Image Registration to Compensate for EPI Distortion in Patients with Brain Tumors: An Evaluation of Tract‐Specific Effects

Norton

et al. 2019

Human Brain Mapping

Self Cite

There are two popular approaches for automated white matter parcellation using diffusion MRI tractography, including fiber clustering strategies that group white matter fibers according to their geometric trajectories and cortical‐parcellation‐based strategies that focus on the structural connectivity among different brain regions of interest. While multiple studies have assessed test–retest reproducibility of automated white matter parcellations using cortical‐parcellation‐based strategies, there are no existing studies of test–retest reproducibility of fiber clustering parcellation. In this work, we perform what we believe is the first study of fiber clustering white matter parcellation test–retest reproducibility. The assessment is performed on three test–retest diffusion MRI datasets including a total of 255 subjects across genders, a broad age range (5–82 years), health conditions (autism, Parkinson's disease and healthy subjects), and imaging acquisition protocols (three different sites). A comprehensive evaluation is conducted for a fiber clustering method that leverages an anatomically curated fiber clustering white matter atlas, with comparison to a popular cortical‐parcellation‐based method. The two methods are compared for the two main white matter parcellation applications of dividing the entire white matter into parcels (i.e., whole brain white matter parcellation) and identifying particular anatomical fiber tracts (i.e., anatomical fiber tract parcellation). Test–retest reproducibility is measured using both geometric and diffusion features, including volumetric overlap (wDice) and relative difference of fractional anisotropy. Our experimental results in general indicate that the fiber clustering method produced more reproducible white matter parcellations than the cortical‐parcellation‐based method.

Section: Discussionmentioning

confidence: 99%

Test–retest reproducibility of white matter parcellation using diffusion MRI tractography fiber clustering

Norton

et al. 2019

Human Brain Mapping

Self Cite

“…In the literature, anatomical-MRI-based segmentation, e.g., the one obtained by SPM, is usually used as the "ground truth" data (Ciritsis et al, 2018;Schnell et al, 2009;Cheng et al, 2020), since the segmentation appears in good agreement with the known anatomy. However, transferring T1w-or T2wbased segmentation into the dMRI space is challenging due to the image distortions in dMRI data, which affected inter-modality registration significantly (Albi et al, 2018;Wu et al, 2008;Jones and Cercignani, 2010). In addition, anatomical MRI data often has higher spatial resolution than dMRI data, resulting in segmentation errors from smoothing and image interpolation when transferring the tissue segmentation computed from the high-resolution anatomical data to the low-resolution dMRI data.…”

Section: Discussionmentioning

confidence: 99%

“…Most current tissue segmentation approaches are based on T1-weighted (T1w) or T2-weighted (T2w) anatomical MRI data (Ashburner and Friston, 2005;Fischl, 2012;Smith et al, 2004), which has high image resolution and image contrast that differentiates between tissue types. However, application of anatomical-MRI-based segmentation to dMRI requires inter-modality registration, which is challenging since dMRI often has echo-planar imaging (EPI) distortions (Wu et al, 2008;Albi et al, 2018;Jones and Cercignani, 2010) and low image resolution (Malinsky et al, 2013). Improved dMRI acquisitions can mitigate these challenges.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Based Segmentation of Brain Tissue from Diffusion MRI

Breger

Cho

et al. 2020

Preprint

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Segmentation of brain tissue types from diffusion MRI (dMRI) is an important task, required for quantification of brain microstructure and for improving tractography. Current dMRI segmentation is mostly based on anatomical MRI (e.g., T1- and T2-weighted) segmentation that is registered to the dMRI space. However, such inter-modality registration is challenging due to more image distortions and lower image resolution in the dMRI data as compared with the anatomical MRI data. In this study, we present a deep learning method that learns tissue segmentation from high-quality imaging datasets from the Human Connectome Project (HCP), where registration of anatomical data to dMRI is more precise. The method is then able to predict a tissue segmentation directly from new dMRI data, including data collected with a different acquisition protocol, without requiring anatomical data and inter-modality registration. We train a convolutional neural network (CNN) to learn a tissue segmentation model using a novel augmented target loss function designed to improve accuracy in regions of tissue boundary. To further improve accuracy, our method adds diffusion kurtosis imaging (DKI) parameters that characterize non-Gaussian water molecule diffusion to the conventional diffusion tensor imaging parameters. The DKI parameters are calculated from the recently proposed mean-kurtosis-curve method that corrects implausible DKI parameter values and provides additional features that discriminate between tissue types. We demonstrate high tissue segmentation accuracy on HCP data, and also when applying the HCP-trained model on dMRI data from a clinical acquisition with lower resolution and fewer gradient directions.

“…This was done by performing an affine registration between the b=0 image of each subject (moving image) and a population-mean T2-weighted image (reference image) using 3D Slicer. We chose T2w data for co-registration because it has similar contrast to the b=0 images for promising inter-MRI-modality registration (Albi et al, 2018). Also, T2w data has a good contrast of the cisternal portion of the TGN and has been widely used to confirm the presence of the TGN (Casselman et al, 2008;Xie et al, 2020).…”

Section: Generation Of Tgn Fiber Clustering Atlasmentioning

confidence: 99%

“…Third, placement of ROIs may require inter-modality registration between dMRI and anatomical MRI (e.g. T2-weighted) data, which is challenging for dMRI with low image resolution (Malinsky et al, 2013) and echo-planar imaging (EPI) distortions (Albi et al, 2018). While ROI placement for TGN identification can be done using dMRI data directly (Behan et al, 2017;Fujiwara et al, 2011;Kabasawa et al, 2007;Moon et al, 2018;Xie et al, 2020), most studies have obtained ROIs from high-resolution anatomical MRI images for a better tissue contrast, requiring a co-registration to the low-resolution dMRI space (David Q.…”

Section: Introductionmentioning

confidence: 99%

Creation of a novel trigeminal tractography atlas for automated trigeminal nerve identification

Xie

Leung

et al. 2020

Preprint

Self Cite

AbstractDiffusion MRI (dMRI) tractography has been successfully used to study the trigeminal nerves (TGNs) in many clinical and research applications. Currently, identification of the TGN in tractography data requires expert nerve selection using manually drawn regions of interest (ROIs), which is prone to inter-observer variability, time-consuming and carries high clinical and labor costs. To overcome these issues, we propose to create a novel anatomically curated TGN tractography atlas that enables automated identification of the TGN from dMRI tractography. In this paper, we first illustrate the creation of a trigeminal tractography atlas. Leveraging a well-established computational pipeline and expert neuroanatomical knowledge, we generate a data-driven TGN fiber clustering atlas using tractography data from 50 subjects from the Human Connectome Project. Then, we demonstrate the application of the proposed atlas for automated TGN identification in new subjects, without relying on expert ROI placement. Quantitative and visual experiments are performed with comparison to expert TGN identification using dMRI data from two different acquisition sites. We show highly comparable results between the automatically and manually identified TGNs in terms of spatial overlap and visualization, while our proposed method has several advantages. First, our method performs automated TGN identification, and thus it provides an efficient tool to reduce expert labor costs and inter-operator bias relative to expert manual selection. Second, our method is robust to potential imaging artifacts and/or noise that can prevent successful manual ROI placement for TGN selection and hence yields a higher successful TGN identification rate.