=Paper=
{{Paper
|id=None
|storemode=property
|title=Nonrigid Motion Compensation of Free Breathing Acquired Myocardial Perfusion Data
|pdfUrl=https://ceur-ws.org/Vol-715/bvm2011_19.pdf
|volume=Vol-715
}}
==Nonrigid Motion Compensation of Free Breathing Acquired Myocardial Perfusion Data==
https://ceur-ws.org/Vol-715/bvm2011_19.pdf
Nonrigid Motion Compensation of Free
Breathing Acquired Myocardial Perfusion Data
Gert Wollny1 , Peter Kellman2 , Andrés Santos1,3 , Marı́a-Jesus Ledesma1,3
1
Biomedical Imaging Technologies, Department of Electronic Engineering, ETSIT,
Universidad Politécnica de Madrid, Spain
2
Laboratory of Cardiac Energetics, National Heart, Lung and Blood Institute,
National Institutes of Health, DHHS, Bethesda, MD, USA
3
Ciber BBN, Spain
gert@die.upm.es
Abstract. In this work, we present a novel method to compensate the
movement in images acquired during free breathing using first-pass ga-
dolinium enhanced, myocardial perfusion magnetic resonance imaging
(MRI). First, we use independent component analysis (ICA) to iden-
tify the optimal number of independent components (ICs) that separate
the breathing motion from the intensity change induced by the contrast
agent. Then, synthetic images are created by recombining the ICs, but
other then in previously published work (Milles et al. 2008), we omit
the component related to motion, and therefore, the resulting reference
image series is free of motion. Motion compensation is then achieved
by using a multi-pass non-rigid image registration scheme. We tested
our method on 15 distinct image series (5 patients) consisting of 58 im-
ages each and we validated our method by comparing manually tracked
intensity profiles of the myocardial sections to automatically generated
ones before and after registration. The average correlation to the man-
ually obtained curves before registration 0.89 ± 0.11 was increased to
0.98 ± 0.02.
1 Introduction
First-pass gadolinium enhanced, myocardial perfusion magnetic resonance imag-
ing (MRI) has been proved to be a reliable tool for the diagnosis of coronary
artery disease that lead to reduced blood supply to the myocardium [1]. In a
typical imaging protocol, images are acquired over 60 sec to cover a full perfu-
sion cycle, (Fig. 1), a time that is generally too long for the average patients
to hold their breath. Therefore, the image series will contain breathing motion
that needs to be compensated for if the myocardial perfusion is to be analyzed
automatically. An additional challenge is posed by the contrast agent passing
through the heart that results in a strong intensity change over time. In the
following we will assume that the patient is breathing freely during image acqui-
sition, which results in a breathing motion that is almost periodic, a property
that can be exploited when this motion is to be compensated for.
Nonrigid Motion Compensation 85
Various image registration methods have been proposed to automatically
compensate breathing movement. Some methods rely on rigid registration only
(e.g., [2, 3]). However, since the heart moves within the non-moving chest, rigid
registration requires the segmentation of a region of interest around the heart,
and it also does not account for the non-rigid deformations of the myocardium.
Also, most proposed methods require the registration of images at different
perfusion phases, and, therefore, need to take the intensity change into account,
e.g. by employing more complex registration criterions that can be directly ap-
plied to images with varying intensity distributions [3, 4]. In [5] a two step
procedure was proposed that exploits the quasi-periodicy of the free breathing
motion that reduces the need to register images from different breathing phases,
but it does not eliminate it completely.
Chao and Ying [6] presented a motion compensation scheme that eliminated
the need to register images from different perfusion phases by modeling an ap-
proximation of ground truth and then register to it. However, the method relies
on an initial rigid registration that requires the extraction of a bounding box.
Milles et al. [2] overcame the need for registration of images of different per-
fusion phases by using ICA to create synthetic references that exhibit similar
intensities like their original counterparts. In addition, they used the ICA and
prior knowledge to extract a bounding box around the left heart ventricle (LV)
making rigid registration possible. However, in [5] it was discussed that the
method does not perform reliably for data acquired with a free breathing pro-
tocol: The synthetic references generally contained a lot of residual movement
that made it impossible to achieve complete registration, and, in some cases it
was impossible to identify the RV and LV cavities, and, hence, to create a proper
bounding box that is required for rigid registration.
We propose to enhance the work of Milles et al. [2] by replacing the three-
component ICA by running a series of ICA that will automatically select the
optimal number of ICs to separate the breathing motion from the intensity
change. Based on this optimal separation, we create a series of reference im-
ages by recombining the optimal ICs omitting the motion component, resulting
in a series of images that is free of motion and whose images exhibit a similar
intensity distribution as their original counterparts, allowing for the application
of the sum of squared differences as registration criterion. Then, non-rigid reg-
Fig. 1. Images from a first-pass gadolinium enhanced, myocardial perfusion MRI study.
From left to right: pre-contrast, RV-peak, LV peak, and myocardial peak.
86 Wollny et al.
istration is used to compensate for the motion, eliminating the need to segment
a bounding box around the heart. For full breathing motion compensation, the
whole scheme is run in a multi-pass fashion.
2 Materials and Methods
Reconsidering myocardial perfusion image series acquired under a free breath-
ing protocol, one takes note that they should actually contain five independent
components (ICs): The baseline, the LV cavity enhancement, the RV cavity en-
hancement, the myocardial perfusion, and the quasi-periodic movement. Hence,
a separation into five ICs should be optimal. However, our experiments show
that depending on the image data, sometimes the perfusion component can not
be separated well, and instead the movement component is split into two differ-
ent ICs which results in more than one mixing curve exhibiting periodic behavior
(Fig. 2, left, solid lines). Here, reducing the number of components can result
in an unambiguous separation of the motion component (Fig. 2,right). In other
cases, intensity change patterns resulting from the imaging process create more
components that can be identified, resulting in a better separation of the move-
ment if more than five components are used. Therefore, we run ICAs using four
to six components and estimate the best separation by identifying periodic com-
ponents based on curve length and mean frequency. The highest number of ICs
that results in only one periodic component is then used for further processing.
To register the whole series, we create synthetic reference images for each
time point by linearly combining all ICs excluding the IC corresponding to the
quasi-periodic movement. By this method, the movement is removed from the
image series, but the intensity change is preserved, resulting in reference images
that exhibit the same intensity distributions as their original counterparts.
Hence, our registration approach uses the sum of squared differences as reg-
istration criterion. It utilizes a B-Spline model for the transformation [7] and
a regularization that is based on the separate norms of the second derivative
of each of the deformation components [8] weighted by a factor κ. In the first
Fig. 2. Left: Mixing matrix obtained using a five component ICA. Note, that the
quasi-periodic movement component is actually split into two components. Using a
four component ICA results in better separation (right).
Nonrigid Motion Compensation 87
pass, we restrict the freedom of the non-rigid registration by employing a high
regularization weight (κ = 20), and by using a large knot spacing (32 pixel) for
the B-spline based transformation. In each subsequent pass, we then reduced
the regularization weights and the B-spline knot spacing by factor of 14 . Initially,
the synthetic references are quite blurry, therefore, we used a multi-pass scheme
like proposed in [2]. Processing stopped after a maximum of three passes, or if in
the the last processing pass no periodic motion component could be identified.
For five subjects first-pass contrast-enhanced myocardial perfusion imaging
data sets were acquired and processed under clinical research protocols. All
data was acquired using a free breathing protocol and motion correction was
performed for three short-axis slices of these five patients covering different levels
of the LV myocardium (basal, mid, and apical levels).
A gold standard for validation was acquired by manually segmenting the
myocardium into 12 distinct sections and tracking their average intensity over
time. Validation was then executed by comparing the manually obtained time-
intensity curves to automatically obtained ones before and after registration.
As a measure of similarity between these curves we used Pearsons correlation
coefficient R2 between the manual and the automatically obtained curves.
3 Results
We were able to achieve full motion compensation for all 15 image series. The av-
erage correlation between the manual and automatically obtained time-intensity
curves before registration 0.89 ± 0.11 was increased to 0.98 ± 0.02 (Fig. 3), an
improvement over the value of 0.96 ± 0.05 that was achieved by employing the
method published in [5].
4 Discussion and Conclusions
The use of ICA as proposed in [2] provides an easy way to create synthetic refer-
ence images with intensity distributions that are close to counterparts from the
Fig. 3. Time intensity curves of two different sections of the myocardium in one image
series. Note the quasi-periodic intensity change in the unregistered series and the good
approximation of the manually segmented curves by the registered series.
88 Wollny et al.
original series making image registration easier. However, the three component
approach and the use of rigid registration is not well suited for perfusion image
series acquired with a free breathing protocol.
Therefore, we presented a change to this ICA based motion compensation
approach that estimates automatically the optimal number of retained ICs to be
used. By identifying the movement component and then eliminating it from the
reference image creation, we were able to create series of reference images that
exhibit no movement at all and also exhibit similar intensity distributions like
the original images. By using nonrigid registration, the need for the segmen-
tation of a bounding box around the LV myocardium was eliminated. Because
of the initial blurriness of the synthetic reference images, a multi-pass scheme
was employed that would put a high penalty on the nonrigid transformations
in the first registration pass, and give more freedom to the transformation in
subsequent passes, when the newly created reference images are less blurry.
In the approach we presented, a free breathing image acquisition protocol is
a requirement, because otherwise it is difficult to identify and hence, eliminate
the movement component by using ICA. However, a free breathing protocol for
image acquisition also makes the procedure easier for the patient.
Currently, our approach uses a registration scheme that is run on a slice-
by-slice bases. Future work will target to achieve a one-step registration of the
whole sequence by modeling the time-dependency of the quasi-periodic motion.
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