Skip Navigation
  Search Menu
Find an Expert

 

Bharath Ambale Venkatesh, Ph.D.

Photo of Dr. Bharath Ambale Venkatesh, Ph.D.

Instructor of Radiology and Radiological Science

Research Interests: cardiovascular imaging; machine learning; artificial intelligence; aging; image analysis; stochastic processes; inverse problems

Background

Dr. Bharath Ambale-Venkatesh has been fascinated by the use of imaging to probe the human body ever since he read about the discovery of functional MRI in the early 90s while still in middle school. He earned his BS from BMS College of Engineering, India, in 2004 and PhD from Auburn University, Alabama, in 2010, with his dissertation focused on understanding myocardial mechanics from cardiac MRI. Bharath then moved to Johns Hopkins University to diversify his research portfolio. Now a faculty member in the department of Radiology, he continues research in cardiovascular imaging, epidemiology, and data science. 

...read more

Titles

  • Instructor of Radiology and Radiological Science

Departments / Divisions

Education

Degrees

  • B.E., B.M.S. College of Engineering - Bangalore (India) (2004)
  • Ph.D., Auburn University (Alabama) (2010)

Research & Publications

Research Summary

Dr. Ambale-Venkatesh has been involved in image processing, machine learning, cardiovascular imaging and epidemiology research since 2004. His current research aims to help clinicians improve patient care by identifying new imaging markers to predict cardiovascular outcomes and assess the success of therapeutic interventions, and evaluating the pathophysiological mechanisms of cardiac diseases. He has been leading aspects of protocol development, data acquisition, image post-processing, imaging data curation and indexing, statistical analysis and conduction of multi-center trials as a co-investigator in the magnetic resonance imaging (MRI) and echocardiography core labs for National Institutes of Health (NIH)-sponsored large population-based studies. He has also taken the lead in designing and developing protocols for assessment of novel MRI-derived biomarkers of stem-cell therapy in those with peripheral arterial disease and myocardial infarction. Specifically, his research includes:

  • Development of novel techniques for image analysis, data modeling, and image acquisition.
  • Predicting adverse outcomes using machine learning in large multi-ethnic populations to generate preventive public health strategies.
  • Using a combination of deep learning and advanced phenotyping to extract information from images and statistical machine learning to provide clinical insight.

Technology Expertise Keywords

MATLAB; Python; Keras; TensorFlow; PyTorch

Selected Publications

View all on Pubmed

Ambale-Venkatesh B, Schiros CG, Himanshu Gupta, Lloyd SG, Dell'Italia L, Denney TS Jr. Three-dimensional plus time biventricular strain from tagged MR images by phase-unwrapped harmonic phase. Journal of Magnetic Resonance Imaging. 2011; 34(4): 799-810.

Ambale-Venkatesh B, Volpe GJ, Donekal S, Mewton N, Liu CY, Shea S, Liu K, Burke G, Wu CO, Bluemke DA, Lima JAC. Association of Longitudinal Changes in Left Ventricular Structure and Function with Myocardial Fibrosis: The MESA study. Hypertension. 2014; 64:508-515.

Ambale-Venkatesh B, Lima JAC. Cardiac MRI: a central prognostic tool in myocardial fibrosis. Nature Reviews Cardiology 12 (1), 18-29, 2015.

Ambale-Venkatesh B, Nauffal V, Noda C, Fujii T, Yang PC, Bettencourt J, Ricketts EP, Murphy MP, Leeper NJ, Moye L, Ebert RF, Muthupillai R, Bluemke DA, Perin EC, Hirsch AT, Lima JAC, Cardiovascular Cell Therapy Research Network (CCTRN). Baseline assessment and comparison of arterial anatomy, hyperemic flow, and skeletal muscle perfusion in peripheral artery disease: The Cardiovascular Cell Therapy Research Network - Patients with Intermittent Claudication Injected with ALDH Bright Cells (CCTRN PACE) study. American Heart Journal, Vol 183, January 2017.

Ambale-Venkatesh B, Yang X, Wu CO, Liu K, WG Hundley, McClelland RL, Gomes SA, Folsom AR, Shea S, Guallar E, Bluemke DA, Lima JAC. Cardiovascular Event Prediction by Machine Learning: The Multi-Ethnic Study of Atherosclerosis. Circ Res. 2017 Oct 13;121(9):1092-1101.

Activities & Honors

Memberships

  • International Society for Magnetic Resonance in Medicine
  • Society for Cardiovascular Magnetic Resonance
  • Radiological Society for North America
  • American Heart Association

Videos & Media

Lectures and Presentations

  • Cardiac Deformation Estimation from Magnetic Resonance Imaging and 3D Modeling
    Seminar, Monthly Engineering Seminar Series, Auburn University, Alabama (10/18/2010)
    Auburn University, Alabama
  • Systolic Function
    Workshop, International Society of Magnetic Resonance in Medicine 24th Annual Meeting, Singapore (05/07/2016)
    International Society of Magnetic Resonance in Medicine
  • Cardiovascular Event Prediction Using Machine Learning
    Grand Rounds, Cardiology Grand Rounds, Johns Hopkins Cardiology Grand Rounds, Baltimore, MD (01/18/2018)
    Johns Hopkins University
  • Machine Learning in Cardiovascular Disease
    Invited Lecture, International Society of Magnetic Resonance in Medicine 26th Annual Meeting, Paris, France (06/18/2018)
    International Society of Magnetic Resonance in Medicine
Is this you? Edit Profile