Nicha Dvornek, PhD
Assistant Professor of Radiology and Biomedical Imaging
Research & Publications
Biography
News
Locations
Research Summary
My research is on the development and application of machine learning algorithms for medical image analysis and processing. My current work focuses on deep learning methods for learning from functional magnetic resonance imaging data with application to autism spectrum disorders. I am driven by the ultimate goal of better understanding neurological disorders and diseases to achieve more personalized medicine.
Coauthors
Research Interests
Autistic Disorder; Biomedical Engineering; Brain; Image Processing, Computer-Assisted; Neural Networks, Computer
Selected Publications
- TAI-GAN: Temporally and Anatomically Informed GAN for Early-to-Late Frame Conversion in Dynamic Cardiac PET Motion CorrectionGuo X, Shi L, Chen X, Zhou B, Liu Q, Xie H, Liu Y, Palyo R, Miller E, Sinusas A, Spottiswoode B, Liu C, Dvornek N. TAI-GAN: Temporally and Anatomically Informed GAN for Early-to-Late Frame Conversion in Dynamic Cardiac PET Motion Correction. 2023, 14288: 64-74. PMID: 38464964, PMCID: PMC10923183, DOI: 10.1007/978-3-031-44689-4_7.
- Learning Sequential Information in Task-Based fMRI for Synthetic Data AugmentationWang J, Dvornek N, Staib L, Duncan J. Learning Sequential Information in Task-Based fMRI for Synthetic Data Augmentation. 2023, 14312: 79-88. DOI: 10.1007/978-3-031-44858-4_8.
- Copy Number Variation Informs fMRI-Based Prediction of Autism Spectrum DisorderDvornek N, Sullivan C, Duncan J, Gupta A. Copy Number Variation Informs fMRI-Based Prediction of Autism Spectrum Disorder. 2023, 14312: 133-142. PMID: 38371906, PMCID: PMC10868600, DOI: 10.1007/978-3-031-44858-4_13.
- MCP-Net: Introducing Patlak Loss Optimization to Whole-Body Dynamic PET Inter-Frame Motion CorrectionGuo X, Zhou B, Chen X, Chen M, Liu C, Dvornek N. MCP-Net: Introducing Patlak Loss Optimization to Whole-Body Dynamic PET Inter-Frame Motion Correction. IEEE Transactions On Medical Imaging 2023, 42: 3512-3523. PMID: 37368811, PMCID: PMC10751388, DOI: 10.1109/tmi.2023.3290003.
- Chapter 13 Deep learning with connectomesDvornek N, Li X. Chapter 13 Deep learning with connectomes. 2023, 289-308. DOI: 10.1016/b978-0-323-85280-7.00013-0.
- Inter-Pass Motion Correction for Whole-Body Dynamic PET and Parametric ImagingGuo X, Wu J, Chen M, Liu Q, Onofrey J, Pucar D, Pang Y, Pigg D, Casey M, Dvornek N, Liu C. Inter-Pass Motion Correction for Whole-Body Dynamic PET and Parametric Imaging. IEEE Transactions On Radiation And Plasma Medical Sciences 2022, 7: 344-353. PMID: 37842204, PMCID: PMC10569406, DOI: 10.1109/trpms.2022.3227576.
- MCP-Net: Inter-frame Motion Correction with Patlak Regularization for Whole-body Dynamic PETGuo X, Zhou B, Chen X, Liu C, Dvornek N. MCP-Net: Inter-frame Motion Correction with Patlak Regularization for Whole-body Dynamic PET. 2022, 13434: 163-172. PMID: 38464686, PMCID: PMC10923180, DOI: 10.1007/978-3-031-16440-8_16.
- Characterization of Early Stage Parkinson's Disease From Resting-State fMRI Data Using a Long Short-Term Memory NetworkGuo X, Tinaz S, Dvornek N. Characterization of Early Stage Parkinson's Disease From Resting-State fMRI Data Using a Long Short-Term Memory Network. Frontiers In Neuroimaging 2022, 1: 952084. PMID: 37555151, PMCID: PMC10406199, DOI: 10.3389/fnimg.2022.952084.
- Multiple-Shooting Adjoint Method for Whole-Brain Dynamic Causal ModelingZhuang J, Dvornek N, Tatikonda S, Papademetris X, Ventola P, Duncan J. Multiple-Shooting Adjoint Method for Whole-Brain Dynamic Causal Modeling. 2021, 12729: 58-70. DOI: 10.1007/978-3-030-78191-0_5.
- A Metamodel Structure For Regression Analysis: Application To Prediction Of Autism Spectrum Disorder SeverityWang S, Dvornek N. A Metamodel Structure For Regression Analysis: Application To Prediction Of Autism Spectrum Disorder Severity. 2021, 00: 1338-1341. DOI: 10.1109/isbi48211.2021.9434009.
- Cross-Modality Segmentation by Self-supervised Semantic Alignment in Disentangled Content SpaceYang J, Li X, Pak D, Dvornek N, Chapiro J, Lin M, Duncan J. Cross-Modality Segmentation by Self-supervised Semantic Alignment in Disentangled Content Space. 2020, 12444: 52-61. DOI: 10.1007/978-3-030-60548-3_6.
- Deep Learning based Respiratory Pattern Classification and Applications in PET/CT Motion CorrectionGuo Y, Dvornek N, Lu Y, Tsai Y, Hamill J, Casey M, Liu C. Deep Learning based Respiratory Pattern Classification and Applications in PET/CT Motion Correction. 2019, 00: 1-5. DOI: 10.1109/nss/mic42101.2019.9059783.
- ShelfNet for Fast Semantic SegmentationZhuang J, Yang J, Gu L, Dvornek N. ShelfNet for Fast Semantic Segmentation. 2019, 00: 847-856. DOI: 10.1109/iccvw.2019.00113.
- Identifying Autism from Resting-State fMRI Using Long Short-Term Memory NetworksDvornek NC, Ventola P, Pelphrey KA, Duncan JS. Identifying Autism from Resting-State fMRI Using Long Short-Term Memory Networks. 2017, 10541: 362-370. PMID: 29104967, PMCID: PMC5669262, DOI: 10.1007/978-3-319-67389-9_42.
- Brain responses to biological motion predict treatment outcome in young children with autismYang D, Pelphrey KA, Sukhodolsky DG, Crowley MJ, Dayan E, Dvornek NC, Venkataraman A, Duncan J, Staib L, Ventola P. Brain responses to biological motion predict treatment outcome in young children with autism. Translational Psychiatry 2016, 6: e948-e948. PMID: 27845779, PMCID: PMC5314125, DOI: 10.1038/tp.2016.213.
- Pivotal response treatment prompts a functional rewiring of the brain among individuals with autism spectrum disorderVenkataraman A, Yang D, Dvornek N, Staib LH, Duncan JS, Pelphrey KA, Ventola P. Pivotal response treatment prompts a functional rewiring of the brain among individuals with autism spectrum disorder. Neuroreport 2016, 27: 1081-1085. PMID: 27532879, PMCID: PMC5007196, DOI: 10.1097/wnr.0000000000000662.
- Prediction of Autism Treatment Response from Baseline fMRI using Random Forests and Tree BaggingDvornek, N.C., Yang, D., Venkataraman, A., Ventola, P., Staib, L.H., Pelphrey, K.A., Duncan, J.S., “Prediction of Autism Treatment Response from Baseline fMRI using Random Forests and Tree Bagging,” In: Sixth International Workshop on Multimodal Learning for Clinical Decision Support, 2016.
- SubspaceEM: A fast maximum-a-posteriori algorithm for cryo-EM single particle reconstructionDvornek NC, Sigworth FJ, Tagare HD. SubspaceEM: A fast maximum-a-posteriori algorithm for cryo-EM single particle reconstruction. Journal Of Structural Biology 2015, 190: 200-214. PMID: 25839831, PMCID: PMC4453989, DOI: 10.1016/j.jsb.2015.03.009.
- Tracking Metastatic Brain Tumors in Longitudinal Scans via Joint Image Registration and LabelingChitphakdithai N, Chiang VL, Duncan JS. Tracking Metastatic Brain Tumors in Longitudinal Scans via Joint Image Registration and Labeling. 2012, 7570: 124-136. PMID: 31187098, PMCID: PMC6559745, DOI: 10.1007/978-3-642-33555-6_11.
- Non-rigid Registration with Missing Correspondences in Preoperative and Postresection Brain ImagesChitphakdithai N, Duncan JS. Non-rigid Registration with Missing Correspondences in Preoperative and Postresection Brain Images. 2010, 13: 367-374. PMID: 20879252, PMCID: PMC3031159, DOI: 10.1007/978-3-642-15705-9_45.
Clinical Trials
Conditions | Study Title |
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Children's Health; Men's Health; Mental Health & Behavioral Research; Women's Health | ACE Multisite Study of Adolescent & Adult Transitions |
Child Development & Autism; Children's Health; Mental Health & Behavioral Research | CBT for Anxiety in Children With Autism |