About the Researcher
Research Interests: Dr. Ahmad's research aims to bring engineering innovations to biomedical imaging. He is particularly interested in developing data acquisition and image reconstruction methods that can improve the diagnostic accuracy of established or emerging imaging modalities. His research expertise encompasses digital signal and image processing, tomographic reconstruction, linear and nonlinear inverse problems, statistical sensitivity analysis, multimodal imaging, and machine learning, with application to cardiovascular magnetic resonance imaging (MRI).
Short Biography: Rizwan Ahmad received a B.S. degree in Electrical Engineering from the University of Engineering and Technology, Lahore, Pakistan. He further received M.S. and Ph.D. degrees in Electrical and Computer Engineering from Ohio State University, Columbus, Ohio. He held post-doctoral and Research Scientist positions at Ohio State University Medical Center. In 2017, he joined the Department of Biomedical Engineering at Ohio State University, where he is currently an assistant professor.
proposed a highly accelerated data acquisition and processing framework that enables whole-heart 4D flow imaging from a five-minute scan. The 4D flow image on the left displays the speed of the blood in the heart and great vessels.4D Flow Imaging: MRI-based 4D flow imaging has emerged as a comprehensive alternative for measuring hemodynamics. Extending the principles of phase-contrast MRI (PC-MRI), 4D flow imaging provides a full volumetric and temporally resolved mapping of the three-dimensional velocity vector, offering an advantage over PC-MRI with regard to anatomical coverage and hemodynamic visualization. Post-processing enables retrospective interrogation of arbitrary slice planes, long after the patient is removed from the magnet. Advanced hemodynamic parameters can also be calculated which may carry additional prognostic value. Despite its advantages, the clinical adoption of 4D flow imaging has been hampered by prohibitively long acquisition times. We recently
Motion Encoding and Compensation: Unaccounted physiological motion, e.g., respiratory motion, can corrupt cardiovascular MRI (CMR). Breath-holding and ECG triggering are strategies commonly used to suppress respiratory motion and to synchronize the acquisition with the cardiac rhythm, respectively. Many patients, however, cannot breath-hold, and placement of ECG leads can be time-consuming. To encode both cardiac and respiratory motions, we are developing methods based on the Pilot Tone (PT) technology. PT is a transmitter that emits electromagnetic waves close to the Larmor frequency. The transmitted signal is modulated by the physiological motions and is picked up by the receive coils. With PT, the physiological motions are seamlessly encoded into the raw MRI data and can be separated from the image content using signal processing techniques.
Accelerated Imaging: MRI acquisition is inherently slow. Therefore, performing volumetric or high-resolution imaging in clinically feasible acquisition times requires high acceleration, which is often achieved by prospective undersampling of the k-space. Recovering diagnostic images from highly undersampled data requires utilizing image priors, which are included as regularizing terms in an optimization problem. We have developed compressed sensing (CS) inspired accelerated imaging techniques that are now clinically used at our institute. Our ongoing efforts are focused on developing and implementing highly accelerated deep learning (DL)-based methods that can preserve fine details that may be lost in sparsity-based CS methods. Target applications include cardiac imaging and neuroimaging in both adult and pediatric populations.