563-02-004 ISMRM Abstract

Prediction of Cartilage Proteoglycan Content from MR Fingerprinting Using Deep Kernel Learning Gaussian Processes (DKL-GP)

Primary: Musculoskeletal - Cartilage

Secondary: Analysis Methods - Classification and Prediction

563-02-004 · Cartilage 2026 · Wednesday, 13 May, 9:15 AM–10:10 AM · Digital Posters Row D

Keywords: MR Fingerprinting Cartilage Machine learning (ML) Deep kernel learning

Accepted

Wajiha Bano¹, Ajinkya Gorad¹, Ville Kantola², Olli Nykänen^2,3, Mikko J Nissi³, Miika T Nieminen^2,4,5, Simo Särkkä¹

¹Department of Electrical Engineering and Automation (EEA), Aalto University, Espoo, Finland

²Research Unit of Health Sciences and Technology, University of Oulu, Oulu, Finland

³Department of Technical Physics, University of Eastern Finland, Kuopio, Finland

⁴Department of Diagnostics, Oulu University Hospital, Oulu, Finland

⁵Medical Research Center Oulu, University of Oulu and Oulu University Hospital, Oulu, Finland

Presenting Author: Victor Casula

Synopsis

Motivation:

Goals:

Approach:

Results:

Full abstract & presentation

The full text, figures, and any recorded presentation for this abstract are not shown here. Log in if you are a member or registered attendee with access.

Full abstracts, figures, and presentations for Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition are available to registered attendees. This content becomes freely available to the public roughly two years after the meeting.

To request or purchase access, contact the ISMRM Central Office at info@ismrm.org.

References

1. Guermazi, A., et al. "Compositional MRI techniques for evaluation of cartilage degeneration in osteoarthritis." Osteoarthritis and cartilage 23.10 (2015): 1639-1653. PMID: 26050864 [pmid]

2. Xia, Yang, Jonathan B. Moody, and Hisham Alhadlaq. "Orientational dependence of T2 relaxation in articular cartilage: A microscopic MRI (μMRI) study." Magnetic Resonance in Medicine: An Official Journal of the International Society for Magnetic Resonance in Medicine 48.3 (2002): 460-469. PMID: 12210910 [pmid]

3. Thüring, J., et al. "Multiparametric MRI and computational modelling in the assessment of human articular cartilage properties: a comprehensive approach." BioMed research international 2018.1 (2018): 9460456. PMID: 29862300 [pmid]

4. Linka, Kevin, et al. "Machine learning-augmented and microspectroscopy-informed multiparametric MRI for the non-invasive prediction of articular cartilage composition." Osteoarthritis and cartilage 29.4 (2021): 592-602. PMID: 33545330 [pmid]

5. Ma, Dan, et al. "Magnetic resonance fingerprinting." Nature 495.7440 (2013): 187-192. PMID: 23486058 [pmid]

6. 6. Kantola, Ville et al. Machine learning assisted prediction of cartilage proteoglycan content using MR fingerprinting. Proceedings of International Society of Magnetic Resonance in Medicine(2024).

7. 7. Wilson, Andrew Gordon, et al. "Deep kernel learning." Artificial intelligence and statistics. PMLR, 2016.

8. 8. Assländer, Jakob, et al. "Low rank alternating direction method of multipliers reconstruction for MR fingerprinting." Magnetic resonance in medicine 79.1 (2018): 83-96.

9. 9. Cloos, Martijn A., et al. "Rapid radial T1 and T2 mapping of the hip articular cartilage with magnetic resonance fingerprinting." Journal of Magnetic Resonance Imaging 50.3 (2019): 810-815. PMID: 28261851 [pmid]

10. 10. Rasmussen, Carl Edward and Williams, Christopher KI. Gaussian processes for machine learning. Vol. 2. No. 3. Cambridge, MA: MIT press, 2006.

11. 11. Chauhan, Nitin Kumar, and Krishna Singh. "A review on conventional machine learning vs deep learning." 2018 International conference on computing, power and communication technologies (GUCON). IEEE, 2018.

12. 12. Van der Wilk, Mark, Carl Edward Rasmussen, and James Hensman. "Convolutional Gaussian processes." Advances in neural information processing systems 30 (2017).

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/563-02-004