Phase-one criteria included the proposed algorithm's potential impact on human health and alignment with FAIR data principles. In phase two, which runs through fall 2027, finalists must test and ultimately demonstrate the proposed algorithm on quantum computing hardware.

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The NYU Langone-Fermilab team, dubbed "quantuMRI," is proposing to address these challenges by exploiting the potential advantages of quantum computing.

The entry is among 10 finalists (and one of just two related to MRI).

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However, the promise of qMRI has yet to be realized in clinical radiology, in part due to computational challenges of modeling the complex interactions between electromagnetic fields and biological tissues at high resolutions.

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Quantitative MRI (qMRI) has the potential go deeper, delivering not just images of anatomical structures but objective metrics of electromagnetic properties of tissue, producing more detailed, more objective, and potentially earlier-stage information about patients' health.

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Quantitative MRI has long been sought to move the imaging technology beyond its current clinical standard in which magnetic resonance images are evaluated qualitatively for appearance of pathology.

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NYU Langone's Riccardo Lattanzi and José E. Cruz Serrallés, Fermilab's Doğa Murat Kürkçüoğlu and Oluwadara Ogunkoya, and NASA's Norm Tubman picked up the gauntlet and proposed a novel quantum algorithm approach to advance quantitative magnetic resonance imaging.

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In the fall of 2024 the NIH's National Center for Advancing Translational Sciences launched the challenge to "address the limitations in technological development and adoption of quantum-enabled technologies to solve translational biomedical problems."

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What would you want to work on in your #PhD research? Join us at the info session & let us know!

The official page of the biomedical imaging and technology PhD training program at NYU Grossman: med.nyu.edu/research/vil...

More students & alumni stories: cai2r.net/tag/phd-prog...

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Ruoxun Zi, alumna ('25), pushed the boundaries of radial sampling & iterative image reconstruction techniques in low-field #MRI, brain cancer imaging, motion tracking, & imaging of wrist motion: cai2r.net/ruoxun-zi-de...
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Jungkyu (JP) Park, alumnus ('25) developed multiple #AI systems for next-generation #BreastCancer screening: cai2r.net/jungkyu-jp-p...
5/

Ryn Flaherty, alumnus ('25) advanced the search for #MRI biomarkers of early neurodegenerative risk among people w/ subjective cognitive decline: cai2r.net/ryn-flaherty...
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Anna Chen, alumna ('25), investigated the replicability & potential of magnetic resonance spectroscopy metrics as biomarkers of mild traumatic brain injury, also known as #concussion: cai2r.net/anna-chen-de...
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Grad students in the program work closely w/ our scientists & make original contributions to #MRI technologies & applications in basic, preclinical, & clinical research. In this thread we highlight what our most recent graduates focused on in the course of their #PhD training.
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What would you want to work on in your #PhD research? Join us at the info session and let us know!

The official page of the biomedical imaging and technology PhD training program at NYU Grossman: med.nyu.edu/research/vil...

Stories featuring our students and alumni: cai2r.net/tag/phd-prog...

7/7

Ruoxun Zi, alumna ('25), pushed the boundaries of radial sampling & iterative image reconstruction techniques in low-field #MRI, brain cancer imaging, motion tracking, & imaging of wrist motion: cai2r.net/ruoxun-zi-de...
6/

Jungkyu (JP) Park, alumnus ('25) developed multiple #AI systems for next-generation #BreastCancer screening: cai2r.net/jungkyu-jp-p...
5/

Ryn Flaherty, alumnus ('25) advanced the search for #MRI biomarkers of early neurodegenerative risk among people w/ subjective cognitive decline: cai2r.net/ryn-flaherty...
4/