Ms. Praitayini Kanakaraj Profile

Praitayini Kanakaraj

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Publications (10)

SPIE Journal Paper | 24 August 2024 Open Access

Characterizing patterns of diffusion tensor imaging variance in aging brains

Chenyu Gao, Qi Yang, Michael Kim, Nazirah Mohd Khairi, Leon Cai, Nancy Newlin, Praitayini Kanakaraj, Lucas Remedios, Aravind Krishnan, Xin Yu, Tianyuan Yao, Panpan Zhang, Kurt Schilling, Daniel Moyer, Derek Archer, Susan Resnick, Bennett Landman, for the Alzheimer’s Disease Neuroimaging Initiative, The BIOCARD Study team

JMI, Vol. 11, Issue 04, 044007, (August 2024) https://doi.org/10.1117/12.10.1117/1.JMI.11.4.044007

KEYWORDS: Diffusion tensor imaging, Image segmentation, Data modeling, Scanners, Brain, Neuroimaging, Motion models, Signal to noise ratio, Matrices, Head

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PurposeAs large analyses merge data across sites, a deeper understanding of variance in statistical assessment across the sources of data becomes critical for valid analyses. Diffusion tensor imaging (DTI) exhibits spatially varying and correlated noise, so care must be taken with distributional assumptions. Here, we characterize the role of physiology, subject compliance, and the interaction of the subject with the scanner in the understanding of DTI variability, as modeled in the spatial variance of derived metrics in homogeneous regions.ApproachWe analyze DTI data from 1035 subjects in the Baltimore Longitudinal Study of Aging, with ages ranging from 22.4 to 103 years old. For each subject, up to 12 longitudinal sessions were conducted. We assess the variance of DTI scalars within regions of interest (ROIs) defined by four segmentation methods and investigate the relationships between the variance and covariates, including baseline age, time from the baseline (referred to as “interval”), motion, sex, and whether it is the first scan or the second scan in the session.ResultsCovariate effects are heterogeneous and bilaterally symmetric across ROIs. Inter-session interval is positively related (p≪0.001) to FA variance in the cuneus and occipital gyrus, but negatively (p≪0.001) in the caudate nucleus. Males show significantly (p≪0.001) higher FA variance in the right putamen, thalamus, body of the corpus callosum, and cingulate gyrus. In 62 out of 176 ROIs defined by the Eve type-1 atlas, an increase in motion is associated (p<0.05) with a decrease in FA variance. Head motion increases during the rescan of DTI (Δμ=0.045 mm per volume).ConclusionsThe effects of each covariate on DTI variance and their relationships across ROIs are complex. Ultimately, we encourage researchers to include estimates of variance when sharing data and consider models of heteroscedasticity in analysis. This work provides a foundation for study planning to account for regional variations in metric variance.

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SPIE Journal Paper | 24 August 2024 Open Access

Field-of-view extension for brain diffusion MRI via deep generative models

Chenyu Gao, Shunxing Bao, Michael Kim, Nancy Newlin, Praitayini Kanakaraj, Tianyuan Yao, Gaurav Rudravaram, Yuankai Huo, Daniel Moyer, Kurt Schilling, Walter Kukull, Arthur Toga, Derek Archer, Timothy Hohman, Bennett Landman, Zhiyuan Li

JMI, Vol. 11, Issue 04, 044008, (August 2024) https://doi.org/10.1117/12.10.1117/1.JMI.11.4.044008

KEYWORDS: Brain, Diffusion weighted imaging, Neuroimaging, Diffusion magnetic resonance imaging, Education and training, Alzheimer disease, Diffusion, Data modeling, Anatomy, Medical imaging

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PurposeIn brain diffusion magnetic resonance imaging (dMRI), the volumetric and bundle analyses of whole-brain tissue microstructure and connectivity can be severely impeded by an incomplete field of view (FOV). We aim to develop a method for imputing the missing slices directly from existing dMRI scans with an incomplete FOV. We hypothesize that the imputed image with a complete FOV can improve whole-brain tractography for corrupted data with an incomplete FOV. Therefore, our approach provides a desirable alternative to discarding the valuable brain dMRI data, enabling subsequent tractography analyses that would otherwise be challenging or unattainable with corrupted data.ApproachWe propose a framework based on a deep generative model that estimates the absent brain regions in dMRI scans with an incomplete FOV. The model is capable of learning both the diffusion characteristics in diffusion-weighted images (DWIs) and the anatomical features evident in the corresponding structural images for efficiently imputing missing slices of DWIs in the incomplete part of the FOV.ResultsFor evaluating the imputed slices, on the Wisconsin Registry for Alzheimer’s Prevention (WRAP) dataset, the proposed framework achieved PSNRb0=22.397, SSIMb0=0.905, PSNRb1300=22.479, and SSIMb1300=0.893; on the National Alzheimer’s Coordinating Center (NACC) dataset, it achieved PSNRb0=21.304, SSIMb0=0.892, PSNRb1300=21.599, and SSIMb1300=0.877. The proposed framework improved the tractography accuracy, as demonstrated by an increased average Dice score for 72 tracts (p<0.001) on both the WRAP and NACC datasets.ConclusionsResults suggest that the proposed framework achieved sufficient imputation performance in brain dMRI data with an incomplete FOV for improving whole-brain tractography, thereby repairing the corrupted data. Our approach achieved more accurate whole-brain tractography results with an extended and complete FOV and reduced the uncertainty when analyzing bundles associated with Alzheimer’s disease.

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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 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 Isgum, 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 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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