A tract-specific framework for white matter morphometry combining macroscopic and microscopic tract features

Zhang, Hui; Awate, Suyash P.; Das, Sandhitsu R.; Woo, John; Melhem, Elias R.; Gee, James C.; Yushkevich, Paul A.

doi:10.1016/j.media.2010.05.002

Cited by 56 publications

(73 citation statements)

References 50 publications

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“…Tract-specific analysis (TSA) (Yushkevich, Zhang, Simon, & Gee, 2008;Zhang et al, 2010) allows computing white matter tract-specific attributes such as FA, ADC (apparent diffusion coefficient, a scalar measurement of overall diffusivity equal to MD), RD and AD across the whole population. In this study, the tensor averaging dimensionality reduction strategy was adopted.…”

Section: Discussionmentioning

confidence: 99%

White matter microstructure in boys with persistent depressive disorder

Vilgis

Vance

Cunnington

et al. 2017

Journal of Affective Disorders

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Section: Discussionmentioning

confidence: 99%

White matter microstructure in boys with persistent depressive disorder

Vilgis

Vance

Cunnington

et al. 2017

Journal of Affective Disorders

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show abstract

“…The tractography representation using CV analysis used two-tailed t-test on each ROI, in order to infer the differences between raw and filtered data. The TSA analysis performs a t-test group analysis internally, which it was chosen a p-value of p=0.05 and 5.000 permutations (Zhang et al, 2010). Figure 2 illustrates the histograms obtained with the RME and SSIM indexes.…”

Section: Discussionmentioning

confidence: 99%

“…The tractography reconstructions were made in 3D Slicer software (Pieper et al, 2004;Xia et al, 2008), which provides a better control set for image visualization than the FSL tractography toolkit. Finally, it was also analyzed the FA variability given by the TSA approach (Zhang et al, 2010). The TSA pipeline calculates the statistical differences between groups, where it was conducted using the N=16 DTI volumes as the reference dataset and the same white matter regions given in the previous tractography evaluation.…”

Section: Image Analysis and Quality Metricsmentioning

confidence: 99%

Enhancing quality in Diffusion Tensor Imaging with anisotropic anomalous diffusion filter

et al. 2017

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Introduction: Diffusion tensor imaging (DTI) is an important medical imaging modality that has been useful to the study of microstructural changes in neurological diseases. However, the image noise level is a major practical limitation, in which one simple solution could be the average signal from a sequential acquisition. Nevertheless, this approach is time-consuming and is not often applied in the clinical routine. In this study, we aim to evaluate the anisotropic anomalous diffusion (AAD) filter in order to improve the general image quality of DTI. Methods: A group of 20 healthy subjects with DTI data acquired (3T MR scanner) with different numbers of averages (N=1,2,4,6,8, and 16), where they were submitted to 2-D AAD and conventional anisotropic diffusion filters. The Relative Mean Error (RME), Structural Similarity Index (SSIM), Coefficient of Variation (CV) and tractography reconstruction were evaluated on Fractional Anisotropy (FA) and Apparent Diffusion Coefficient (ADC) maps. Results: The results point to an improvement of up to 30% of CV, RME, and SSIM for the AAD filter, while up to 14% was found for the conventional AD filter (p<0.05). The tractography revealed a better estimative in fiber counting, where the AAD filter resulted in less FA variability. Furthermore, the AAD filter showed a quality improvement similar to a higher average approach, i.e. achieving an image quality equivalent to what was seen in two additional acquisitions. Conclusions: In general, the AAD filter showed robustness in noise attenuation and global image quality improvement even in DTI images with high noise level.

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“…For this reason, normalization of FODs is more desirable for downstream applications that use groupwise fiber tractography (30)(31)(32), as well as the recently presented apparent fiber density measure (13). Hong et al (15) proposed the first method to reorient FODs for normalization.…”

Section: Discussionmentioning

confidence: 99%

Reorientation of fiber orientation distributions using apodized point spread functions

Raffelt¹,

Tournier²,

Crozier³

et al. 2011

Magnetic Resonance in Med

110

105

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Using high angular resolution diffusion-weighted images, spherical deconvolution enables multiple white matter fiber populations to be resolved within a single voxel by computing the fiber orientation distribution (FOD). Higher order information provided by FODs could improve several methods for investigating population differences in white matter, including image registration, voxel-based analysis, atlas-based segmentation and labeling, and group average fiber tractography. All of these methods require spatial normalization of FODs. In this article, a novel method to reorient the FOD is presented, which is an important step required for FOD spatial normalization. The proposed method was assessed using both qualitative and quantitative experiments, with numerical simulations and in vivo human data. Results demonstrate that the proposed method improves FOD reorientation accuracy, removes undesired artefacts, and decreases computation time compared to a previous approach. The utility of the proposed method is illustrated by nonlinear FOD spatial normalization of 10 human subjects. Key words: fiber orientation distribution; reorientation; normalization; diffusion MRI Diffusion-weighted imaging provides a unique method to investigate the architecture of white matter in vivo. To investigate group differences in the diffusion properties of white matter, correspondence between images is required to ensure that the same anatomical structures are being compared. The most common method for obtaining correspondence involves transforming data from each individual subject into a common co-ordinate or template space (a process called spatial normalization). For the remainder of this text, we refer to spatial normalization as simply normalization.The diffusion of water within each imaging voxel is commonly modeled using a diffusion tensor (1). In recent years, normalization of diffusion tensor measures such as fractional anisotropy (FA) (2) has been widely used to investigate voxelwise differences between diseased and healthy populations (see Assaf and Pasternak (3) for a recent review). However, the popular rank-2 diffusion tensor is only capable of modeling a single fiber population within each voxel. This is a serious limitation given recent work suggesting that 90% of white matter voxels contain multiple fiber populations (4). Consequently, in the majority of white matter, population differences in diffusion tensor measures can be influenced not only by differences in so-called white matter "integrity", but also by partial volume effects within crossing fiber regions (5,6).Using high angular resolution diffusion-weighted imaging, higher order models enable crossing fibers to be resolved and, therefore, provide more information than the diffusion tensor at a subvoxel level. Spherical deconvolution is one method that can resolve crossing fibers by estimating the so-called fiber orientation distribution (FOD), a continuous distribution representing the partial volume of the underlying fibers as a function of orientation (7-12...

show abstract

A tract-specific framework for white matter morphometry combining macroscopic and microscopic tract features

Cited by 56 publications

References 50 publications

White matter microstructure in boys with persistent depressive disorder

White matter microstructure in boys with persistent depressive disorder

Enhancing quality in Diffusion Tensor Imaging with anisotropic anomalous diffusion filter

Reorientation of fiber orientation distributions using apodized point spread functions

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