Rohan Dharmakumar

Bio: Rohan Dharmakumar is an academic researcher from Cedars-Sinai Medical Center. The author has contributed to research in topics: Myocardial infarction & Magnetic resonance imaging. The author has an hindex of 27, co-authored 150 publications receiving 2536 citations. Previous affiliations of Rohan Dharmakumar include Northwestern University & University of California, Los Angeles.

T1 Mapping: Basic Techniques and Clinical Applications

Abstract: In cardiac magnetic resonance (CMR) imaging, the T1 relaxation time for the 1H magnetization in myocardial tissue may represent a valuable biomarker for a variety of pathological conditions. This possibility has driven the growing interest in quantifying T1, rather than just relying on its effect on image contrast. The techniques have advanced to where pixel-level myocardial T1 mapping has become a routine component of CMR examinations. Combined with the use of contrast agents, T1 mapping has led an expansive investigation of interstitial remodeling in ischemic and nonischemic heart disease. The purpose of this review was to introduce the reader to the physical principles of T1 mapping, the imaging techniques developed for T1 mapping, the pathophysiological markers accessible by T1 mapping, and its clinical uses.

Adversarial Image Synthesis for Unpaired Multi-modal Cardiac Data

Abstract: This paper demonstrates the potential for synthesis of medical images in one modality (e.g. MR) from images in another (e.g. CT) using a CycleGAN [24] architecture. The synthesis can be learned from unpaired images, and applied directly to expand the quantity of available training data for a given task. We demonstrate the application of this approach in synthesising cardiac MR images from CT images, using a dataset of MR and CT images coming from different patients. Since there can be no direct evaluation of the synthetic images, as no ground truth images exist, we demonstrate their utility by leveraging our synthetic data to achieve improved results in segmentation. Specifically, we show that training on both real and synthetic data increases accuracy by 15% compared to real data. Additionally, our synthetic data is of sufficient quality to be used alone to train a segmentation neural network, that achieves 95% of the accuracy of the same model trained on real data.

Cardiovascular Magnetic Resonance in Acute ST-Segment-Elevation Myocardial Infarction: Recent Advances, Controversies, and Future Directions.

Abstract: Although mortality after ST-segment elevation myocardial infarction (MI) is on the decline, the number of patients developing heart failure as a result of MI is on the rise. Apart from timely reperfusion by primary percutaneous coronary intervention, there is currently no established therapy for reducing MI size. Thus, new cardioprotective therapies are required to improve clinical outcomes after ST-segment-elevation MI. Cardiovascular magnetic resonance has emerged as an important imaging modality for assessing the efficacy of novel therapies for reducing MI size and preventing subsequent adverse left ventricular remodeling. The recent availability of multiparametric mapping cardiovascular magnetic resonance imaging has provided new insights into the pathophysiology underlying myocardial edema, microvascular obstruction, intramyocardial hemorrhage, and changes in the remote myocardial interstitial space after ST-segment-elevation MI. In this article, we provide an overview of the recent advances in cardiovascular magnetic resonance imaging in reperfused patients with ST-segment-elevation MI, discuss the controversies surrounding its use, and explore future applications of cardiovascular magnetic resonance in this setting.

Functionalizing nanoparticles with biological molecules: developing chemistries that facilitate nanotechnology.

Abstract: Chemistries that Facilitate Nanotechnology Kim E. Sapsford,† W. Russ Algar, Lorenzo Berti, Kelly Boeneman Gemmill,‡ Brendan J. Casey,† Eunkeu Oh, Michael H. Stewart, and Igor L. Medintz*,‡ †Division of Biology, Department of Chemistry and Materials Science, Office of Science and Engineering Laboratories, U.S. Food and Drug Administration, Silver Spring, Maryland 20993, United States ‡Center for Bio/Molecular Science and Engineering Code 6900 and Division of Optical Sciences Code 5611, U.S. Naval Research Laboratory, Washington, D.C. 20375, United States College of Science, George Mason University, 4400 University Drive, Fairfax, Virginia 22030, United States Department of Biochemistry and Molecular Medicine, University of California, Davis, School of Medicine, Sacramento, California 95817, United States Sotera Defense Solutions, Crofton, Maryland 21114, United States