I am interested in transcranial focused ultrasound and its applications for studying complex cognitive functions. 

My research focuses on developing novel methods to quantify Chemical Exchange Saturation Transfer (CEST) MRI using machine learning and deep learning, and applying these methods to tumors, ischemic stroke, muscle diseases, and other pathologies.

My current projects include

1. Developing a framework for partially synthetic data generation to train deep learning models for CEST MRI applications. These models are used to improve imaging in brain tumors, ischemic stroke, and muscle disorders such as ALS. This work has also been extended in our lab to improve the quantification of a newly identified nuclear Overhauser enhancement (NOE) signal at -1.6 ppm, which shows potential for detecting tumors and stroke. In addition, our lab works on enhancing the quality and SNR of CEST images using deep learning, with the aim of achieving more reliable quantification and enabling clinical translation.
2. Applying advanced techniques such as double saturation power CEST (DSP-CEST) to enable rapid and specific quantification of CEST signals.
3. Applying CEST MRI across different pathologies, with ongoing studies extending into Alzheimer's disease.

Publications:

Viswanathan M, Yin L, Kurmi Y, Zu Z. Machine learning-based amide proton transfer imaging using partially synthetic training data. Magn Reson Med. 2024; 91: 1908-1922. doi: 10.1002/mrm.29970

Viswanathan M, Yin L, Kurmi Y, Afzal A, Zu Z. Enhancing amide proton transfer imaging in ischemic stroke using a machine learning approach with partially synthetic data. NMR in Biomedicine. 2025; 38(1):e5277. doi:10.1002/nbm.5277

Viswanathan M, Kurmi Y, Zu Z. Nuclear Overhauser enhancement imaging at −1.6 ppm in rat brain at 4.7T. Magn Reson Med. 2024; 91: 615-629. doi: 10.1002/mrm.29896

Viswanathan M, Kurmi Y, Zu Z. A rapid method for phosphocreatine-weighted imaging in muscle using double saturation power-chemical exchange saturation transfer. NMR in Biomedicine. 2024; 37(4):e5089. doi:10.1002/nbm.5089

Kurmi Y, Viswanathan M, Zu Z. Enhancing SNR in CEST imaging: A deep learning approach with a denoising convolutional autoencoder. Magn Reson Med. 2024; 92: 2404-2419. doi: 10.1002/mrm.30228

Having a career in medicine spanning over 15 years, I am a Nuclear Medicine/PET Technologist specializing in advanced diagnostic imaging techniques, with a passion for improving patient outcomes through medical technology research.

I hold an undergraduate degree in Radiologic Technology with a concentration in Diagnostic Medical Imaging, which laid the foundation for my experience in precision imaging and patient care. In 2024, I completed Graduate School with a Master's degree in Radiologic Sciences, further honing my skills and deepening my understanding of the field of research.

I have combined technical proficiency with compassionate care, ensuring each participant receives the highest level of service. My goal is to continue contributing to the field of diagnostic imaging through my experiences and education to advance imaging research, and positively impact patient health.

I am a Ph.D. candidate in the Department of Physics and Astronomy at Vanderbilt University, working under the mentorship of Prof. John C. Gore. My research focuses on magnetic resonance microscopy, with an emphasis on developing hardware, pulse sequences, and image-processing methods to achieve ultra-high spatial resolution imaging (~10–40 µm) on a 15.2 Tesla preclinical MRI system.

A central part of my work involves designing and fabricating highly SNR-efficient micro-imaging coils tailored for ultra-high field systems, while also leveraging fast pulse sequences such as FSE, EPI, and GRASE. I am further developing approaches that integrate compressed sensing acceleration with magnetic resonance microscopy, enabling high-resolution imaging of larger mammalian neural tissue samples.

Together, these advances are applied to the study of spinal cord injury and other disease models, where multiple MR contrasts—including T1/T2, diffusion, and quantitative magnetization transfer (qMT)—provide insights into tissue microstructure and mechanisms of recovery.