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Peper, E.

Publications and source records attributed to Peper, E..

2 recordsLinked to original sources

Improved 3D Radial Phyllotaxis Trajectories for Uniform Density Distribution of Readout Directions and Sequential Binning

PurposeTo develop 3D radial spiral phyllotaxis trajectories that provide a uniform density distribution of readout directions and support retrospective sequential binning, thereby reducing ringing artifacts and improving image quality. MethodsUPhy trajectory redefines the polar angle to achieve uniform density distribution of readout directions. FlexiPhy further decouples the azimuthal and polar ordering of interleaves through a randomized permutation, improving robustness to sequential binning. The proposed trajectories were evaluated in vivo on 10 healthy volunteers using two gradient-echo sequences on a 3T MRI scanner. Sequential temporal reconstructions were compared with reference reconstructions using structural similarity and relative L2 error metrics. ResultsUPhy presents analytically demonstrated uniform density distribution of readout directions. Quantitative analysis shows significantly higher SSIM values and lower relative L2 errors for FlexiPhy compared with both the original phyllotaxis and UPhy trajectories after Bonferroni correction (pcorrected < 0.05). ConclusionFlexiPhy enables more reliable sequential binning reconstructions by reducing trajectory-induced ringing artifacts and temporal inconsistencies. Moreover, its randomized construction is not tied to a specific binning strategy, making it broadly compatible with retrospective binning approaches used in dynamic and motion-resolved MRI.

bioinformatics↗

Monalisa: An Open Source, Documented, User-Friendly MATLAB Toolbox for Magnetic Resonance Imaging Reconstruction

PurposeAn 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. MethodsMonalisa 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 l1 (Compressed Sensing (CS)) and l2 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. ResultsResults of the comparison demonstrate competitive performance, yielding higher Structural Similarity Index (SSIM) and lower l2 error. Notably, Monalisa reconstructions exhibited fewer visible artifacts than BART. ConclusionBy 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.

bioinformatics↗