Ms. Praitayini Kanakaraj Profile

Praitayini Kanakaraj

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Publications (8)

Proceedings Article | 2 April 2024 Poster + Paper

Evaluation of mean shift, ComBat, and CycleGAN for harmonizing brain connectivity matrices across sites

Hanliang Xu, Nancy Newlin, Michael Kim, Chenyu Gao, Praitayini Kanakaraj, Aravind Krishnan, Lucas Remedios, Nazirah Mohd Khairi, Kimberly Pechman, Derek Archer, Timothy Hohman, Angela Jefferson, Ivana Išgum, Yuankai Huo, Daniel Moyer, Kurt Schilling, Bennett Landman

Proceedings Volume 12926, 129261X (2024) https://doi.org/10.1117/12.3005563

KEYWORDS: Matrices, Brain, Scanners, Alzheimer disease, Biological research, Anatomy

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Proceedings Article | 2 April 2024 Poster + Paper

Predicting age from white matter diffusivity with residual learning

Chenyu Gao, Michael Kim, Ho Hin Lee, Qi Yang, Nazirah Mohd Khairi, Praitayini Kanakaraj, Nancy Newlin, Derek Archer, Angela Jefferson, Warren Taylor, Brian Boyd, Lori Beason-Held, Susan Resnick, Yuankai Huo, Katherine Van Schaik, Kurt Schilling, Daniel Moyer, Ivana Išgum, Bennett Landman

Proceedings Volume 12926, 129262I (2024) https://doi.org/10.1117/12.3006525

KEYWORDS: Brain, Diffusion tensor imaging, Data modeling, White matter, Neuroimaging, Deep learning, Alzheimer disease, Convolutional neural networks, Computer vision technology, Image registration

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Imaging findings inconsistent with those expected at specific chronological age ranges may serve as early indicators of neurological disorders and increased mortality risk. Estimation of chronological age, and deviations from expected results, from structural magnetic resonance imaging (MRI) data has become an important proxy task for developing biomarkers that are sensitive to such deviations. Complementary to structural analysis, diffusion tensor imaging (DTI) has proven effective in identifying age-related microstructural changes within the brain white matter, thereby presenting itself as a promising additional modality for brain age prediction. Although early studies have sought to harness DTI’s advantages for age estimation, there is no evidence that the success of this prediction is owed to the unique microstructural and diffusivity features that DTI provides, rather than the macrostructural features that are also available in DTI data. Therefore, we seek to develop white-matter-specific age estimation to capture deviations from normal white matter aging. Specifically, we deliberately disregard the macrostructural information when predicting age from DTI scalar images, using two distinct methods. The first method relies on extracting only microstructural features from regions of interest (ROIs). The second applies 3D residual neural networks (ResNets) to learn features directly from the images, which are nonlinearly registered and warped to a template to minimize macrostructural variations. When tested on unseen data, the first method yields mean absolute error (MAE) of 6.11 ± 0.19 years for cognitively normal participants and MAE of 6.62 ± 0.30 years for cognitively impaired participants, while the second method achieves MAE of 4.69 ± 0.23 years for cognitively normal participants and MAE of 4.96 ± 0.28 years for cognitively impaired participants. We find that the ResNet model captures subtler, non-macrostructural features for brain age prediction.

Proceedings Article | 2 April 2024 Presentation + Paper

Learning-based free-water correction using single-shell diffusion MRI

Tianyuan Yao, Derek Archer, Praitayini Kanakaraj, Nancy Newlin, Shunxing Bao, Daniel Moyer, Kurt Schilling, Bennett Landman, Yuankai Huo

Proceedings Volume 12926, 1292607 (2024) https://doi.org/10.1117/12.3006901

KEYWORDS: Data modeling, Deep learning, Diffusion magnetic resonance imaging, White matter

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Proceedings Article | 2 April 2024 Presentation + Paper

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

Aravind Krishnan, Kaiwen Xu, Thomas Li, Chenyu Gao, Lucas Remedios, Praitayini Kanakaraj, Ho Hin Lee, Shunxing Bao, Kim Sandler, Fabien Maldonado, Ivana Išgum, Bennett Landman

Proceedings Volume 12926, 129261D (2024) https://doi.org/10.1117/12.3006608

KEYWORDS: Emphysema, Bone, CT reconstruction, Computed tomography, Data modeling, Lung, Image analysis, Medical image reconstruction

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Proceedings Article | 2 April 2024 Poster + Paper

Learning site-invariant features of connectomes to harmonize complex network measures

Nancy Newlin, Praitayini Kanakaraj, Thomas Li, Kimberly Pechman, Derek Archer, Angela Jefferson, Bennett Landman, Daniel Moyer

Proceedings Volume 12930, 129302E (2024) https://doi.org/10.1117/12.3009645

KEYWORDS: Education and training, Brain, Scanners, Diffusion, Alzheimer disease, Diffusion magnetic resonance imaging, Data modeling, Biological imaging, Matrices, Magnetic resonance imaging

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Proceedings Article | 1 April 2024 Poster + Paper

Nonlinear gradient field estimation in diffusion MRI tensor simulation

Praitayini Kanakaraj, Tianyuan Yao, Nancy Newlin, Leon Cai, Kurt Schilling, Baxter Rogers, Adam Anderson, Daniel Moyer, Bennett Landman

Proceedings Volume 12925, 1292549 (2024) https://doi.org/10.1117/12.3005364

KEYWORDS: Diffusion, Signal to noise ratio, Calibration, Scanners, Matrices, Statistical analysis, Distortion, Diffusion magnetic resonance imaging, Solids, Magnetic resonance imaging

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Proceedings Article | 3 April 2023 Poster + Paper

Mapping the impact of approximate gradient nonlinearity fields correction on tractography

Praitayini Kanakaraj, Francois Rheault, Leon Cai, Nancy Newlin, Fang-Cheng Yeh, Baxter Rogers, Kurt Schilling, Bennett Landman

Proceedings Volume 12464, 1246427 (2023) https://doi.org/10.1117/12.2653884

KEYWORDS: Diffusion, Diffusion tensor imaging, Scanners, Reproducibility, Distortion, Brain

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Proceedings Article | 4 April 2022 Presentation + Paper

Mapping the impact of non-linear gradient fields on diffusion MRI tensor estimation

Praitayini Kanakaraj, Colin Hansen, Francois Rheault, Leon Cai, Karthik Ramadass, Baxter Rogers, Kurt Schilling, Bennett Landman

Proceedings Volume 12032, 1203203 (2022) https://doi.org/10.1117/12.2611900

KEYWORDS: Diffusion, Tissues, Lawrencium, Anisotropy, Magnetism, Error analysis, Distortion, Diffusion magnetic resonance imaging, Scanners, Optical spheres

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Showing 5 of 8 publications

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