Students Projects

Bachelor and Master projects at MRISM Lab – SPRING 2026

Optimization and evaluation of a DL reconstruction model for 3D T1ρ/T2 mapping of the knee cartilage

Project description:

Background: Relaxometry on the transverse plane (T2) and in the rotating frame (T1ρ) play an important role in the study of brain pathologies as well as cartilage osteoarthritis. However, conventional spin-echo-based sequences for T2/ T1ρ mapping are limited to 2D or 3D-slab imaging with anisotropic resolutions due to high energy deposition and long acquisition time. In this project, we investigate magnetization preparation approaches (T2 or T1ρ) combined with fast gradient echo sequences that allow for volumetric and isotropic high-resolution mapping of the entire organ of interest.
Our current technique employs incoherent undersampled data acquisition and compressed sensing (CS) image reconstruction. To further reduce acquisition times and/or improve robustness and signal-to-noise, we have recently implemented a deep learning (DL) framework for reconstruction. The network has been trained on different gradient echo images of the brain and finetuned at different field strengths. This already showed improved SNR of knee T1rho maps at 3 T and promises to reduce acquisition times by few minutes, making the technique more suitable for clinical workflow.

Goals:

  • To characterize the performance of the current DL framework in comparison to CS in a standardized phantom (ISMRM-NIST) and in healthy subjects (e.g. linear regression and Bland-Altman analysis of T2/T1rho estimates).
  • To finetune the current DL model with QuantoRAGE images of knee and hip cartilage.
  • To characterize the finetuned DL model in comparison to current DL model and CS in phantom and healthy subjects.
  • (Optional) To investigate other accelerated acquisition strategies (GRAPPA/CAIPIRINHA) in combination with DL reconstruction.

The student will have the opportunity to:

  • Learn about CS and DL reconstructions
  • Learn about acceleration strategies for acquisition
  • Have access to source DL recon for finetuning
  • Acquire MR data in phantoms and in humans
  • Perform comparison analysis of different methods (statistics)

Requirements:

  • Knowledge of signal and image processing.
  • Basic knowledge of MR physics.
  • Basic knowledge of Machine Learning.
  • MATLAB, Python programming. C/C++ is an advantage.
  • Independent worker / Problem solving attitude
  • Good command of English.

Duration: 6 months

Supervisors:

Day-to-day supervision and location: Gabriele Bonanno, PhD  ([email protected]), Translational Imaging Center of sitem-insel, Bern

Academic supervisors: Jonathan Stelter, Prof. Dimitrios Karampinos

 

References:

  1. Bonanno G, et al. Program number 1475. Intl. Soc. Mag. Reson. Med. 29 (2021)
    https://cds.ismrm.org/protected/21MPresentations/abstracts/1475.html
  2. Forman C, et al. High-resolution 3D whole-heart coronary MRA: a study on the combination of data acquisition in multiple breath-holds and 1D residual respiratory motion compensation. Magn Reson Mater Phy 2014;27:435-443.
  3. Nezafat R et al., B1-Insensitive T2 Preparation for Improved Coronary Magnetic Resonance Angiography at 3 T. Magnetic Resonance in Medicine 55:858–864 (2006)
  4. Sharafi A, Xia D, Chang G, Regatte RR. Biexponential T1ρ relaxation mapping of human knee cartilagein vivoat 3 T. NMR Biomed. 2017;30(10):e3760. 10.1002/nbm.3760
  5. Wetzl J, Forman C, Wintersperger BJ, et al. High‐resolution dynamic CE‐MRA of the thorax enabled by iterative TWIST reconstruction. Magn Reson Med. 2017;77:833–
  6. Bathla, G. et al. Deep Learning–Based Reconstruction of 3D T1 SPACE Vessel Wall Imaging Provides Improved Image Quality with Reduced Scan Times: A Preliminary Study. J. Neuroradiol. (2024) doi:10.3174/ajnr.A8382.
  7. Yaman, Burhaneddin, Seyed Amir Hossein Hosseini, Steen Moeller, Jutta Ellermann, Kâmil Uğurbil, and Mehmet Akçakaya. “Self‐supervised learning of physics‐guided reconstruction neural networks without fully sampled reference data.” Magnetic resonance in medicine 84, no. 6 (2020): 3172-3191. 
  1. Batson, Joshua, and Loic Royer. “Noise2self: Blind denoising by self-supervision.” In International Conference on Machine Learning, pp. 524-533. PMLR, 2019.