Research Summary

Dr. Andreas Rauschecker is a neuroradiologist who cares for both adults and children. He specializes in using advanced imaging technologies, such as CT and MRI, to diagnose nervous system disorders. He also treats patients by means of image-guided procedures and uses his expertise to assist colleagues in managing and treating their patients.

In research, Rauschecker focuses on using modern technology to understand the brain in conditions of health and disease. Specifically, he uses artificial intelligence and advanced image-processing techniques to investigate the brain and its appearance in relation to disease, with the goal of improving the information he can provide to patients and other doctors.

Education

Fellowship in Neuroradiology, 06/2020 - Neuroradiology, University of California, San Francisco
02/2020 - Diversity, Equity, and Inclusion Champion Training, University of California
Residency, 06/2018 - Diagnostic Radiology, University of Pennsylvania
MD PhD, 06/2013 - Neuroscience, Medical School, Stanford University
M.Sc., 08/2005 - Neuroscience, Oxford University
B.S., 06/2004 - Biology & Psychology, Georgetown University

Honors & Awards

  • ASNR Trainee Award, 2020
  • RSNA Research Scholar Award, 2020
  • ASNR/ASfNR MIT-E Scholarship, 2019
  • RSNA Roentgen Fellow Research Award, 2019
  • Roger A. Bauman, MD Award, SIIM, 2019
  • ACR-AUR Research Scholar Program, 2018
  • NVIDIA GPU Seed Grant, 2018
  • RSNA Roentgen Resident Research Award, 2018
  • RSNA Resident Research Grant, 2017
  • RSNA Magna Cum Laude Award, 2016
  • RSNA Certificate of Merit Award, 2015
  • Mary Duke Biddle Clinical Scholars Program, Stanford University, 2013
  • Stanford Bio-X Graduate Student Fellowship, Stanford University, 2008
  • Clarendon Fund Scholarship, Oxford University, 2004
  • Howard Hughes Undergraduate Research Scholar, Georgetown University, 2000-2004

Selected Publications

  1. Chen JV, Chaudhari G, Hess CP, Glenn OA, Sugrue LP, Rauschecker AM, Li Y. Deep Learning to Predict Neonatal and Infant Brain Age from Myelination on Brain MRI Scans. Radiology. 2022 Jul 19; 211860.  View on PubMed
  2. Kline C, Stoller S, Byer L, Samuel D, Lupo JM, Morrison MA, Rauschecker AM, Nedelec P, Faig W, Dubal DB, Fullerton HJ, Mueller S. An Integrated Analysis of Clinical, Genomic, and Imaging Features Reveals Predictors of Neurocognitive Outcomes in a Longitudinal Cohort of Pediatric Cancer Survivors, Enriched with CNS Tumors (Rad ART Pro). Front Oncol. 2022; 12:874317.  View on PubMed
  3. Narsinh KH, Hui F, Saloner D, Tu-Chan A, Sharon J, Rauschecker AM, Safoora F, Shah V, Meisel K, Amans MR. Diagnostic Approach to Pulsatile Tinnitus: A Narrative Review. JAMA Otolaryngol Head Neck Surg. 2022 05 01; 148(5):476-483.  View on PubMed
  4. Calabrese E, Rudie JD, Rauschecker AM, Villanueva-Meyer JE, Clarke JL, Solomon DA, Cha S. Combining radiomics and deep convolutional neural network features from preoperative MRI for predicting clinically relevant genetic biomarkers in glioblastoma. Neurooncol Adv. 2022 Jan-Dec; 4(1):vdac060.  View on PubMed
  5. Rauschecker AM, Gleason TJ, Nedelec P, Duong MT, Weiss DA, Calabrese E, Colby JB, Sugrue LP, Rudie JD, Hess CP. Interinstitutional Portability of a Deep Learning Brain MRI Lesion Segmentation Algorithm. Radiol Artif Intell. 2022 Jan; 4(1):e200152.  View on PubMed
  6. Gu W, Rauschecker AM, Hsu E, Zorn KC, Sucu Y, Federman S, Gopez A, Arevalo S, Sample HA, Talevich E, Nguyen ED, Gottschall M, Nourbakhsh B, Gold CA, Cree BAC, Douglas VC, Richie MB, Shah MP, Josephson SA, Gelfand JM, Miller S, Wang L, Tihan T, DeRisi JL, Chiu CY, Wilson MR. Detection of Neoplasms by Metagenomic Next-Generation Sequencing of Cerebrospinal Fluid. JAMA Neurol. 2021 11 01; 78(11):1355-1366.  View on PubMed
  7. Weiss DA, Saluja R, Xie L, Gee JC, Sugrue LP, Pradhan A, Nick Bryan R, Rauschecker AM, Rudie JD. Automated multiclass tissue segmentation of clinical brain MRIs with lesions. Neuroimage Clin. 2021; 31:102769.  View on PubMed
  8. Rudie JD, Duda J, Duong MT, Chen PH, Xie L, Kurtz R, Ware JB, Choi J, Mattay RR, Botzolakis EJ, Gee JC, Bryan RN, Cook TS, Mohan S, Nasrallah IM, Rauschecker AM. Brain MRI Deep Learning and Bayesian Inference System Augments Radiology Resident Performance. J Digit Imaging. 2021 08; 34(4):1049-1058.  View on PubMed
  9. Calabrese E, Rudie JD, Rauschecker AM, Villanueva-Meyer JE, Cha S. Feasibility of Simulated Postcontrast MRI of Glioblastomas and Lower-Grade Gliomas by Using Three-dimensional Fully Convolutional Neural Networks. Radiol Artif Intell. 2021 Sep; 3(5):e200276.  View on PubMed
  10. Li Y, Thompson WK, Reuter C, Nillo R, Jernigan T, Dale A, Sugrue LP, ABCD Consortium, Brown J, Dougherty RF, Rauschecker A, Rudie J, Barch DM, Calhoun V, Hagler D, Hatton S, Tanabe J, Marshall A, Sher KJ, Heeringa S, Hermosillo R, Banich MT, Squeglia L, Bjork J, Zucker R, Neale M, Herting M, Sheth C, Huber R, Reeves G, Hettema JM, Howlett KD, Cloak C, Baskin-Sommers A, Rapuano K, Gonzalez R, Karcher N, Laird A, Baker F, James R, Sowell E, Dick A, Hawes S, Sutherland M, Bagot K, Bodurka J, Breslin F, Morris A, Paulus M, Gray K, Hoffman E, Weiss S, Rajapakse N, Glantz M, Nagel B, Ewing SF, Goldstone A, Pfefferbaum A, Prouty D, Rosenberg M, Bookheimer S, Tapert S, Infante M, Jacobus J, Giedd J, Shilling P, Wade N, Uban K, Haist F, Heyser C, Palmer C, Kuperman J, Hewitt J, Cottler L, Isaiah A, Chang L, Edwards S, Ernst T, Heitzeg M, Puttler L, Sripada C, Iacono W, Luciana M, Clark D, Luna B, Schirda C, Foxe J, Freedman E, Mason M, McGlade E, Renshaw P, Yurgelun-Todd D, Albaugh M, Allgaier N, Chaarani B, Potter A, Ivanova M, Lisdahl K, Do E, Maes H, Bogdan R, Anokhin A, Dosenbach N, Glaser P, Heath A, Casey BJ, Gee D, Garavan HP, Dowling G, Brown S. Rates of Incidental Findings in Brain Magnetic Resonance Imaging in Children. JAMA Neurol. 2021 05 01; 78(5):578-587.  View on PubMed
  11. Rudie JD, Weiss DA, Colby JB, Rauschecker AM, Laguna B, Braunstein S, Sugrue LP, Hess CP, Villanueva-Meyer JE. Three-dimensional U-Net Convolutional Neural Network for Detection and Segmentation of Intracranial Metastases. Radiol Artif Intell. 2021 May; 3(3):e200204.  View on PubMed
  12. Rudie JD, Rauschecker AM, Xie L, Wang J, Duong MT, Botzolakis EJ, Kovalovich A, Egan JM, Cook T, Bryan RN, Nasrallah IM, Mohan S, Gee JC. Subspecialty-Level Deep Gray Matter Differential Diagnoses with Deep Learning and Bayesian Networks on Clinical Brain MRI: A Pilot Study. Radiol Artif Intell. 2020 Sep; 2(5):e190146.  View on PubMed
  13. Duong MT, Rauschecker AM, Mohan S. Diverse Applications of Artificial Intelligence in Neuroradiology. Neuroimaging Clin N Am. 2020 Nov; 30(4):505-516.  View on PubMed
  14. Rauschecker AM, Rudie JD, Xie L, Wang J, Duong MT, Botzolakis EJ, Kovalovich AM, Egan J, Cook TC, Bryan RN, Nasrallah IM, Mohan S, Gee JC. Artificial Intelligence System Approaching Neuroradiologist-level Differential Diagnosis Accuracy at Brain MRI. Radiology. 2020 06; 295(3):626-637.  View on PubMed
  15. Bryan RN, Davatzikos C, Herskovits EH, Mohan S, Rudie JD, Rauschecker AM. Medical Image Analysis: Human and Machine. Acad Radiol. 2020 01; 27(1):76-81.  View on PubMed
  16. Rudie JD, Weiss DA, Saluja R, Rauschecker AM, Wang J, Sugrue L, Bakas S, Colby JB. Multi-Disease Segmentation of Gliomas and White Matter Hyperintensities in the BraTS Data Using a 3D Convolutional Neural Network. Front Comput Neurosci. 2019; 13:84.  View on PubMed
  17. Duong MT, Rauschecker AM, Rudie JD, Chen PH, Cook TS, Bryan RN, Mohan S. Artificial intelligence for precision education in radiology. Br J Radiol. 2019 Nov; 92(1103):20190389.  View on PubMed
  18. Duong MT, Rudie JD, Wang J, Xie L, Mohan S, Gee JC, Rauschecker AM. Convolutional Neural Network for Automated FLAIR Lesion Segmentation on Clinical Brain MR Imaging. AJNR Am J Neuroradiol. 2019 08; 40(8):1282-1290.  View on PubMed
  19. Rudie JD, Rauschecker AM, Bryan RN, Davatzikos C, Mohan S. Emerging Applications of Artificial Intelligence in Neuro-Oncology. Radiology. 2019 03; 290(3):607-618.  View on PubMed
  20. Silson EH, Aleman TS, Willett A, Serrano LW, Pearson DJ, Rauschecker AM, Maguire AM, Baker CI, Bennett J, Ashtari M. Comparing Clinical Perimetry and Population Receptive Field Measures in Patients with Choroideremia. Invest Ophthalmol Vis Sci. 2018 07 02; 59(8):3249-3258.  View on PubMed

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