Library of Congress Cataloging-in-Publication Data

Sordillo, Laura A., editor. | Sordillo, Peter P., editor.Title: Short-wavelength infrared optical windows for biomedical applications / Laura A.Sordillo and Peter P. Sordillo.

Description: Bellingham, Washington, USA : SPIE–The International Society forOptical Engineering, [2022] | Includes bibliographical references and index.

Identifiers: LCCN 2021039236 | ISBN 9781510646230 (hardcover) | ISBN9781510646247 (pdf)

Subjects: LCSH: Biomedical materials. | Artificial intelligence.Classification: LCC R857.M3 S53 2021 | DDC 610.28–dc23/eng/20211007LC record available at https://lccn.loc.gov/2021039236

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The content of this book reflects the work and thought of the authors and editors.Every effort has been made to publish reliable and accurate information herein, butthe publisher is not responsible for the validity of the information or for any outcomesresulting from reliance thereon.

Cover design by Laura A. Sordillo

Printed in the United States of America.First printingFor updates to this book, visit http://spie.org and type “PM336” in the search field.

Contents

Preface xvContributors xixAcronyms and Abbreviations xxiii

Part I SWIR Techniques 1

1 Optical Properties of Tissues Using SWIR Light 3Francisco J. Salgado-Remacha, Sebastián Jarabo, andAna Sánchez-Cano

1.1 Introduction 41.2 Optical Properties of Major Tissue Components 5

1.2.1 SWIR light sources and detectors 61.3 Novel SWIR Supercontinuum Source 91.4 Effective Filtering of Scattered Light 12

1.4.1 Experimental setup for direct light measurement 131.4.2 Theory 141.4.3 Comparison of both experimental methods for

assessment of the attenuation coefficient in ascattering media phantom 15

1.5 Spectral Attenuation Measurements of Brain and RetinalTissues in the SWIR Region: Experiment I 17

1.5.1 Materials and methods 171.5.2 Results and discussion 181.5.3 Conclusions from experiment I 20

1.6 Measurement of Optical Properties of ex vivo BrainTissues in the SWIR Range: Experiment II 21

1.6.1 Theory 211.6.2 Experimental procedure 231.6.3 Results and discussion 241.6.4 Comparison with the literature and final remarks 29

1.7 Conclusion 32References 33

v

2 Luminescence Nanothermometry and Photothermal ConversionEfficiency for Particles Operating in the SWIR Region 41

Albenc Nexha, Joan Josep Carvajal, and Maria Cinta Pujol

2.1 Light–Matter Interactions 422.2 Luminescence Nanothermometry in the SWIR Region 45

2.2.1 QD-based luminescent nanothermometers 452.2.1.1 PbS@CdS@ZnS QD-based luminescent

nanothermometers 462.2.1.2 Ag2S QD-based luminescent nanothermometers 49

2.2.2 TM-based luminescent nanothermometers 582.2.3 Lanthanide-based luminescent nanothermometers 62

2.2.3.1 Nd3þ-based luminescent nanothermometers 632.2.3.2 Er3þ-based luminescent nanothermometers 672.2.3.3 Tm3þ/Ho3þ-based luminescent nanothermometers 73

2.3 Photothermal Conversion Agents 762.3.1 Self-assessed ex vivo photothermal conversion agents 80

2.4 Concluding Remarks 82References 83

3 SWIR Properties of Rare-Earth-Doped Nanoparticles forBiomedical Applications 91

Yang Sheng, Zhenghuan Zhao, and Mei Chee Tan

3.1 Introduction 923.2 Design and Synthesis of Rare-Earth-Doped Nanoparticles (REDNs) 94

3.2.1 Materials selection for host and dopants 943.2.2 Core/shell structure 953.2.3 Synthesis methods 96

3.3 Upconversion and SWIR for Biomedical Photoluminescence Imaging 983.3.1 Upconversion photoluminescence imaging 983.3.2 SWIR imaging 100

3.4 Photoacoustic Imaging 1053.5 Multifunctional Platforms Based on SWIR-Emitting REDNs 108

3.5.1 REDN-based multimodal imaging 1093.5.1.1 SWIR and photoacoustic imaging 1103.5.1.2 PL and CT imaging 1123.5.1.3 PL/PET/SPECT imaging 1123.5.1.4 PL/MRI imaging 114

3.5.1.4.1 PL/T1 combination 1143.5.1.4.2 PL/T2 combination 1163.5.1.4.3 PL/T1/T2 triple modalities 117

3.5.2 REDN-based theranostic platforms 1173.5.2.1 SWIR/chemotherapy 1183.5.2.2 SWIR/photodynamic therapy 1193.5.2.3 SWIR/photothermal therapy 120

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3.6 Toxicity 1233.7 Summary and Perspectives 126References 127

4 Short-Wave Infrared Meso-Patterned Imaging forQuantitative and Label-Free Tissue Characterization 141

Yanyu Zhao, Anahita Pilvar, Mark C. Pierce, and Darren Roblyer

4.1 Introduction to SWIR-Meso-Patterned Imaging 1414.2 The Unique Capabilities of SWIR Light for Quantifying

Water and Lipid Content in Tissue 1434.3 SWIR-MPI Instrumentation 1484.4 Advantages of SWIR-MPI in Probing Depth and Spatial Resolution 1504.5 Examples of Potential Biomedical Applications for SWIR-MPI 1544.6 Future Directions 155References 155

5 Short-Wavelength Infrared Hyperspectral Imaging for BiomedicalApplications 161

Lise Lyngsnes Randeberg, Julio Hernández, and Emilio Catelli

5.1 Introduction 1625.2 Medical Hyperspectral Imaging 1645.3 Hyperspectral Instrumentation and Setup 1665.4 Hyperspectral Data Collection 1735.5 Hyperspectral Data Analysis 1755.6 Data Analysis and Simulations 1755.7 Chemometric Tools and Methods from Spectroscopy 176

5.7.1 Spectral preprocessing 1785.7.2 Unsupervised methods 180

5.7.2.1 Principal component analysis 1805.7.2.2 Cluster analysis 1835.7.2.3 Methods for purest spectra extraction 185

5.7.3 Supervised method: regression 1895.8 Machine Learning and Artificial Intelligence 191Acknowledgments 193References 193

6 VIS–SWIR Wideband Lens-Free Microscopic Imaging 209Ziduo Lin, Abdulkadir Yurt, Geert Vanmeerbeeck, Murali Jayapala,Zhenxiang Luo, Jiwon Lee, Joo Hyoung Kim, Vladimir Pejovic,Epimitheas Georgitzikis, Pawel Malinowski, and Andy Lambrechts

6.1 Introduction 2096.2 System Development and Evaluation 211

6.2.1 System development 2116.2.2 Quantum dot sensor 212

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6.2.3 System performance evaluation 2136.3 Applications 215

6.3.1 Silicon inspection and measurement 2156.3.2 Cell and tissue imaging 2176.3.3 Wide-range multispectral LFI 219

6.4 Future Prospects 220References 220

Part II Applications—Cancers 227

7 SWIR Fluorescence and Monte Carlo Modeling of Tissues forMedical Applications 229

Tatsuto Iida, Shunsuke Kiya, Kosuke Kubota, Akitoshi Seiyama,Takashi Jin, and Yasutomo Nomura

7.1 Introduction 2307.2 Monte Carlo Models in Multilayered Media (MCML) 232

7.2.1 Calculation routine 2327.2.2 SWIR photon migration 234

7.3 Fluorescence Monte Carlo Simulation 2367.3.1 Point source of fluorescence MCML for cerebral angiography 2367.3.2 Spherical source of fluorescence MCML for breast cancer 241

7.3.2.1 Overview of the model 2417.3.2.2 Fluorescence photon migration in MCML:

breast model 2437.4 SWIR Fluorescence Monte Carlo Model in Voxelized Media (MCVM)

for Breast Cancer 2447.4.1 The breast model 2457.4.2 Image processing and implementation of the model 2467.4.3 Excitation gradient 2477.4.4 Setting optical parameters that reflect duct morphology 2487.4.5 SWIR for detection of small breast cancer in deep tissue 250

7.5 Conclusions and Perspectives 251References 252

8 Multimodal SWIR Laser Imaging for Assessment and Detection ofUrothelial Carcinomas 261

Gustavo Castro-Olvera, Simone Morselli, Mauro Gacci, Sergio Serni,Andrea Liaci, Rosella Nicoletti, and Pablo Loza-Alvarez

8.1 Introduction 2628.1.1 Epidemiology 2628.1.2 Aetiology 2628.1.3 Histopathology and staging 2628.1.4 Clinical presentation 2648.1.5 Diagnosis 2648.1.6 Treatment 265

viii Contents

8.1.7 Diagnostic needs in clinical practice 2658.2 Role of Multimodal SWIR Laser Imaging 266

8.2.1 SWIR 2678.2.2 Multimodal microscopy 2678.2.3 Nonlinear optics for microscopy 2688.2.4 Two-photon excited fluorescence (TPEF) 2708.2.5 Second-harmonic generation (SHG) 2708.2.6 Third-harmonic generation (THG) 271

8.3 SWIR Optical Windows 2728.3.1 First biological window 2728.3.2 Second and third biological windows 273

8.4 Damage and Image Optical Thresholds 2748.5 Conclusion 277References 277

9 SWIR Fluorescence Endoscopy for Tumor Imaging 287Yongkuan Suo, Hongguang Liu, and Zhen Cheng

9.1 Introduction 2879.2 Endoscopic Imaging 2889.3 SWIR Fluorescence Endoscopic Imaging 292References 294

10 Short-Wavelength Infrared Hyperspectral Imaging to AssessGastrointestinal Stromal Tumors during Surgery 301

Toshihiro Takamatsu, Hiroaki Ikematsu, Hiroshi Takemura,Hideo Yokota, and Kohei Soga

10.1 Short-Wavelength Infrared Imaging 30210.2 Hyperspectral Imaging 30310.3 Data Processing Methods for Hyperspectral Imaging 30510.4 Distinction of Gastrointestinal Stromal Tumors by SWIR–HSI 30810.5 Wavelength Band Reduction Method for Hyperspectral Data 31110.6 Development of SWIR-HSI Devices for Clinical Applications 31510.7 Summary 318Acknowledgments 318References 318

Part III Applications: Diseases of the Heart, Brain, Skin, and Other Organs 325

11 SWIR for the Assessment of Heart Failure 327Aaron G. Smith, Shona Stewart, Marlena B. Darr, Robert C. Schweitzer,Matthew Nelson, Patrick J. Treado, and J. Christopher Post

11.1 Introduction 32711.2 Current Methods of Heart Failure Patient Assessment 32811.3 Molecular Chemical Imaging 33011.4 Application of SWIR-MCI to Heart Failure Space 33211.5 SWIR Clinical Studies Results 335

ixContents

11.5.1 Introduction and clinical perspective 33511.5.2 Methods and results 336

11.6 Discussion 34511.7 Future Directions 346References 348

12 Transparent Polycrystalline Ceramic Cranial Implant with PhotonicFunctionality in the SWIR 357

Santiago Camacho-López, Nami Davoodzadeh, David L. Halaney,Juan A. Hernández-Cordero, Guillermo Aguilar, Gabriel R. Castillo,Antonio Cisneros-Martínez, Beatriz Coyotl-Ocelotl, Roger Chiu,Julio C. Ramírez-San-Juan, and Rubén Ramos-García

12.1 Introduction 35812.2 Theranostic Cranial Implant for Hyperspectral Light Delivery and

Microcirculation Imaging without Scalp Removal 35912.2.1 Ex vivo proof of concept: optical transmittance

measurements 36212.2.2 In vivo demonstration of optical access for LSI of brain

microvasculature 36512.3 Femtosecond Laser-Written Waveguides in nc-YSZ for WttB in

the SWIR 37012.3.1 Waveguide writing 37012.3.2 Depressed cladding waveguides: discrete versus continuous 37312.3.3 Characterization of waveguides for SWIR 374

12.4 Imaging through Highly Scattering Media 37612.4.1 Laser speckle contrast imaging (LSCI) 37712.4.2 Wavefront shaping: focusing light through a ceramic cranial

implant 38012.4.3 Single-pixel imaging (SPI) 38212.4.4 Lensless camera 384

12.5 Optical Fiber Probes for Diagnostics and Therapeutics 38812.5.1 Fiber-optic temperature sensors 38812.5.2 Fiber-optic polymer microbubble sensors for temperature and

deformation 39012.5.3 Photothermal probes 392

12.6 Conclusion 393Funding 394Acknowledgments 394References 394

x Contents

13 SWIR Hyperspectral Imaging to Assess Neurocognitive DisordersUsing Blood Plasma Samples 407

Raquel Leon, Abian Hernandez, Himar Fabelo, Samuel Ortega,Francisco Balea-Fernández, and Gustavo M. Callico

13.1 Introduction 40813.2 Materials and Methods for Generating a Hyperspectral SWIR Blood

Plasma Database 41113.2.1 Blood plasma sample preparation and HS-SWIR setup for

data acquisition 41113.2.2 HS image preprocessing 41413.2.3 Blood plasma HS dataset partition 41513.2.4 Statistical preprocessing approach 417

13.3 Processing Framework of HS-SWIR Blood Plasma Samples 41913.3.1 Machine learning approach 42013.3.2 Deep learning approach 42113.3.3 Evaluation metrics 422

13.4 Experimental Results and Discussion 42313.4.1 Validation classification results 42313.4.2 Test classification results 42713.4.3 Limitations 428

13.5 Conclusions 429Acknowledgments 430References 430

14 Hyperspectral SWIR Imaging of Skin Inflammation 439Leonid Shmuylovich and Mikhail Y. Berezin

14.1 Introduction 43914.1.1 Contact dermatitis as a model inflammatory skin disease 44014.1.2 Skin imaging landscape 442

14.2 Extending Beyond the Visible and Near-Infrared to the SWIR 44314.3 SWIR Hyperspectral Imaging of Allergic Contact Dermatitis 445

14.3.1 HSI hardware and software for image analysis 44514.3.2 Applying SWIR-HSI and IDCube analysis to ACD 44514.3.3 Evidence of pigmentation-independent imaging in the SWIR

spectral range 44714.3.4 Designing SWIR multispectral imaging from

hyperspectral data 44814.4 Conclusions 450References 450

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15 Use of a SWIR Otoscope in the Assessment of Pediatric and OtherConditions 455

Nirvikalpa Natarajan, Yu-Jin Lee, and Tulio A. Valdez

15.1 Introduction 45515.2 Middle Ear Anatomy 45615.3 Pathophysiology of Middle Ear Infections 45815.4 Diagnosis: Current Modalities and Challenges 45915.5 SWIR 46015.6 Preclinical Studies: Optical Properties of the Human Tympanic

Membrane 46115.7 Preclinical Studies to Evaluate SWIR Imaging 462

15.7.1 Ex vivo analysis of human tympanic fluid 46215.7.2 Analysis of a middle ear fluid phantom in a middle ear model 465

15.8 Fluorescence Chemical Sensors in Conjunction with SWIR ImagingTools for Detecting Otitis Media in a Murine Model 466

15.9 SWIR Otoscope Design 46915.10 Clinical Studies 470

15.10.1 SWIR imaging of human middle ear anatomy in adults 47015.10.2 SWIR otoscopy in a pediatric population 471

15.11 Conclusion 473References 473

16 Use of an OCT System in the Short-Wavelength Infrared Region:Applications 481

Pauline John, Vani Damodaran, and Nilesh J. Vasa

16.1 Introduction 48216.2 Optical Coherence Tomography (OCT) 484

16.2.1 Basic principles of the OCT technique 48416.2.2 Different types of OCT systems 485

16.2.2.1 Time domain OCT system 48616.2.2.2 Frequency domain OCT system 486

16.2.2.2.1 Spectral domain OCT 48716.2.2.2.2 Swept source OCT 488

16.2.2.3 Functional OCT: polarization-sensitive OCT 48916.3 Application I: SWIR OCT for Dental Imaging 491

16.3.1 Dental caries 49116.3.2 Imaging of dental caries 49416.3.3 Restoration and secondary caries 497

16.4 Application II: SWIR OCT for Glucose Monitoring in the AnteriorChamber of the Human Eye 499

16.4.1 Diabetes 499

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16.4.2 Glucose monitoring techniques 50016.4.2.1 Principles of spectral-domain differential-absorption

low-coherence interferometry (SD-DA-LCI) 50216.4.2.2 Theory of DA-based SD-LCI 504

16.4.3 Glucose monitoring using a supercontinuum laser source inthe anterior chamber of a human eye model 506

16.5 Conclusion 509References 510

17 SWIR Imaging of Lesions on Tooth Surfaces 521Daniel Fried

17.1 Introduction 52117.2 Optical Properties of Dental Hard Tissues and Lesion Contrast at

SWIR Wavelengths 52317.3 Detection of Caries Lesions on Proximal and Occlusal Surfaces 52717.4 Detection of Caries Lesions and Dental Calculus on Root Surfaces 53317.5 Detection of Secondary Caries 53517.6 Characterization of Developmental Defects 53717.7 Imaging Cracks in Teeth 53817.8 Assessment of Lesion Activity 54017.9 Summary 542References 543

Part IV Artificial Intelligence 555

18 Advances in SWIR Deep Tissue Imaging Using Machine Learning 557Laura A. Sordillo and Diana C. Sordillo

18.1 Introduction 55718.2 Short-Wavelength Infrared (SWIR) 55818.3 Deep Learning Models 562

18.3.1 Overview of deep learning 56218.3.2 Popular DL models in biophotonics 564

18.4 Machine Learning Techniques, SWIR, and Disease 56518.4.1 Machine learning and biophotonics 56518.4.2 Machine learning and SWIR 567

18.5 Conclusion 569References 569

Index 577

xiiiContents

Preface

One of the most exciting, recent developments in photonics, particularly inregard to its use in medicine and disease, is the utilization of light atwavelengths beyond the visible range and the slightly longer range of short-wavelength infrared (SWIR) wavelengths at 1100–1350 nm, now known as thesecond optical window. Once ignored because of a lack of sensitive detectors,a third (at 1600–1870 nm) and a fourth (at 2100–2350 nm) optical window arenow being utilized extensively. These wavelengths are situated at areasbetween water peak maxima (where absorption of light is reduced). Due tominimal absorption and scattering of light at these wavelengths, the use ofthese SWIR optical windows can provide less blurring, better-contrast images,and deeper penetration into tissue media compared with visible light.

With the use of these windows, extensive progress has been made in thestudy of diseases such as cancer, heart failure, neurocognitive disorders, anddiseases of the bone, eyes, skin and teeth. In Part I of this book, investigatorsreview new and emerging techniques based on SWIR light, including thefabrication and use of SWIR nanoparticles as luminescent nanothermometersand photothermal agents, and recent advances in the design, structure andSWIR-related biomedical applications of rare-earth doped nanoparticles(REDNs). REDNs are among the most exceptionally bright and biocompati-ble SWIR emitters. SWIR imaging techniques—including SWIR hyperspec-tral imaging for biomedical applications, and a novel wideband (VISþSWIR)digital holographic microscopic method, based on a novel quantum-dot (QD)image sensor—are also discussed.

In Part II of this book, we explore biomedical applications that employthe SWIR optical windows for the assessment and detection of cancer. SWIRfluorescence and Monte Carlo modeling of breast cancer tissues can revealimportant information on how SWIR light interacts with complex media.Recent advances in the study of urothelial carcinoma, a cancer that recursfrequently, are reviewed. SWIR light with multimodal microscopy can beutilized as a minimally invasive diagnostic technique for evaluation of thiscancer. Investigators also show how SWIR light can be coupled withfluorescence endoscopy for tumor imaging, and how the assessment ofgastrointestinal stromal tumors during surgery can be made using SWIRhyperspectral imaging.

xv

In Part III of this book, we discuss biomedical applications of SWIR lightin important diseases of the heart, brain, skin, and other organs. SWIR lightcan be utilized in the assessment of heart failure and to access difficult-to-reach areas of the brain. Investigators use SWIR imaging techniques toevaluate neurocognitive disorders and skin inflammation. SWIR light canalso be employed using an otoscope to assess pediatric conditions, and withoptical coherence tomography and other imaging modalities in dentistry.

Finally, Part IV provides a discussion of how artificial intelligence andmachine learning can greatly enhance our ability to use SWIR windows todetect and study disease.

Acknowledgments

We would like to thank Ms. Dara Burrows, SPIE Press Senior Editor, for herinvaluable help in preparing this book. We would also particularly like toacknowledge Ms. Diana C. Sordillo, M.S., who not only co-authored achapter, but also made valuable notes and recommendations throughout thebook. Finally, we would also like to thank Mr. Vincent T. Sordillo for histechnical help, and especially Mrs. Ellen R. Sordillo, M.P.A., for her support.

Laura A. Sordillo, M.S., M.Phil., Ph.D.Peter P. Sordillo, M.D., Ph.D., M.S.

December 2021

Laura A. Sordillo, M.S., M.Phil., Ph.D. is an interdisciplinary researcher inoptics and biophotonics whose work focuses on the discovery of novel short-wavelength infrared techniques for deep tissue imaging of the brain, bone andbreast cancer, the use of spectroscopy for the assessment of neurodegenerativediseases such as Parkinson’s and Alzheimer’s, as well as of cancer, and thestudy of quantum effects in the brain and in photosynthetic systems. She iscurrently investigating the application of photonics to quantum computing.She is Director of Biophotonics at Allosteric Bioscience, Inc. and is a researchassistant professor at The Institute for Ultrafast Spectroscopy and Lasers inthe physics and electrical engineering departments at The City College of NewYork. She is the recipient of the Kaylie Entrepreneur Award, the MSKCC-CCNY Graduate Research Award, the 2016–2017 Grove School ofEngineering Graduate Fellowship, the 2017–2018 Corning Inc. Ph.D.Fellowship Award, and the 2018–2019 Corning Inc. Ph.D. FellowshipAward. She has published more than 60 papers, holds 13 patents, and iscoeditor (along with Dr. Peter P. Sordillo M.D., Ph.D., M.S.) of the recentlypublished book Biophotonics, Tryptophan and Disease from Elsevier/Academic Press.

xvi Preface

Peter P. Sordillo, M.D., Ph.D., M.S. is a physician and cancer researcherwhose specialty is the treatment of extremely rare cancers. In addition to hisM.D., he holds three graduate degrees in philosophy (causality) (ColumbiaUniversity), and a graduate degree in physics (New York University). He isVice-President and Chief Scientific Officer at SignPath Pharma Inc., abiotechnology company; Attending Physician in Medical Oncology, Hema-tology and Internal Medicine at Lenox Hill Hospital in New York City; andresearch consultant at The Institute for Ultrafast Spectroscopy and Lasers,Physics Department, The City College of the City University of New York,USA. He has published more than 190 papers, holds 15 patents, and iscoeditor (along with Laura A. Sordillo, M.S., M.Phil., Ph.D.) of the recentlypublished book Biophotonics, Tryptophan and Disease from Elsevier/Academic Press.

xviiPreface

Chapter 1

Optical Properties of TissuesUsing SWIR LightFrancisco J. Salgado-Remacha, Sebastián Jarabo, and Ana Sánchez-CanoUniversidad de Zaragoza, Zaragoza, Spain

1.1 Introduction1.2 Optical Properties of Major Tissue Components

1.2.1 SWIR light sources and detectors1.3 Novel SWIR Supercontinuum Source1.4 Effective Filtering of Scattered Light

1.4.1 Experimental setup for direct light measurement1.4.2 Theory1.4.3 Comparison of both experimental methods for assessment of the

attenuation coefficient in a scattering media phantom1.5 Spectral Attenuation Measurements of Brain and Retinal Tissues in the

SWIR Region: Experiment I1.5.1 Materials and methods1.5.2 Results and discussion1.5.3 Conclusions from experiment I

1.6 Measurement of Optical Properties of ex vivo Brain Tissues in the SWIRRange: Experiment II

1.6.1 Theory1.6.2 Experimental procedure1.6.3 Results and discussion1.6.4 Comparison with the literature and final remarks

1.7 ConclusionReferences

3

performed using this device. The purpose of the first experiment (experiment I)was to validate our system and show how efficient and feasible thesupercontinuum (SC)–SWIR system is at measuring the spectral attenuationfrom 1100–2240 nm from brain and retina tissue. Finally, we calculate theabsorption and scattering coefficients in the SWIR range between 1100 and2300 nm, which are based exclusively on experimental measurements.

1.2 Optical Properties of Major Tissue Components

Biological tissues are heterogeneous materials that contain, e.g., cells andtheir organelles, protein fibers, DNA, or lipids, and are surrounded by tissuefluids, such as the cytoplasm and interstitial fluid. The attenuation of light intissue increases at wavelengths corresponding to the absorption bands of thetissue components, while strong light scattering is created by refractive indexchanges between the tissue component interfaces. The primary absorptionbands for the tissue components in the visible (VIS) through the SWIR lightrange are shown in Fig. 1.1.24 Key bands are located at 970, 1180, 1450,1775, 1930, and 1975 nm (due to water); at approximately 760, 830, 920,1040, 1210, 1430, 1730, 1760, and 1900–2600 nm (lipids); at 420 and 550 nm[deoxygenated hemoglobin (Hb)]; and at 410, 540, and 575 nm [oxygenatedhemoglobin (HbO2)].

10,24,25 In the ultraviolet (UV) range, the primaryabsorption bands from common tissue components are observed at 200 and230 nm (proteins); at 260 nm (DNA and RNA); at 275 and 345 nm (HbO2);and at 275 and 360 nm (Hb).

Figure 1.1 Spectral absorption coefficients of some tissue components (water, melanin,fat, HbO2, and Hb) in the VIS and SWIR regions. Question marks in the key indicateestimated plots. (Reprinted from Ref. 24.)

5Optical Properties of Tissues Using SWIR Light

experimental results obtained in the following sections utilize the fibercollimator system shown in Fig. 1.5(a).

1.5 Spectral Attenuation Measurements of Brain and RetinalTissues in the SWIR Region: Experiment I

In this section, we measured the spectral attenuation of biological tissues usinga SWIR-based SC device and double GRIN-lens filter (as described in theprevious section). The spectral attenuation coefficients were measuredbetween 1100 and 2250 nm for two different types of tissues (brain andretina), using the fiber collimator system shown in Fig. 1.5(a). By comparingthe signal attenuation versus the tissue depth at different wavelengths, wedemonstrate quantitatively the optimum wavelength range for deep brain andretina SWIR imaging.

1.5.1 Materials and methods

Rats used in the experiments were obtained from Dr. M. LaVail (Universityof California, San Francisco, California, USA) and were bred in a colony atthe University of Zaragoza, Zaragoza, Spain. Animals were housed andhandled with the authorization and supervision of the Institutional AnimalCare and Use Committee from the University of Zaragoza. Procedures wereperformed in accordance with the ARVO Statement for the Use of Animals inOphthalmic and Vision Research. Two six-month-old Long–Evans normalrats were injected with a mixture of ketamine (41.7 mg/kg) and xylazine(2.5 mg/kg). The depth of anesthesia was verified using tail and toe pinchreflexes of the lower extremities, and the animals were sacrificed byadministering a lethal dose of pentobarbital. The brain and retina wereremoved, fixed in 4% w/v paraformaldehyde for 1 hour at room temperature,washed in phosphate buffer (pH 7.4 and osmolarity 300 mOsm/kg), andsequentially cryoprotected in 15% w/v sucrose for 1 hour and 30% w/v sucroseovernight at 4 °C. The tissues were sliced at different thicknesses (from15–100mm) using a cryostat (Leica Biosystems Nussloch GmbH, Nussloch,Germany).

The ballistic photons (direct transmitted light) were collected using a fibercollimator system. Light transmission spectra were obtained using a custom-made spectroscopic system. A complete description of the laser system can befound in Section 1.3 and in Refs. 34 and 38–40 (Fig. 1.4). The SWIR powerwas generated from 1100–2250 nm in a highly nonlinear silica fiber, NL-1550-Zero type (Yangtze Optical Fibre and Cable Joint Stock Company Ltd.,Wuhan, Hubei, China) pumped with a periodic train of ultrashort pulses.The nominal dispersion of this nonlinear fiber at 1550 nm is null with adispersion slope of less than 0.025 ps · nm–2 · km–1. The nonlinear coefficientis greater than 10 W–1 · km–1, and the Raman gain coefficient is greater than

17Optical Properties of Tissues Using SWIR Light

emission with the use of optical fiber collimators [Fig. 1.5(a)]. With theseoptical tools, the system can filter efficiently scattered light, leading toan accurate measurement of attenuation values. Spectral attenuation from1100–2240 nm was measured for both rat brain and retinal tissues. Theabsorption mechanism has only a small effect on the attenuation coefficient,while the scattering coefficient decreases at longer wavelengths in the SWIRregion. This strongly suggest that longer wavelengths in the SWIR range canimprove the performance of biophotonic devices by minimizing the spectralattenuation and allowing for deeper penetration into tissue. This also showsthat our SC–SWIR system can be used directly on ex vivo tissues without theuse of optical clearing, with potential clinical applications. Additionally,because diseased tissues commonly have different optical properties comparedto healthy tissues, the proposed SWIR technique could be utilized todistinguish healthy and diseased tissues. In the next section, we extend theapplications of our SC–SWIR system to rat brain tissue of various thicknessesand a custom-made IR spectrophotometer system.

1.6 Measurement of Optical Properties of ex vivo Brain Tissuesin the SWIR Range: Experiment II

In experiment I, we were able to validate our SC–SWIR experimental systemusing brain and retina tissue samples. In experiment II, we performed a deeperanalysis on the optical coefficients of biological tissues using two distinctsetups: the SC–SWIR system and a custom-made IR spectrophotometer(to measure transmittance from 1100–2300 nm). While absorption is due tothe presence of chromophores in tissue, scattering is produced bydiscontinuities in the refractive index at the microscopic level.55 Scatteringat short wavelengths (primarily from 400–1300 nm) limits the imagingdepth.56 The NIR optical window near 1700 nm (window III) in the SWIRrange can provide deep tissue penetration when both scattering andabsorption are considered.57 Several devices that operate at approximately1300 nm both in vivo and ex vivo have been studied.1,2,58 Both opticalcoherence tomography (OCT) using a SC light source4 and three-photonmicroscopy57 at 1700 nm are currently used to study structures noninvasivelyin a living mouse brain. However, the primary characteristics of long-wavelength OCT for deep imaging in biological soft tissue have not beensufficiently studied. In this section, we measured the spectral properties ofdifferent biological tissues in the SWIR range using a specific experimentalsetup,39 and compared those results with the literature.

1.6.1 Theory

The transmittance of different tissues was measured using a single-mode fibercollimator based on a GRIN lens, as described in Section 1.4. The acceptance

21Optical Properties of Tissues Using SWIR Light

Chapter 2

LuminescenceNanothermometry andPhotothermal ConversionEfficiency for ParticlesOperating in the SWIR RegionAlbenc Nexha, Joan Josep Carvajal, and Maria Cinta PujolUniversitat Rovira i Virgili, Tarragona, Spain

2.1 Light–Matter Interactions2.2 Luminescence Nanothermometry in the SWIR Region

2.2.1 QD-based luminescent nanothermometers2.2.1.1 PbS@CdS@ZnS QD-based luminescent

nanothermometers2.2.1.2 Ag2S QD-based luminescent nanothermometers

2.2.2 TM-based luminescent nanothermometers2.2.3 Lanthanide-based luminescent nanothermometers

2.2.3.1 Nd3þ-based luminescent nanothermometers2.2.3.2 Er3þ-based luminescent nanothermometers2.2.3.3 Tm3þ/Ho3þ-based luminescent nanothermometers

2.3 Photothermal Conversion Agents2.3.1 Self-assessed ex vivo photothermal conversion agents

2.4 Concluding RemarksReferences

41

2.1 Light–Matter Interactions

The study of the interactions of light with matter continues to be a major areaof research, particularly on the nanoscale. When light illuminates ananoparticle (NP), different optical phenomena can occur, ranging fromluminescence to heat. Luminescence arises from radiative processes, whileheat can be generated by nonradiative processes (which can take place withinthe structure after being excited by light; Fig. 2.1). When the luminescence ofa material is temperature dependent, the material can act as a luminescentthermometer.1,2 If the heat generated by a material falls within thehyperthermia region (from 314 K to 321 K),3 then the material can be usedas a photothermal agent to treat cancer and other diseases.3,4 Luminescentnanothermometers can provide the temperature at the intratumor orintracellular level,5,6 detect tumors at early stages,7 monitor brain activity,8

Figure 2.1 Nanoparticle (NP) illuminated by light. Radiative processes lead to lumines-cence, and nonradiative processes lead to heat. Typical luminescence materials—includingquantum dots (QDs: PbS, Ag2S), transition metals (TMs: Ni2þ, Mn5þ, and Cr4þ), andlanthanide-doped (Nd3þ, Er3þ, Tm3þ, and Ho3þ) materials operating in the SWIR region—are shown together with their corresponding wavelength ranges of operation.

42 Chapter 2

2.3 Photothermal Conversion Agents

A particle can absorb light upon illumination. The absorbed light can beconverted into luminescence, which can be further used for temperaturesensing if the generated luminescence displays temperature-dependentproperties as described previously. The absorbed light could also be convertedinto heat (Fig. 2.1). One way to measure the ability of a material to convertabsorbed light into heat is through the photothermal conversion efficiency h.Materials that display this property can be used in photothermal therapy.3

This process is crucial in preventing and treating cancer and other diseasesthrough control of the heat generated by the particles of the material. In atemperature interval from 314 to 321 K, the heat generated can induce proteinaggregation, long-term cell inactivation, and cell death.3

Several methods can be used to determine the photothermal conversionefficiency of a material.3,20 Examples of these methods include: thermalrelaxation, based on a study of the kinetics of heating and cooling afterswitching the excitation source on (heating) and off (cooling);60 and theintegrating sphere method, based on determining the power of the lightreflected, transmitted, scattered, and generated by a sample after irradiationwith a suitable laser source in an integrating sphere.61 These two methods areused to determine h for different materials operating as photothermal agentsand luminescent thermometers in the SWIR.

Among the types of luminescent materials operating in the SWIR region,only QDs and Ln3þ-doped materials have been tested as photothermal agents.For a full overview of photothermal agents—including largely thoseabsorbing light in the VIS and NIR spectral regions—the reader is referredto other references.3,4,20 To date, to the best of our knowledge, no report hasexplored the applicability of TM luminescent materials as photothermalagents, probably because these materials are typically excited with UV or VISlight sources. Nevertheless, as the ability of a material to generate heat isattributable to nonradiative processes occurring among their electronic states(excluding plasmonic particles), TMs should in theory exhibit this property;even more so if the TMs are combined with Ln3þ ions.

Before discussing QDs and Ln3þ-based photothermal agents, it is essentialto emphasize that a proper comparison between these types of agents couldnot be done, as several parameters reported for these two different typesof materials are different. Hence, the excitation wavelength, power density,area of illumination, concentration of the particles, and type of methodfor determination of h greatly influence the h values reported.3 Furthermore,for these materials, often only a temperature increase of the medium in whichthey are used has been reported, without specifying the actual value of h(for example, in Ag@Ag2S QDs30 or PbS@CdS@ZnS combined with Nd3þ

ions in a hybrid structure).26 Therefore, to have a comparison of theirperformances, we have extracted from the literature the reported h values and

76 Chapter 2

Chapter 3

SWIR Properties of Rare-Earth-Doped Nanoparticles forBiomedical ApplicationsYang ShengChangzhou University, Changzhou, China

Zhenghuan ZhaoChongqing Medical University, Chongqing, China

Mei Chee TanSingapore University of Technology and Design, Singapore

3.1 Introduction3.2 Design and Synthesis of Rare-Earth-Doped Nanoparticles (REDNs)

3.2.1 Materials selection for host and dopants3.2.2 Core/shell structure3.2.3 Synthesis methods

3.3 Upconversion and SWIR for Biomedical Photoluminescence Imaging3.3.1 Upconversion photoluminescence imaging3.3.2 SWIR imaging

3.4 Photoacoustic Imaging3.5 Multifunctional Platforms Based on SWIR-Emitting REDNs

3.5.1 REDN-based multimodal imaging3.5.1.1 SWIR and photoacoustic imaging3.5.1.2 PL and CT imaging3.5.1.3 PL/PET/SPECT imaging3.5.1.4 PL/MRI imaging

3.5.1.4.1 PL/T1 combination3.5.1.4.2 PL/T2 combination3.5.1.4.3 PL/T1/T2 triple modalities

91

3.2 Design and Synthesis of REDNs

In general, the host–dopant coupling is tailored to synthesize brightly emittingREDNs. Oxide and fluoride materials are frequently used as hosts due to theirhigh optical transparency in the visible to NIR range and low phononvibration energy. The rare-earth or lanthanide dopants, which usually formtrivalent cations (Ln3þ), are generally selected based on their desirableexcitation and emission wavelengths. Extensive studies on Ln3þ have shownthat the single-dopant system generally exhibits relatively poor luminescenceefficiency due in part to low absorption cross sections and low-concentrationquenching thresholds. Therefore, the majority of REDNs studied today arebased on a co-dopant scheme, where one dopant acts as a sensitizer and theother acts as a luminescence emitter, known as an activator. The sensitizerabsorbs photon energy from the excitation source and then transfers theenergy to the activator, resulting in radiative PL as well as heat due tononradiative relaxation processes (Fig. 3.2). The energy transfer mechanism isintroduced in Section 3.3.

3.2.1 Materials selection for host and dopants

Selection of the host material is critical to the synthesis of REDNs withdesirable PL properties. In general, besides transparency in the UV to NIRrange (~400–2000 nm), the host material should have a low lattice phononenergy and small lattice mismatch to the dopants. Low lattice phonon energyis essential to minimizing nonradiative energy loss and maximizing theradiative emission. A small lattice mismatch reduces crystal defects such asvacancies and interstitial ions.12 To date, the most successful host materialsare oxide and fluoride materials such as Y2O3, NaYF4, and CaF2.

The host materials themselves do not exhibit PL emission. Therefore,Ln3þ is doped into the host materials as localized emitting centers. Er3þ,Tm3þ, and Ho3þ are the most frequently used activators since their

Figure 3.2 Illustration of the PL process of a co-dopant REDN system.

94 Chapter 3

leads to undesired heat as well as reduced penetration depths, which limitsthe practical application of REDNs for safe and high-resolution in vivoimaging. An effective approach to address this issue is to shift the excitationwavelength from 975 nm to either 745 or 800 nm, through use of other rare-earth ions (e.g., Nd3þ). By shifting the excitation wavelength, the length of thepenetration depth is extended, and the heating effects are reducedsignificantly. Liang et al. designed core/shell NaYF4:Nd,Yb/NaYF4:Yb,Tmnanoparticles, which achieved 803-nm emission under 745-nm excitation byefficient energy transfer through the Nd3þ→Yb3þ→Yb3þ→Tm3þ pathway.66

This study demonstrates that tissue exposed to 980-nm laser irradiation suffersmore than five-fold heating in comparison with tissue irradiated at 745 nmwhile experimental conditions were maintained the same. The in vitro cellviability assay also suggests the significant drawback of the overheating issue,where the hyperthermia produced by 975-nm excitation reduced the cellviability even at low exposure durations, whereas the 745-nm excitationshowed no obvious effect on the cell viability.

3.4 Photoacoustic Imaging

Photoacoustic imaging (PAI) has attracted immense attention for high-resolution and deep-tissue visualization, especially for diagnosis of tumorpathophysiological status, minimally invasive characteristics, rapid operation,and accurate quantification.67,68 In general, PAI is a hybrid imaging modalitythat combines both optics and ultrasound—providing high ultrasonic spatialresolution (up to several centimeters of penetration depth) and intrinsicoptical absorption contrast.69,70 When a target such as a biological sample isirradiated by a short-pulsed laser beam, which can be either in the visible orSWIR range, a portion of the light is absorbed by the target’s constituents andthen partially converted into heat, which generates a pressure increase throughthermo-elastic expansion.71 This initial pressure increase, determined by thelocal optical energy deposition and other thermal and mechanical propertiesof the target, propagates as an acoustic wave in the biological tissue.72 Whenthe acoustic wave is detected by highly sensitive ultrasound receivers, thedistribution of signal in the sample can be reconstructed.73

It is well-established that hemoglobin and melanin are among the mostimportant naturally occurring contrast agents for enhancing PAI contrast.74

However, the limitations of these intrinsic contrast agents have driven thedevelopment of exogenous contrast agents. For example, these intrinsiccontrast agents absorb light in the visible spectral region where strongabsorption and scattering events occurs, leading to shallow penetrationdepths. In addition, some diseased sites show a low PA signal due to the lowintrinsic optical response of the local tissue chromophore (e.g., lymphaticsystem).75

105SWIR Properties of Rare-Earth-Doped Nanoparticles for Biomedical Applications

Polyethylene glycol (PEG)-modified Fe3O4@b-NaGdF4:Yb/Er and Yb/Tmwere successfully synthesized by Chen et al.119 They found that the obtainednanostructure still displayed bright PL emissions in the absence of a silica orpolymer spacer layer. Furthermore, the coating on the surface of the IONPsdid not affect their T2 contrast ability where a high T2 relaxivity with the valueof 204 mM–1s–1 was measured.

Apart from IONPs, dysprosium was also studied to synthesize PL/T2

dual-mode contrast agents due to its high magnetic moment (10.6 mB) andshort electron relaxation time. Yuan et al. synthesized NaLuF4:Yb,Tm/NaLuF4/NaDyF4 nanocrystals with enhanced UCL and MRI signals.120 A116-fold enhancement of the emission intensity was observed by coating thelayer of NaDyF4 with a measured effective r2 value of 40 mM–1s–1. Due to theexcellent optical and T2 contrast properties, orthotopic pancreatic cancerlesions were detected using these multimodal NaLuF4:Yb,Tm/NaLuF4/NaDyF4 nanocrystals.

3.5.1.4.3 PL/T1/T2 triple modalities

Since Gd and Dy can enhance MRI signals by decreasing the relaxation timeof a nearby proton in the T1 or T2 modes, doping REDNs with both Gd andDy could potentially create tri-modal PL/T1/T2 contrast agents. Zhang et al.synthesized NaDyF4:Yb/NaGdF4:Yb,Er nanoparticles that circumventedthe upconverting poisoning effect of Dy3þ ions and generated efficient UCL(Fig. 3.13).121 In addition, the NaDyF4:Yb/NaGdF4:Yb,Er nanoparticlesoffered not only significant dark T2-weighted MRI contrast but also brightT1-weighted MRI contrast properties arising from Dy3þ and Gd3þ. However,the T1 contrast ability of this nanosystem was relatively low at 0.32 mM–1s–1,limiting its further application as a T1 contrast agent. The effects of couplingMn2þ to the surface of NaDyF4 nanoparticles on the optical, T1, and T2

imaging properties were investigated.122 Whereas a Mn-complex-modifiedNaDyF4:Yb@NaLuF4:Yb,Er@polydopamine core/shell nanocompositeexhibited a narrow intrinsic emission band and characteristic UCL emissions,this structure exhibited high T1 and T2 contrast ability with values of 4.97 and277.63 mM–1s–1, respectively, which enabled tumor detection in both the T1

and T2 modes.

3.5.2 REDN-based theranostic platforms

Investigations on rationally designed REDN-based theranostic platformshave been extensively performed. In the early stages of such research, REDN-based theranostic platforms were used as simple nanocarriers with PL contrastability to monitor and subsequently overcome the limitation of chemotherapy(e.g., drug dependence, drug resistivity, and poor targeting efficiency).Recently, the unique features of REDNs, including the UC properties andionizing radiation absorption, have been considered key to address the

117SWIR Properties of Rare-Earth-Doped Nanoparticles for Biomedical Applications

Chapter 4

Short-Wave InfraredMeso-Patterned Imaging forQuantitative and Label-FreeTissue CharacterizationYanyu ZhaoBeihang University, Beijing, China

Anahita PilvarBoston University, Boston, USA

Mark C. PierceRutgers, The State University of New Jersey, Piscataway, USA

Darren RoblyerBoston University, Boston, USA

4.1 Introduction to SWIR-Meso-Patterned Imaging4.2 The Unique Capabilities of SWIR Light for Quantifying Water and Lipid

Content in Tissue4.3 SWIR-MPI Instrumentation4.4 Advantages of SWIR-MPI in Probing Depth and Spatial Resolution4.5 Examples of Potential Biomedical Applications for SWIR-MPI4.6 Future DirectionsReferences

4.1 Introduction to SWIR-Meso-Patterned Imaging

The absorption coefficient ma and reduced scattering coefficient ms0 describe

the magnitude of optical absorption and scattering, respectively, in turbidmedia such as tissue, and can serve as important physiological biomarkers.1,2

141

crossed polarizers to ensure measurement of diffuse light. Images acquired atthe three AC illumination phases are demodulated to obtain the tissueresponse at the corresponding spatial frequency. The demodulation isconducted following Eq. (4.1), which computes the pixel-by-pixel modulationdepth (amplitude) at the illuminating AC frequency:

I ¼ffiffiffi2

p

3

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiðI1 � I2Þ2 þ ðI2 � I3Þ2 þ ðI3 � I1Þ2

q, (4.1)

where I1, I2, and I3 represent raw images corresponding to the three projectionphases.

The demodulated images of the tissue are then calibrated against theircounterparts from a phantom with known diffuse reflectance to producediffuse reflectance maps of the tissue under DC and AC illumination. Withdiffuse reflectance measurements at a minimum of two spatial frequencies,one can calculate ma and ms

0 values at each pixel through an analytical orMonte Carlo-based inverse model.5,7

After measuring ma at multiple wavelengths, the concentration of tissuechromophores such as oxy- and deoxyhemoglobins can be extracted usingBeer’s law, shown in matrix form in Eq. (4.2), where ε and c refer to thechromophore extinction coefficient and chromophore concentration, respec-tively:

264maðl1Þ

..

.

maðljÞ

375 ¼

264ε1ðl1Þ : : : εiðl1Þ

..

. . .. ..

.

ε1ðljÞ : : : εiðljÞ

375264c1...

ci

375: (4.2)

The ε values of chromophores (such as oxy- and deoxyhemoglobin, water,and lipids) are available in the literature.8,9 Equation (4.2) can be solvedfor unknown tissue chromophore concentrations c1, c2,. . . , ci using matrixinversion or regression methods, provided measurements of ma have beenobtained at j ≥ i independent wavelengths.

4.2 The Unique Capabilities of SWIR Light for QuantifyingWater and Lipid Content in Tissue

Almost all prior SFDI work utilizes VIS- to NIR-wavelength bands, oftenbetween 640 and 850 nm,10 where the absorption peaks of oxy- anddeoxyhemoglobins are prominent. These bands and the absorption spectra ofthe major tissue chromophores in the visible, NIR, and SWIR wavelengthregions—based on typical concentrations found in premenopausal breasttissue—are shown in Fig. 4.1.17 Oxy- and deoxyhemoglobins are importantphysiological parameters that can be used to track tissue oxidative

143Short-Wave Infrared Meso-Patterned Imaging

4.5 Examples of Potential Biomedical Applications forSWIR-MPI

SWIR-MPI has multiple potential applications in both preclinical and clinicalsettings. We briefly introduce several examples, including inflammationmonitoring, tumor lipid mapping, brown fat classification, and in vivo bloodlipid characterization. The experimental conditions and full analyses arepresented in Zhao et al.13

First, SWIR-MPI was used for longitudinal tracking of edema, which isoften associated with inflammation. As an experimental demonstration,10 mL of phosphate-buffered saline (PBS) or 10 mL of proinflammatory12-O-tetradecanoylphorbol 13-acetate (TPA) was injected subcutaneouslyinto the flank of BALB/c mice. SWIR-MPI was used to track the local watercontent for 2 hours post-injection. The measured changes in water content 2hours after injection are shown in Fig. 4.7(a), demonstrating a significant

Figure 4.7 (a) Changes of water content induced by inflammation in an in vivo mousemodel. (b) Lipid content mapping within a tumor cross section. (c) Subcutaneous brown fatclassification through intact skin (BAT: brown adipose tissue; WAT: white adipose tissue).(d) SWIR-MPI-based index for blood lipids, measured on the back of the hand in a humansubject.

154 Chapter 4

Chapter 5

Short-Wavelength InfraredHyperspectral Imaging forBiomedical ApplicationsLise Lyngsnes RandebergNorwegian University of Science and Technology (NTNU), Trondheim, Norway

Julio HernándezHySpex, Norsk Elektro Optikk AS, Oslo, Norway

Emilio CatelliUniversity of Bologna, Ravenna Campus, Italy and Norwegian University ofScience and Technology (NTNU), Trondheim, Norway

5.1 Introduction5.2 Medical Hyperspectral Imaging5.3 Hyperspectral Instrumentation and Setup5.4 Hyperspectral Data Collection5.5 Hyperspectral Data Analysis5.6 Data Analysis and Simulations5.7 Chemometric Tools and Methods from Spectroscopy

5.7.1 Spectral preprocessing5.7.2 Unsupervised methods

5.7.2.1 Principal component analysis5.7.2.2 Cluster analysis5.7.2.3 Methods for purest spectra extraction

5.7.3 Supervised method: regression5.8 Machine Learning and Artificial IntelligenceReferences

161

genitourinary systems.”However, despite their extensive review, Ortega et al.5

and Gao and Smith9 list few papers on SWIR, leaving SWIR as a relativityunexplored area of HSI and an area for future efforts.

Gowen et al.19 review applications of HSI in microbiology and coverseveral applications in the SWIR range, but the applications are mainlyrelated to food science. However, applications such as detection of fungi onsurfaces and pathogenic bacteria (such as Staphylococcus epidermidis andEnterobacteria in water, and biofilms from Escherichia coli on microfluidiccells) are highly relevant for biomedical applications.101–105 In general, foodsciences have provided a large number of relevant studies on characterizationand detection of bacteria that could be adapted to a medical context.

Skin—the most accessible organ on the human body and one of the largestorgans—is the organ most explored by HSI, including characterization ofhealthy tissue and conditions of pathologies affecting the skin. Initial studiesinclude examples on circulatory changes and skin oxygenation in diabeticfeet.93 Allergic contact dermatitis, burns, and ulcers are more recent examplesof HSI of skin75,93,94 (Fig. 5.5). Randeberg75 presents hyperspectral data in theSWIR range, discusses spectral characteristics of spectra from a burn model,and shows that unsupervised classification methods such as k-means could beapplied for burn classification.

In the field of forensics, HSI has been used to investigate crime scenes, toidentify stains of biological fluids and blood on soft materials such as clothing,and for characterizing skin bruises.73,74,106–108 Bruises are caused by rupturedblood vessels and leakage of blood into the tissue. Immediately after thetrauma, there will be a wheal and flare reaction, which is characterized byswelling and fluid accumulation.109 It has been shown that, in the SWIRspectral range, it is possible to differentiate between blood and other fluidsaccumulating in the injured area74 (Figs. 5.6 and 5.7 show the spectra frombruised skin in subjects with skin types II and IV). These spectra clearly showthat the influence from pigmentation is limited in the SWIR spectral range.Paintballs induce circular bruises with an undamaged central spot, due tocentral compression of the skin directly underneath the paintball, and theshear force at the edge of the paintball causes vessel rupture. When imaging afresh paintball bruise in the SWIR, the central area will appear swollen, whilea rapidly developing hemorrhage can be seen at the edges.

5.3 Hyperspectral Instrumentation and Setup

HSI was initially developed for remote sensing and satellite imaging. In 2005,Sellar and Boreman classified imaging spectrometers for HSI34 and defined ataxonomy for imaging spectrometers. According to Boreman’s definitions,and highlighted in an overview provided by Wu and Sun,13 hyperspectralinstrumentation and data collection is divided into four types. The first type is

166 Chapter 5

cross-validation (CV) temporarily removes part of the data to simulate anindependent test set.166

With either a new validation test set or an independent validation test set,we can evaluate the results of the calibration model using two equations: theroot mean-square error (RMSE) equation and the R2 coefficient, both ofwhich are terms for the calibration, cross-validation, and predictive ability.Low values of the RMSE with an R2 coefficient close to 1 indicate goodperformances of the model in terms of predictive ability:

RMSE ¼

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiXni¼1

ðyi � yiÞ2

n

vuuut(5.9)

R2 ¼ 1�

Xni¼1

ðyi � yiÞ2

Xni¼1

ðyi � yiÞ2, (5.10)

where y and y (mean) are experimental, y is the predicted value for sample i,and n is the total number of samples.

5.8 Machine Learning and Artificial Intelligence

It is no doubt that machine learning [a form of artificial intelligence (AI)] andespecially deep learning can greatly enhance HSI. By using appropriateauto-encoders, the algorithms can work on data without dimensionalityreduction, noise removal, or separate feature extraction. However, there aredisadvantages. In 2018, a workshop was held to develop a roadmap forfoundational research on AI in medical imaging.167 Researchers highlightedseveral state-of-the-art applications of AI in medicine, which are as follows:

1. New image reconstruction methods that efficiently produce imagessuitable for human interpretation from source data.

2. Automated image labeling and annotation methods—includinginformation extraction from the imaging report, electronic phenotyp-ing, and prospective structured image reporting.

3. New machine-learning methods for clinical imaging data—such astailored, pretrained model architectures, and federated machinelearning methods.

4. Machine-learning methods that can explain the advice they provide tohuman users (i.e., explainable AI).

191Short-Wavelength Infrared Hyperspectral Imaging for Biomedical Applications

Chapter 6

VIS–SWIR Wideband Lens-FreeMicroscopic ImagingZiduo Lin, Abdulkadir Yurt, Geert Vanmeerbeeck, Murali Jayapala, ZhenxiangLuo, Jiwon Lee, Joo Hyoung Kim, Vladimir Pejovic, Epimitheas Georgitzikis,Pawel Malinowski, and Andy LambrechtsImec—Interuniversity Microelectronics Center, Leuven, Belgium

6.1 Introduction6.2 System Development and Evaluation

6.2.1 System development6.2.2 Quantum dot sensor6.2.3 System performance evaluation

6.3 Applications6.3.1 Silicon inspection and measurement6.3.2 Cell and tissue imaging6.3.3 Wide-range multispectral LFI

6.4 Future ProspectsReferences

6.1 Introduction

Microscopes are an integral part of most biomedical research. Over the lastseveral years, microscopy has been utilized with wavelengths of light beyondthe visible spectrum in the short-wavelength infrared (SWIR). SWIRmicroscopy provides several advantages over visible and near-infrared(NIR) microscopy, including lack of (or minimal) autofluorescence, low lightabsorption by blood and tissues, and reduced scattering. However, extendingthe spectral range of microscopic instruments from the visible to the SWIR ischallenging because of optical equipment such as sensors and othercomponents. Digital image sensors typically provide good sensitivity butonly in a limited spectral range. They can be used for imaging in the visible or

209

Chapter 7

SWIR Fluorescence and MonteCarlo Modeling of Tissues forMedical ApplicationsTatsuto Iida, Shunsuke Kiya, and Kosuke KubotaMaebashi Institute of Technology, Maebashi, Japan

Akitoshi SeiyamaKyoto University, Kyoto, Japan

Takashi JinRIKEN Center for Biosystems Dynamics Research, Suita, Japan and OsakaUniversity, Osaka, Japan

Yasutomo NomuraMaebashi Institute of Technology, Maebashi, Japan and RIKEN Center forBiosystems Dynamics Research, Suita, Japan

7.1 Introduction7.2 Monte Carlo Models in Multilayered Media (MCML)

7.2.1 Calculation routine7.2.2 SWIR photon migration

7.3 Fluorescence Monte Carlo Simulations7.3.1 Point source of fluorescence MCML for cerebral angiography7.3.2 Spherical source of fluorescence MCML for breast cancer

7.3.2.1 Overview of the model7.3.2.2 Fluorescence photon migration in MCML: breast model

7.4 SWIR Fluorescence Monte Carlo Model in Voxelized Media (MCVM) forBreast Cancer

7.4.1 The breast model7.4.2 Image processing and implementation of the model7.4.3 Excitation gradient

229

restricted by absorption and scattering, and the absorption and scatteringcoefficients at 488 nm are larger than those at 780 nm (Table 7.2). Notably,almost no fluence is observed at 488 nm at a depth less than 1.0 cm, eventhough a fluorophore-labeled cancer with a diameter of 1.0 cm is localized in adeeper area of the circle, which is indicated by dotted lines [Fig. 7.9(a)].Therefore, no fluorophores within the cancer are excited and no emissionphotons are observed [Fig. 7.9(b)].

In contrast, NIR excitation photons at 780 nm extensively propagate in themodel, and the weights are partially absorbed [Fig. 7.9(c)]. Compared with theregion near the origin where excitation photons enter, fluence in the cancer isweak and the difference in fluence among grids within the cancer is alsominor. Therefore, as a first step to establish the breast cancer model, it may beappropriate to generate emission photons with a constant weight at arandomly determined position, but generation of emission photons with aninitial weight that is dependent on the position is necessary in subsequent steps,as described in the next section. As illustrated in Fig. 7.9(d), each photon thatemits from the fluorophore-labeled cancer propagates isotropically.

SWIR-excitation photons migrate the widest and deepest among the threetypes of excitation photons, and the grid area of the cancer has the highestfluence [Fig. 7.9(e)]. SWIR-emission photons migrate more widely and deeperthan NIR-emission photons [Fig. 7.9(f)]. In order to simplify the calculation,the heterogeneity due to the finite cancer volume is ignored, although theremay be an excitation gradient within a cancer having a finite volume. This isan important problem to be solved and is discussed in the next section.

7.4 SWIR Fluorescence Model in Voxelized Media (MCVM) forBreast Cancer

In the final section of this chapter, a fluorescence-labeled cancer is embeddedinto the model reflecting a realistic breast structure. In the previous section,the breast tissue is assumed to have a multilayered structure made of skin andfat. However, in addition to the two components of the tissue, breast tissuecontains mammary glands with a complex morphology, which is made ofducts and lobes. Breast cancer patients usually develop breast cancer in theduct. To develop a realistic breast cancer model, an anatomical dataset of thebreast is implemented in the Monte Carlo model.58 In the previous section,fluorophores within the spherical cancer receive a constant energy fluencefrom the excitation photons. Here, fluorophores are excited in a manner thatdepends on the excitation energy fluence of each voxel within the cancer.Using MCVM, which is more realistic than MCML, the significance of SWIRfluorescence imaging of breast cancer is discussed.

244 Chapter 7

Chapter 8

Multimodal SWIR Laser Imagingfor Assessment and Detectionof Urothelial CarcinomasGustavo Castro-OlveraICFO, The Barcelona Institute of Science and Technology, Castelldefels, Spain

Simone Morselli, Mauro Gacci, Sergio Serni, Andrea Liaci, andRosella NicolettiUniversity of Florence, Florence, Italy

Pablo Loza-AlvarezICFO, The Barcelona Institute of Science and Technology, Castelldefels, Spain

8.1 Introduction8.1.1 Epidemiology8.1.2 Aetiology8.1.3 Histopathology and staging8.1.4 Clinical presentation8.1.5 Diagnosis8.1.6 Treatment8.1.7 Diagnostic needs in clinical practice

8.2 Role of Multimodal SWIR Laser Imaging8.2.1 SWIR8.2.2 Multimodal microscopy8.2.3 Nonlinear optics for microscopy8.2.4 Two-photon excited fluorescence (TPEF)8.2.5 Second-harmonic generation (SHG)8.2.6 Third-harmonic generation (THG)

8.3 SWIR Optical Windows8.3.1 First biological window8.3.2 Second and third biological windows

261

Chapter 9

SWIR Fluorescence Endoscopyfor Tumor ImagingYongkuan Suo and Hongguang LiuNortheastern University, Shenyang, China

Zhen ChengStanford University, Stanford, United States

9.1 Introduction9.2 Endoscopic Imaging9.3 SWIR Fluorescence Endoscopic ImagingReferences

9.1 Introduction

Interventional therapy, supported by medical imaging (angiography,computed tomography, magnetic resonance, or endoscopy), uses the naturallumen of the human body to treat a blood vessel or lesioned part of the body.Interventional therapy has the advantages of minimal trauma, few complica-tions, quick curative effects, strong reproducibility, and short recovery time forpatients. In addition, interventional therapy has been widely used in medicaltreatments of vascular thrombolysis, in vascular stents, and for diseases ofinternal organs—such as the lungs, esophagus, stomach, and intestines—andof the abdominal cavity.

For the treatment to be done correctly, the process of interventional therapyneeds to be guided and monitored by an imaging device in order to preciselyreach the lesion. For example, coronary angiography, thrombolysis, and stentimplantation in the treatment of angina pectoris and acute myocardial infarctionare typical vascular interventional therapy technology applications. However,because of the poor opacity of blood and the complexity of the vascular networkthrough various tissues, the images are usually obtained by radiological imaging

287

Chapter 10

Short-Wavelength InfraredHyperspectral Imaging toAssess GastrointestinalStromal Tumors During SurgeryToshihiro TakamatsuTokyo University of Science, Noda, Japan and National Cancer Center, Kashiwa,Japan

Hiroaki IkematsuNational Cancer Center, Kashiwa, Japan and National Cancer Center HospitalEast, Kashiwa, Japan

Hiroshi TakemuraTokyo University of Science, Noda, Japan

Hideo YokotaRIKEN Center for Advanced Photonics, Wako, Japan

Kohei SogaTokyo University of Science, Noda, Japan

10.1 Shortwave Infrared Imaging10.2 Hyperspectral Imaging10.3 Data Processing Methods for Hyperspectral Imaging10.4 Distinction of Gastrointestinal Stromal Tumors by SWIR-HSI10.5 Wavelength Band Reduction Method for Hyperspectral Data10.6 Development of SWIR-HSI Devices for Clinical Applications10.7 SummaryReferences

301

Chapter 12

Transparent PolycrystallineCeramic Cranial Implantwith Photonic Functionalityin the SWIRSantiago Camacho-LópezCentro de Investigación Científica y de Educación Superior de Ensenada,Ensenada, Mexico

Nami Davoodzadeh and David L. HalaneyUniversity of California, Riverside, United States

Juan A. Hernández-CorderoUniversidad Nacional Autónoma de México, Ciudad de México, Mexico

Guillermo AguilarUniversity of California, Riverside, United States and Texas A&M University,College Station, United States

Gabriel R. CastilloCentro de Investigación Científica y de Educación Superior de Ensenada,Ensenada, Mexico; and Universidad Nacional Autónoma de México, Ensenada,Mexico

Antonio Cisneros-Martínez and Beatriz Coyotl-OcelotlInstituto Nacional de Astrofísica, Óptica, y Electrónica, Tonantzintla, México

Roger ChiuUniversidad de Guadalajara, Lagos de Moreno Jalisco, México

Julio C. Ramírez-San-Juan and Rubén Ramos-GarcíaInstituto Nacional de Astrofísica, Óptica, y Electrónica, Tonantzintla, México

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Camacho-López are working on different strategies to efficiently coupleoptical fibers in and out of the waveguides, to make it possible to bring lightfrom an external source into the brain tissue, for the purpose of diagnosticsand/or therapeutic treatment of specific brain lesions. We have alsoconsidered the possibility of drug photoactivation under appropriate lightexposure, aimed at localized tissue that is the target of the drug providedthrough systemic delivery.

Researchers broadly recognize nc-YSZ as one of the most useful high-temperature structural materials due to its favorable ionic conductingproperties,58 high-temperature stability,59 record toughness,46 and provenbiocompatibility.26 While these properties make nc-YSZ a promising materialfor extreme integrated photonic applications, this material has been largelyoverlooked. This is because YSZ is not generally considered an opticalmaterial, owing to the poor/extant optical quality of conventionally producedYSZ polycrystals. However, thanks to work by Garay et al.46 on the CAPADtechnique, nc-YSZ is now produced in optical grade. Thus, a multidisciplinaryeffort has been made to integrate the WttB platform based on the nc-YSZceramic.

We can summarize our findings by highlighting the fact that wedemonstrated the feasibility of femtosecond laser writing of waveguides inpolycrystalline nc-YSZ. Prior to our work, direct laser writing (DLW) ofphotonic structures in ceramics was largely untested. This is because todaythere are only a limited number of groups worldwide that can produce optical-grade NC ceramics. This DLW in ceramics technique represents a newdirection for integrated photonics. Moreover, NC ceramics offer hightemperature/chemical stability, superior mechanical toughness, and greaterfabrication flexibility when compared to glasses and single crystals.

12.4 Imaging through Highly Scattering Media

Propagation of light in biological tissues is a popular research topic since lightcan provide useful information for diagnosis and treatment of many medicalconditions; in particular, traumatic brain injury and brain disorders (such asAlzheimer’s disease, dementias, brain cancer, epilepsy, mental disorders,Parkinson’s, stroke, and transient ischemic attack) are the most-difficult-to-treat medical conditions, given the difficulty of accessing the brain. For suchreasons, light-based techniques are promising medical options. However, lightpenetration is hindered by light absorption and scattering from the tissue andskull. Fortunately, tissue and bones are more translucent—allowing light topenetrate deeper—but are also heterogeneous, resulting in distorted wave-fronts and light scattering in many directions. Visualization of blood vesselsis critical to identify abnormalities and measurement of blood flow, butimage-bearing light is rapidly attenuated, and the spatial information is lost as

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12.5 Optical Fiber Probes for Diagnostics and Therapeutics

It is clear that nc-YSZ provides attractive optical and mechanical featuresfor the WttB platform. In particular, it offers adequate transparency overthe SWIR optical regions that allow for implementing procedures fortherapeutics and diagnostics with optical techniques. For instance, imagingmethods and PDT can be readily performed through this material, and useof OCAs may further extend the versatility of the WttB platform. Someprocedures however may still require access to deeper regions of the brain,and it is for these purposes that we have been exploring novel approachesfor developing optical fiber probes. In particular, we have focused onfabrication of sensors and devices that could serve either as auxiliary toolsfor therapeutic and diagnostic procedures, or as building blocks for fiber-optic probes—enabling thermal-based therapeutics such as ablation andhyperthermia. These are of interest for treating cancerous tissue and,combined with other established therapeutic techniques (e.g., chemotherapyor radiotherapy), have yielded promising results for reduction or eliminationof brain tumors.

Our approach for fabricating fiber devices is based on combiningsilica-based fibers with polymeric materials of different functionalities. Wehave explored, in particular, the use of PDMS—a polymer with remarkablethermal, mechanical, rheological, and biological properties85,86 that iscommon and easy to process. Through simple mixing procedures, we havedemonstrated that PDMS can serve as a host for different nanomaterials,including carbon-based structures and fluorescent powders.87,88 This methodyields polymer composites that can be easily fabricated and processed to beincorporated into optical fibers, or simply used as membranes withinteresting effects that can be triggered by infrared light irradiation.87 Whilefluorescent polymer composites are useful for developing sensors forfluorescent thermometry,88 PDMS composites incorporating carbon nano-particles (CNPs) serve for generating thermal effects in a highly efficientmanner. These PT effects are useful for a wide range of applications (refer toRef. 87 for some examples) and can be further exploited to fabricate otherdevices such as fiber-optic sensors. We describe temperature and deforma-tion sensors as well as PT devices that have been developed within the WttBresearch framework. These devices are sought as building blocks for more-complex devices such as fiber-optic probes for therapeutic procedures.

12.5.1 Fiber-optic temperature sensors

The temperature sensors are based on the fluorescence features of the PDMScomposites; these incorporate rare-earth-doped materials that have beenpreviously optimized to generate green upconversion (UC) luminescence.

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Chapter 13

SWIR Hyperspectral Imagingto Assess NeurocognitiveDisorders Using Blood PlasmaSamplesRaquel Leon, Abian Hernandez, and Himar FabeloInstituto Universitario de Microelectrónica Aplicada, Universidad de Las Palmas deGran Canaria, Las Palmas de Gran Canaria, Spain

Samuel OrtegaInstituto Universitario de Microelectrónica Aplicada, Universidad de Las Palmas deGran Canaria, Las Palmas de Gran Canaria, Spain and Nofima—The NorwegianInstitute of Food, Fisheries and Aquaculture Research, Tromsø, Norway

Francisco Balea-Fernández and Gustavo M. CallicoInstituto Universitario de Microelectrónica Aplicada, Universidad de Las Palmas deGran Canaria, Las Palmas de Gran Canaria, Spain

13.1 Introduction13.2 Materials and Methods for Generating a Hyperspectral-SWIR Blood

Plasma Database13.2.1 Blood plasma sample preparation and HS-SWIR setup for data

acquisition13.2.2 HS image preprocessing13.2.3 Blood plasma HS dataset partition13.2.4 Statistical preprocessing approach

13.3 Processing Framework of HS-SWIR Blood Plasma Samples13.3.1 Machine learning approach13.3.2 Deep learning approach13.3.3 Evaluation metrics

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13.4 Experimental Results and Discussion

In this section, the results obtained using the proposed ML and DLapproaches for processing the SWIR blood plasma samples are presented. Thesection is divided into the presentation of the results obtained using thevalidation and test sets, and also employing different data normalizations.

13.4.1 Validation classification results

The validation set was employed to optimize the hyperparameters of the MLalgorithms as explained in Section 13.3.1. The results obtained afterperforming a grid search using the ML approach are presented in Table 13.2,where the selected hyperparameters were those that achieved higher AUCvalues. Furthermore, one can observe in Table 13.2 that the AUC values wereobtained applying different data normalizations, where the SVM classifierwith a linear kernel (SVM-L) achieved the higher AUC value for each datanormalization type. The data normalization has no effect on the AUC resultsobtained using the RF classifier. In the case of the SVM-L and KNNclassifiers, the data normalization deteriorates the AUC results with respect tothe use of the data without normalization. These results suggest that datanormalization does not necessarily correspond to an improvement in theclassification performance. Hence, the best classification model is obtainedwith the SVM-L without applying data normalization, achieving an AUC of0.72.

Considering the DL approach based on the MLP classifier described inSection 13.3.2, the results obtained are presented in Table 13.3 with andwithout applying different data normalizations. Unlike the previous results

Table 13.2 Grid search results using different ML classifiers with and without applying thedifferent data normalizations (where HP refers to hyperparameters).

Normalization type Classifier HP

Grid search

Initial/step/final Optimal HP AUC

None SVM-L C 2–20/22/220 2–14 0.720KNN NT 1/2/600 17 0.612RF KNN 1/2/600 197 0.625

NOR SVM-L C 2–20/22/220 2–8 0.707KNN NT 1/2/600 11 0.573RF KNN 1/2/600 197 0.625

SOR SVM-L C 2–20/22/220 2–8 0.706KNN NT 1/2/600 7 0.573RF KNN 1/2/600 197 0.625

BOR SVM-L C 2–20/22/220 2–8 0.707KNN NT 1/2/600 7 0.572RF KNN 1/2/600 55 0.625

423SWIR Hyperspectral Imaging to Assess Neurocognitive Disorders

Chapter 15

Use of a SWIR Otoscope in theAssessment of Pediatric andOther ConditionsNirvikalpa Natarajan, Yu-Jin Lee, and Tulio A. ValdezStanford University, Stanford, United States

15.1 Introduction15.2 Middle Ear Anatomy15.3 Pathophysiology of Middle Ear Infections15.4 Diagnosis: Current Modalities and Challenges15.5 SWIR15.6 Preclinical Studies: Optical Properties of the Human Tympanic

Membrane15.7 Preclinical Studies to Evaluate SWIR Imaging

15.7.1 Ex vivo analysis of human tympanic fluid15.7.2 Analysis of a middle ear fluid phantom in a middle ear model

15.8 Fluorescence Chemical Sensors in Conjunction with SWIR ImagingTools for Detecting Otitis Media in a Murine Model

15.9 SWIR Otoscope Design15.10 Clinical Studies

15.10.1 SWIR imaging of human middle ear anatomy in adults15.10.2 SWIR otoscopy in a pediatric population

15.11 ConclusionReferences

15.1 Introduction

Otitis media (ot-ear, itis-inflammation, media-middle ear) is one of the mostcommon illnesses diagnosed in children. Globally, with an incidence of over709 million,1–3 it is the most common reason for administering antibiotics to

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Chapter 16

Use of an OCT System in theShort-Wavelength InfraredRegion: ApplicationsPauline JohnSri Sivasubramaniya Nadar (SSN) College of Engineering, Kalavakkam, India

Vani DamodaranSRM Institute of Science and Technology, Kattankulattur, India

Nilesh J. VasaIndian Institute of Technology Madras (IITM), India

16.1 Introduction16.2 Optical Coherence Tomography (OCT)

16.2.1 Basic principles of the OCT technique16.2.2 Different types of OCT systems

16.2.2.1 Time domain OCT system16.2.2.2 Frequency domain OCT system

16.2.2.2.1 Spectral domain OCT16.2.2.2.2 Swept source OCT

16.2.2.3 Functional OCT: polarization-sensitive OCT16.3 Application I: SWIR OCT for Dental Imaging

16.3.1 Dental caries16.3.2 Imaging of dental caries16.3.3 Restoration and secondary caries

16.4 Application II: SWIR OCT for Glucose Monitoring in the AnteriorChamber of the Human Eye

16.4.1 Diabetes16.4.2 Glucose monitoring techniques

16.4.2.1 Principles of spectral-domain differential-absorptionlow-coherence interferometry (SD-DA-LCI)

16.4.2.2 Theory of DA-based SD-LCI

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Chapter 18

Advances in SWIR DeepTissue Imaging UsingMachine LearningLaura A. SordilloAllosteric Bioscience, Inc., New York, United States

Diana C. SordilloVanderbilt University, Nashville, United States

18.1 Introduction18.2 Short-Wavelength Infrared (SWIR)18.3 Deep Learning Models

18.3.1 Overview of deep learning18.3.2 Popular DL models in biophotonics

18.4 Machine Learning Techniques, SWIR, and Disease18.4.1 Machine learning and biophotonics18.4.2 Machine learning and SWIR

18.5 ConclusionReferences

18.1 Introduction

Recently, machine learning (ML), a form of artificial intelligence (AI), hasemerged as an important technique in biophotonics, an interdisciplinary fieldof science that merges the studies of light (photons) and medicine. Applica-tions of ML algorithms to medicine include data preprocessing, featuredetection, drug discovery, and disease classification, among others.1 In fact, ithas been suggested that the use of ML algorithms, in particular deep learning(DL), may revolutionize biophotonics.

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the SWIR range at ~1200 and ~1700 nm, corresponding to NIR windows IIand III, respectively.

The Leukos SC laser operates with a 1550-nm pump and delivers 40-pspulses at 6 MHz. Figure 18.3 shows the power spectrum of the SC laser lightsource with a maximum power of ~500 mW/nm in the SWIR range.

When compared with a conventional broadband lamp light source, the SClaser light source, which provides a greater number of outgoing photons, wasable to reach a greater depth of penetration and give higher-quality images ofabnormalities hidden behind thick tissue. While SWIR light can providemajor benefits, such as an increase in penetration depth and a reduction ofabsorption and scattering of light, acquisition of high-quality optical imagesthrough thick tissue (even with a powerful SC laser light source) remains achallenge. Thus, there is still a need for additional techniques that can provideefficient assessment and detection as well as high-quality optical images ofabnormalities hidden beneath thick tissue. ML techniques may be utilized toenhance these biophotonic techniques.

18.3 Deep Learning Models

18.3.1 Overview of deep learning

Machine learning (ML) is a branch of artificial intelligence (AI) that usessample data to make predictions about new, previously unseen data.20,21 Deeplearning (DL) is a class of ML algorithms based on artificial neural networks(ANNs). An ANN is a ML algorithm that learns through labeled data(supervised learning) and was originally modeled after the learning process ofthe human brain. Introduced in 1958 by Rosenblatt, the first ANN was calledthe perceptron. In the perceptron model, inputs x = (x1, . . . , xd) are first

Figure 18.3 Pump spectrum from the Leukos SC laser light source, with windows I, II, III,and IV highlighted.

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probability of x, given y), whereas discriminative models calculate theconditional probability p(y|x) (the probability of y, given x).31

In the simplest case of a GAN, both G and D can be taken to be MLPs.Given a training set, G generates new data with the same statistics as thetraining set. D is trained to maximize the probability of assigning the correctlabel to both the training samples and the samples generated by G.Simultaneously, G is trained to maximize the error rate of D.30 GANs canbe for image classification, image manipulation and generation, and super-resolution.32 Shrivastava et al. developed a GAN capable of generating highlyrealistic images.33 Thus, GANs can be used to create more training sampleswhen there is limited labeled training data available. Ledig et al. developedSRGAN, a GAN for image super-resolution.34

ResNets were introduced by He et al. in 2015, winning first place in theclassification task at ILSVRC 2015 by achieving a 3.57% error on theImageNet test set.35 In ResNets, instead of hoping the stacked layers fitthe desired underlying mapping HðxÞ, the stacked nonlinear layers fit themapping F ðxÞ ¼ HðxÞ � x. Then, the original mapping can be recast intoF ðxÞ þ x, which can be realized by a feed-forward neural network with skipconnections. ResNets can be trained by backpropagation and are consideredto be easier to optimize than CNNs and ANNs. They are used in classificationtasks and data processing.

In 2015, Ronneberger et al. proposed the U-Net, a modification of a fullyconnected convolutional network for biomedical image segmentation.36 Fullyconvolutional networks (FCNs) were first introduced by Long et al. in 2015.37

FCNs take the usual contracting CNN architecture and supplement it withsuccessive layers, but the pooling layers are replaced by upsampling operators.This allows for increased resolution of the output.

U-Nets are a modification of the FCN architecture. The U-Netarchitecture consists of a contracting path and an expansive path. Thecontracting path has the usual CNN architecture. The expansive path consistsof upsampling of feature maps followed by convolution. Only a few trainingimages are needed for U-Nets, which yield a more-precise segmentation thanFCNs.37

18.4 Machine Learning Techniques, SWIR, and Disease

18.4.1 Machine learning and biophotonics

The intersection of two advanced fields of science—AI (or ML) andbiophotonics—has caused an explosion of studies focusing on ways to assessand detect disease better than previously possible.38–43 ML has been appliedto biophotonic techniques, such as fluorescence and Raman spectroscopy, andto optical imaging (Fig. 18.5).

565Advances in SWIR Deep Tissue Imaging Using Machine Learning