Medical-image Analysis and Statistical Interpretation (MASI) Lab

MICCAI-CDMRI 2023 QuantConn Challenge Findings on Achieving Robust Quantitative Connectivity through Harmonized Preprocessing of Diffusion MRI

Sep. 3, 2024—Newlin, N. R., Schilling, K., Koudoro, S., Chandio, B. Q., Kanakaraj, P., Moyer, D., Kelly, C. E., Genc, S., Chen, J., Yang, J. Y.-M., Wu, Y., He, Y., Zhang, J., Zeng, Q., Zhang, F., Adluru, N., Nath, V., Pathak, S., Schneider, W., … Landman, B. A. (2024). MICCAI-CDMRI 2023 QuantConn Challenge Findings on Achieving Robust...

Empirical Assessment of the Assumptions of ComBat with Diffusion Tensor Imaging

May. 24, 2024—Michael E. Kim, Chenyu Gao, Leon Y. Cai, Qi Yang, Nancy R. Newlin, Karthik Ramadass, Angela Jefferson, Derek Archer, Niranjana Shashikumar, Kimberly R. Pechman, Katherine A. Gifford, Timothy J. Hohman, Lori L. Beason-Held, Susan M. Resnick, Stefan Winzeck, Kurt G. Schilling, Panpan Zhang, Daniel Moyer, Bennett A. Landman, Empirical assessment of the assumptions of ComBat...

Figure 3. We perform kernel conversion on paired hard and soft CT kernel images using a pix2pix GAN. The generator, a U-Net, takes as input the source CT kernel image and predicts a residue image. The output kernel is obtained by adding the residue image to the input source kernel image. The discriminator, a PatchGAN, takes as input the generated image and the input kernel, and outputs the probability of the generated image being a translated version of the input.

Lung CT harmonization of paired reconstruction kernel images using generative adversarial networks

Apr. 24, 2024—Aravind R. Krishnan, Kaiwen Xu, Thomas Li, Lucas W. Remedios, Kim L. Sandler, Fabien Maldonado, Bennett A. Landman. “Lung CT harmonization of paired reconstruction kernel images using generative adversarial networks.”Med Phys. 2024;1-14.https://doi.org/10.1002/mp.17028 Abstract Background The kernel used in CT image reconstruction is an important factor that determines the texture of the CT image. Consistency of reconstruction...

Enhancing Hierarchical Transformers for Whole Brain Segmentation with Intracranial Measurements Integration

Dec. 20, 2023—Xin Yu, Yucheng Tang, Qi Yang, Ho Hin Lee, Shunxing Bao, Yuankai Huo, and Bennett A. Landman. “Enhancing Hierarchical Transformers for Whole Brain Segmentation with Intracranial Measurements Integration.” SPIE Medical Imaging 2024 Full text: NIHMSID Abstract: Whole brain segmentation with magnetic resonance imaging (MRI) enables the non-invasive measurement of brain regions, including total intracranial volume (TICV) and...

Nucleus subtype classification using inter-modality learning

Dec. 19, 2023—Lucas W. Remedios, Shunxing Bao, Samuel W. Remedios, Ho Hin Lee, Leon Y. Cai, Thomas Li, Ruining Deng, Can Cui, Jia Li, Qi Liu, Ken S. Lau, Joseph T. Roland, Mary K. Washington, Lori A. Coburn, Keith T. Wilson, Yuankai Huo, Bennett A. Landman (2024). Nucleus subtype classification using inter-modality learning. SPIE Medical Imaging 2024 :...

Exploring shared memory architectures for end-to-end gigapixel deep learning

Dec. 19, 2023—Lucas W. Remedios, Leon Y. Cai, Samuel W. Remedios, Karthik Ramadass, Aravind Krishnan, Ruining Deng, Can Cui, Shunxing Bao, Lori A. Coburn, Yuankai Huo, Bennett A. Landman (2023). Exploring shared memory architectures for end-to-end gigapixel deep learning. MIDL 2023 short paper track Full text: NIHMSID Abstract Deep learning has made great strides in medical imaging, enabled by hardware advances in GPUs. One major constraint for the development...

Longitudinal Multimodal Transformer Integrating Imaging and Latent Clinical Signatures from Routine EHRs for Pulmonary Nodule Classification.

Dec. 13, 2023—Thomas Z. Li, John M. Still, Kaiwen Xu, Ho Hin Lee, Leon Y. Cai, Aravind R. Krishnan, Riqiang Gao, Mirza S. Khan, Sanja Antic, Michael Kammer, Kim L. Sandler, Fabien Maldonado, Bennett A. Landman, Thomas A. Lasko. Abstract The accuracy of predictive models for solitary pulmonary nodule (SPN) diagnosis can be greatly increased by...

Nonlinear Gradient Field Estimation in Diffusion MRI Tensor Simulation

Dec. 8, 2023—Praitayini Kanakaraj, Tianyuan Yao, Nancy R. Newlin, Leon Y. Cai, Kurt G. Schilling, Baxter P. Rogers, Adam Anderson, Daniel Moyer, Bennett A. Landman. “Nonlinear Gradient Field Estimation in Diffusion MRI Tensor Simulation”. Physics of Medical Imaging SPIE 2024. Accepted Abstract Gradient nonlinearities not only induce spatial distortion in magnetic resonance imaging (MRI), but also introduce...

In resting state BOLD fMRI signals there are expected correlations between grey matter voxels which can be used to
identify anatomical regions7
(left). In a similar manner, we hypothesize correlations can be identified between white matter voxels
(right), providing evidence there may be informative white matter correlations in resting state BOLD fMRI data.

Identification of functional white matter networks in BOLD fMRI

Dec. 6, 2023—Alexa L. Eby, Lucas W. Remedios, Michael E. Kim, Muwei Li, Yurui Gao, John C. Gore, Kurt G. Schilling, Bennett A. Landman “Identification of functional white matter networks in BOLD fMRI” Full Text Abstract White matter signals in resting state blood oxygen level dependent functional magnetic resonance (BOLD-fMRI) have been largely discounted, yet there is...

A cycle GAN consists of a forward and backward path. In the forward path, the source encoder and target decoder combine together to form a U-Net that generates a synthetic image with the style of the target domain. The synthetic image and the real target domain image are passed as inputs to a discriminator DB which distinguishes whether the generated image is real or fake. In the backward path, a synthetic image with the style of the source domain is generated which is fed to discriminator DA along with the source domain image.

Inter-vendor harmonization of CT reconstruction kernels using unpaired image translation

Dec. 1, 2023—Aravind R. Krishnan, Kaiwen Xu, Thomas Li, Chenyu Gao, Lucas W. Remedios, Praitayini Kanakaraj, Ho Hin Lee, Shunxing Bao, Kim L. Sandler, Fabien Maldonado, Ivana Išgum, and Bennett A. Landman “Inter-vendor harmonization of CT reconstruction kernels using unpaired image translation”, Proc. SPIE 12926, Medical Imaging 2024: Image Processing, 129261D (2 April 2024); https://doi.org/10.1117/12.3006608 Abstract The reconstruction kernel in computed tomography (CT) generation determines the texture of the image. Consistency...

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