Dr. Joshua Kaggie is a distinguished Senior Research Associate in the Department of Radiology at the University of Cambridge, specializing in the development and application of novel Magnetic Resonance Imaging (MRI) techniques. His research primarily focuses on advancing medical diagnostics and understanding disease progression, with significant contributions in cancer imaging and osteoarthritis detection, augmented by his expertise in machine learning and MRI hardware development.
Academic Background and Affiliations: Dr. Kaggie earned his Ph.D., Master of Science, and Bachelor of Science degrees in Physics from the University of Utah, following an Associate’s degree from Salt Lake Community College. He began his tenure at the University of Cambridge in February 2015 as a Senior Research Associate, also serving as an MRI Physicist since 2015 (Kaggie, n.d.-a; The Org, n.d.). His affiliations within Cambridge include the Advanced Cancer Imaging Programme at the CRUK Cambridge Centre and positions as a Bye-Fellow and College Teaching Associate at Downing College since September 2019 (CRUK Cambridge Centre, n.d.; The Org, n.d.). Earlier in his Cambridge career, he held a Research Associate position at Trinity Hall from 2015 to 2019 (The Org, n.d.).
Core Research Areas and Innovations: Dr. Kaggie’s work pushes the boundaries of MRI technology through a multidisciplinary approach combining physics, electronics, machine learning, and clinical challenges (Kaggie, n.d.-a). His primary research interests encompass:
- Advanced MRI Technique Development: He has made significant contributions to Sodium MRI, particularly for breast imaging, enhancing image quality and sensitivity. His work also focuses on MR Fingerprinting, an innovative technique aimed at achieving faster quantitative imaging without increasing scan times, which holds potential for revolutionizing clinical MRI scans. This includes studies on its multi-site repeatability in the brain at different field strengths (Kaggie, n.d.-a; Kaggie et al., n.d.). Furthermore, he explores X-nuclei MRI, which involves imaging atoms other than hydrogen to provide unique biochemical information (Kaggie, n.d.-a).
- Clinical Applications: A central theme of his research is the enhancement of disease detection and monitoring. He is particularly focused on improving the detection of osteoarthritis and various cancers, including breast cancer, high-grade serous ovarian cancer (HGSOC), and glioblastoma (Kaggie, n.d.-a; The Org, n.d.). His work with hyperpolarized Carbon-13 MRI, for instance, has demonstrated its potential in assessing early response to neoadjuvant chemotherapy in both breast and ovarian cancers, providing crucial insights into metabolic flux changes associated with treatment response (Beer et al., 2025; Woitek et al., 2021).
- Machine Learning Integration: Dr. Kaggie actively applies machine learning to medical imaging, developing automated techniques for image analysis, such as identifying knee tissues in MRI images. He views machine learning as both a hobby and a job, applying neural networks to both 1-dimensional (e.g., genomics) and 3-dimensional (imaging) data for pattern recognition and prediction (Kaggie, n.d.-a; Kaggie, 2019). His GitHub profile showcases practical implementations, such as a PyTorch-based library for Voronoi diagrams and Delaunay triangulations (kaggie, n.d.).
- MRI Hardware Development: He contributes to the development of radiofrequency (RF) coils and arrays, which are crucial for improving imaging resolution and sensitivity. This includes filing patents related to circuit robustness and dual resonant breast coil designs for sodium and proton MRI (Kaggie, n.d.-a).
Key Projects and Contributions: Dr. Kaggie is a key member of the STARSTEM consortium, an EU Horizon 2020 research and innovation program. Within STARSTEM, he has presented extensive research, including studies on magnetic resonance tracking of iron-labeled stem cells for osteochondral defect repair, automated textural classification of osteoarthritis MRI, and various advanced quantitative MRI techniques (STARSTEM, 2018; STARSTEM, 2022). He has also engaged in public outreach, giving talks like “Imaging Light in Medicine” to explain the diverse applications of light in medicine, including MRI, PET, and photoacoustic imaging (STARSTEM, 2022).
His research output is substantial, with over 66 peer-reviewed papers, one book, and two book chapters, covering topics such as non-proton MRI (e.g., carbon-13, deuterium metabolic imaging), MR physics, deep learning, and RF coil developments. His academic impact is reflected in an h-index of 25 and an i10-index of 42 (Kaggie, n.d.-a). He actively contributes to the academic community as a Category Chair for the European Molecular Imaging Meeting 2025, a regular reviewer for MRI journals, and by representing junior researchers on University of Cambridge committees and the UK Research Staff Association, where he helped organize national postdoctoral conferences (Kaggie, n.d.-a; University of Liverpool, n.d.).
Broader Engagements: Beyond his primary research, Dr. Kaggie has participated in panels, such as the 2019 London Festival of Genomics, where he offered an image analysis perspective on unstructured data and AI (Kaggie, 2019). He also has a notable history of community involvement, having founded the non-profit support group SLC Postmos for former members of Mormonism and serving as a Board Member for the Exmormon Foundation (Exmormon Foundation, n.d.).
In summary, Dr. Joshua Kaggie’s research at the University of Cambridge represents a significant advancement in medical imaging, bridging fundamental physics with clinical application through innovative MRI techniques and the strategic integration of machine learning. His extensive publications, patents, and leadership roles underscore his profound impact on the field of advanced medical diagnostics.
Bibliography
Beer, L., Bura, V., Ursprung, S., Woitek, R., McLean, M. A., Ang, J. E., Jimenez-Linan, M., Gill, A. B., Kaggie, J., Locke, M., Frary, A., Field-Rayner, J., Patterson, I., Reinius, M., Graves, M. J., Deen, S., Funingana, G., Rundo, L., Priest, A., … Sala, E. (2025). Assessment of early response to neoadjuvant chemotherapy in multi-site high-grade serous ovarian cancer using hyperpolarized-13C MRI. EJNMMI Research, 15(40). https://link.springer.com/article/10.1186/s13550-025-01219-5
CRUK Cambridge Centre. (n.d.). Dr Joshua Kaggie. Retrieved May 15, 2024, from https://crukcambridgecentre.org.uk/users/joshua-kaggie
Exmormon Foundation. (n.d.). Board Members. Retrieved May 15, 2024, from http://exmormonfoundation.org/board.html
Kaggie, J. (n.d.-a). About Me. kaggie.com. Retrieved May 15, 2024, from https://www.kaggie.com/about-me/
Kaggie, J. (n.d.-b). Thank you to Nicola from the NIHR Cambridge Biomedical Research Centre for putting this together! LinkedIn. Retrieved May 15, 2024, from https://www.linkedin.com/posts/kaggie_spotlight-on-researchers-cambridge-biomedical-activity-6760487947037884416-jntN
Kaggie, J. (2019, January 24). Machine Learning: A Hobby and a Job. Joshua Kaggie’s Weblog. https://kaggie.wordpress.com/
Kaggie, J., Buonincontri, G., Biagi, L., Retico, A., & McLean, M. (n.d.). Data supporting: “MR Fingerprinting Repeatability in the Brain”. University of Cambridge Repository. Retrieved May 15, 2024, from https://www.repository.cam.ac.uk/items/b71f380c-effb-4205-93e7-e993b4958313
kaggie. (n.d.). kaggie. GitHub. Retrieved May 15, 2024, from https://github.com/kaggie
Newton Gateway to Mathematics. (2016, October 19). Developments in Healthcare Imaging – Connecting with Industry. Isaac Newton Institute. https://www.newton.ac.uk/event/tgmw37/
STARSTEM. (2018, June 12). Dr Joshua Kaggie presents STARSTEM studies at ISMRM-ESMRMB meeting in Paris. https://starstem.eu/dr-joshua-kaggie-presents-starstem-studies-at-ismrm-esmrmb-meeting-in-paris/
STARSTEM. (2022, May 25). “Imaging Light in Medicine” with Dr Joshua Kaggie, #LivefromLucy International Day of Light. https://starstem.eu/imaging-light-in-medicine-talk/
The Org. (n.d.). Joshua Kaggie – Senior Research Associate at University of Cambridge. Retrieved May 15, 2024, from https://theorg.com/org/university-of-cambridge/org-chart/joshua-kaggie
University of Cambridge Department of Surgery. (n.d.). Dr Joshua Kaggie. Retrieved May 15, 2024, from https://sciences.musculoskeletal.group.cam.ac.uk:443/members/dr-joshua-kaggie/
University of Liverpool. (n.d.). The panel. Researcher Hub. Retrieved May 15, 2024, from https://www.liverpool.ac.uk/researcher/postdoc-appreciation-week/npdc/programme/panel/
Woitek, R., McLean, M., Ursprung, S., Rueda, O. M., Garcia, R. M., Locke, M. J., Beer, L., Baxter, G., Rundo, L., Provenzano, E., Kaggie, J., Patterson, A., Frary, A., Field Rayner, J., Papalouka, V., Kane, J., Benjamin, A. J. V., Gill, A. B., Priest, A. W., … Gallagher, F. A. (2021, October 8). Hyperpolarized Carbon-13 MRI for Early Response Assessment of Neoadjuvant Chemotherapy in Breast Cancer Patients. Cancer Research, 81(23), 6004–6017.
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