Berkin Bilgic

3D Magnetic Resonance Fingerprinting (MRF) with Hybrid Sliding-Window and GRAPPA Reconstruction

We are pleased to announce the release of our 3D MRF sequence and off-line reconstruction packages for fast quantitative imaging applications. This package is developed by research groups at Stanford University, Martinos Center for Biomedical Imaging / MGH, and Center for Brain Imaging Science and Technology (CBIST) at Zhejiang University (ZJU).

Pulse sequence for 3D MRF is only available for the Siemens Prisma and Skyra Scanners with VE11C/E platforms. The off-line reconstruction script is based on MATLAB 2014b or later versions.

To request the software, instructions and example protocols, please navigate to


Magnetic Resonance Fingerprinting (MRF) (1) has shown great potential for efficient multi-parameter mapping. 3D-MRF acquisitions (2–6) enjoy an SNR efficiency benefit over their 2D counterparts, and could help achieve high SNR at high resolutions. However, high resolution imaging with whole-brain coverage can lead to lengthy scans which, in turn, increases motion sensitivity. To accelerate 3D-MRF, our work (6) combines stack-of-spiral acquisition with hybrid sliding-window (7) and GRAPPA (8) (SW+GRAPPA) reconstruction (Fig.1). This enables >10x acceleration through

  • 3-fold acceleration in kz encoding and
  • 3.6-fold reduction in the number of TRs for pattern matching.



3D FISP sequence (9) was implemented for MRF. The partition-by-partition sampled sequence that also incorporates a low-flip-angle training data acquisition into the wait period. The total acquisition time for each partition is 8 seconds for a 420 time-points (tps) acquisition.

SW+GRAPPA recon:

3D coil sensitivity profiles were estimated from the center fully-sampled k-space region of the training data using ESPIRiT (10-12). SW and NUFFT (13-15) were used to remove in-plane aliasing and create a Cartesian dataset that is fully sampled in-plane. This then allows for a direct application of Cartesian GRAPPA reconstruction to overcome Rz acceleration. More details can be found in (6).


Dictionary generation and pattern recognition:

The dictionary was generated by 2-step extended phase graph (EPG) (16) simulation using variable TRs and FAs. The effect of the low-flip-angle GRAPPA training acquisitions and the T1 recovery during the waiting period between each partition were also included in the dictionary generation process. The SW+GRAPPA reconstructed 3D volumes were then normalized and pattern matched voxel-wise to the corresponding dictionary using the maximum inner product method to obtain T1 and T2 maps.



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Thanks to Congyu Liao (Stanford), Xiaozhi Cao (Stanford), Mary Kate Manhard (Cincinnati Children's), Bo Zhao (UT Austin), Jianhui Zhong (ZJU), Lawrence Wald (MGH), and Kawin Setsompop (Stanford) for their efforts in developing this software.