Monalisa: an open source, documented MATLAB toolbox for magnetic resonance imaging reconstruction
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Purpose
An open-source, user-friendly MATLAB framework for Magnetic Resonance Imaging (MRI) reconstruction was developed to simplify the reconstruction process, with a specific focus on non-Cartesian imaging and dynamic applications in the presence of motion.
Methods
Monalisa is decomposing the reconstruction pipeline into clear modular steps, including raw data reading with flexible file-type abstraction, trajectory computation, density compensation, advanced coil sensitivity mapping, and tailored binning strategies through its “mitosius” preprocessing stage. The framework supports a suite of reconstruction methods, including iterative-SENSE (also named CG-SENSE), GeneRalized Autocalibrating Partial Parallel Acquisition (GRAPPA) reconstructions, and regularized reconstructions supporting both spatial and temporal regularization using l 1 (Compressed Sensing (CS)) and l 2 techniques, accommodating both Cartesian and non-Cartesian acquisitions. We performed benchmark experiments comparing Monalisa with the Berkeley Advanced Reconstruction Toolbox (BART) toolbox on simulated 2D radial acquisitions.
Results
Results of the comparison demonstrate competitive performance, yielding higher Structural Similarity Index (SSIM) and lower l 2 error. Notably, Monalisa reconstructions exhibited fewer visible artifacts than BART.
Conclusion
By providing comprehensive documentation, Monalisa serves not only as a powerful tool for research and clinical imaging but also as an educational platform to facilitate innovation in MRI reconstruction.
