Registration Category

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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....

Constructing Statistically Unbiased Cortical Surface Templates Using Feature Space Covariance

Dec. 19, 2017—Citation: ” Constructing statistically unbiased cortical surface templates using feature-space covariance”. Prasanna Parvathaneni, Ilwoo Lyu, Justin A. Blaber, Yuankai Huo, Allison E. Hainline, Neil D. Woodward, Hakmook Kang, Bennett A. Landman In SPIE Medical Imaging, International Society for Optics and Photonics, 2018 (Accepted). Abstract The choice of surface template plays an important role in cross-sectional subject analyses...

Gray Matter Surface based Spatial Statistics in Neuroimaging Studies

Jun. 1, 2017—Citation: Gray Matter Surface based Spatial Statistics in Neuroimaging Studies. Authors: Prasanna Parvatheni, Baxter P. Rogers, Yuankai Huo, Kurt G. Schilling, Allison E. Hainline, Adam W. Anderson, Neil D. Woodward, Bennett A. Landman. Frontiers in Biomedical Imaging Science VI. May 2017. Abstract. Abstract In this study, we propose gray matter surface based spatial statistics (GS-BSS)...

Qualitative comparisons between body-wise and organ-wise approaches on spleen (red), right kidney (yellow), and left kidney (blue). Padded bounding boxes in correspondent colors indicate the ROIs for organ-wise registrations.

Evaluation of Body-Wise and Organ-Wise Registrations for Abdominal Organs

Apr. 15, 2016—Zhoubing Xu, Sahil A. Panjwani, Christopher P. Lee, Ryan P. Burke, Rebeccah B. Baucom, Benjamin K. Poulose, Richard G. Abramson, Bennett A. Landman, “Evaluation of Body-Wise and Organ-Wise Registrations for Abdominal Organs”, In Proceedings of the SPIE Medical Imaging Conference. San Diego, California, February 2016. Oral presentation. Full Text: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4845963/ Abstract Identifying cross-sectional and...

The proposed general pipeline for conducting registrations and followed by metric analyses. Initially, each of 20 CT scans were pairwise linear registered using affine IRTK. Using the linear registration as a baseline, the registrations went through four non-rigid registrations. The output non-rigid registrations are evaluated against the manual segmentation via the DSC overlap, mean surface distance, and Hausdorff distance for each organ of interest. The comparison of the four non-rigid registrations was first based on all pairs of inter-subject registrations for all organs. Then another round of comparison was applied to the top 1 and top 5 registrations selected retrospectively for each target CT scan.

Evaluation of Five Image Registration Tools for Abdominal CT: Pitfalls and Opportunities with Soft Anatomy.

Feb. 12, 2015—Christopher P. Lee, Zhoubing Xu, Ryan P. Burke, Rebeccah B. Baucom, Benjamin K. Poulose, Richard G. Abramson, Bennett A. Landman. “Evaluation of Five Image Registration Tools for Abdominal CT: Pitfalls and Opportunities with Soft Anatomy.” In Proceedings of the SPIE Medical Imaging Conference. Orlando, Florida, February 2015. † Full text: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4405654/ Abstract Image registration has...

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