We offer free open-source tools to support imaging science and foster innovation.

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Magnetic Resonance imaging Lab (MRiLab)

MRiLab is a numerical MRI simulation package developed and optimized to simulate MR signal formation, k-space acquisition, and MR image reconstruction. MRiLab simulation platform combined with various toolboxes can be applied to customize virtual MR experiments, which can be a prior stage for prototyping and testing new MR techniques and applications.

Liu F, Velikina JV, Block W, Kijowski R, Samsonov A: Fast Realistic MRI Simulations Based on Generalized Multi-Pool Exchange Tissue Model. IEEE Trans Med Imaging, 2017; 36:527-37.
Matrix User (MatrixUser)

MatrixUser is a software package designed and optimized for manipulating multi-dimensional image data. MatrixUser provides a user-friendly graphical environment for multi-dimensional image display, matrix (image stack) processing, and volume rendering. This lightweight software is a great tool for researchers who use Matlab for image processing and analysis.

Liu F, Samsonov A, Wilson J, Blankenbaker D, Block W, Kijowski R: Rapid In Vivo Multi-Component T2 Mapping of Human Knee Menisci. J Magn Reson Imaging. 2015; 42(5): 1321-8.
Generalized Physics-Guided Self-Supervised Learning for RF (GPS-RF)

GPS-RF is a comprehensive framework designed for creating MRI RF pulses using a physics-guided, self-supervised learning approach. This algorithm can generate RF pulses that meet flexible design requirements by utilizing a physics module, the Bloch equation, to guide the learning and optimization process.

Jang A, He X, Liu F: Physics-Guided Self-Supervised Learning: Demonstration for Generalized RF Pulse Design. Magn Reson Med. 2024, DOI: 10.1002/mrm.30307
POSition Encoding for improved qMRI (POSE)

    POSE is a technique that uses subvoxel shifting as a source of encoding for accelerating quantitative MRI. The POSE framework applies unique subvoxel shifts along the acquisition parameter dimension, thereby creating an extra source of encoding. Combining with a biophysical signal model of interest, accelerated and enhanced resolution maps of biophysical parameters are obtained. (coming soon)

    Jang A, Liu F: POSE: POSition Encoding for accelerated quantitative MRI. Magn Reson Imaging. 2024, DOI: 10.1016/j.mri.2024.110239