Evaluation of Rigid and Non-rigid Motion Compensation of Cardiac Perfusion MRI

Hui-feng, Xue; Guehring, Jens; Srinivasan, Latha; Zuehlsdorff, Sven; Saddi, Kinda Anna; Chefd’hotel, Christophe; Hajnal, Joseph V.; Rueckert, Daniel

doi:10.1007/978-3-540-85990-1_5

Cited by 19 publications

(18 citation statements)

References 16 publications

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“…Milles et al (2008); Xue et al (2008). As discussed in Wollny et al (2010a), a validation based on comparing manually segmented shapes is not a reliable option for our target application.…”

Section: Experiments and Validationmentioning

confidence: 99%

Automatic motion compensation of free breathing acquired myocardial perfusion data by using independent component analysis

Wollny

Kellman

Santos

et al. 2012

Medical Image Analysis

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Images acquired during free breathing using first-pass gadolinium-enhanced myocardial perfusion magnetic resonance imaging (MRI) exhibit a quasiperiodic motion pattern that needs to be compensated for if a further automatic analysis of the perfusion is to be executed. In this work, we present a method to compensate this movement by combining independent component analysis (ICA) and image registration: First, we use ICA and a time-frequency analysis to identify the motion and separate it from the intensity change induced by the contrast agent. Then, synthetic reference images are created by recombining all the independent components but the one related to the motion. Therefore, the resulting image series does not exhibit motion and its images have intensities similar to those of their original counterparts. Motion compensation is then achieved by using a multi-pass image registration procedure. We tested our method on 39 image series acquired from 13 patients, covering the basal, mid and apical areas of the left heart ventricle and consisting of 58 perfusion images each. We validated our method by comparing manually tracked intensity profiles of the myocardial sections to automatically generated ones before and after registration of 13 patient data sets (39 distinct slices). We compared linear, non-linear, and combined ICA based registration approaches and previously published motion compensation schemes. Considering run-time and accuracy, a two-step ICA based motion compensation scheme that first optimizes a translation and then for non-linear transformation performed best and achieves registration of the whole series in 32 ± 12s on a recent workstation. The proposed scheme improves the Pearsons correlation coefficient between manually and automatically obtained times-intensity curves from .84 ± .19 before registration to .96 ± .06 after registration.

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“…Milles et al (2008); Xue et al (2008). As discussed in Wollny et al (2010a), a validation based on comparing manually segmented shapes is not a reliable option for our target application.…”

Section: Experiments and Validationmentioning

confidence: 99%

Automatic motion compensation of free breathing acquired myocardial perfusion data by using independent component analysis

Wollny

Kellman

Santos

et al. 2012

Medical Image Analysis

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“…Aside from through-plane motion, rigid techniques do not consider deformations that can occur in the myocardium throughout the first-pass image acquisition. Registration methods that use a global (affine) motions model [15] account for some aspects of the more complex deformations. Non-rigid motion models that account for local deformations [16]–[18] provide better alignment if there is deformation of the heart during breathing, but they are more susceptible to noise and are more computationally intensive.…”

Section: Introductionmentioning

confidence: 99%

“…Techniques by Bidaut and Vallé [11], and Gupta et al [12] are based on the sum-of-squared differences metric, which is well-suited to correcting for rotations [19]. Other groups have employed metrics based on normalized mutual information [15], and cross-correlation [12]. Other methods move away from the intensity-based approach and use metrics that assess spatial gradients [20] or independent component analysis [21].…”

Section: Introductionmentioning

confidence: 99%

“…Widespread adoption of any technique in the clinical environment requires thorough validation. Xue et al [15] did a validation study on two registration techniques across a multi-center dataset. They used a combination of qualitative and quantitative measures to assess the performance of the methods, but did not evaluate clinically relevant parameters on this dataset.…”

Section: Introductionmentioning

confidence: 99%

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An Open Benchmark Challenge for Motion Correction of Myocardial Perfusion MRI

Pontré

Cowan

DiBella

et al. 2017

IEEE J. Biomed. Health Inform.

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Cardiac magnetic resonance perfusion examinations enable non-invasive quantification of myocardial blood flow. However, motion between frames due to breathing must be corrected for quantitative analysis. Although several methods have been proposed, there is a lack of widely available benchmarks to compare different algorithms. We sought to compare many algorithms from several groups in an open benchmark challenge. Nine clinical studies from two different centers comprising normal and diseased myocardium at both rest and stress were made available for this study. The primary validation measure was regional myocardial blood flow based on the transfer coefficient (Ktrans), which was computed using a compartment model and the myocardial perfusion reserve (MPR) index. The ground truth was calculated using contours drawn manually on all frames by a single observer, and visually inspected by a second observer. Six groups participated and 19 different motion correction algorithms were compared. Each method used one of three different motion models: rigid, global affine or local deformation. The similarity metric also varied with methods employing either sum-of-squared differences, mutual information or cross-correlation. There were no significant differences in Ktrans or MPR compared across different motion models or similarity metrics. Compared with the ground truth, only Ktrans for the sum of squared differences metric, and for local deformation motion models, had significant bias. In conclusion, the open benchmark enabled evaluation of clinical perfusion indices over a wide range of methods. In particular, there was no benefit of non-rigid registration techniques over the other methods evaluated in this study. The benchmark data and results are available from the Cardiac Atlas Project (www.cardiacatlas.org).

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“…In order to successfully apply the KLT filter to clinical perfusion datasets with significant myocardial motion, we hereby propose to apply non-rigid registration to compensate for respiratory motion and myocardial deformation between image frames. This novel combined approach of non-rigid registration prior to filtering is expected to improve the effectiveness of KLT filtering by concentrating the information content into a smaller number of eigenimages [11]. …”

Section: Introductionmentioning

confidence: 99%

Non-rigid registration and KLT filter to improve SNR and CNR in GRE-EPI myocardial perfusion imaging

Mihai¹,

Ding²,

Hui-feng³

et al. 2012

JBiSE

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The purpose of the study was to evaluate the effect of motion compensation by non-rigid registration combined with the Karhunen-Loeve Transform (KLT) filter on the signal to noise (SNR) and contrast-to-noise ratio (CNR) of hybrid gradient-echo echoplanar (GRE-EPI) first-pass myocardial perfusion imaging. Twenty one consecutive first-pass adenosine stress perfusion MR data sets interpreted positive for ischemia or infarction were processed by non-rigid Registration followed by KLT filtering. SNR and CNR were measured in abnormal and normal myocardium in unfiltered and KLT filtered images following non-rigid registration to compensate for respiratory and other motions. Image artifacts introduced by filtering in registered and nonregistered images were evaluated by two observers. There was a statistically significant increase in both SNR and CNR between normal and abnormal myocardium with KLT filtering (mean SNR increased by 62.18% ± 21.05% and mean CNR increased by 58.84% ± 18.06%; p = 0.01). Motion correction prior to KLT filtering reduced significantly the occurrence of filter induced artifacts (KLT only-artifacts in 42 out of 55 image series vs. registered plus KLT-artifacts in 3 out of 55 image series). In conclusion the combination of non- rigid registration and KLT filtering was shown to increase the SNR and CNR of GRE-EPI perfusion images. Subjective evaluation of image artifacts revealed that prior motion compensation significantly reduced the artifacts introduced by the KLT filtering process.

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Evaluation of Rigid and Non-rigid Motion Compensation of Cardiac Perfusion MRI

Cited by 19 publications

References 16 publications

Automatic motion compensation of free breathing acquired myocardial perfusion data by using independent component analysis

Automatic motion compensation of free breathing acquired myocardial perfusion data by using independent component analysis

An Open Benchmark Challenge for Motion Correction of Myocardial Perfusion MRI

Non-rigid registration and KLT filter to improve SNR and CNR in GRE-EPI myocardial perfusion imaging

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