Guidance of transseptal punctures for

left heart interventions using personalized

biomechamical models and volumetric

ultrasound imaging

Thesis Plan

Doctoral Program in Biomedical Engineering

Student:

Pedro André Gonçalves Morais Student nº UP201400020

Supervision team:

João Manuel R. S. Tavares (DEMec/FEUP, Portugal)

João Luís Araújo Martins Vilaça (ICVS/3B’s, Portugal)

Jan D’hooge (KULeuven, Belgium)

June of 2015

Summary

Access to the left atrium (LA) of the heart is required for several minimally

invasive cardiac interventions of the left heart, such as mitral valve replacement,

catheter ablation for atrial fibrillation or left atria appendage closure. Hereto, the atrial

septum is punctured using a catheter inserted in the right atrium (RA) via the venous

system under bidimensional fluoroscopic guidance. Although this approach has been

used for many years, complications and procedural failure are common. Moreover, the

exact location at which the septum needs to be traversed, in order to avoid these

complications and/or to enable reaching a given LA target site, is currently entirely

based on the physician’s experience.

The aim of this project is therefore to develop technologies to assist the

physician in performing transseptal punctures (TSPs). Hereto, subject-specific

biomechanical models based on finite element methods that allow optimizing the TSP

location pre-operatively will be developed. In addition, this interventional plan will be

fused with peri-interventional transesophageal volumetric echocardiography, for TSP

guidance. In this way, intra-procedural radiation exposure will be reduced,

complications will be avoided, and surgery time will be reduced.

i

Contents

1. Introduction ................................................................................................................... 2

1.1. Percutaneous Cardiac Interventions ...................................................................... 2

1.2. Transcatheter left-atria procedures ........................................................................ 2

1.2.1. Atrial anatomy ................................................................................................ 3

1.2.2. Left atrium access route .................................................................................. 5

1.2.3. Transseptal puncture technique ...................................................................... 5

1.3. Motivation .............................................................................................................. 8

1.4. Aims and Contributions ......................................................................................... 9

1.5. Document outline ................................................................................................. 10

2. State of the art ............................................................................................................. 12

2.1 Patient-specific anatomical cardiac models .......................................................... 12

2.2 Biomechanical simulation .................................................................................... 16

2.3 Image-fusion and catheter tracking techniques .................................................... 18

3. Methods….. ................................................................................................................ 26

3.1 Work package 1 – Development and validation of an intra-procedural guidance

framework … .............................................................................................................. 26

3.2 Work package 2 – Building a patient-specific anatomical model ........................ 28

3.3 Work package 3 – Creating a software environment for interventional planning

and simulation ............................................................................................................ 29

3.4 Work package 4 – Real-time image fusion for interventional guidance ................. -

……………………………………………………………………………………….30

3.5 Timetable .............................................................................................................. 32

4. Final Remarks ............................................................................................................. 34

5. References …………………………………………………………………………..36

ii

iii

Abbreviations

2D Two-dimensional

3D Three-dimensional

ASM Active shape model

BEAS B-spline Explicit Active Surface

BRK Brockenbrough

CS Coronary Sinus

CT Computed Tomography

CTA Computed Tomography Angiography

DOFs Degrees of freedom

DRRs Digitally reconstructed radiographs

ECG Electrocardiography

EM Electromagnetic sensor

FO Fossa ovalis

FOV Field of view

GHT Generalized Hough Transform

GPU Graphics processing unit

IAS Interatrial septum

iASD Iatrogenic Atrial septal defects

ICP Iterative closest point

IVC Inferior vena cava

LA Left atrium

LAA Left atrial appendage

LAO Left anterior oblique

LV Left ventricle

MV Mitral valve

MRI Magnetic resonance imaging

PCI Percutaneous cardiac interventions

PFO Patent Foramen ovale

RA Right atrium

RAO Right anterior oblique

RV Right ventricle

SVC Superior vena cava

SURF Speed up robust features

TA Transaortic

TEE Transesophageal echocardiography

TSP Transseptal puncture

US Ultrasound

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v

Figures list

Figure 1 - Heart anatomy [15]. ......................................................................................... 4

Figure 2 - Schematic of (a) transaortic route and (b) transseptal puncture technique

(adapted from [24]). .......................................................................................................... 6

Figure 3 - Transseptal puncture technique. (a) A dilator and sheath are placed into the

SVC using a guidewire; (b) a needle is inserted into the dilator until c) the SVC; the

needle is pull-down and two movements are detected, namely: (d) entrance into the RA

and (e) entrance into FO; (f) after FO identification the puncture is performed (adapted

from: [35]). ....................................................................................................................... 7

Figure 4 - Ecabert et al. methodology [57]. a) A rough contour is initially estimated

through a generalized hough transform approach. In (b) and (c) a global and local

adaptation of the model is performed, respectively. Finally, in (d) a deformable model

strategy is used to refine the contour. ............................................................................. 12

Figure 5 - Overview of the technique presented by Zheng et al. [63]. ........................... 14

Figure 6 - Schematic used by atlas-based approaches to segment MR images. This

method uses an affine registration (1) to roughly align the unseen image with the atlas.

The obtained transformations are posteriorly used to map the label images (2) into the

unseen image and generate a region-of-interest through a majority vote strategy (3).

Using only this region, a non-rigid registration (4) is used to align the different datasets.

The resulting deformation fields are used to transform the labels and generate the final

contour (5) [65]. .............................................................................................................. 15

Figure 7 - (a) Calibration cage used to align US world with probe position and (b)

radiopaque markers to fuse X-ray and US world [87]. ................................................... 20

Figure 8 - Workflow used to align MRI/CT, X-ray and US images. During the pre-

intervention stage, surface model of the target structure is generated. Additionally, the

esophagus centerline was manually segmented and included into the model. Regarding

intra-procedural stage, an initial alignment between the ultrasound and X-ray image is

performed via the US probe position. The abovementioned model is then manually

positioned in the X-ray image before being automatically registered via the ultrasound

image [88]. ...................................................................................................................... 21

Figure 9 - Overview of the proposed project. During the pre-procedural stage an

anatomical atrial model will be constructed from CT images. Moreover, biomechanical

vi

simulation will be used to estimate the optimal puncture position and to estimate the

optimal trajectory. During the intra-procedural stage, the high-resolution model will be

fused with real-time image acquisition techniques (3D TEE), transferring consequently

the planning data to the intra-procedural for the real procedure. Finally, during the intra-

procedural stage an augmented reality framework will be developed to guide the expert

for the optimal transseptal puncture location. ................................................................ 26

vii

Tables list

Table 1 – Overview of the main anatomic variation of the atria. ..................................... 8

viii

Introduction

1

Introduction

Introduction

2

1. Introduction

1.1. Percutaneous Cardiac Interventions

Percutaneous cardiac interventions (PCI) cover the minimally invasive

procedures where access to the heart is performed via blood circulation system. Instead

of the traditional open-chest surgery that requires a long surgical cut in the chest wall to

access the target organ, minimally invasive interventions use only a small hole through

the skin to access the vascular system and, consequently, to access the cardiac structure.

Since vascular access is used throughout PCI, a direct visualization of the target

is not possible and surgical instruments manipulation is cumbersome. As such, several

kind of equipment are required to guarantee a safe medical procedure, namely imaging

modalities (e.g. fluoroscopy or ultrasound imaging) to guide the expert throughout the

entire procedure; and transcatheter surgical tools (e.g. electrocautery needles, guidewire,

dilators, sheaths, contrast-injection tools) to ease the instruments manipulation.

Several advantages are associated with these minimally invasive techniques,

namely: less postoperative pain, reduced procedure time, improved cosmetics, less

blood loss, fewer procedural complications, lower costs and shorter hospital stay [1].

However, these techniques require experienced experts/operators and high-level

equipment in the surgical room (e.g. fluoroscopy-image acquisition, electrocautery

system). Furthermore, interventional complications can result in open-chest surgery and

interventional failures are possible.

Regarding the medical application of PCI, a high number of cardiac procedures

are currently performed using this approach, such as: cardiac valves replacement or

repair [2, 3], atrial or ventricular septal defect closure [4, 5], ablation for atrial

fibrillation [6], ablation for control of ventricular tachycardia [7], coronary interventions

[8] and cardiogenic shock [9].

1.2. Transcatheter left-atria procedures

A huge number of PCI are performed in left atrium (LA) chamber, being usually

applied in interventions, such as: catheter ablation for atrial fibrillation, pulmonary vein

isolation and left atrial appendage (LAA) closure. The importance of atrial fibrillation

was addressed in a projection study published in 2013 [10], reporting a total of 8.8

Introduction

3

million of patients with atrial fibrillation per year in the Europe Union, with predictions

indicating a 2-fold increase by 2060.

However, LA is the most difficult cardiac chamber to access percutaneously

[11]. Direct physical access to this structure is not possible, consequently hampering the

entire transcatheter procedure. As such, in the last years a high number of LA access

techniques were described in literature. These strategies reach the LA chamber via one

of the remaining cardiac chambers, which reduces the catheter dexterity and increases

the number of risks throughout the intervention.

During the next sub-sections these techniques are described and the advantages

and main complications of each approach are pointed out. Furthermore, some

considerations about atrial anatomy will be made.

1.2.1. Atrial anatomy

The atria are divided in two chambers (right and left atrium) by a muscular

septal wall.

The right atrium (RA) shows larger volume and thinner walls (approximately 2

mm) than LA. Anatomically, the superior RA is composed by the superior vena cava

(SVC) and the right atrial appendage (Figure 1) [12]. The inferior RA is constituted by

the inferior vena cava (IVC) and the tricuspid valve (Figure 1). The SVC receives the

blood from the superior part of the body, while the IVC returns the blood from the

inferior one. The tricuspid valve controls the blood circulation between the RA and the

right ventricle (RV). Furthermore, particular attention with the coronary sinus (CS)

position is required. The CS is a set of vessels that collect blood from the myocardium

draining into the RA. This structure is positioned between the orifice of the IVC and the

tricuspid valve (Figure 1) [12, 13].

On the other hand, LA is smaller with thicker walls (approximately 3 mm). LA

presents a cuboidal shape, being limited superiorly by four pulmonary veins and the left

atrial appendage (Figure 1) [12]. Inferiorly, the mitral valve (MV) is responsible by the

control of the blood circulation on the left heart [13]. As a final remark, it should be

noticed that: 1) aorta artery and pulmonary artery cover externally the LA [14]; and 2)

LA is separated from the esophagus by a thin fibrous pericardium [14].

A muscular structure, termed interatrial septal wall (IAS), is found between the

two atria. IAS is formed from the fusion of the septum primum (LA septum) and the

Introduction

4

septum secundum (RA septum) [1]. The fusion region is termed limbus (or “true

septum”) presenting a larger thickness. However, a depression can be detected in the

middle of the limbus, which is called fossa ovalis (FO) [9]. The FO is the thinnest

region of the IAS and it is composed by thin fibrous tissue [9]. Additionally, it has an

oval or circular shape and can only be detected from the RA [1]. Anatomically, FO

presents an expected average area between 1.5-2.4 cm2 and it is situated at the lower

part of the septum, between the IVC and the CS [1]. Since lowest thickness is found at

the FO, transseptal access of the LA is traditionally performed through this structure.

[12]. Beyond the FO, His Bundle can be also detected at the inferior IAS wall and it is

composed by myocardial cells that propagate the electric pulse from the atrioventricular

node until the ventricles [9].

Figure 1 - Heart anatomy [15].

Introduction

5

1.2.2. Left atrium access route

Two techniques are commonly used to access LA chamber, namely: transaortic

(TA) access and transseptal puncture (TSP). Recent studies indicated that both

strategies show similar success rates, procedure time and complication rate [11, 16].

In TA access, a catheter inserted over the femoral artery is retrogradely

advanced through the aortic valve towards the left-ventricle (LV). Posteriorly, the

catheter is rotated 180° and advanced throughout the MV until the LA chamber

(Figure 2a). Regarding the TSP, a catheter is inserted into the RA via the venous

system, through which a needle can be moved forward, in order to puncture the IAS

wall and consequently to access the LA (Figure 2b). Note that TSP technique

establishes a “more direct” access route, when compared with TA approach.

Nonetheless, in complex situations TSP technique can perforate a large vessel (e.g.

aorta) resulting in serious complications for the patient.

Despite the similar performance and safety between the two techniques, TA

route requires a 180° rotation of the catheter, complicating the manipulation of the

catheter and hampering, therefore, the procedure. As such, in the last years a superior

number of procedures based on TSP were registered [11].

1.2.3. Transseptal puncture technique

In the current section, a briefly description of the TSP procedure is presented.

Moreover, an overview of the traditional procedural complications is stated. TSP

procedure has been widely explained in literature [11, 17, 18], reporting the guidance

equipment and catheters used to safe puncture the IAS wall. The technique is guided

using the bidimensional fluoroscopy imaging and it is performed using a mechanical

Brockenbrough needle (BRK, St. Jude Medical, Minneapolis, MN, USA). Furthermore,

several auxiliary catheters are used to prevent puncture of vital structures. For instance,

catheters at the aorta, CS and His Bundle are commonly used. Regarding procedural

time, frequently 1 to 15 minutes are required to perform this task. [19-23].

Introduction

6

Figure 2 - Schematic of (a) transaortic route and (b) transseptal puncture technique (adapted from

[24]).

The procedure starts with the insertion of a guidewire (0.81-0.89 mm) into the

SVC using the right femoral vein access. This step is guided by anterioposterior

fluoroscopy view. The guidewire is used to define a safe route between the femoral vein

and the SVC. A dilator and sheath are also positioned into the SVC using the guidewire.

At this stage, the guidewire is replaced by the BRK needle with the needle being

maintained inside the sheath to prevent inadvertent punctures. Then, the assembly

(needle, sheath and dilator) is positioned on the FO region. The assembly is rotated until

4-5 clock position and posteriorly pull-down using left anterior oblique (LAO)

fluoroscopy view to control the assembly rotation. At this point, two movements will be

detected: the first indicates the entrance of the assembly into the RA; and the second,

which is less perceptible, occurs when the assembly is inside the FO region. At this

point, two movements will be detected: the first indicates the entrance of the assembly

into the RA; and the second, which is less perceptible, occurs when the assembly is

inside the FO region. A confirmation of the assembly position is achieved using the

right anterior oblique (RAO) direction of the fluoroscopy. Since puncture outside of the

FO region increases the risk of vital structures perforation and limit the maneuverability

of the catheter in the LA, exhaustive confirmation of the needle position should be

Introduction

7

performed [14]. Additionally, a confirmation of the actual position of the aorta, CS and

His Bundle is required to ensure a safe route for the puncture.

Finally, the puncture can be performed and the surgical tool can be introduced

into the LA, being used left atria pressure variation or contrast agents to confirm the

needle position. It should be noticed that a repetition of this entire procedure is required

when the assembly is not aligned with the FO or when the expert has doubts about the

assembly position.

Regarding the number of complications and failures, a low rate (approximately

1% of the procedures [11]) is commonly associated with the TSP technique. However,

the physician should be aware that: aortic root puncture, arterial air embolism,

pericardial tamponade, right or left atrial wall puncture, transient ST-segment elevation,

pleuritic chest pain, persistence of atrial septal defect and death are complications that

can be caused by this intervention [11, 20, 22, 25-29]. Furthermore, since TSP creates a

hole in the IAS, post-procedural complications are reported, such as persistent

iatrogenic atrial septal defects (iASD). iASD can originate serious complications (e.g.

mitral valve calcification, lower cardiac output, increased rate of paradoxical

embolism), consequently requiring a second procedure [24, 30-33].

As a final remark, it should be noticed that a different LA access site is required

when an abnormal anatomy is identified. These modifications are crucial to ensure the

maximum safety of the procedure and reduce the number of complications [34]. Table 1

presents an overview of the most common anatomical variations of the atria region.

Figure 3 - Transseptal puncture technique. (a) A dilator and sheath are placed into the SVC using a

guidewire; (b) a needle is inserted into the dilator until c) the SVC; the needle is pull-down and two

movements are detected, namely: (d) entrance into the RA and (e) entrance into FO; (f) after FO

identification the puncture is performed (adapted from: [35]).

Introduction

8

1.3. Motivation

Although TSP appears as a safe technique (complication rate lower than 1%)

with large application around the world, complications and failures are still a reality.

The optimal location at which the septum needs to be punctured in order to access the

LA is currently based on experience [11], which it is a suboptimal strategy that can

result in serious procedural complications, such as aortic root puncture, damage of the

atrial wall, atrial septal defect and ultimately in death [17].

Despite the high-success rate normally achieved by experienced surgeons the

same is not observed with non-experienced physicians. Yao et al. and Bayrak et al.

proved that significant higher failure rate (20% higher) and larger procedural time (two

times slower) are achieved by unexperienced interventionists, raising question about the

real safety of the TSP procedure and claiming for novel solutions in order to trainee the

physicians [38, 39].

This scenario is aggravated when a second TSP procedure is performed, due to

the difficulty to identify the scarred FO position. Hu et al. presented a comparison study

between one TSP and repeated TSP procedure, and the results proved that a higher

Table 1 – Overview of the main anatomic variation of the atria.

Anatomic

variations Description Difficulties Solution

Patent

foramen

ovale (PFO)

- Direct route between the

RA and the LA [36].

- LA access without any

puncture [36].

Since the PFO is located at the

anterior and superior part of the

IAS wall, pulmonary vein

procedures is not

straightforward [34, 36].

TSP should be

used, even in the

presence of PFO

[34].

Left atrium

dilation

- LA dilation results in a

posterior position of the FO

[37].

Higher risk of puncture an

undesirable structure [37].

Different TSP

site should be

used [37].

Abnormal

mechanical

properties

of the IAS

- Heart diseases can result

in elastic or thickened IAS

wall [21].

- Patients with previous

TSP procedure, present a

thickened IAS wall [20].

TSP can result in serious

complication for the patient,

such as, atria roof puncture or

aortic route puncture.

Moreover, TSP can fail [21].

Radio-frequency

(RF) needles

should be used

[21].

Abnormal

position of

the FO

- Superior position of the

FO is detected [14].

Superior LA access reduces the

maneuverability of catheter in

pulmonary veins procedures

[14].

Puncture the

inferior part of

the FO [14].

Introduction

9

number of procedural complications/failures (three times higher) were achieved with

repeated TSP [40].

Moreover, in the presence of abnormal atrial anatomy, different puncture

orientation and position should be used. As such, the physician needs to identify the

anatomical variation, assess the surrounding structures and correct the needle

orientation, using a simple bidimensional X-ray image. Obviously, this task is not

straightforward and novel equipment was included into the surgical room in order to

simplify the puncture procedure. RF needles [20, 26, 41], pre-planning based on high-

resolution imaging [42, 43], real-image acquisition through fiber optics systems [44],

electroanatomic mapping [45-47], intra-procedural 3D image acquisition through

transesophageal [48-50] and intracardiac echocardiography were proposed [22, 51, 52].

Nonetheless, each expert uses a different strategy with different equipment, missing a

consensus about the optimal strategy to perform this task. Furthermore, augmented-

reality frameworks that combine information obtained from multiple modalities and

guide the physician for the optimal puncture position are still missing [53, 54].

Finally, determining the optimal puncturing site is not straightforward, as not

only the ease of puncturing depends on the location but, also, the target site to be

reached in the LA has to be taken into account, as crossing the septum limits the

maneuverability and dexterity of the catheters and, thus, their ability to reach LA target

sites [34, 39, 55]. To date, this decision is entirely based on the experience of the

physician and secondary punctures are required regularly (15% of the procedures) as the

first one turns out inadequate. Should be noticed that secondary puncture is time-

consuming, frustrating, require large radiation dose and can originate serious

complications [56].

1.4. Aims and Contributions

The aim of this PhD project is to develop an integrated interventional planning

and guidance framework to assist the physician in successfully performing TSPs.

Hereto, patient-specific anatomical models will be combined with biomechanical

simulations and real-time image fusion. Specifically, the following algorithms and tools

will be developed, implemented and tested:

An algorithm for the automatic segmentation of the LA, RA and the

proximal parts of the inferior and superior vena cava on pre-interventional

Introduction

10

computed-tomography (CT) data, in order to build patient-specific

anatomical models.

Computational models for the most used LA catheters, in order to simulate

their maneuverability during the TSP intervention.

A patient-specific biomechanical (based on the finite element method) model

of the inter-atrial septum in order to simulate the puncturing at a given

location considering the local tissue properties, and estimate the trajectory of

the catheter and the target locations inside the LA.

Real-time image fusion of the anatomical model and the TSP planning with

intra-interventional transesophageal volumetric ultrasound images for TSP

guidance.

1.5. Document outline

The current document is divided into three chapters, namely: introduction, state-

of-the methods and methods.

In the next chapter, an overview of pre-procedural and intra-procedural

strategies is presented, indicating the techniques currently available to generate patient-

specific anatomical models, biomechanical simulation and image fusion approaches.

The third chapter presents a detailed explanation about the methods that we

intend to develop during the PhD project, in order to accurately guide the physician

throughout the TSP procedure.

Finally, the fourth chapter presents the final remarks of this work, indicating the

expected results and advantages of this novel framework.

State of the art

11

State of the art

State of the art

12

2. State of the art

Reaching the LA using a TSP technique was initially proposed in 1959 and has

become the standard approach for several left heart interventions [17, 48]. Since a

transcatheter approach is used, non-invasive image acquisition of the patient anatomy is

crucial. Moreover, pre-procedural planning based on high-resolution imaging

techniques is required to recognize complex procedures, to identify safe puncture site

and to define the optimal puncture route.

As such, in this chapter we present an overview of pre-planning cardiac

procedures techniques, namely: generation of patient-specific anatomical models and

biomechanical simulation techniques. Finally, an overview of intra-procedural strategies

is given.

2.1 Patient-specific anatomical cardiac models

In order to aid the physician throughout the pre-procedural planning, several

authors have proposed automatic and semi-automatic strategies to generate patient-

specific anatomical models from: CT, magnetic resonance (MRI) and ultrasound (US)

imaging. During this sub-section a particular interest on patient-specific anatomical

models of the atrial region is given, due to the importance of these chambers for the

current work.

Ecabert et al. (Philips Research Institute) presented a novel strategy for fully

automatic segmentation of the whole heart in 3D CT [57]. The proposed method relies

in two main-stages, namely: 1) heart localization (Figure 4a); and 2) segmentation

refinement using a deformable model (Figure 4b-d).

Figure 4 - Ecabert et al. methodology [57]. a) A rough contour is initially estimated through a

generalized hough transform approach. In (b) and (c) a global and local adaptation of the model is

performed, respectively. Finally, in (d) a deformable model strategy is used to refine the contour.

State of the art

13

The heart localization method was performed through an adapted 3D generalized

Hough transform (GHT), due to the high robustness and versatility to detect any

arbitrary shape in the target image presented by this strategy (Figure 4a). During the

second phase, a shape-constrained deformable model was applied to refine the whole-

heart mesh. This refinement was performed iteratively using two alternating steps,

combining therefore, parametric and deformable adaptation. Parametric adaptation starts

with a global resize (Figure 4b) of the mesh followed by a local adaptation of each

contour (Figure 4c). Finally, deformable adaptation was applied to guarantee optimal fit

between the resulting mesh and the patient anatomy (Figure 4d). Note that shape

knowledge prior was used to constrain the model evolution.

The current solution was tested on 37 3D-CT datasets with surface-to-surface

errors lower than 1 mm in all the cardiac structures. Regarding the computational time,

a total of 22 seconds per dataset was reported.

Daoudi et al. presented a deformable model strategy to segment the LA in 2D-

CT [58]. This method starts with contrast enhancement based on adaptive histogram

equalization, followed by morphological operators and a region growing technique to

create a coarse contour of the LA chamber. A refinement step is, finally, performed

using gradient vector flow technique.

The strategy was tested on 20 CT datasets, however only visual assessment was

performed. Furthermore, since bidimensional segmentation is performed, relevant

clinical indicators were not extracted from these images.

Almeida et al. suggested a semi-automatic strategy to delineate the LA chamber

in US image [59]. This strategy focused on the B-spline Explicit Active Surface

(BEAS) framework, which was previously proposed for left-ventricle segmentation in

US and MRI data [60, 61]. In both situations, high accuracy and lower computational

time were achieved. However, since LA presents a less regular shape than LV, an

adaptation of the BEAS parameters was presented. The current methodology was tested

on 20 volumetric sequences, proving that LA functional parameters can be derived from

the semi-automatic contours. Nevertheless, since the current work is only a preliminary

report, exhaustive validation of the method is missing.

Haak et al. proposed a novel strategy based on active shape models (ASM) for

segment multiple heart chambers in 3D ultrasound imaging [62]. Since ultrasound

shows a limited field-of-view (FOV), wide-view ultrasound images were manually

generated using several individual US records. The wide-view image was posteriorly

State of the art

14

semi-automatically segmented using a three-stage approach: 1) heart pose estimation; 2)

heart pose and shape estimation; and 3) refinement of the contour obtained in each

chamber. In each stage, a gamma mixture model was used to generate a blood-tissue

probability map, which it was subsequently fitted with an ASM model of the heart

chambers, generating consequently the optimal contour. Regarding the validation step,

single US image and wide-view US images were segmented using the abovementioned

techniques, showing a considerable improvement of Dice coefficient for the fused data.

Instead of deformable models approaches, Zheng et al. (Siemens Corporate

Research) proposed a novel machine learning strategy to automatically segment the

whole-heart in 3D CT volumes [63]. The proposed technique, termed marginal space

learning, identifies the optimal heart pose through a classification approach. A 9-

dimensional vector with position, orientation and scaling was used to train the classifier

method and generate, consequently, a full parameter space with a large number of

hypotheses (Figure 5). During the test step, a multi-stage strategy was used to identify

the optimal pose for each chamber. The method starts with a restricted number of

possibilities and increases the dimensionality of the problem in each stage. Finally, a

mean shape model of the heart model was deformed until the estimated optimal heart

pose, creating the final segmentation (Figure 5).

A total of 323 3D-CT volumes were used throughout the experimental

validation, presenting surface-to-surface errors lower than 1.6 mm for all the structures.

Regarding the computational time, a total of 2 seconds per volume were required [63].

Figure 5 - Overview of the technique presented by Zheng et al. [63].

State of the art

15

Based on machine learning techniques, Margeta et al. also presented a

supervised learning method for fully automatic left atrium segmentation from 3D

cardiac MR datasets [64]. The method starts with a blood-pool region extraction

through a simple threshold. However, since intensity homogeneity was observed

between the different chambers, multiple cardiac structures were detected. As such, the

authors applied a learning technique based on decision forest to identify the LA in the

resulting refined region. The current strategy was tested on 10 different datasets through

a leave-one-out approach. Regarding the results, unsatisfactory dice coefficients lower

than 70% was achieved, proving that the proposed strategy was not able to delineate the

entire cavity.

Zuluaga et al. focused on a multi-atlas approach to accurately segment the

whole heart from 3D MR and 3D computed tomography angiography (CTA) sequences

[65].

Figure 6 - Schematic used by atlas-based approaches to segment MR images. This method uses an

affine registration (1) to roughly align the unseen image with the atlas. The obtained

transformations are posteriorly used to map the label images (2) into the unseen image and

generate a region-of-interest through a majority vote strategy (3). Using only this region, a non-

rigid registration (4) is used to align the different datasets. The resulting deformation fields are

used to transform the labels and generate the final contour (5) [65].

State of the art

16

The method proposed by Zuluaga et al. relies on two steps: 1) region of interest

(ROI) localization through affine alignment between the unseen image and the atlas (see

(1) and (2) in Figure 6 and 2) whole-heart segmentation based on non-rigid registration

between the resulting ROI and the atlas (see (3-5) in Figure 6). The resulting

deformation fields were posteriorly combined to transform the labels from the atlas to

the unknown image, and consequently generate the final contour. Since a set of non-

rigid alignments were required to delineate the final contour, high computational time

was required.

This strategy was tested on 23 and 8 MRI and CTA datasets, respectively.

Interesting dice scores of 90.8% and 89% for the whole-heart chambers were reported

for each situation, validating this methodology. Regarding the computational time, a

total of 30 and 60 minutes were required in MR and CTA datasets, respectively.

Similarly, Kirisili et al. proposed a multi-atlas-based approach to segment the

whole heart from CTA data. The current strategy was validated into a large-scale,

multicenter and multivendor study with a total of 1380 datasets [66]. Note that 8 labeled

CTA datasets were used to generate the atlas. The experts evaluated the result obtained

in the large database and they indicated that 49% of the cases were very accurately

segmented (errors below 1 mm) and 29% of the database results were accurately

segmented (error between 2 and 3 mm), demonstrating the accuracy and robustness of

the atlas-based technique. Furthermore, eight fully segmented datasets were used to

estimate the surface-to-surface error, showing an error lower than 1.5 mm for the

different chambers. Regarding the computational time, approximately 20 minutes per

volume was required.

2.2 Biomechanical simulation

The recent advances in non-invasive high-resolution image acquisition

techniques have made them feasible to generate accurate patient-specific anatomical

models and consequently create novel computational/biomechanical models relevant to

the clinical practice.

Initially, several authors used these novel image techniques to develop generic

simulation models capable to provide useful information about the atrial conduction

system [67-69], motion pattern characterization [70, 71], hemodynamics of the heart

State of the art

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[72], influence and function of the LAA [73] and study of different pathologies [74].

However, since generic models were used, the individual information of each patient

was neglected limiting the application of this solution in the clinical practice. As such,

several authors suggested complex methodologies in order to develop accurate patient-

specific models [75-78]. This novel model combines the abovementioned generic

models with patient-specific data obtained through multiple image acquisition or

multiple clinical measurements, such as multiple acquisition of the electrocardiography

(ECG) signal [75, 76].

Regarding the minimal invasive cardiac procedures, Mansi et al. (Siemens

Corporate Research) proposed a novel framework to simulate the procedure used to

treat MV regurgitation, namely MitralClip [79]. As such, they generated a patient-

specific model of the MV from ultrasound imaging, being posteriorly used a finite

element method to simulate the valve closure and the entire correction procedure. The

generated patient-specific MV model was tested on 11 patients with an average point-

to-mesh error of 1.47 ± 0.27 mm. Moreover, the entire simulation framework was

applied/tested on one patient with results qualitatively similar to the real surgical

outcome [79].

Similarly, Stevanella et al. proposed a finite element model of the mitral valve in

order to predict the outcome of mitral annuloplasty procedures [80]. In this case,

patient-specific models were obtained from MR data. Furthermore, hyperelastic

anisotropic mechanical properties were assigned to the MV tissues. The current strategy

was tested on one healthy and one unhealthy patient, obtaining similar results to the real

procedure [80].

Instead of mitral valve procedures, Wang et al. developed a patient-specific

model to quantify and characterize the interaction between the transcatheter stent and

the stenotic aortic valve [81]. In this case, finite element models of the patient-specific

anatomy and the transcatheter stent were generated. The anatomic model was created

from CT data, being applied anisotropic hyperelastic materials to simulate the tissue

mechanical properties. Regarding the transcatheter stent, an eight-node hexahedral was

used to generate the solid element and a four-node quadrilateral element was selected to

model the balloon. The proposed methodology was tested on one patient, simulating

with success the entire valve-replacement procedure. As such, the authors proved that

this approach can be used to extract relevant pre-planning information such as optimal

State of the art

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stent position, procedural risks and possible post-intervention complication (e.g.

paravalvular leakage).

Similarly, Morganti et al. proposed a novel framework to simulate the

transcatheter aortic valve implantation [82]. The aortic model was obtained from CT

images, including the native structure of this valve, namely: leaflets and calcific

plaques. Isotropic and homogeneous materials were used to define the mechanical

properties of this valve. Regarding the prosthesis model, the authors have focused on

the commercially available Edwards SAPIEN valve, creating it structure from a micro-

CT image. Moreover, the Von Mises plasticity model with isotropic hardening was used

to represent the mechanical parameters of the prosthesis virtually modulated.

This novel framework was tested on two patients. The results obtained for each

patient indicated that the proposed simulator can be used to realistic simulation of the

minimal invasive procedure, presenting the patient-specific stress distribution of the

aortic wall and possible risk of post-procedural complications, namely paravalvular

leakage [82].

Finally, Jayender et al. proposed an approach to estimate the optimal puncture

location by combining these pre-interventional models with a mechanical model of the

catheter to be used for the LA intervention [83]. The pre-interventional models were

obtained from CT datasets through a semi-automatic strategy. Regarding the catheter

model, it was considered being made up infinitesimal rigid links along a backbone

curve. As such, the optimal puncturing site could be estimated based on the thickness of

the septal wall and the mechanical maneuverability of the catheter at all the positions of

the LA. The current system was only tested in one offline dataset, missing exhaustive

validation, mainly in abnormal septal wall situations. Moreover, since different

catheters can be used to puncture the septal wall, namely mechanical-based and RF-

based, different catheter models should be used.

2.3 Image-fusion and catheter tracking techniques

During the previous sub-sections, pre-interventional planning methods were

presented, where accurate and robust planning of the entire procedure is performed.

Nonetheless, the pre-procedural planning data need to be combined with intra-

procedural information (e.g. image acquisition) in order to guide the physician during

State of the art

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the entire procedure. As such, in this sub-section an overview of image-fusion and

catheter tracking techniques are described.

External tracking hardware, such as electromagnetic sensor (EM, e.g Aurora

NDI) or optical infrared systems (e.g. Polaris, NDI, Canada), were suggested to guide

image-based procedures [84]. These systems are coupled with the surgical catheter,

being consequently used to combine pre-procedural and intra-procedural data through a

set of markers. Nonetheless, these systems can suffer of electromagnetic interference

caused by the remaining surgical equipment. Moreover, a complex initial setup is

required.

Based on this technique, Jeevan et al. suggested EM sensors integrated on the

catheter tip to guide the transseptal puncture procedure [85]. In this case, the catheter

was rigidly aligned with a patient-specific atria geometry, which was obtained from a

pre-interventional MRI. This method was only validated in one phantom model, proving

that this system reduces the procedure time, has no learning curve and can reduce the

number of complications. However, this system was only tested in static models without

any real-time image acquisition (e.g. X-ray or US imaging), being far from being

applied in real situations.

Hatt et al. focused on an EM-tracking framework to fuse X-ray, MRI and US

image [86]. Note that US and X-ray are real-time image acquisition modalities that are

required throughout the intervention, while the MRI is obtained during the planning

stage. The method requires two pre-intervention calibrations, namely: 1) between the

EM and US probe and 2) between EM world and X-ray image. During the first

calibration step, two EM sensors were coupled with the US probe and a surgical needle,

respectively. Posteriorly, ultrasound image acquisition of the surgical needle was

performed. The differences between tool position into US image and physical distance

measured through EM sensor were used to calibrate the system. Regarding the second

stage, two-custom-built phantoms with metal beads were used to calibrate the

fluoroscopy image with the EM sensor. The difference between the metal beads

measured through fluoroscopy and EM sensor were used to define the optimal

transformation between these two systems. Finally, an intra-operative calibration of

MRI and EM system was performed, based on fiducial markers. As such, a set of

markers was externally positioned on the patient, being the markers easily detected in

the MRI image. Moreover, the EM sensor was used to generate a 3D-world with the

State of the art

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different position of these markers. The position differences measured through MRI and

EM sensor were used to align the different worlds.

The current system was tested on phantom and animal model, obtaining an

accuracy error lower than 5 mm for the worst situation [86].

Lang et al. suggested a novel image-strategy to register US, CT and fluoroscopy

imaging without any tracking device [87]. The novel method requires two calibration

steps, namely: 1) alignment between probe position and transesophageal

echocardiography (TEE/US) world; and 2) alignment between the probe position and

fluoroscopy world. The first stage is performed through a calibration cage (Figure 7a),

while the second require a set of tracking beads coupled with US probe (Figure 7b).

Thus, a transformation that maps the US world into the fluoroscopy world was obtained.

Then, a semi-automatic segmentation is applied to delineate the relevant

structure from 3D CT data. The obtained mesh is registered with a set of manual

landmarks extracted from US data through an iterative closest point (ICP) approach.

Moreover, since a transformation between US and fluoroscopy worlds were obtained

during the calibration phase, the patient-specific data obtained from CT images can be

also transferred for the fluoroscopy world [87].

The algorithm was tested on excised porcine hearts datasets, with an acceptable

accuracy of 2.6 mm for tracked US to CT. Nonetheless, since the required landmarks

are difficult to be detected in US imaging, high registration errors between CT and US

can be obtained, showing therefore that the proposed method is dependent of the user

input.

Figure 7 - (a) Calibration cage used to align US world with probe position and (b) radiopaque

markers to fuse X-ray and US world [87].

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Similarly, Housden et al. (Philips Healthcare) proposed a novel approach to

align CT, US and fluoroscopy images [88]. The method requires an initial calibration

between the probe and TEE data through a calibration cage (see (1) in Figure 8). Then, a

semi-automatic strategy was proposed to align the fluoroscopy with the US worlds,

through a rigid alignment between a virtual model of the probe and a projection of US

probe in bidimensional X-ray (see (1) in Figure 8). For that, the initial manual

identification of the probe position into fluoroscopy image was required. After the

initial alignment, a high-resolution surface model of the target structure was generated

(Figure 8). Furthermore, a manual segmentation of esophagus centerline is created and

included into the abovementioned high-resolution model.

Finally, a simple downhill iterative optimization algorithm was used to align the

high-resolution model with the US data (see (2) in Figure 8). Note that, the esophagus

position was used to constrain the optimization.

The method was only tested on phantom model, missing its application in real

situations. Moreover, the current methodology is static being not able to guarantee

optimal fit between the pre-procedural image (3D CT) and the real-time intra-

procedural image throughout the cardiac cycle.

Figure 8 - Workflow used to align MRI/CT, X-ray and US images. During the pre-intervention

stage, surface model of the target structure is generated. Additionally, the esophagus centerline was

manually segmented and included into the model. Regarding intra-procedural stage, an initial

alignment between the ultrasound and X-ray image is performed via the US probe position. The

abovementioned model is then manually positioned in the X-ray image before being automatically

registered via the ultrasound image [88].

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Huang et al. proposed a different solution to align pre-operative data, 3D CT or

3D MR, with 2D ultrasound imaging [89]. In this formulation, both datasets were

spatially registered using a rigid transformation, while their time alignment/consistency

was obtained using a simultaneously recorded ECG. However, since only rigid

transformations were considered, a periodic heart motion is assumed, neglecting the

heart deformation caused by the respiration and surgical procedures. The current

strategy was tested on beating heart phantom and animal models, with an accuracy of

1.7 ± 0.4 mm.

Gao et al. suggested a strategy to align 3D TEE data with fluoroscopy imaging

[90]. The method starts with an automatic US probe identification on X-ray imaging,

through graphics processing unit (GPU)-based image-registration between a 3D virtual

model of the probe and the 2D fluoroscopy image. Then, using a pre-interventional

calibration of the US probe and US image, a transformation map between the

fluoroscopy world and US world can be achieved. The method was tested on a realistic

heart phantom, obtaining a target registration error lower than 2 mm. Furthermore, an

offline patient dataset was also used, resulting in mean registration errors between 1.5-

4.2 mm. However, it should be noticed that the probe estimation is a time-consuming

(2-15 seconds) method, hampering their application in real-time procedures and failing

to follow the cardiac structure throughout the respiratory cycle. In addition, exhaustive

offline and online validation of the proposed technique is missing [90].

Recently, Lang et al. presented a novel strategy to align US and CT/MR data. In

order to perform this alignment, two different registration techniques were described

and compared, namely: 1) surface-based registration and 2) image registration [91].

The first approach relies on a segmentation technique based on continuous max-flow

algorithm to delineate the relevant structure in CT/MR and US, followed by a mesh-

alignment based on ICP strategy. Regarding the second technique, an image-alignment

was performed based on non-rigid registration with mutual information metric. Note

that any feature/contour extraction was required in this technique.

Moreover, a GPU-implementation of the tracking method was used. Both

methods were tested on 18 datasets. Registrations errors lower than 2.5 mm, dice

coefficients higher than 80% and low computational time were obtained with surface

and image registration approaches, proving that both approaches have potential to be

used in image-guidance procedures. Nonetheless, these approaches were only tested in

offline aortic replacement procedures, being required further clinical validation.

State of the art

23

Furthermore, since fluoroscopy imaging is not used, catheter tracking approaches are

required to identify the surgical equipment’s into the ultrasound imaging, which was not

addressed in the current study [91].

Grbic et al. (Siemens Healthcare) proposed a different strategy to align pre-

operative 3D-CT and intra-operative rotational-angiography based on surrogate

anatomical structures position [92]. The selected surrogate anatomical structure relies

on the trachea bifurcation, which it is visible in both modalities without contrast. As

such, in each image modality a probabilistic boosting classifier is used to estimate the

global position of the trachea bifurcation. Then, a rigid-registration between the two

meshes is performed in order to align the CT and rotational-angiography worlds. The

method was tested on 28 patient datasets obtaining with an accuracy of 7.57 ± 3.22 mm.

Instead of multi-machines image acquisitions, the novel C-Arm technologies can

be used to acquire 3D CT volumes and 2D fluoroscopy images that are intrinsically

registered. However, since 3D CT is only acquired in one temporal moment, patient

anatomy variations caused by the catheter insertion are not considered. As such, Liao et

al. (Siemens Corporation) proposed a fully-automatic solution to align the

bidimensional X-ray images with the 3D dataset, compensating the motion variations

expected throughout the procedure [93]. Note that the proposed strategy is only used

when agent contrast is emitted. Thus, the method automatically detects the contrast

injection on X-ray images based on histogram analysis and a likelihood ratio test. Then,

an optimized alignment based on rigid transformations is performed between the

contrast-based fluoroscopy image under study and a set of bidimensional digitally

reconstructed radiographs (DRRs). Should be noticed that these 2D-DRRs were

extracted from 3D CT volumes using various plane orientations. The current solution

was tested on 34 datasets, presenting a mean registration error of 0.66±0.47 mm and a

computational time of 2.5 seconds per alignment. Although high accuracy was

achieved, the current solution is far from being applicable in real-time. Moreover, since

a static 3D volume is rigidly aligned, the dynamics of the heart (e.g. aortic valve closure

and opening) are ignored.

Although the previous approaches focused on solutions to combine pre-

interventional with intra-interventional data, surgical instruments are not considered on

these models, hampering their application in real image-guidance procedures.

As such, Brost et al. (Siemens Corporation) focused on solutions to

identify/track the surgical catheter in fluoroscopy imaging, which can be used to

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accurately guide the expert in several minimal invasive interventions, such as:

transseptal puncture or catheter ablation [94]. In this formulation, the catheter structure

is segmented through machine learning technique. The authors presented a real-time

strategy to identify the catheter through a cascade of boosted classifier combined with

haar-features. Regarding the tracking approach, a consecutive segmentation approach

was used. This technique was tested in 12 offline datasets, showing a tracking error

lower than 0.7 mm [94].

Moreover, Buck et al. also proposed a novel solution to track the target catheter

from X-ray imaging [95]. The method relies on template-matching combined with

Kalman filters to estimate the catheter tip and reduce the search space, respectively.

Note that this template is a bidimensional projection of a virtual 3D cylindrical model

with rounded tip. The current solution was tested on 14 fluoroscopy sequences

presenting a maximum tracking error of 1.7 mm. Furthermore, clinical validation of this

technique was performed, being well accepted by the experts. Nonetheless, the presence

of multiple catheters caused some tracking errors.

Contrarily, some authors focused on catheter tracking and catheter identification

from US imaging. Since US image shows a small FOV with several artifacts, automatic

identification of the catheter structure is challenging. Thus, Wu et al. solved the

abovementioned problem through a four-stage technique: 1) automatic or semi-

automatic catheter identification in X-ray imaging based on speeded up robust features

(SURF) and Frangi vesselness filter [96]; 2) catheter tracking in X-ray imaging using

Kalman filters; 3) fast registration of the X-ray and US imaging based on US probe

position into X-ray images; and 4) catheter segmentation/tracking in US images using

the displacement field estimated throughout stage (2).

The current methodology was tested on 5 porcine models and 4 patient datasets.

The catheter tracking presented an error lower than 2 mm. Regarding the computational

time, a total of 1.3 seconds per frame were required, hampering its application on real-

time procedures [96].

Methods

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Methods

Methods

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3. Methods

The PhD project consists of four main work packages (WPs) executed in two

stages. In the first stage (WP1) a preliminary augmented reality framework will be

developed to guide the TSP procedure, presenting therefore a proof-of concept about the

main-topic addressed throughout this project. During the final stage, novel modules will

be developed to automate the abovementioned framework and to improve the efficacy

of TSP procedure (Figure 9).

3.1 Work package 1 – Development and validation of an intra-

procedural guidance framework

In this WP, a preliminary augmented-reality framework will be developed to

guide the surgeon throughout the transseptal puncture procedure. This novel system will

fuse different imaging modalities to ease the recognition of the optimal puncture site.

Moreover, the fluoroscopy will be removed from the intervention, being only used

volumetric ultrasound as intra-procedural image acquisition.

Figure 9 - Overview of the proposed project. During the pre-procedural stage an anatomical atrial

model will be constructed from CT images. Moreover, biomechanical simulation will be used to

estimate the optimal puncture position and to estimate the optimal trajectory. During the intra-

procedural stage, the high-resolution model will be fused with real-time image acquisition

techniques (3D TEE), transferring consequently the planning data to the intra-procedural for the

real procedure. Finally, during the intra-procedural stage an augmented reality framework will be

developed to guide the expert for the optimal transseptal puncture location.

Methods

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Task 1.1 – Building an augmented-reality framework: Within this task, a novel

framework will be developed to combine pre-CT operative data with intra-operative

TEE data, transferring the pre-procedural planning, namely fossa ovalis position, for the

real procedure. This initial alignment will be performed through a set of landmarks

placed manually at specific position of the atria region in CT and TEE images. Then, an

ICP strategy will be used to minimize the distance between landmarks and align both

datasets.

Regarding the catheter/needle position, a commercially available

electromagnetic tracking device (Aurora NDI) will be placed on the tip of catheter,

guiding the surgeon for the optimal puncture location using the developed augmented

reality framework.

Task 1.2 – Development of patient-specific phantom models: The entire

framework will be, initially, validated using a mock model of the atria region. Hereto,

an elastic model will be created using stereolithography and polyvinyl alcohol. This

model will be extracted from clinical CT datasets, which were previously manually

segmented, resulting in an identical experimental and in-silico geometry. The

contraction of the heart will be simulated through passive inflation via a pumping

system similar to what was done by the co-supervising lab for the left ventricle.

Task 1.3 – Validation of the augmented reality framework: The framework

developed throughout Task 1.1 will be tested in silico models. A total of 10 different

dynamic phantoms will be created, including normal and non-normal atrial anatomy.

With this validation, we intend to present a proof-of-concept about the aim of this

project, proving that quick and safe puncture can be achieved with this novel strategy,

even with non-experienced surgeons. Moreover, we will present a novel solution where

the intra-procedural radiation was completely removed.

As a final remark, should be noticed that this initial WP is dependent of the user

input, which can result in procedural failures and complication, mainly when

manipulated by unexperienced physicians. As such, during the remaining task of this

project, we intend to develop solutions/modules to automate the proposed framework.

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3.2 Work package 2 – Building a patient-specific anatomical model

In this WP, a patient-specific anatomical model will be built based on pre-

interventional CT. In order to avoid time consuming manual segmentation and its

associated inter-operator variability, this WP will focus on automation of the

segmentation.

Task 2.1 Automatic segmentation of the RA, LA and venae cava from CT: In

order to build patient-specific anatomical models, an accurate segmentation of the

anatomical structures from CT is required, namely: LA, RA and venae cava. The co-

supervising lab has already developed a robust, accurate and real-time segmentation

framework using a level-set like formulation, termed BEAS framework [60]. It was

designed and validated for segmentation of the left ventricle from ultrasound [60] and

magnetic resonance imaging [97]. However, the left-ventricle shows a more regular

shape when compared with the atria region, being required several modifications in the

current segmentation framework in order to segment the abovementioned cardiac

structure. As such, within this WP an adaptation of the BEAS framework will be

proposed, namely: 1) adaption of the segmentation energies to cope with the specific

CT challenges; 2) reformulation of the parametric space in order to allow coupled

segmentation of both atria and to be able to cope with the more complex geometric of

the atria; 3) competitive contours will be implemented to delineate with maximum

accuracy the atrial septal wall, which show low thickness and low contrast; and 4) a

strategy to delineate the epicardium at the atrial region will be proposed.

Finally, strategies will be derived to allow for fully automatic/automated

initialization of the segmentation process, based on mean shape models of the heart or

template-matching strategies.

Task 2.2 Validation of the segmentation approach: The developed automatic

segmentation method will be validated against manual contouring by experts.

Methods

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3.3 Work package 3 – Creating a software environment for

interventional planning and simulation

In order to identify the optimal puncture location, software tools will be

developed and implemented to simulate the intervention, in this WP. Hereto, a catheter

specific mechanical model will be created and combined with the anatomical model

derived from WP2.

Task 3.1 Building a mechanical catheter model: Within this WT, a Computer

Aided Design (CAD) model will be generated that completely defines the mechanical

properties of the catheter most used for TSP (NRG®

RF Transseptal Needle and BRKTM

Transseptal Needle). Since these catheters normally have a flexible region with few

degrees of freedom (DOF) in the proximal region and a higher number of DOF on the

distal region, it will be described as a multi-body system with rigid links and flexible

joints, which is a common approach for modeling catheters [98, 99].

Task 3.2: Experimental validation of the catheter model: The mechanical

catheter model will be validated experimentally by comparing the effective catheter

behavior against the simulated one. In order to quantify the 3D trajectory of the real

catheter, it will be integrated with an electromagnetic tracking system (Aurora NDI).

Task 3.3: Simulation of the intervention: In this task, the anatomical model

(WP1) will used to generate a finite element mesh within a finite element analysis

package (namely, Abaqus), in order to obtain patient-specific biomechanical models of

the atria and in particular the inter-atrial septum. In the same virtual environment, the

mechanical catheter model will be inserted. In this way, virtual manipulations can be

performed allowing testing different possible trajectories of the catheters from the vena

cave to the septum. Based on mechanical properties of the atrial tissue available in

literature [100, 101], the interaction of the catheter with the septal wall as well as the

force required for puncturing a given septal site can be mimicked. Finally, the trajectory

through the LA (after TSP) towards a predefined target site can be simulated.

Task 3.4 Trajectory optimization: This above virtual intervention will be used in

an optimization framework. Hereto, one of more target sites will be defined in the LA,

Methods

30

after which, the optimization scheme will determine the optimal path given the patient-

specific constraints.

Task 3.5: Experimental validation of the simulation environment: The entire

biomechanical framework will be validated using the developed phantom models (Task

1.2). At this stage, we intend to verify if the system can estimate the optimal puncture

location and assess the differences when compared with the preliminary version

developed during WP1. Furthermore, we intend to prove that the estimated trajectory

(Task 3.4) guarantee maximum catheter dexterity at the target site, preventing second

puncture procedures.

3.4 Work package 4 – Real-time image fusion for interventional

guidance

During the second stage, an image fusion methodology will be developed in

order to combine the pre-interventional plan (WP3) with a peri-interventional

ultrasound imaging-based guidance system in real-time.

Task 4.1 Image fusion: The multi-modality registration algorithm will be based

on a non-rigid transformation using mutual information and a temporal alignment,

based on the ECG. In order to run this registration step in real-time – required for

guidance – the algorithm will be optimized for GPU processing. The catheter will be

guided using the image from the 3D TEE registered with the CT, creating an augmented

intervention system capable to indicate the optimal puncture position.

Task 4.2 Experimental validation: The final framework will be initially validated

using the dynamics phantom models developed throughout the task 1.2. Then, the entire

framework will be tested and validated in an experimental animal setting by planning

the intervention and verifying the accuracy of the puncture site and the target location.

Both experienced and inexperienced surgeons will be involved. All this experimental

work will be done in the Life and Health Sciences Research Institute (Braga) and at the

department of Cardiovascular Sciences (Leuven), both of which have the required

expertise, animal facilities and instrumentation.

Methods

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The integrated interventional planning and guidance framework proposed will

allow the physician to obtain objective information on the theoretical optimal site for

TSP and will show him this information during the intervention, as guidance. This will

not only avoid complications and the need for secondary punctures, but also decrease

the interventional time (and thus cost) and radiation dose.

As a final remark, an overview of the project timeline is presented in next

section.

Methods

32

3.5 Timetable

Final Remarks

33

Final

Remarks

Final Remarks

34

4. Final Remarks

In this project, we intend to develop an integrated framework to guide the

physician throughout the TSP puncture based on a patient-specific biomechanical

model. As such, this novel framework will combines the intra-procedural planning

performed by the expert, with the real-time data obtained from three-dimensional

ultrasound imaging, in order to align/guide the transseptal needle position with the

optimal puncture site.

With the proposed framework, we expect to increase the success rate of TSPs

and avoid complications or the need for secondary punctures. Moreover, it will increase

the level of confidence of the physician during the procedure, thereby reducing the

interventional time and thus, its costs. The advantages are particularly relevant for

surgeons with less experience (e.g. trainees) and in patients with an abnormal septum, in

which TSP remains difficult even for the most experienced operator. Finally, the use of

ionizing radiation can be reduced, as the intra-procedural image guidance would be

based on volumetric ultrasound imaging.

Final Remarks

35

References

36

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[4] R. Yang, Y. Sheng, K. Cao, X. Kong, D. Xu, Y. Yong, L. Zhou, H. Zhang, L.

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[5] T. Akagi, Y. Kijima, Y. Takaya, K. Nakagawa, H. Oe, S. Sano, and H. Ito,

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CLOSURE," Journal of the American College of Cardiology, vol. 65, 2015.

[6] R. Weerasooriya, P. Khairy, J. Litalien, L. Macle, M. Hocini, F. Sacher, N.

Lellouche, S. Knecht, M. Wright, and I. Nault, "Catheter ablation for atrial

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American College of Cardiology, vol. 57, pp. 160-166, 2011.

[7] J. Mallidi, G. N. Nadkarni, R. D. Berger, H. Calkins, and S. Nazarian, "Meta-

analysis of catheter ablation as an adjunct to medical therapy for treatment of

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Acknowledgement

45

Acknowledgement

This work was supported by Fundação para a Ciência e Tecnologia, Portugal, in

the scope of the PhD grant SFRH/BD/95438/2013.