‘Neuroimaging’

A comparison of the number of learnable parameters and the types and number of skip connections in our five models. Note that the skip connections type column lists operations which show how residual information is reintegrated when the skip / residual connection or parallel processing branch rejoins the network.

Technical Note: Comparison of Convolutional Neural Networks for Detecting Large Vessel Occlusion on Computed Tomography Angiography

Jul. 21, 2021—Lucas W. Remedios, Sneha Lingam, Samuel W. Remedios, Riqiang Gao, Stephen W. Clark, Larry T. Davis, Bennett A. Landman. Technical Note: Comparison of Convolutional Neural Networks for Detecting Large Vessel Occlusion on Computed Tomography Angiography. Medical Physics, 2021 Full Text Abstract Purpose: Artificial intelligence diagnosis and triage of large vessel occlusion may quicken clinical response for...

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...

Assessment of Bias in MRI Diffusion Tensor Imaging Parameters Using SIMEX

Mar. 2, 2013—Carolyn B. Lauzon, Ciprian Crainiceanu, Brian C. Caffo, Bennett A. Landman, “Assessment of Bias in MRI Diffusion Tensor Imaging Parameters Using SIMEX”, Magnetic Resonance in Medicine. 2013 Mar 1;69(3):891-902. PMC22611000 † Full Text: https://www.ncbi.nlm.nih.gov/pubmed/22611000 Abstract: Diffusion tensor imaging enables in vivo investigation of tissue cytoarchitecture through parameter contrasts sensitive to water diffusion barriers at the...

Correcting Power and p-Value Calculations for Bias in Diffusion Tensor Imaging.

Mar. 2, 2013—Carolyn B. Lauzon and Bennett A. Landman, “Correcting Power and p-Value Calculations for Bias in Diffusion Tensor Imaging.” Magnetic Resonance Imaging. 2013 Mar 2. pii: S0730-725X(13) PMC23465764 † Full Text: https://www.ncbi.nlm.nih.gov/pubmed/?term=Correcting+Power+and+p-Value+Calculations+for+Bias+in+Diffusion+Tensor+Imaging. Abstract: Diffusion tensor imaging (DTI) provides quantitative parametric maps sensitive to tissue microarchitecture (e.g., fractional anisotropy, FA). These maps are estimated through computational processes and...

mpact of outliers in BPM. (A) is one of the regressor images without outliers. The main modality images are: Y = 3X inside a spherical region, Y = constant outside the region. (B) is the Beta estimation when there are no outliers. (C) is the significant results when there are no outliers, (D) is the shifted regressor image, the center cube shifts to another region. The center cube of the paired main modality image shifts to the same region. (E) is the estimated Beta from original BPM when there are outliers. (F) is the significant results from original BPM when there are outliers.

“Robust Biological Parametric Mapping: An Improved Technique for Multimodal Brain Image Analysis”

Feb. 1, 2011—Xue Yang, Lori Beason-Held, Susan M. Resnick, Bennett A. Landman. “Robust Biological Parametric Mapping: An Improved Technique for Multimodal Brain Image Analysis”, In Proceedings of the SPIE Medical Imaging Conference. Lake Buena Vista, Florida, February 2011 PMC3103184 † Full text Abstract Mapping the quantitative relationship between structure and function in the human brain is an...