Honors & Awards


  • SCMR Early Career Award - Basic Science, SCMR (2023)
  • The OSU College of Engineering Exemplary Graduate Student Research Award, The Ohio State University (2022)
  • 2nd Place Poster Award for ISMRM Workshop on Data Sampling & Image Reconstruction, ISMRM (2020)
  • 1st Place for ISMRM Cardiac MR Study Group Abstract Award, ISMRM (2018)
  • ISMRM Summa Cum Laude Merit Award, ISMRM (2018)

Professional Education


  • Doctor of Philosophy, Ohio State University (2022)
  • Master of Science, Ohio State University (2017)
  • Bachelor of Science, Nanjing University of Aeronautics and Astronautics (2015)
  • Postdoc, Stanford University, Signal Processing and Medical Imaging (2023)
  • Ph.D., The Ohio State University, Electrical and Computer Engineering (2022)
  • M.S., The Ohio State University, Electrical and Computer Engineering (2017)
  • B.S., Nanjing University of Aeronautics and Astronautics, Electrical and Electronics Engineering (2015)

Stanford Advisors


Patents


  • Shen Zhao, Rizwan Ahmad, Lee Potter. "United States Patent US20220244333A1 High-dimensional fast convolutional framework (HICU) for calibrationless MRI", Ohio State Innovation Foundation, Aug 4, 2022
  • Shen Zhao, Rizwan Ahmad, David Tucker, Lee C. Potter.. "United States Patent T2022-060 Venc design and velocity estimation for phase-contrast MRI", Ohio State Innovation Foundation, Sep 16, 2021

All Publications


  • Venc Design and Velocity Estimation for Phase Contrast MRI IEEE TRANSACTIONS ON MEDICAL IMAGING Zhao, S., Ahmad, R., Potter, L. C. 2022; 41 (12): 3712-3724

    Abstract

    In phase-contrast magnetic resonance imaging (PC-MRI), spin velocity contributes to the phase measured at each voxel. Therefore, estimating velocity from potentially wrapped phase measurements is the task of solving a system of noisy congruence equations. We propose Phase Recovery from Multiple Wrapped Measurements (PRoM) as a fast, approximate maximum likelihood estimator of velocity from multi-coil data with possible amplitude attenuation due to dephasing. The estimator can recover the fullest possible extent of unambiguous velocities, which can greatly exceed twice the highest venc. The estimator uses all pairwise phase differences and the inherent correlations among them to minimize the estimation error. Correlations are directly estimated from multi-coil data without requiring knowledge of coil sensitivity maps, dephasing factors, or the actual per-voxel signal-to-noise ratio. Derivation of the estimator yields explicit probabilities of unwrapping errors and the probability distribution for the velocity estimate; this, in turn, allows for optimized design of the phase-encoded acquisition. These probabilities are also incorporated into spatial post-processing to further mitigate wrapping errors. Simulation, phantom, and in vivo results for three-point PC-MRI acquisitions validate the benefits of reduced estimation error, increased recovered velocity range, optimized acquisition, and fast computation. A phantom study at 1.5T demonstrates 48.5% decrease in root mean squared error using PRoM with post-processing versus a conventional "dual-venc" technique. Simulation and 3T in vivo results likewise demonstrate the proposed benefits.

    View details for DOI 10.1109/TMI.2022.3193132

    View details for Web of Science ID 000907324600020

    View details for PubMedID 35862337

    View details for PubMedCentralID PMC9837712

  • High-dimensional fast convolutional framework (HICU) for calibrationless MRI (vol 86, pg 1212, 2021) MAGNETIC RESONANCE IN MEDICINE Zhao, S., Potter, L. C., Ahmad, R. 2022; 87 (6): 3027

    View details for DOI 10.1002/mrm.29195

    View details for Web of Science ID 000778071600039

  • Alias-Free Arrays. IEEE signal processing letters Tucker, D., Zhao, S., Ahmad, R., Potter, L. C. 2022; 29: 2457-2461

    Abstract

    Nonuniform array geometries provide freedom for increased aperture and reduced mutual coupling. A necessary and sufficient condition is given for an array of isotropic sensor elements to be unambiguous for any specified set of directions of arrival. The set of unambiguous spatial frequencies is shown to be a parallelepiped, admitting simple geometrical interpretation. Results are used in design of linear, planar, and 3D arrays.

    View details for DOI 10.1109/LSP.2022.3224834

    View details for PubMedID 36530478

  • MAXIMIZING UNAMBIGUOUS VELOCITY RANGE IN PHASE-CONTRAST MRI WITH MULTIPOINT ENCODING Zhao, S., Ahmad, R., Potter, L. C., IEEE IEEE. 2022

    Abstract

    In phase-contrast magnetic resonance imaging (PC-MRI), the velocity of spins at a voxel is encoded in the image phase. The strength of the velocity encoding gradient offers a trade-off between the velocity-to-noise ratio (VNR) and the extent of phase aliasing. Phase differences provide invariance to an unknown background phase. Existing literature proposes processing a reduced set of phase difference equations, simplifying the phase unwrapping problem at the expense of VNR or unaliased range of velocities, or both. Here, we demonstrate that the fullest unambiguous range of velocities is a parallelepiped, which can be accessed by jointly processing all phase differences. The joint processing also maximizes the velocity-to-noise ratio. The simple understanding of the unambiguous parallelepiped provides the potential for analyzing new multi-point acquisitions for an enhanced range of unaliased velocities; two examples are given.

    View details for DOI 10.1109/ISBI52829.2022.9761589

    View details for Web of Science ID 000836243800188

    View details for PubMedID 35646241

    View details for PubMedCentralID PMC9136874

  • CALIBRATIONLESS MRI RECONSTRUCTION WITH A PLUG-IN DENOISER Zhao, S., Potter, L. C., Ahmad, R., IEEE IEEE. 2021: 1846-1849

    Abstract

    Magnetic Resonance Imaging (MRI) is a noninvasive imaging technique that provides excellent soft-tissue contrast without using ionizing radiation. MRI's clinical application may be limited by long data acquisition time; therefore, MR image reconstruction from highly under-sampled k-space data has been an active research area. Calibrationless MRI not only enables a higher acceleration rate but also increases flexibility for sampling pattern design. To leverage non-linear machine learning priors, we pair our High-dimensional Fast Convolutional Framework (HICU) [1] with a plug-in denoiser and demonstrate its feasibility using 2D brain data.

    View details for DOI 10.1109/ISBI48211.2021.9433815

    View details for Web of Science ID 000786144100392

    View details for PubMedID 35211244

    View details for PubMedCentralID PMC8865188

  • CONVOLUTIONAL FRAMEWORK FOR ACCELERATED MAGNETIC RESONANCE IMAGING Zhao, S., Potter, L. C., Lee, K., Ahmad, R., IEEE IEEE. 2020: 1065-1068

    Abstract

    Magnetic Resonance Imaging (MRI) is a noninvasive imaging technique that provides exquisite soft-tissue contrast without using ionizing radiation. The clinical application of MRI may be limited by long data acquisition times; therefore, MR image reconstruction from highly undersampled k-space data has been an active area of research. Many works exploit rank deficiency in a Hankel data matrix to recover unobserved k-space samples; the resulting problem is non-convex, so the choice of numerical algorithm can significantly affect performance, computation, and memory. We present a simple, scalable approach called Convolutional Framework (CF). We demonstrate the feasibility and versatility of CF using measured data from 2D, 3D, and dynamic applications.

    View details for Web of Science ID 000578080300217

    View details for PubMedID 35211242

    View details for PubMedCentralID PMC8865187