rebuilding ivus images from raw data of the rf signal: a...
TRANSCRIPT
Rebuilding IVUS Images From Raw Data Of The RF
Signal Exported by IVUS Equipment
Marco Aurélio Granero¹,³, Marco Antônio Gutierrez², Eduardo Tavares Costa¹
¹ Department of Biomedical Engineering– DEB/FEEC/UNICAMP, Campinas, Brazil
² Division of Informatics/Heart Institute – HCFMUSP, São Paulo, Brazil
³ Federal Institute of Education, Science and Technology S. Paulo – IFSP, São Paulo, Bra
Abstract - The study of composition and classification
of atherosclerotic plaque has been a very active
research field, both in cardiology and image
processing. Intravascular ultrasound (IVUS) is an
effective tool, which can insights about the cross-
section of blood vessels, with sufficient accuracy to
allow an accurate assessment of CT slices. This
enables information about blood vessel structures to be
determined. During an IVUS medical examination,
physicians subjectively adjust a set of parameters to
improve the visualization of a Region Of Interest (ROI)
and produce corresponding images in Digital Imaging
and Communications (DICOM) format, for later
analysis and study. DICOM is appropriate for
storage, transportation and access, but limits
subsequent changes to image parameters, such as
contrast or brightness. This makes comparison across
patient populations difficult and restricts image
processing operations. This paper details an
alternative to using DICOM, which is to rebuild IVUS
images from raw radiofrequency signal (RF) data. The
main advantage of this process is the independence of
the acquisition parameters adjusted during the exam.
This advantage makes possible the comparison
between exams and can be used to monitor the
evolution of cardiovascular disease. Beyond this, once
the reconstructed images and the RF signal are stored,
operations relating to texture and spectral analysis can
be carried out and automatic classifiers employed.
From a clinical point of view these reconstructed
images share the same characteristics as DICOM
images with an advantage that the former have a
higher contrast than the latter, allowing deeper
regions to be seen.
Keywords: RF signal, IVUS image, rebuilding
process, ultrasound image, RF raw data.
1. Introduction
Among the different modalities of medical images,
ultrasound is arguably the most difficult in which to
perform segmentation. This is evident from a study of
the first papers on segmentation, in which it was only
possible to apply a threshold to the image in order to
separate the background from foreground due to the
poor quality of the acquired data (Noble, 2010).
At the same time, subsequent technological
development has greatly increased the quality of
ultrasound images, especially in terms of signal to
noise ratio (SNR) and contrast to noise ratio (CNR),
resulting in improvements to image quality. Several
studies have been highlighted that aim to develop
algorithms for the design of edges on objects contained
in ultrasound images (Noble, 2010).
Ultrasonic Tissue Characterization (UTC) has
become a well-established research field since its first
publication (Mountford and Wells, 1972). Thijssen
(2003) defines UTC as the assessment by ultrasound of
quantitative information about the characteristics of
biological tissue, and their pathology. This quantitative
information is extracted from echographic data from
RF data.
UTC applications abound in the literature and
include classification of breast tissue (Tsui et al. 2008
and Molthen et. al. 1998), liver (Molthen et al., 1998),
heart (Clifford et al., 1998 and Nillesen et. al., 2008),
eyes (Lizzi et. al., 1983), skin (Raju et. al., 2003),
kidney (Engelhorn et. al., 2012) and prostate (Moradi,
2008).
Szabo (2004) defines two general goals for
ultrasonic tissue characterization which can be applied
to the above areas (Szabo, 2004):
i. Reveal the properties of tissues by analyzing
the RF signal backscattered by ultrasound
transducer and
ii. Use information about the properties of the
tissue to distinguish between the state of
tissue (healthy or diseased), or to detect
changes in these properties when subjected to
stimuli or long periods of time in response to
natural processes or medication.
Reaching these goals can be challenging since the
interaction between biological tissue and sound waves
is extremely difficult to model and the process evolved
in image segmentation is strongly influenced by the
quality of data and by the different parameters used
during the acquisition process of an image.
Parameters like contrast, brightness and gain are
adjusted by physicians to improve the visualization of
regions during the examination. These changes
determine the DICOM images that are recorded and
the result cannot be changed after the image has been
acquired. This greatly complicates the comparison
between patients and the use of images in studies of
groups of patients.
Thus, to avoid these complications and make
image reconstructed independent of the parameters set
by the physician a reconstruction method from IVUS
images is proposed. This method is based on the RF
signal stored by the equipment during medical imaging
examinations of intravascular ultrasound.
The process of rebuilding starts with applying a
band-pass filter to the RF signal to eliminate signals
that do not come from the transducer. In the next step,
a time gain compensation (TGC) function is applied to
compensate for attenuation loss. After this, the
envelope of the signal is computed and the result is
log-compressed and normalized in a grayscale image.
After the process of rebuilding, the grayscale
image, in polar coordinators, is submitted to a Digital
Development Process (DDP) responsible for enhancing
the contrast and edge emphasis. So, the image is
interpolated to cartesian coordinators. The cartesian
image is further processed with an intensity
transformation function to improve the contrast of the
final cartesian grayscale IVUS image.
The above processes are described in more detail
in section 2 and the results obtained are shown in
section 3. In section 4 a comparison is made between
reconstructed images and DICOM images from an
examination. Finally, section 5 shows conclusions and
possibilities for future work.
2. Method for IVUS image
reconstruction
An IVUS examination is carried out by inserting a
catheter into coronary arteries via femoral or brachial
vessels. At the tip of this catheter there is an ultrasound
emitter and a piezoelectric transducer that collect the
echoes reflected by internal structures of the vessel as
RF signal.
A schematic representation of the execution of an
IVUS examination is shown in Figure 1(a), where the
IVUS equipment collects data from patient and stores
it in the workstation. Figure 1(b) shows an IVUS
rotational catheter.
During an IVUS exam, the acquired images are
stored in DICOM format and exported to the databank
of the clinical centre to be used for clinical diagnosis.
In addition to the images in DICOM format, the
equipment allows the RF signal to be recorded, which
are used in the manufacture of images in a proprietary
format.
The proposal of this paper is to process the RF
signal data according to the steps shown in Figure 2.
These steps are detailed below.
Figure 1: (a) IVUS in-vivo analysis typical scenario, (b) Rotational IVUS catheter. Extracted from Ciompi, (2008).
Figure 2: Block diagram of reconstruction
process.
2.1 – RF dataset
The data was taken from examinations in the
Department of Hemodynamics in the Heart Institute of
the Medical School of the University of São Paulo
(Heart Institute – HCFMUSP), Brazil, using iLab
IVUS (Boston Scientific, Fremont, USA), equipped
with a 40 MHz catheter Atlantis SR 40 Pro and
anonymised to avoid the identification of the patient
and used only for research purposes.
The RF File Reader (designed by Boston
Equipment) is an xml file that contains information
about the examination. This file allows us to identify
the number of rows, columns and frames from each
exam. Beyond this, the reader contains the distance
from each pixel in the image, in millimetres.
Once image attributes have been found using the
RF File Reader, it is possible to extract the data. These
data were placed in a tri-dimensional matrix. The rows
of this matrix represent the lines in A-mode, each line
with radial information about the vessel, the columns
represent the distance to the tip of catheter and the
slices, third dimension, represent each time frames of
the exam. The study of IVUS used in this work results
RF raw data
Bandpass filter
TGC
Log-compression
DDP
Polar Image
Envelope
in a 3D matrix, where the dimensions represents the
size of each image and the third dimension being the
number of frames.
After this, each frame was submitted to the
reconstruction shown in Figure 2.
2.2 - Bandpass filter
A Butterworth bandpass filter was applied to
dataset in order to eliminate frequencies that do not
come from the transducer. The manufacture of
transducer describes the central frequency emitted by
transducer at the tip of catheter as 40 MHz and
frequency sample rate as 200 MHz.
Each line in A-mode was filtered by a Butterworth
finite impulse response filter (FIR filter).
The frequency range was adjusted between 20 and
60 MHz as can be viewed in Figure 3(a).
Figure 3: (a) Frequency response of the
Butterworth FIR filter and
(b) Profile of TGC function.
2.3 - TGC (Time Gain Compensation)
The ultrasound beam is attenuated as it penetrates
the tissue. To compensate for this loss in signal
intensity TGC is applied to each line in A-mode, which
is defined as
rerT 1)( (1)
where is the coefficient of attenuation and is the
radial distance from tip of catheter.
The range of the radial distance was extracted
from the RF File Reader of exam ranging until 4.48cm.
In Ciompi (2008), RF signal of in-vivo and ex-
vivo was used to develop a multiclass classifier to the
problem of characterization of the atherosclerotic
plaque. They define a value for the coefficient of
attenuation as , which was
adopted in this work.
The profile of TGC function is shown in
Figure 3(b).
2.4 - Signal envelope
To show the changes stemming from the texture
and not from the wave profile, the envelope of the
signal is obtained simply applying the Hilbert
transform to each line in A-mode from the RF signal.
Figure 4: RF envelope is shown as a black line over the
wave profile of RF signal gray line (Data).
2.5 - Log-compression
The next stage in the procedure described in
Figure 2, is to normalize the RF signal providing
values between 0 and 1 in order to permit work with a
homogeneous range for all IVUS images. After this,
the RF signal undergoes a transformation whose
purpose is to map a narrow range of grayscale values
in an input image to a wide range of output levels
(Gonzalez and Woods, 2004). This transformation is
defined as
nor
t Iet
I 11log1
log (2)
where norI represents the RF signal normalized and t
is empirically obtained to improve the log-
compression.
2.6 - Digital Development Process
In order to emphasize the edges borders and
improve contrast gain, a Digital Development Process
(DDP) (Gonzalez and Woods, 2004) was applied to the
RF signal.
(a)
(b)
Each pixel value of an image was modified by
equation (3) to produce an image with better Contrast
Noise Ratio (CRN).
baX
XkY
ji
ji
ji
}{ (3)
where }{ jiX is obtained by applying a Gaussian low-
pass filter to the original image and the parameters k ,
a , and b were empirically determined to improve the
CRN.
After this, the image was converted and
interpolated to cartesian form, resulting in an image
with 512x512 pixels and 256 gray levels.
Finally, an intensity transformation was applied to
image in order to expand the saturation of the gray
level dynamic band and the image was Gaussian
filtered.
3. Experimental Results
The results of the rebuilding process of the IVUS
images are shown in Figure 5, which the mayor
structures visible in an IVUS examination are
identified.
Figure 5(a) shows the segmentation of lumen and
the media-adventitia borders. 5(b) the stent and an
artifact generated by the wire guide. 5(c) shows a
region with calcification and the acoustic shadow
behind it, with an arrow pointing to an artifact
generated by the wire guide.
Figures 5(e) and (h) show a bifurcation region,
with calcification. A stent is visible in 5(d) and (i), and
it is possible to identify the malposition of the stent in
5(i).
Figure 5(f) shows the shadow of the pericardium
and 5(g) the acoustic shadow of a big calcification and
the lumen and media-adventitia borders.
Figure 5: Images from the reconstruction process.
(a) (b) (c)
(d) (e) (f)
(g) (h) (i)
Figure 6 show both the rebuild image and the DICOM
images.
Figure 6: (a), (b), (c) and (d) DICOM images.
(e), (f), (g) and (h) Rebuilt images.
As can be seen, the rebuilt images show the same
structures as DICOM images, and in all cases the
contrast of the rebuild images are better than DICOM
images. What is perhaps most noticeable is the
difference in visibility in the outer region of the lumen.
The reconstructed image shows fine detail where the
DICOM shows only a black region.
4. Discussion, Conclusion and Future
Work
IVUS is an examination that can provide a good
quality image of the cross-section of blood vessel
allowing the assessment of inner structures.
In an IVUS medical examination, sets of hundreds
or even thousands of images are acquired and used as
the basis for a medical diagnosis.
These images are subject to a variability of
interpretation inter and intra operator because a set of
parameter are adjusted to improve the visualization of
a ROI. Once the images are acquired these parameters
cannot be changed, restricting the comparison between
different examinations or patients.
To avoid this limitation, this article describes a
methodology for reconstructing IVUS images from RF
raw data, which are independent of the parameters
adjusted by the physician during the exam and which
can be processed to improve the CNR of the image.
The RF signal is processed according to the
theoretic model proposed in section 2 and illustrated in
Figure 2. The parameters used in the model were
adjusted to maximize CNR enabling identification of
the main structures of the vessel.
The results of the proposed model were presented
in Figures 5 and 6 and compared with the DICOM
images generated by the equipment. The proposed
model produces images with superior CNR which can
be used for clinical purposes.
In the figures it is possible to see the main
structures of the vessel and this result can be used to
perform segmentation to help the physician in
diagnosis process. Beyond this, it is possible to identify
bifurcations and calcifications regions to be submitted
a percutaneous coronary intervention - PCI.
Considering the data used in this work, the
propose method was proved to be robust with regard to
fidelity in the reconstruction of structures in
comparison with DICOM image and, in all cases the
CNR in reconstructed images was greater than DICOM
images, figure 6.
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ACKNOWLEDGEMENTS
This work is supported by the Brazilian National
Institute of Science and Technology in Medicine
Assisted by Scientific Computing (INCT - MAAC)
and National Council for Scientific and Technological
Development (CNPq).