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A) Middle axial slice of a human brain acquired using DW-MRI. B, C) CSD FOD's of the same subject on two different scanners showing inconsistency which cannot be validated without ground truth. D) Coronal slice of a Squirrel Monkey. E, F, G, H) Right: CSD FOD's at b-value of 3000 s/mm2 Left: Ground truth reconstruction using aggregated histological structural tensors depicting a loss in precision.

Deep learning reveals untapped information for local white-matter fiber reconstruction in diffusion-weighted MRI

Jan. 17, 2020—Nath V, Schilling KG, Parvathaneni P, Hansen CB, Hainline AE, Huo Y, Blaber JA, Lyu I, Janve V, Gao Y, Stepniewska I, Anderson AW, Landman BA. Deep learning reveals untapped information for local white-matter fiber reconstruction in diffusion-weighted MRI. Magnetic resonance imaging. 2019 Oct 1;62:220-7. Abstract PURPOSE: Diffusion-weighted magnetic resonance imaging (DW-MRI) is of critical importance...

Fig. 2. Distortion and correction in diffusion MRI. (A) EPI susceptibility distortion occurs along the phase encode direction, with phase encoding in the posterior (PE-P) direction leading to displacements of identical distance but opposite direction of that in the anterior (PE-A) direction. (B) State of the art distortion correction (topup) typically uses distortions in two opposite directions to iteratively estimate the undistorted image. (C) The proposed method uses an undistorted T1-weighted image to synthesize an undistorted volume with b0 contrast, which can be used to correct the distorted (in this case, PE-P) image without requiring an additional phase encoding acquisition. The blue circles highlight areas of observable signal distortion. Yellow arrows indicate phase encode direction. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Synthesized b0 for diffusion distortion correction (Synb0-DisCo).

Dec. 16, 2019—Kurt G Schilling, Yuankai Huo, Allen Newton, Colin Hansen, Vishwesh Nath, Andrea T. Shafer, Owen Williams, Susan M. Resnick, Baxter Rogers, Adam W Anderson, Bennett A Landman. “Synthesized b0 for diffusion distortion correction (Synb0-DisCo).” Magnetic Resonance Imaging. 2019 May 7. pii: S0730-725X(18)30617-9. doi: 10.1016/j.mri.2019.05.008 Full text: https://www.ncbi.nlm.nih.gov/pubmed/?term=Synthesized+b0+for+diffusion+distortion+correction+(Synb0-DisCo). Abstract Diffusion magnetic resonance images typically suffer from spatial...

Improving Human Cortical Sulcal Curve Labeling in Large Scale Cross-Sectional MRI using Deep Neural Networks

Nov. 11, 2019—Prasanna Parvathaneni, Vishwesh Nath, Maureen McHugo, Yuankai Huo, Susan Resnick, Neil Woodward, Bennett Landman, Ilwoo Lyu. “Improving Human Cortical Sulcal Curve Labeling in Large Scale Cross-Sectional MRI using Deep Neural Networks”. Journal of Neuroscience Methods, 324, 108311, 2019. [Full text] Abstract BACKGROUND: Human cortical primary sulci are relatively stable landmarks and commonly observed across the population....

Hierarchical Spherical Deformation for Cortical Surface Registration

Nov. 11, 2019—Ilwoo Lyu, Hakmook Kang, Neil D. Woodward, Martin A. Styner and Bennett A. Landman. “Hierarchical Spherical Deformation for Cortical Surface Registration”. Medical Image Analysis, 57, pp. 72-88, 2019. [Full text][Code][Docker] Abstract We present hierarchical spherical deformation for a group-wise shape correspondence to address template selection bias and to minimize registration distortion. In this work, we...

An overview of the proposed method. Three geometric features (iH, SD, H) are used for training the spherical U-Net to predict 49 cortical parcellation labels. For each geometric property, intermediate deformation fields draw a total of 11+11 respective samples after boundary and geometric alignment for data augmentation. The cortical parcellation is then performed using the original geometric features of testing subjects.

Cortical Surface Parcellation using Spherical Convolutional Neural Networks

Jun. 6, 2019—*Prasanna Parvathaneni, *Shunxing Bao, Vishwesh Nath, David H. Zald, Neil D. Woodward, Daniel O. Claassen, Carissa Cascio, Yuankai Huo, Bennett A. Landman, Ilwoo Lyu, “Cortical Surface Parcellation using Spherical Convolutional Neural Networks”, MICCAI 2019, LNCS11766, 501-509. Abstract We present cortical surface parcellation using spherical deep convolutional neural networks. Traditional multi-atlas cortical surface parcellation requires inter-subject surface registration using geometric features with high processing...

Fig. 1. Effects of incorrect b-vector tables. Diffusion encoded color (DEC) maps and vector maps are shown for a correct gradient table (A), a table with a flipped component (B), and one with permuted components (C). With a flipped component, the DEC map may appear correct, but unit vectors will be flipped in one plane (coronal plane in this example), and will appear correct in another (axial). For a permuted table, both the color maps and vectors will be erroneous. In both (B) and (C), directions are incorrect, and subsequent analysis and tractography will also be erroneous. DEC and vector maps are colored red, green, and blue for diffusion primarily in the right/left, anterior/posterior, and superior/inferior directions, respectively. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

A fiber coherence index for quality control of B-table orientation in diffusion MRI scans.

May. 16, 2019—Kurt G Schilling, Fang-Cheng Yeh, Vishwesh Nath, Colin Hansen, Owen Williams, Susan Resnick, Adam W. Anderson, Bennett A. Landman. “A fiber coherence index for quality control of B-table orientation in diffusion MRI scans”. Magnetic Resonance Imaging. 2019. doi: 10.1016/j.mri.2019.01.018 Full text: https://www.ncbi.nlm.nih.gov/pubmed/?term=fiber+coherence+b-table Abstract Purpose The diffusion MRI “b-vector” table describing the diffusion sensitization direction can be flipped and...

Compensation of Gradient Field Nonlinearity and Signal Drift in Diffusion Weighted MRI

Apr. 1, 2019—Colin B. Hansen, Vishwesh Nath, Kurt G. Schilling, Justin A. Blaber, Baxter P. Rogers, Bennett A. Landman, “Compensation of Gradient Field Nonlinearity and Signal Drift in Diffusion Weighted MRI.” ISBI, Venice, Italy, 2019 Diffusion weighted magnetic resonance imaging (DW-MRI) represents an important method of studying local brain tissue microarchitecture and structural connectivity. Interpretation of DW-MRI is challenging...

Consideration of Cerebrospinal Fluid Intensity Variation in Diffusion Weighted MRI

Feb. 7, 2019—Colin B. Hansen, Vishwesh Nath, Allison E. Hainline, Kurt G. Schilling, Prasanna Parvathaneni, Roza G. Bayrak, Justin A. Blaber, Owen Williams, Susan Resnick, Lori Beason-Held, Okan Irfanoglu, Carlo Pierpaoli, Adam W. Anderson, Baxter P. Rogers, Bennett A. Landman, “Consideration of Cerebrospinal Fluid Intensity Variation in Diffusion Weighted MRI.” SPIE Medical Imaging, San Diego, CA, 2019 Abstract Diffusion weighted MRI (DW-MRI) depends on...

Imaging Biomarkers in Thyroid Eye Disease and their Clinical Associations

Dec. 18, 2018—Shikha Chaganti, Katrina Nelson, Kevin Mundy, Robert Harrigan, Robert Galloway, Louise A. Mawn, Bennett Landman, “Imaging biomarkers in thyroid eye disease and their clinical associations,” Journal of Medical Imaging. (2018), doi: 10.1117/1.JMI.5.4.044001. Abstract Purpose: The purpose of this study is to understand the phenotypes of thyroid eye disease (TED) through data derived from a multi-atlas...

Electronic Medical Record Context Signatures Improve Diagnostic Classification using Medical Image Computing

Dec. 17, 2018—Shikha Chaganti, Louise A. Mawn, Hakmook Kang, Josephine Egan, Susan M. Resnick, Lori L. Beason-Held, Bennett A. Landman, Thomas A. Lasko. “Electronic Medical Record Context Signatures Improve Diagnostic Classification using Medical Image Computing”. IEEE Journal of Biomedical and Health Informatics. (In Press) Abstract Composite models that combine medical imaging with electronic medical records (EMR) improve...

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