Automated Diffeomorphic Registration of Anatomical Structures with Rigid Parts: Application to Dynamic Cervical MRI

Commowick, Olivier; Wiest-Daesslé, Nicolas; Prima, Sylvain

doi:10.1007/978-3-642-33418-4_21

Cited by 21 publications

(29 citation statements)

References 15 publications

(24 reference statements)

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“…However, one of the primary motivations for this work was to demonstrate that typical approaches, which are often highly successful on oft-studied structures (e.g., the brain), are problematic when applied to new, highly difficult structures (i.e., structures exhibiting large imaging and anatomical variability). As a result, reasonable segmentation of the spinal cord’s internal structures through 3-D deformable registrations often require (1) a priori structural information, (2) a highly-tuned application-specific registration framework – e.g.,(Commowick et al, 2012b), or (3) a multi-contrast cost function (e.g., using Tl- and T2*-weighted images). Additionally, and potentially most importantly, pairwise 3-D non-rigid registration algorithms can often take upwards of an hour to perform each individual registration on a modern cpu.…”

Section: Discussionmentioning

confidence: 99%

Groupwise multi-atlas segmentation of the spinal cord’s internal structure

Asman

Bryan

Smith

et al. 2014

Medical Image Analysis

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The spinal cord is an essential and vulnerable component of the central nervous system. Differentiating and localizing the spinal cord internal structure (i.e., gray matter vs. white matter) is critical for assessment of therapeutic impacts and determining prognosis of relevant conditions. Fortunately, new magnetic resonance imaging (MRI) sequences enable clinical study of the in vivo spinal cord’s internal structure. Yet, low contrast-to-noise ratio, artifacts, and imaging distortions have limited the applicability of tissue segmentation techniques pioneered elsewhere in the central nervous system. Additionally, due to the inter-subject variability exhibited on cervical MRI, typical deformable volumetric registrations perform poorly, limiting the applicability of a typical multi-atlas segmentation framework. Thus, to date, no automated algorithms have been presented for the spinal cord’s internal structure. Herein, we present a novel slice-based groupwise registration framework for robustly segmenting cervical spinal cord MRI. Specifically, we provide a method for (1) pre-aligning the slice-based atlases into a groupwise-consistent space, (2) constructing a model of spinal cord variability, (3) projecting the target slice into the low-dimensional space using a model-specific registration cost function, and (4) estimating robust segmentations using geodesically appropriate atlas information. Moreover, the proposed framework provides a natural mechanism for performing atlas selection and initializing the free model parameters in an informed manner. In a cross-validation experiment using 67 MR volumes of the cervical spinal cord, we demonstrate sub-millimetric accuracy, significant quantitative and qualitative improvement over comparable multi-atlas frameworks, and provide insight into the sensitivity of the associated model parameters.

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Section: Discussionmentioning

confidence: 99%

Groupwise multi-atlas segmentation of the spinal cord’s internal structure

Asman

Bryan

Smith

et al. 2014

Medical Image Analysis

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“…We adapt this approach to a block-matching algorithm [23], [24] by constraining the transformation to be aligned with the PED as assumed in the distortion model. The block-matching algorithm enables a simple and effective incorporation of this constraint on the deformation field.…”

Section: B Block-matching For Distortion Correctionmentioning

confidence: 99%

“…This extrapolation aims at computing a dense field of transformation logarithmsR .,i (i = 1, ..., M representing each voxel) from the sparseÂ .,j . This is performed utilizing an Msmoothing algorithm in the log-Euclidean space on affine transformations [30] as proposed in [24]:…”

Section: Transformation Extrapolation and Compositionmentioning

confidence: 99%

“…This cost function is optimized through an iterative scheme, more detailed in [24]. The obtained transformation logarithmsR .,i are then applied to their respective positions x i to compute the SVFs δS + and δS − : δS .…”

Section: Transformation Extrapolation and Compositionmentioning

confidence: 99%

“…We therefore present a new algorithm for distortion correction falling in the same category. Block-matching based registration has been successfully proposed for registration in medical imaging both for rigid [23] and non-linear registration [24]. As a registration framework, block-matching has the advantage of being very generic and easily adaptable to different transformation priors, both to match blocks in the floating image [25] and for the global transformation (linear or non-linear).…”

Section: Introductionmentioning

confidence: 99%

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Block-Matching Distortion Correction of Echo-Planar Images With Opposite Phase Encoding Directions

Hédouin

Commowick

Bannier

et al. 2017

IEEE Trans. Med. Imaging

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Abstract-By shortening the acquisition time of MRI, Echo Planar Imaging (EPI) enables the acquisition of a large number of images in a short time, compatible with clinical constraints as required for diffusion or functional MRI. However such images are subject to large, local distortions disrupting their correspondence with the underlying anatomy. The correction of those distortions is an open problem, especially in regions where large deformations occur.We propose a new block-matching registration method to perform EPI distortion correction based on the acquisition of two EPI with opposite phase encoding directions (PED). It relies on new transformations between blocks adapted to the EPI distortion model, and on an adapted optimization scheme to ensure an opposite symmetric transformation. We present qualitative and quantitative results of the block-matching correction using different metrics on a phantom dataset and on in-vivo data. We show the ability of the blockmatching to robustly correct EPI distortion even in strongly affected areas.

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Measurement of magnetization transfer ratio (MTR) from cervical spinal cord: Multicenter reproducibility and variability

Combès

Monteau

Bannier

et al. 2018

Magnetic Resonance Imaging

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Background Assessing the multicenter variability of magnetization transfer ratio (MTR) measurements in the spinal cord of healthy controls is the first step toward investigating its clinical use as a biomarker. Purpose To analyze the between‐session, between‐participant, and between‐scanner variability of MTR measurements in automatically extracted regions of interest in the cervical cord of healthy controls. Study Type Control study. Population Forty‐four participants, distributed across five MRI scanners (all from the same manufacturer). Ten participants were scanned twice in the same scanner, and 10 others were scanned twice in two different scanners. Field Strength/Sequence 3D‐gradient echo images, centered on C5, without and with magnetization transfer prepulse at 3T. Assessment We calculated the mean MTR for different vertebral levels in the whole cord (WC), as well as in the white matter and gray matter, and determined the between‐session, between‐participant, and between‐scanner variabilities. Statistical Tests Coefficients of variation and intraclass correlations (ICCs) for the different variabilities and their associated confidence intervals. Results The MTR measurements for Levels C4‐C6 (near the slab center) exhibited a mean value in WC of 34.6 pu and a pooled standard deviation of 0.9 pu. The between‐session coefficient of variation was estimated as 2.3% (ICC = 0.63), the between‐participant coefficient as 1.6% (ICC = 0.32), and the between‐scanner coefficient as 0.7% (ICC = 0.05). The resulting aggregate coefficient of variation was 2.9%, which was sufficiently low to detect an MTR reduction of 1 pu between groups of about 45 participants (Type‐I error rate: 0.05; Type‐II error rate: 0.10). Data Conclusion The good between‐scanner reproducibility and low overall variability in cervical spinal cord MTR measurements in a control population might pave the way for multicenter analyses in various neurological diseases with moderate cohort sizes. Level of Evidence: 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:1777–1785.

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Automated Diffeomorphic Registration of Anatomical Structures with Rigid Parts: Application to Dynamic Cervical MRI

Cited by 21 publications

References 15 publications

Groupwise multi-atlas segmentation of the spinal cord’s internal structure

Groupwise multi-atlas segmentation of the spinal cord’s internal structure

Block-Matching Distortion Correction of Echo-Planar Images With Opposite Phase Encoding Directions

Measurement of magnetization transfer ratio (MTR) from cervical spinal cord: Multicenter reproducibility and variability

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