SlicerDMRI: Diffusion MRI and Tractography Research Software for Brain Cancer Surgery Planning and Visualization

Zhang, Fan; Noh, Thomas; Juvekar, Parikshit; Frisken, Sarah; Rigolo, Laura; Norton, Isaiah; Kapur, Tina; Pujol, Sonia; Wells, William M.; Yarmarkovich, Alex; Kindlmann, Gordon; Wassermann, Demián; Estépar, Raúl San Jośe; Rathi, Yogesh; Kikinis, Ron; Johnson, Hans J.; Westin, Carl‐Fredrik; Pieper, Steve; Golby, Alexandra J.; O’Donnell, Lauren J.

doi:10.1200/cci.19.00141

Cited by 74 publications

(43 citation statements)

References 51 publications

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“…The dMRI data was preprocessed using in-house data processing pipeline ( github.com/pnlbwh/pnlpipe ), including brain masking using the SlicerDMRI extension ( dmri.slicer.org ) ( Norton et al, 2017 ; Zhang et al, 2020b ) in 3D Slicer ( www.slicer.org ) and eddy current-induced distortion correction, outlier replacement and motion correction using Eddy ( Andersson et al, 2016 ; Andersson and Sotiropoulos, 2016 ), FSL ( Jenkinson et al, 2012 ). Motion parameters were derived from the FSL permutations and compared between the CHR and HC subjects, where no significant differences were identified ( p = 0.52).…”

Section: Methodsmentioning

confidence: 99%

MK-Curve improves sensitivity to identify white matter alterations in clinical high risk for psychosis

et al. 2021

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Diffusion kurtosis imaging (DKI) is a diffusion MRI approach that enables the measurement of brain microstructural properties, reflecting molecular restrictions and tissue heterogeneity. DKI parameters such as mean kurtosis (MK) provide additional subtle information to that provided by popular diffusion tensor imaging (DTI) parameters, and thus have been considered useful to detect white matter abnormalities, especially in populations that are not expected to show severe brain pathologies. However, DKI parameters often yield artifactual output values that are outside of the biologically plausible range, which diminish sensitivity to identify true microstructural changes. Recently we have proposed the mean-kurtosis-curve (MK-Curve) method to correct voxels with implausible DKI parameters, and demonstrated its improved performance against other approaches that correct artifacts in DKI. In this work, we aimed to evaluate the utility of the MK-Curve method to improve the identification of white matter abnormalities in group comparisons. To do so, we compared group differences, with and without the MK-Curve correction, between 115 individuals at clinical high risk for psychosis (CHR) and 93 healthy controls (HCs). We also compared the correlation of the corrected and uncorrected DKI parameters with clinical characteristics. Following the MK-curve correction, the group differences had larger effect sizes and higher statistical significance (i.e., lower p-values), demonstrating increased sensitivity to detect group differences, in particular in MK. Furthermore, the MK-curve-corrected DKI parameters displayed stronger correlations with clinical variables in CHR individuals, demonstrating the clinical relevance of the corrected parameters. Overall, following the MK-curve correction our analyses found widespread lower MK in CHR that overlapped with lower fractional anisotropy (FA), and both measures were significantly correlated with a decline in functioning and with more severe symptoms. These observations further characterize white matter alterations in the CHR stage, demonstrating that MK and FA abnormalities are widespread, and mostly overlap. The improvement in group differences and stronger correlation with clinical variables suggest that applying MK-curve would be beneficial for the detection and characterization of subtle group differences in other experiments as well.

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Section: Methodsmentioning

confidence: 99%

MK-Curve improves sensitivity to identify white matter alterations in clinical high risk for psychosis

et al. 2021

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“…visually ranked the four RGVPs from each subject, as follows. The four tractography files were loaded into the 3D Slicer software (Kikinis et al, 2014; Norton et al, 2017; Zhang, Noh, et al, 2020), overlaid on the anatomical T1w and b0 images. The expert was blinded to the origin of each RGVP: tractography files were anonymized so that there was no information about which RGVP was from which tractography method.…”

Section: Methodsmentioning

confidence: 99%

Comparison of multiple tractography methods for reconstruction of the retinogeniculate visual pathway using diffusion MRI

Zhang

Xie

et al. 2020

Preprint

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The retinogeniculate visual pathway (RGVP) conveys visual information from the retina to the lateral geniculate nucleus. Anatomically, the RGVP can be separated into four subdivisions, including two decussating and two non-decussating fiber pathways, which cannot be identified by conventional magnetic resonance imaging (MRI). Diffusion MRI tractography has the potential to trace these subdivisions and is increasingly used to study the anatomy of the RGVP. However, it is not yet known which fiber tracking strategy is most suitable for tractographic reconstruction of the RGVP. In this study, four different tractography algorithms, including constrained spherical deconvolution (CSD) model based probabilistic (iFOD1) and deterministic (SD-Stream) methods, and multi-fiber (UKF-2T) and single-fiber (UKF-1T) unscented Kalman filter (UKF) tractography methods, are compared for reconstruction of the RGVP. Experiments are performed using diffusion MRI data of 57 subjects in the Human Connectome Project. The RGVP is identified using regions of interest created by two clinical experts. Anatomical measurements are used to assess the advantages and limitations of the four tracking strategies, including the reconstruction rate of the four RGVP subdivisions, the percentage of decussating fibers, the correlation between volumes of the traced RGVPs and a T1w-based RGVP segmentation, and an expert judgment to rank the anatomical appearance of the reconstructed RGVPs. Overall, we conclude that UKF-2T and iFOD1 produce the best RGVP reconstruction results. The iFOD1 method can better quantitatively estimate the percentage of decussating fibers, while the UKF-2T method produces reconstructed RGVPs that are judged to better correspond to the known anatomy.

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“…From each dMRI dataset, we compute the following data that is needed as input to our proposed method as well as other comparison methods (see Section 4). We first compute diffusion tensor images using a least squares estimation, followed by computation of the FA image from the tensor image, using the SlicerDMRI extension 2 [58], [59] in 3D Slicer 3 . We also calculate multi-shell multi-tissue constrained spherical deconvolution (CSD) data, followed by FOD peak extraction with a maximum number of three peaks per voxel, using MRtrix 4 [60], [61].…”

Section: Experimental Datasetsmentioning

confidence: 99%

Deep Diffusion MRI Registration (DDMReg): A Deep Learning Method for Diffusion MRI Registration

Wells

2021

Preprint

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In this paper, we present a deep learning method, DDMReg, for fast and accurate registration between diffusion MRI (dMRI) datasets. In dMRI registration, the goal is to spatially align brain anatomical structures while ensuring that local fiber orientations remain consistent with the underlying white matter fiber tract anatomy. To the best of our knowledge, DDMReg is the first deep-learning-based dMRI registration method. DDMReg is a fully unsupervised method for deformable registration between pairs of dMRI datasets. We propose a novel registration architecture that leverages not only whole brain information but also tract-specific fiber orientation information. We perform comparisons with four state-of-the-art registration methods. We evaluate the registration performance by assessing the ability to align anatomically corresponding brain structures and ensure fiber spatial agreement between different subjects after registration. Experimental results show that DDMReg obtains significantly improved registration performance. In addition, DDMReg leverages deep learning techniques and provides a fast and efficient tool for dMRI registration.

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SlicerDMRI: Diffusion MRI and Tractography Research Software for Brain Cancer Surgery Planning and Visualization

Cited by 74 publications

References 51 publications

MK-Curve improves sensitivity to identify white matter alterations in clinical high risk for psychosis

MK-Curve improves sensitivity to identify white matter alterations in clinical high risk for psychosis

Comparison of multiple tractography methods for reconstruction of the retinogeniculate visual pathway using diffusion MRI

Deep Diffusion MRI Registration (DDMReg): A Deep Learning Method for Diffusion MRI Registration

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