Nonrigid registration of 3D tensor medical data

Ruiz-Alzola, Juan; Westin, Carl‐Fredrik; Warfield, Simon K.; Alberola, C.; Maier, Stephan E.; Kikinis, Ron

doi:10.1016/s1361-8415(02)00055-5

Cited by 127 publications

(87 citation statements)

References 38 publications

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“…Our laboratory has had extensive experience with image-based matching models for a variety of brain matching applications (Warfield et al, 1995;Warfield, 1996;Nakajima et al, 1997;Hata et al, 1998;Warfield et al, 1999;Ferrant et al, 1999b;Hata et al, 2000;Kaus, 2000;Ruiz-Alzola et al, 2001;Rexilius and Warfield, 2001). However, in the context of intraoperative brain matching, such methods have not yet been able to provide a robust and clinically applicable solution.…”

Section: Intraoperative Registration Methodsmentioning

confidence: 99%

Serial registration of intraoperative MR images of the brain

Ferrant

Nabavi

Macq

et al. 2002

Medical Image Analysis

179

156

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Section: Intraoperative Registration Methodsmentioning

confidence: 99%

Serial registration of intraoperative MR images of the brain

Ferrant

Nabavi

Macq

et al. 2002

Medical Image Analysis

179

156

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“…Indeed, tensors are supposed to remain aligned with underlying tissues under any transform. We chose the finite strain reorientation strategy proposed in [10] to reorient the tensors with respect to the Jacobian of the deformation. Note that the principal direction of diffusion preserving method [11] could be used as well.…”

Section: Construction Of a Diffusion Tensor Atlas Of Controlsmentioning

confidence: 99%

Detection of DTI White Matter Abnormalities in Multiple Sclerosis Patients

Commowick

Fillard

Clatz

et al. 2008

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2008

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The emergence of new modalities such as Diffusion Tensor Imaging (DTI) is of great interest for the characterization and the temporal study of Multiple Sclerosis (MS). DTI indeed gives information on water diffusion within tissues and could therefore reveal alterations in white matter fibers before being visible in conventional MRI. However, recent studies generally rely on scalar measures derived from the tensors such as FA or MD instead of using the full tensor itself. Therefore, a certain amount of information is left unused.In this article, we present a framework to study the benefits of using the whole diffusion tensor information to detect statistically significant differences between each individual MS patient and a database of control subjects. This framework, based on the comparison of the MS patient DTI and a mean DTI atlas built from the control subjects, allows us to look for differences both in normally appearing white matter but also in and around the lesions of each patient. We present a study on a database of 11 MS patients, showing the ability of the DTI to detect not only significant differences on the lesions but also in regions around them, enabling an early detection of an extension of the MS disease.

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“…Using the presented approach, the difficult problem of tensor re-orientation was elegantly overcome. A severe drawback of approaches currently available for tensor reorientation (Guimond et al, 2002;Ruiz-Alzola et al, 2002;Sierra, 2001) is the significantly increased computational cost due to their algorithmic complexity. In addition to that, these techniques have not yet been fully quantified and theoretically proven, for this reason re-orientation of tensors is circumvented in our registration framework.…”

Section: Discussionmentioning

confidence: 99%

Correction of susceptibility artifacts in diffusion tensor data using non-linear registration

Merhof

Soza

Stadlbauer

et al. 2007

Medical Image Analysis

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Diffusion tensor imaging can be used to localize major white matter tracts within the human brain. For surgery of tumors near eloquent brain areas such as the pyramidal tract this information is of importance to achieve an optimal resection while avoiding post-operative neurological deficits. However, due to the small bandwidth of echo planar imaging, diffusion tensor images suffer from susceptibility artifacts resulting in positional shifts and distortion. As a consequence, the fiber tracts computed from echo planar imaging data are spatially distorted. We present an approach based on non-linear registration using Bézier functions to efficiently correct distortions due to susceptibility artifacts. The approach makes extensive use of graphics hardware to accelerate the non-linear registration procedure. An improvement presented in this paper is a more robust and efficient optimization strategy based on simultaneous perturbation stochastic approximation (SPSA).Since the accuracy of non-linear registration is crucial for the value of the presented correction method, two techniques were applied in order to prove the quality of the proposed framework. First, the registration accuracy was evaluated by recovering a known transformation with non-linear registration. Second, landmark-based evaluation of the registration method for anatomical and diffusion tensor data was performed. The registration was then applied to patients with lesions adjacent to the pyramidal tract in order to compensate for susceptibility artifacts. The effect of the correction on the pyramidal tract was then quantified by measuring the position of the tract before and after registration. As a result, the distortions observed in phase encoding direction were most prominent at the cortex and the brainstem. The presented approach allows correcting fiber tract distortions which is an important prerequisite when tractography data are integrated into a stereotactic setup for intra-operative guidance.

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Nonrigid registration of 3D tensor medical data

Cited by 127 publications

References 38 publications

Serial registration of intraoperative MR images of the brain

Serial registration of intraoperative MR images of the brain

Detection of DTI White Matter Abnormalities in Multiple Sclerosis Patients

Correction of susceptibility artifacts in diffusion tensor data using non-linear registration

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