Improved Correspondence for DTI Population Studies Via Unbiased Atlas Building

Goodlett, Casey; Davis, Bradley Moore; Jean, Remi; Gilmore, John H.; Gerig, Guido

doi:10.1007/11866763_32

Cited by 36 publications

(38 citation statements)

References 11 publications

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“…The comparison of different registration methods will therefore be interesting to quantify this dependency. This evaluation will also include the use of registration methods taking into account the specificities of the DTI such as [14], allowing to register more precisely regions such as the white matter, which has a uniform intensity in conventional MRI.…”

Section: Resultsmentioning

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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Section: Resultsmentioning

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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show abstract

“…Second-order statistics on the whole diffusion tensor were computed for model-based diffusion tensor tractography [35] in the brain but only with a Euclidean metric. A population study of brain diffusion tensors used statistics with the Log-Euclidean metric but was limited to their averaging [36].…”

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confidence: 99%

A Computational Framework for the Statistical Analysis of Cardiac Diffusion Tensors: Application to a Small Database of Canine Hearts

Peyrat

Sermesant

Pennec

et al. 2007

IEEE Trans. Med. Imaging

119

106

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Abstract-We propose a unified computational framework to build a statistical atlas of the cardiac fiber architecture from diffusion tensor magnetic resonance images (DT-MRIs). We apply this framework to a small database of nine ex vivo canine hearts. An average cardiac fiber architecture and a measure of its variability are computed based on most recent advances in diffusion tensor statistics. This statistical analysis confirms the already established good stability of the fiber orientations and a higher variability of the laminar sheet orientations within a given species. The statistical comparison between the canine atlas and a standard human cardiac DT-MRI shows a better stability of the fiber orientations than their laminar sheet orientations between the two species. The proposed computational framework can be applied to larger databases of cardiac DT-MRIs from various species to better establish intra-and inter-species statistics on the anatomical structure of cardiac fibers. This information will be useful to guide the adjustment of average fiber models onto specific patients from in vivo anatomical imaging modalities.

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“…The BrainWeb data does not contain the diffusion tensor image information, so we align mean diffusion tensor of a separate population to the BrainWeb data. The diffusion tensor field for the population are created using the method described by Goodlett et al [9] All image alignment or registration are performed using affine transformations and deformable transformations parametrized using B-splines with the mutual information image match metric [10].…”

Section: Methodsmentioning

confidence: 99%

Brain Lesion Segmentation through Physical Model Estimation

Prastawa

Gerig

2008

Advances in Visual Computing

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Abstract. Segmentations of brain lesions from Magnetic Resonance (MR) images is crucial for quantitative analysis of lesion populations in neuroimaging of neurological disorders. We propose a new method for segmenting lesions in brain MRI by inferring the underlying physical models for pathology. We use the reaction-diffusion model as our physical model, where the diffusion process is guided by real diffusion tensor fields that are obtained from Diffusion Tensor Imaging (DTI). The method performs segmentation by solving the inverse problem, where it determines the optimal parameters for the physical model that generates the observed image. We show that the proposed method can infer reasonable models for multiple sclerosis (MS) lesions and healthy MRI data. The method has potential for further extensions with different physical models or even non-physical models based on existing segmentation schemes.

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Improved Correspondence for DTI Population Studies Via Unbiased Atlas Building

Cited by 36 publications

References 11 publications

Detection of DTI White Matter Abnormalities in Multiple Sclerosis Patients

Detection of DTI White Matter Abnormalities in Multiple Sclerosis Patients

A Computational Framework for the Statistical Analysis of Cardiac Diffusion Tensors: Application to a Small Database of Canine Hearts

Brain Lesion Segmentation through Physical Model Estimation

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