Unsupervised motion-compensation of multi-slice cardiac perfusion MRI

Stegmann, Mikkel Bille; Ólafsdóttir, Hildur; Larsson, Henrik

doi:10.1016/j.media.2004.10.002

Cited by 58 publications

(52 citation statements)

References 37 publications

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“…The majority of them focuses to compensate the rigid-body motion using image registration [4][5][6][7][8][9][10], while an exception is recently published [11] with the ICA (independent component analysis) being used to analyze the myocardium intensity patterns and to estimate the 2D translation. To correct the possible non-rigid deformation of myocardium during the contrast uptake, methods based on active contour [12] and active shape model [13] have been developed.…”

Section: Introductionmentioning

confidence: 99%

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

Hui-feng

Guehring

Srinivasan

et al. 2008

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2008

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Abstract. Although the evaluation of cardiac perfusion using MRI could be of crucial importance for the diagnosis of ischemic heart diseases, it is still not a routinely used technique. The major difficulty is that MR perfusion images are often corrupted by inconsistent myocardial motion. Although motion compensation methods have been studied throughout the past decade, no clinically accepted solution has emerged. This is partly due to the lack of comprehensive validation. To address this deficit we collected a large multi-centre MR perfusion dataset and used this to characterize typical myocardial motion and confirmed that under clinically relevant conditions motion correction is a frequent requirement (67% of all 586 cases). We then developed a proposed solution which includes both rigid/affine and the non-rigid image registration. Quantitative validation has been conducted using 6 different statistics to provide a comprehensive evaluation, showing the proposed techniques to be highly robust to different myocardial anatomy and motion patterns as well as to MR imaging acquisition parameters.

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Section: Introductionmentioning

confidence: 99%

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

Hui-feng

Guehring

Srinivasan

et al. 2008

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2008

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“…These approaches have been successfully employed to segment structures in cardiac MRIs [16] or for registration in functional heart imaging [19]. In [17] vertebrae in the spine were delineated, and in [20] active shape models were utilised for bone densiometry.…”

Section: Introductionmentioning

confidence: 99%

Sparse MRF Appearance Models for Fast Anatomical Structure Localisation

Donner

Micusik

Bischof³

2007

Procedings of the British Machine Vision Conference 2007

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Image segmentation methods like active shape models, active appearance models or snakes require an initialisation that guarantees a considerable overlap with the object to be segmented. In this paper we present an approach that localises anatomical structures in a global manner by means of Markov Random Fields (MRF). It does not need initialisation, but finds the most plausible match of the query structure in the image. It provides for precise, reliable and fast detection of the structure and can serve as initialisation for more detailed segmentation steps.Sparse MRF Appearance Models (SAMs) encode a priori information about the geometric configurations of interest points, local features at these points and local features along the edges of adjacent points. This information is used to formulate a Markov Random Field and the mapping of the modeled object (e.g. a sequence of vertebrae) to the query image interest points is performed by the MAX-SUM algorithm.The local image information is captured by novel symmetry-based interest points and local descriptors derived from Gradient Vector Flow. Experimental results are reported for two data-sets showing the applicability to complex medical data.

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“…Recently Stegmann et al (12) proposed the use of active-appearance models to register the perfusion images. The active-appearance models capture the shape variability of the heart from a representative training set and use the models to fit the new perfusion images and register them.…”

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confidence: 99%

Model‐based registration for dynamic cardiac perfusion MRI

Adluru

DiBella

Schabel

2006

Magnetic Resonance Imaging

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Purpose:To assess the accuracy of a model-based approach for registration of myocardial dynamic contrastenhanced (DCE)-MRI corrupted by respiratory motion. Materials and Methods:Ten patients were scanned for myocardial perfusion on 3T or 1.5T scanners, and short-and long-axis slices were acquired. Interframe registration was done using an iterative model-based method in conjunction with a mean square difference metric. The method was tested by comparing the absolute motion before and after registration, as determined from manually registered images. Regional flow indices of myocardium calculated from the manually registered data were compared with those obtained with the model-based registration technique. Results:The mean absolute motion of the heart for the short-axis data sets over all the time frames decreased from 5.3 Ϯ 5.2 mm (3.3 Ϯ 3.1 pixels) to 0.8 Ϯ 1.3 mm (0.5 Ϯ 0.7 pixels) in the vertical direction, and from 3.0 Ϯ 3.7 mm (1.7 Ϯ 2.1 pixels) to 0.9 Ϯ 1.2 mm (0.5 Ϯ 0.7 pixels) in the horizontal direction. A mean absolute improvement of 77% over all the data sets was observed in the estimation of the regional perfusion flow indices of the tissue as compared to those obtained from manual registration. Similar results were obtained with two-chamber-view long-axis data sets. Conclusion:The model-based registration method for DCE cardiac data is comparable to manual registration and offers a unique registration method that reduces errors in the quantification of myocardial perfusion parameters as compared to those obtained from manual registration. DYNAMIC MR EVALUATION of myocardial perfusion is becoming a powerful tool for the detection of coronary artery disease (1-3). The standard approach is to rapidly acquire T1-weighted images to track the uptake and washout of the contrast agent Gd-DTPA. Multiple 2D images are acquired each heartbeat. The slices are acquired using ECG-gated sequences so that a given slice is always acquired during the same phase of the cardiac cycle. The time-series data are analyzed visually or with semiquantitative or quantitative models (4,5). Depending on the analysis technique used, at least 20 -40 seconds of data are needed to determine regional perfusion values in the left ventricular (LV) myocardium. Many patients cannot hold their breath long enough to provide a motion-free study. A breathhold of any appreciable length becomes more difficult when pharmacological stress is induced by vasodilating agents, which is necessary for detection of coronary artery disease. Respiration causes motion of the heart, which makes qualitative visual analysis difficult and can cause incorrect estimation of semiquantitative and quantitative parameters.A number of registration methods have been proposed to correct the motion of the heart in dynamic contrast MRI acquisitions (6 -10). In the method of Bidaut and Vallee (6), the mean squared difference between images in the perfusion sequence and a reference image in a region defined by a cardiac mask is minimized. The initial reference image is c...

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Unsupervised motion-compensation of multi-slice cardiac perfusion MRI

Cited by 58 publications

References 37 publications

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

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

Sparse MRF Appearance Models for Fast Anatomical Structure Localisation

Model‐based registration for dynamic cardiac perfusion MRI

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