Neuroimaging Category

Generalizing deep whole-brain segmentation for post-contrast MRI with transfer learning

Dec. 26, 2020—Bermudez, C., Remedios, S. W., Ramadass, K., McHugo, M., Heckers, S., Huo, Y., & Landman, B. A. (2020). Generalizing deep whole-brain segmentation for post-contrast MRI with transfer learning. Journal of Medical Imaging, 7(6), 064004. Full Text: https://pubmed.ncbi.nlm.nih.gov/33381612/ Abstract Purpose: Generalizability is an important problem in deep neural networks, especially with variability of data acquisition in...

Roadmap of the challenges of microstructure biophysical modeling. The main objective of this review is to propose a roadmap for development of a reliable biophysical model of diffusion in a given tissue. Discrete steps along this course are shown above, although the entire process is very much a continuous and iterative process of validation, adaptation, and application

Challenges for biophysical modeling of microstructure

Dec. 9, 2020—Ileana O. Jelescu, Marco Palombo, Francesca Bagnato, Kurt G. Schilling. “Challenges for biophysical modeling of microstructure”. (2020) Journal of Neuroscience Methods 108861. Full text: https: https://www.sciencedirect.com/science/article/pii/S0165027020302843 Abstract The biophysical modeling efforts in diffusion MRI have grown considerably over the past 25 years. In this review, we dwell on the various challenges along the journey of...

Joint cortical surface and structural connectivity analysis of Alzheimer’s Disease

Nov. 20, 2020—Leon Y. Cai, Cailey I. Kerley, Chang Yu, Katherine S. Aboud, Lori L. Beason-Held, Andrea T. Shafer, Susan M. Resnick, Lori C. Jordan, Adam W. Anderson, Kurt G. Schilling, Ilwoo Lyu, Bennett A. Landman. Joint cortical surface and structural connectivity analysis of Alzheimer’s Disease. SPIE Medical Imaging, 2021. Full Text: NIHMSID Abstract Joint independent component...

Joint analysis of structural connectivity and cortical surface features: correlates with mild traumatic brain injury

Nov. 18, 2020—Cailey I. Kerley, Leon Y. Cai, Chang Yu, Logan M. Crawford, Jason M. Elenberger, Eden S. Singh, Kurt G. Schilling, Katherine S. Aboud, Bennett A. Landman, Tonia S. Rex, “Joint analysis of connectivity and cortical surface features: correlates with mild traumatic brain injury.” (2021, Feb) SPIE Medical Imaging. San Diego, CA. (Accepted) Full text: NIHMSID, arXiv...

Validation of group-wise registration for surface-based functional MRI analysis

Oct. 20, 2020—Chang Yu, Yue Liu, Leon Cai, Cailey Kerley, Kaiwen Xu, Katherine Aboud, Warren Taylor, Hakmook Kang, Andrea Shafer, Lori Beason-Held, Susan Resnick, Bennett Landman, Ilwoo Lyu. “Validation of group-wise registration for surface-based functional MRI analysis”. SPIE Medical Imaging 2021. [Full text][Code] Abstract Resting-state functional MRI (rsfMRI) provides important information for studying and mapping the activities...

Automatic Labeling of Cortical Sulci using Convolutional Neural Networks in a Developmental Cohort

Apr. 20, 2020—Lingyan Hao, Shunxing Bao, Yucheng Tang, Riqiang Gao, Prasanna Parvathaneni, Jacob Miller, Willa Voorhies, Jewelia Yao, Silvia Bunge, Kevin Weiner, Bennett Landman, Ilwoo Lyu. “Automatic Labeling of Cortical Sulci using Convolutional Neural Networks in a Developmental Cohort”. IEEE International Symposium on Biomedical Imaging (ISBI) 2020, IEEE, 412-415, Iowa City, Iowa, USA, 2020. [Full text][Code] Abstract...

Different classes of methods have been used to infer tissue microstructure from single shell and multi shell DW-MRI data. The gap addressed herein is in data-driven machine learning models for multi-shell DW-MRI data.

Enabling Multi-shell b-Value Generalizability of Data-Driven Diffusion Models with Deep SHORE

Jan. 17, 2020—Nath V, Lyu I, Schilling KG, Parvathaneni P, Hansen CB, Huo Y, Janve VA, Gao Y, Stepniewska I, Anderson AW, Landman BA. Enabling Multi-shell b-Value Generalizability of Data-Driven Diffusion Models with Deep SHORE. In International Conference on Medical Image Computing and Computer-Assisted Intervention 2019 Oct 13 (pp. 573-581). Springer, Cham. Full text: https://arxiv.org/ftp/arxiv/papers/1907/1907.06319.pdf Abstract Intra-voxel...

The base regression neural network is depicted in the center which describes the parameters of the fullyconnected dense layers with respective activation functions. The plot at left: Depicts three inputs where the center input
comes from corresponding DW-MRI with histology. The other two are pairwise inputs from corresponding voxels of
scanner 1.5T and 3T. The plot at right: Depicts the loss function which uses the hypothesis that the outcome/prediction
should be same irrespective of the scanner gradient strength.

Harmonizing 1.5 T/3T diffusion weighted MRI through development of deep learning stabilized microarchitecture estimators

Jan. 17, 2020—Nath V, Remedios S, Parvathaneni P, Hansen CB, Bayrak RG, Bermudez C, Blaber JA, Schilling KG, Janve VA, Gao Y, Huo Y. Harmonizing 1.5 T/3T diffusion weighted MRI through development of deep learning stabilized microarchitecture estimators. In Medical Imaging 2019: Image Processing 2019 Mar 15 (Vol. 10949, p. 109490O). International Society for Optics and Photonics....

Violin plots of intrasession submissions across both the scanners per tract. (A) Dice similarity coefficients, (B) intraclass correlation coefficients. The top row depicts the median of the top five intrasession submissions. The tracts are in the following order (L/R): a) Uncinate, b) Fornix, c) Fminor & Fmajor, d) Cingulum, e) Corticospinal tract, f) Inferior longitudinal fasciculus, g) Superior longitudinal fasciculus, h) Inferior fronto‐occipital tract.

Tractography reproducibility challenge with empirical data (TRAceD): The 2017 ISMRM diffusion study group challenge

Jan. 17, 2020—Nath V, Schilling KG, Parvathaneni P, Huo Y, Blaber JA, Hainline AE, Barakovic M, Romascano D, Rafael‐Patino J, Frigo M, Girard G. Tractography reproducibility challenge with empirical data (traced): The 2017 ISMRM diffusion study group challenge. Journal of Magnetic Resonance Imaging. 2020 Jan;51(1):234-49. Full text: https://www.ncbi.nlm.nih.gov/pubmed/31179595 Abstract BACKGROUND: Fiber tracking with diffusion-weighted MRI has become an...

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...

« Previous Page