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Ahmad Sultan, M.Sc.

Ph.D. Candidate, Electrical & Biomedical Engineering

I work on developing and applying signal processing, machine learning and unsupervised deep learning techniques for accelerated cardiac magnetic resonance imaging (MRI) reconstruction. My doctoral research focuses on motion-robust, whole-heart imaging for fast, reliable, and accesible MRI, funded by NIH Grant R01HL151697. I am advised by Rizwan Ahmad at The Ohio State University. I hold a M.Sc. in Biomedical Engineering and a B.Sc. in Electrical & Computer Engineering.

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Research Summary

  • Motion-robust Accelerated Cardiac Imaging: MRI data acquisition is inherently slow and susceptible to motion artifacts. Achieving clinically feasible imaging speeds requires a high degree of acceleration. Patients with arrhythmias or those unable to hold their breath, present further challenges. To address this, we have developed motion-robust self-supervised deep learning-based methods that preserve fine image details (often lost with conventional compressed sensing) at high accelerations, eliminating the need for breath-holds. Our approach produced higher-quality 2D cine, first-pass perfusion, and late gadolinium enhancement (LGE) images than traditional techniques, making cardiac MRI faster, more reliable and more accessible to patients who struggle with breath-holding or irregular heart rhythms.

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