The papers below are generally up to date publications that have been peer reviewed and published. As we know, traditional peer review can be a bit slow, so to stay up to date with the latest research, please keep an eye out on our Google Scholar page.
Peer Reviewed Publications
2023:
1. Xiang T, Yurt M, Syed A, Setsompop K, Chaudhari A. DDM2: Self-Supervised Diffusion MRI Denoising with Generative Diffusion Models. In Proceedings of The International Conference on Learning Representations (2023). doi: 10.48550/arXiv.2302.03018.
2. Schmidt A, Desai A, Watkins L, Crowder H, Black M, Mazzoli V, Rubin E, Lu Q, MacKay J, Boutin R, Kogan F, Gold G, Hargreaves B, Chaudhari A. Generalizability of Deep Learning Segmentation Algorithms for Automated Assessment of Cartilage Morphology and MRI Relaxometry. Journal Magnetic Resonance Imaging. 2023 Apr;57(4):1029-1039. doi: 10.1002/jmri.28365 3. Dam E, Desai A, Deniz C, Rajamohan H, Regatte R, Iriondo C, Pedoia V, Majumdar S, Perslev M, Igel C, Pai A, Gaj S, Yang M, Nakamura K, Li X, Maqbool H, Irmakci I, Song S, Bagci U, Hargreaves B, Gold G, Chaudhari A. Towards automatic cartilage quantification in clinical trials – Continuing from the 2019 IWOAI knee segmentation challenge. Osteoarthritis Imaging 3 (2023) 100087. doi: 10.1016/j.ostima.2023.100087 4. Dominic J, Bhaskhar N, Desai A, Schmidt A, Rubin E, Gunel B, Gold G, Hargreaves B, Lenchik L, Boutin R, Chaudhari A. Improving Data-Efficiency and Robustness of Medical Imaging Segmentation Using Inpainting-Based Self-Supervised Learning. Bioengineering 2023, 10(2), 207; doi: 10.3390/bioengineering10020207 5. Tan T, Gatti A, Fan B, Shea K, Sherman S, Uhlrich S , Hicks J, Delp S, Shull P, Chaudhari A. A scoping review of portable sensing for out-of-lab anterior cruciate ligament injury prevention and rehabilitation. npj Digit. Med. 6, 46 (2023). doi: 10.1038/s41746-023-00782-2 6. Huang, SC., Pareek, A., Jensen, M. Lungren M, Yeung S, Chaudhari A. Self-supervised learning for medical image classification: a systematic review and implementation guidelines. npj Digit. Med. 6, 74 (2023).doi: 10.1038/s41746-023-00811-0 7. Barbieri M, Chaudhari A, Moran C, Gold G, Hargreaves B, Kogan F. A method for measuring B0 field inhomogeneity using quantitative double-echo in steady-state. Magnetic Resonance in Medicine. 2023 Feb;89(2):577-593. doi: 10.1002/mrm.29465 8. Barbieri M, Watkins L, Mazzoli V, Desai A, Rubin E, Schmidt A, Gold G, Hargreaves B, Chaudhari A, Kogan F. B1 Field inhomogeneity correction for qDESS mapping: application to rapid bilateral knee imaging. Magn Reson Mater Phy (2023). doi: 10.1007/s10334-023-01094-y. 9. Hirvasniemi J, Runhaar J, van der Heijden R, Zokaeinikoo M, Yang M, Li X, Tan J, Rajamohan H, Zhou Y, Deniz C, Caliva F, Iriondo C, Lee J, Liu F, Martinez A, Namiri N, Pedoia V, Panfilov E,. Bayramoglu N, Nguyen H, Nieminen M, Saarakkala S, Tiulpin A, Lin E, Li A, Li V, Dam E, Chaudhari A, Kijowski R, Bierma-Zeinstra S, Oei E, Klein S. The KNee OsteoArthritis Prediction (KNOAP2020) challenge: An image analysis challenge to predict incident symptomatic radiographic knee osteoarthritis from MRI and X-ray images. Osteoarthritis Cartilage. 2023 Jan;31(1):115-125. doi: 10.1016/j.joca.2022.10.001. 2022:10. Desai A, Gunel B, Ozturkler B, Beg H, Vasanawala S, Hargreaves B, Ré C, Pauly J, Chaudhari A. VORTEX: Physics-Driven Data Augmentations Using Consistency Training for Robust Accelerated MRI Reconstruction. Proceedings of The 5th International Conference on Medical Imaging with Deep Learning, PMLR 172:325-352, 2022 11. Cohen J, Viviano J, Bertin P, Morrison P, Torabian P, Guarrera M, Lungren M, Chaudhari A, Brooks R, Hashir M, Bertrand H. TorchXRayVision: A library of chest X-ray datasets and models. Proceedings of The 5th International Conference on Medical Imaging with Deep Learning, PMLR 172:1–19, 2022 12. Blankemeier L, Gallegos I, Chaves J, Maron D, Sandhu A, Rodriguez F, Rubin D, Patel B, Willis M, Boutin R, Chaudhari A. Opportunistic Incidence Prediction of Multiple Chronic Diseases from Abdominal CT Imaging Using Multi-task Learning. Medical Image Computing and Computer Assisted Intervention – MICCAI 2022. Lecture Notes in Computer Science book series (LNCS,volume 13437). doi: 10.1007/978-3-031-16449-1_30. 13. Gunel B, Sahiner A, Desai A, Chaudhari A, Vasanawala S, Pilanci M, Pauly J. Scale-Equivariant Unrolled Neural Networks for Data-Efficient Accelerated MRI Reconstruction. Medical Image Computing and Computer Assisted Intervention – MICCAI 2022. Lecture Notes in Computer Science book series (LNCS,volume 13437). doi: 10.1007/978-3-031-16446-0_70 14. Chambon P, Bluethgen C, Langlotz C, Chaudhari A. Adapting Pretrained Vision-Language Foundational Models to Medical Imaging Domains. In Proceedings of the Foundation Models for Decision Making Workshop at The 36th Conference Conference and Workshop on Neural Information Processing Systems. (2022) 15. Huang S, Akshay A, Langlotz C, Shah N, Yeung S, Lungren M. Developing medical imaging AI for emerging infectious diseases. Nat Commun 13, 7060 (2022). doi: 10.1038/s41467-022-34234-4 16. Blankemeier L, Tinn R, Kiblawi S, Gu Y, Chaudhari A, Poon H, Zhang S, Wei M, Preston S. KRISS-Search: A Contextual Span Recommender for Biomedical Text. In Proceedings of the InterNLP Workshop at the 36th Conference on Neural Information Processing Systems (NeurIPS 2022). 17. Delbrouck JB, Saab K, Varma M, Eyuboglu S, Chambon P, Dunnmon J, Zambrano J, Chaudhari A, Langlotz C. ViLMedic: a framework for research at the intersection of vision and language in medical AI. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, 23-34 (2022). 18. Eckstein F, Chaudhari A, Fuerst D, Gaisberger M, Kemnitz J, Baumgartner C, Konukoglu E, Hunter D, and Wirth W. Detection of Differences in Longitudinal Cartilage Thickness Loss Using a Deep-Learning Automated Segmentation Algorithm: Data From the Foundation for the National Institutes of Health Biomarkers Study of the Osteoarthritis Initiative. Arthritis Care & Research. (2022). doi: 10.1002/acr.24539 2021:19. Gokyar S, Robb F, Kainz W, Chaudhari A, Winkler S. MRSaiFE: An AI-Based Approach Towards the Real-Time Prediction of Specific Absorption Rate. IEEE Access 9 (2021): 140824-140834. 20. Chaudhari A, Mittra E, Davidzon G, Gulaka P, Brown A, Gandhi H, Zhang T, Gong E, Zaharchuk G, and Jadvar H. Low-Count Whole-Body PET with Deep Learning in a Multicenter and Externally Validated Study. NPJ digital medicine (2021), 4(1). doi: 10.1038/s41746-021-00512-6 21. Desai A, Schmidt A, Rubin E, Sandino C, Black M, Mazzoli V, Stevens K, Boutin R, Re C, Gold G, Hargreaves B, and Chaudhari A. SKM-TEA: A Dataset for Accelerated MRI Reconstruction with Dense Image Labels for Quantitative Clinical Evaluation. Thirty-fifth Conference on Neural Information Processing Systems (NeurIPS) Datasets and Benchmarks Track, 2021. 22. Cohen J, Brooks R, En S, Zucker E, Pareek A, Lungren M, and Chaudhari A. Gifsplanation via Latent Shift: A Simple Autoencoder Approach to Counterfactual Generation for Chest X-rays. Medical Imaging with Deep Learning. 2021. -One of 18 papers selected for Long Oral Presentation out of 124 submissions (top 15% of papers). Also Best Paper finalist. 23. Darestani MZ, Chaudhari AS, Heckel R. Measuring Robustness in Deep Learning Based Compressive Sensing. In: Proceedings of the 38th International Conference on Machine Learning. Vol 139. Proceedings of Machine Learning Research. PMLR; 2021:2433-2444. -One of 166 papers selected for Long Oral Presentation out of 5,513 submissions (top 3% of papers) 24. Chaudhari A, Grissom M, Fang Z, Sveinsson B, Lee JH, Gold G, Hargreaves B, and Stevens K. Diagnostic Accuracy of Quantitative Multi-Contrast 5-Minute Knee MRI Using Prospective Artificial Intelligence Image Quality Enhancement. American Journal of Roentgenology (2021) 216:6, 1614-1625. doi:10.2214/AJR.20.24172 25. Desai A, Caliva F, Iriondo C, Khosravan N, Mortazi A, Jambawalikar S, Torigian D, Ellerman J, Akcakaya M, Bagci U, Tibrewala R, Flament I, O’Brian M, Majumdar S, Perslev M, Pai A, Igel C, Dam E, Gaj S, Yang M, Nakamura K, Li X, Deniz C, Juras V, Regatte, Gold G, Hargreaves B, Pedoia V, and Chaudhari A. The International Workshop on Osteoarthritis Imaging Knee MRI Segmentation Challenge: A Multi-Institute Evaluation and Analysis Framework on a Standardized Dataset. Radiology: Artificial Intelligence (2021) 3:3. doi: 10.1148/ryai.2021200078 -#2 Most cited paper in Radiology: Artificial Intelligence in 2021 26. Sveinsson B, Chaudhari A, Zhu B, Koonjoo N, Torriani M, Gold G, and Rosen M. Synthesizing Quantitative T2 Maps in Right Lateral Knee Femoral Condyles from Multi-Contrast Anatomical Data with a Conditional GAN. Radiology: Artificial Intelligence (2021). doi: 10.1148/ryai.2021200122 27. Thoenen J, Stevens K, Turmezei T, Chaudhari A, Watkins L, McWalter E, Hargreaves B, Gold G, MacKay J, and Kogan F. Non-contrast MRI of synovitis in the knee using quantitative DESS. European Radiology (2021). doi: 10.1007/s00330-021-08025-2 28. Wirth W, Eckstein F, Kemnitz J, Baumgartner C, Konukoglu E, Furst D, and Chaudhari A. Accuracy and Longitudinal Reproducibility of Quantitative Femorotibial Cartilage Measures Derived from Automated U-Net-based Segmentation of Two Different MRI Contrasts – Data from the Osteoarthritis Initiative Healthy Reference Cohort. Magnetic Resonance Materials in Physics, Biology and Medicine (2021). 34(3):337-354. doi: 10.1007/s10334-020-00889-7 29. Tian Q, Biglic B, Fan Q, Ngamsombat C, Zaretskaya N, Ohringer N, Chaudhari A, Hu Y, Witzel T, Setompop K, Polimeni J, and Huang S. Improving in-vivo Human Cerebral Cortical Surface Reconstruction using Deep Learning-Based Super-Resolution. Cerebral Cortex, (2021) 31(1):463-482. doi: 10.1093/cercor/bhaa237 2020:30. Chaudhari A, Kogan F, Pedoia V, Majumdar S, Gold G, and Hargreaves B. Rapid Knee MRI Acquisition and Analysis Techniques for Imaging Osteoarthritis. Journal of Magnetic Resonance Imaging (2019). 2020 Nov;52(5):1321-1339. doi: 10.1002/jmri.26991Chaudhari A, Sandino C, Cole E, Larson D, Gold G, Vasanawala S, Lungren M, Hargreaves B, and Langlotz C. Prospective Deployment of Deep Learning in MRI: A Framework for Important Considerations, Challenges, and Recommendations for Best Practices. Journal of Magnetic Resonance Imaging (2020). doi: 10.1002/jmri.27331 31. Eckstein F, Chaudhari A, Fuerst D, Gaisberger M, Kemnitz J, Baumgartner C, Konukoglu E, Hunter D, Wirth W. A Deep Learning Automated Segmentation Algorithm Accurately Detects Differences in Longitudinal Cartilage Thickness Loss – Data from the FNIH Biomarkers Study of the Osteoarthritis Initiative. Arthritis Care & Research (2020). doi: 10.1002/acr.24539 32. Fürst D, Chaudhari A, Wirth W, and Eckstein F. Layer-Specific Analysis of Femorotibial Cartilage T2 Relaxation Time Based on Registration of Segmented Double Echo Steady State (DESS) to Multi Echo Spin Echo (MESE) Images. (2020). Magnetic Resonance Materials in Physics, Biology and Medicine. 33 (2020): 819-828. doi: 10.1007/s10334-020-00852-6 33. Eijgenraam S, Chaudhari A, Reijmam, Bierma-Zeinstra S, Hargreaves B, Runhaar J, Heijboer F, Gold G, and Oei E.H. Time-saving Opportunities in Knee Osteoarthritis: Structural Imaging and T2 mapping in the Knee using a Single 5-minute MRI Scan. (2020), European Radiology, 30(4), 2231-2240. doi: 10.1007/s00330-019-06542-9 34. Fritz R, Chaudhari A, and Boutin R. Preoperative MRI of Articular Cartilage in the Knee: A Practical Approach. (2020). The Journal of Knee Surgery, 33(11), 1088-1099. doi: 10.1055/s-0040-1716719 35. Winkler S, Sanior I, Chaudhari A, Fraser R, Vaughan T. MRSaiFE: Tissue Heating Prediction for MRI: A Feasibility Study. IEEE MTT-S International Microwave Biomedical Conference (IMBioC) (2020) pp. 1-3, doi: 10.1109/IMBIoC47321.2020.9385044 2019:36. Chaudhari A, Stevens K, Wood J, Chakraborty A, Gibbons E, Fang Z, Desai A, Lee JH, Gold G, Hargreaves B. Utility of Deep Learning Super-Resolution in the Context of Osteoarthritis MRI Biomarkers. Journal of Magnetic Resonance Imaging. (2019). 51 (3), 768-779. doi: 10.1002/jmri.26872 37. Chaudhari A, Stevens K, Sveinsson B, Wood J, Beaulieu C, Oei E.H., Rosenberg J, Kogan F, Alley M, Gold G, and Hargreaves B. Combined 5-Minute Double-Echo in Steady-State with Separated Echoes and 2-Minute Proton-Density-Weighted 2D FSE Sequence for Comprehensive Whole-Joint Knee MRI Assessment. Journal of Magnetic Resonance Imaging. (2019), 49(7), e183-e194. doi: 10.1002/jmri.26582. 38. Gibbons E, Hodgson K, Chaudhari A, Richards L, Majersik J, Adluru G, and DiBella E. Simultaneous NODDI and GFA Parameter Map Generation from Subsampled q-space Imaging using Deep Learning. Magnetic Resonance in Medicine (2019), 81(4), 2399-2411. doi: 10.1002/mrm.27568 39. Winkler S, Corea J, Lechene B, O’Brien K, Bonanni J, Chaudhari A, Alley M, Taviani V, Grafendorfer T, Robb F, Scott G, Pauly J, Lustig M, Arias A, and Vasanawala S. A Pilot Clinical Study Evaluating a Flexible 12-Channel Screen-Printed Pediatric MRI Coil. Radiology (2019), 291(1), 180-185. doi: https://doi.org/10.1148/radiol.2019181883 40. Kemnitz J, Baumgartner C, Eckstein F, Chaudhari A, Ruhdorfer A, Wirth W, Eder S, and Konukoglu E. Clinical Evaluation of Fully-Automated Thigh Muscle and Adipose Tissue Segmentation Using A U-Net Deep Learning Architecture in Context of Osteoarthritic Knee Pain. (2019). Magnetic Resonance Materials in Physics, Biology and Medicine. doi: 10.1007/s10334-019-00816-5 2018:41. Chaudhari A, Fang Z, Kogan F, Wood J, Stevens K, Gibbons E, Lee J.H, Gold G, and Hargreaves B. Super-Resolution Musculoskeletal MRI Using Deep learning. Magnetic Resonance in Medicine (2018), 80(5):2139-2154. doi: 10.1002/mrm.27178 42. Chaudhari A, Black M, Eijgenraam S, Wirth W, Maschek S, Sveinsson B, Eckstein F, Oei, E.H, Gold G, and Hargreaves B. 5-Minute Knee MRI for Simultaneous Morphometry and T2 Relaxometry of Cartilage and Meniscus and for Semi-Quantitative Radiological Assessment using Double-Echo in Steady-State at 3T. Journal of Magnetic Resonance Imaging (2018), 47(5), 1328-1341. doi: 10.1002/jmri.25883 43. Chaudhari A, Fang Z, Lee J.H, Gold G, and Hargreaves B. Deep Learning Super-Resolution Enables Rapid Simultaneous Morphological and Quantitative Magnetic Resonance Imaging. Medical Image Computing and Computer Assisted Intervention Machine Learning for Medical Image Reconstruction (pp. 3-11). Springer, Cham. (2018) pre-print: arXiv:1808.04447 44. Kogan F, Levine E, Chaudhari A, Monu U, Epperson K, Oei E.H, Gold G, and Hargreaves B. Simultaneous Bilateral-Knee MR Imaging. Magnetic Resonance in Medicine (2018). 80(2):529-537. doi: 10.1002/mrm.27045 45. Kim Y, Cha J, Shin Y, Chaudhari A, Suh Y, Yoon S, and Gold G. 3D ultrashort TE MRI for evaluation of cartilaginous endplate of cervical disk in vivo: feasibility and correlation with disk degeneration in T2-weighted spin-echo sequence. American Journal of Roentgenology 210.5 (2018): 1131-1140. doi: 10.2214/ajr.17.17855 2017:46. Chaudhari A, Sveinsson B, Moran C, McWalter E, Johnson E, Zhang T, Gold G, and Hargreaves B. Imaging and T2 relaxometry of short‐T2 connective tissues in the knee using ultrashort echo‐time double‐echo steady‐state (UTEDESS). Magnetic Resonance in Medicine (2017), 78:2136–2148. doi: 10.1002/mrm.26577 47. Sveinsson, B, Chaudhari, A. Gold G, and Hargreaves B. A simple analytic method for estimating T2 in the knee from DESS. Magnetic Resonance Imaging (2017), 38, 63-70. doi: 10.1016/j.mri.2016.12.018 2010:48. Peng L, Ivetac A, Chaudhari A, Van S, Zhao G, Yu L, Howell S, McCammon J, and Gough D. Characterization of a clinical polymer‐drug conjugate using multiscale modeling. Biopolymers (2010), 93(11), 936– 951. doi: 10.1002/bip.21474 |