Registration Category

Predicting Age from White Matter Diffusivity with Residual Learning

Dec. 1, 2023—Chenyu Gao, Michael E. Kim, Ho Hin Lee, Qi Yang, Nazirah Mohd Khairi, Praitayini Kanakaraj, Nancy R. Newlin, Derek B. Archer, Angela L. Jefferson, Warren D. Taylor, Brian D. Boyd, Lori L. Beason-Held, Susan M. Resnick, The BIOCARD Study Team, Yuankai Huo, Katherine D. Van Schaik, Kurt G. Schilling, Daniel Moyer, Ivana Išgum, Bennett A....

Characterizing Low-cost Registration for Photographic Images to Computed Tomography

Dec. 1, 2023—Michael E. Kim, Ho Hin Lee, Karthik Ramadass, Chenyu Gao, Katherine Van Schaik, Eric Tkaczyk, Jeffrey Spraggins, Daniel C. Moyer, Bennett A. Landman Biorxiv Link: https://www.biorxiv.org/content/10.1101/2023.09.22.558989v1 Figure 1. We map surfaces of phantom objects obtained from low-cost photogrammetry to surfaces obtained from volumetric CT scans of the phantoms in order to examine if similar...

Hierarchical Particle Optimization for Cortical Shape Correspondence in Temporal Lobe Resection

Aug. 31, 2023—Yue Liu, Shunxing Bao, Dario J. Englot, Victoria L. Morgan, Warren D. Taylor, Ying Wei, Ipek Oguz, Bennett A. Landman, Ilwoo Lyu Paper: https://pubmed.ncbi.nlm.nih.gov/36525831/ Abstract Background: Anterior temporal lobe resection is an effective treatment for temporal lobe epilepsy. The post-surgical structural changes could influence the follow-up treatment. Capturing post-surgical changes necessitates a well-established cortical shape correspondence...

Distortion correction of functional MRI without reverse phase encoding scans or field maps

Aug. 31, 2023—Tian Yu, Leon Y. Cai, Salvatore Torrisi, An Thanh Vu, Victoria L. Morgan, Sarah E. Goodale, Karthik Ramadass, Steven L. Meisler, Jinglei Lv, Aaron E.L. Warren, Dario J. Englot, Laurie Cutting, Catie Chang, John C. Gore, Bennett A. Landman, Kurt G. Schilling Paper: https://www.sciencedirect.com/science/article/pii/S0730725X23001121 Code: https://github.com/MASILab/SynBOLD-DisCo Abstract Functional magnetic resonance images (fMRI) acquired using echo planar sequences...

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Multi-Contrast Computed Tomography Healthy Kidney Atlas

Dec. 28, 2020—Ho Hin Lee, Yucheng Tang, Kaiwen Xu, Shunxing Bao, Agnes B. Fogo, Raymond Harris, Mark P. de Caestecker, Mattias Heinrich, Jeffery M. Spraggins, Yuankai Huo, Bennett A. Landman, “Multi-Contrast Computed Tomography Healthy Kidney Atlas.” arXiv preprint arXiv:2012.12432 (2020). Full Text Abstract The construction of three-dimensional multi-modal tissue maps provides an opportunity to spur interdisciplinary innovations across temporal and...

Sample manuscript showing specifications and style - Adobe Acrobat Reader DC 12_6_2020 7_27_32 PM (2)

Construction of a Multi-Phase Contrast Computed Tomography Kidney Atlas

Dec. 6, 2020—Ho Hin Lee, Yucheng Tang, Kaiwen Xu, Shunxing Bao, Agnes B. Fogo, Raymond Harris, Mark P. de Caestecker, Mattias Heinrich, Jeffrey Spraggins, Yuankai Huo, Bennett A, Landman, Construction of a Multi-Phase Contrast Computed Tomography Kidney Atlas, SPIE 2021 Medical Imaging Full Text Abstract The Human BioMolecular Atlas Program (HuBMAP) seeks to create a molecular atlas at the cellular level of...

Establishing Surface Correspondence for Post-surgical Cortical Thickness Changes in Temporal Lobe Epilepsy

Dec. 5, 2020—Yue Liu, Dario J. Englot, Victoria L. Morgan, Warren D. Taylor, Ying Wei, Ipek Oguz, Bennett A. Landman, Ilwoo Lyu. Establishing Surface Correspondence for Post-surgical Cortical Thickness Changes in Temporal Lobe Epilepsy. SPIE Medical Imaging, 2021. Full Text Abstract In pre- and post-surgical surface shape analysis, establishing shape correspondence is necessary to investigate the post-operative...

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...

Figure. Four consecutive modules of the atlas creation pipeline. (A) Preprocessing module: (A.1) original moving scan; (A.2) the overlap between the ambient removed scan and lung & body segmentation masks. (B) Affine registration module based on NiftyReg toolbox: (B.1) moving scan with intensity window (0, 1000); (B.2) axial view of reference scan; (B.3) reference scan with intensity window (0, 1000); (B.4) affine registered intensity windowed moving scan; (B.5) interpolation of the preprocessed moving scan with the affine registration transformation matrix. (C) Non-rigid registration module based on corrField (registration) and NiftyReg (interpolation): (C.1) affine registered moving scan overlapped with lung and body segmentation masks; (C.2) overlapped with non-NaN region mask; (C.3) reference scan with non-NaN region mask; (C.4) non-rigid deformed moving scan overlapped with deformed non-NaN region mask; (C.5) non-rigid deformed moving scan overlapped with deformed lung / body masks. (D) Atlas creation module: (D.1) average atlas of HU; (D.2) average atlas of Jacobian determinant map.

Development and Characterization of a Chest CT Atlas

Oct. 13, 2020—Kaiwen Xu, Riqiang Gao, Mirza S. Khan, Shunxing Bao, Yucheng Tang, Steve A. Deppen, Yuankai Huo, Kim L. Sandler, Pierre P. Massion, Mattias P. Heinrich, Bennett A. Landman. Development and characterization of a chest CT atlas. SPIE Medical Imaging, 2021. Full text: https://arxiv.org/abs/2012.03124 Abstract A major goal of lung cancer screening is to identify individuals...

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....

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