imr-framework

Focus

The Accessible Magnetic Resonance Laboratory (AMRL) is a research group that focuses on developing and deploying MRI tools and methods to enable accessible imaging of pediatric patients with cancer, infectious diseases or mental health challenges. We are a part of the Division of Cancer Imaging Research at Johns Hopkins University.

Projects

Want to work on advancing accessible MRI? Check out our research projects below.

Tools

Check out some cool open source tools we have built here.

Education

Want to learn more about MR? Check out our educational initiatives here.

Ongoing Projects

Practical magnetic resonance imaging for all

This educational project will overcome access limitations and deliver a seminal, cutting-edge curriculum using an online game-based digital platform that allows students to build and play with their own educational MR scanner.

Design and development of 0.2T portable MR systems

Successful design and development of a portable 0.2T MRI for the head and spine will yield structural, functional and metabolic MRI capabilities required for downstream clinical use in an accessible manner.

Accessible imaging of high consequence pathogens

Tracking brain changes due to infection in restricted environments require portable solutions providing image quality similar to clinical scanners

Past Projects

Indian National Mission on Indigenous 1.5T MRI - Academic and prototyping site

We were involved in building a 1.5T MRI from scratch indigenously in India.

Pediatric brain tumor imaging using tailored MR Fingerprinting

We developed a non-synthetic approach to simultaneously obtain multiple qualitative and qunatitative MR images. We designed, implemented and evaluated the sequence on pediatric brain tumor patients

Featured Publications

1) Oiye, I.E., Poojar, P., Qian, E., Vaughan, J.T. and Geethanath, S., 2025. A standalone and cost-effective MR-compatible computer numerical control machine for simultaneous, multi-parameter mapping. Measurement Science and Technology, 36(4), p.045902. [Article]

2) Manso Jimeno, M., Ravi, K.S., Fung, M., Oyekunle, D., Ogbole, G., Vaughan Jr, J.T. and Geethanath, S., 2025. Automated detection of motion artifacts in brain MR images using deep learning. NMR in Biomedicine, 38(1), p.e5276. [Article]

3) Block, K.T., Zhang, C., Ciancia, V., Cooley, C., Geethanath, S., Stockmann, J., Verghese, G. and Alon, L., 2025. MRI4ALL - A Week‐Long Hackathon for the Development of an Open‐Source Ultra‐Low‐Field MRI System. Journal of Magnetic Resonance Imaging. [Article]

4) Poojar, P., Oiye, I.E., Aggarwal, K., Jimeno, M.M., Vaughan, J.T. and Geethanath, S., 2024. Repeatability of image quality in very low‐field MRI. NMR in Biomedicine, 37(10), p.e5198. [Article]

5) Tong, G., Ananth, R., Vaughan Jr, J.T. and Geethanath, S., 2024. Expanding access to magnetic resonance education through open-source web tutorials. NMR in Biomedicine, pp.e5109-e5109. [Article]