Purpose To present a method that automatically, rapidly, and in a noniterative manner determines the regularization weighting for wavelet‐based compressed sensing reconstructions. This method determines level‐specific regularization weighting factors from the wavelet transform of the image obtained from zero‐filling in k‐space. Methods We compare reconstruction results obtained by our method, λauto, to the ones obtained by the L‐curve, λLcurve, and the minimum NMSE, λNMSE. The comparisons are done using in vivo data; then, simulations are used to analyze the impact of undersampling and noise. We use NMSE, Pearson’s correlation coefficient, high‐frequency error norm, and structural similarity as reconstruction quality indices. Results Our method, λauto, provides improved reconstructed image quality to that obtained by λLcurve regardless of undersampling or SNR and comparable quality to λNMSE at high SNR. The method determines the regularization weighting prospectively with negligible computational time. Conclusion Our main finding is an automatic, fast, noniterative, and robust procedure to determine the regularization weighting. The impact of this method is to enable prospective and tuning‐free wavelet‐based compressed sensing reconstructions.
Purpose To develop an open‐source, high‐performance, easy‐to‐use, extensible, cross‐platform, and general MRI simulation framework (Koma). Methods Koma was developed using the Julia programming language. Like other MRI simulators, it solves the Bloch equations with CPU and GPU parallelization. The inputs are the scanner parameters, the phantom, and the pulse sequence that is Pulseq‐compatible. The raw data is stored in the ISMRMRD format. For the reconstruction, MRIReco.jl is used. A graphical user interface utilizing web technologies was also designed. Two types of experiments were performed: one to compare the quality of the results and the execution speed, and the second to compare its usability. Finally, the use of Koma in quantitative imaging was demonstrated by simulating Magnetic Resonance Fingerprinting (MRF) acquisitions. Results Koma was compared to two well‐known open‐source MRI simulators, JEMRIS and MRiLab. Highly accurate results (with mean absolute differences below 0.1% compared to JEMRIS) and better GPU performance than MRiLab were demonstrated. In an experiment with students, Koma was proved to be easy to use, eight times faster on personal computers than JEMRIS, and 65% of test subjects recommended it. The potential for designing acquisition and reconstruction techniques was also shown through the simulation of MRF acquisitions, with conclusions that agree with the literature. Conclusions Koma's speed and flexibility have the potential to make simulations more accessible for education and research. Koma is expected to be used for designing and testing novel pulse sequences before implementing them in the scanner with Pulseq files, and for creating synthetic data to train machine learning models.
Time constraints placed on magnetic resonance imaging often restrict the application of advanced diffusion MRI (dMRI) protocols in clinical practice and in high throughput research studies. Therefore, acquisition strategies for accelerated dMRI have been investigated to allow for the collection of versatile and high quality imaging data, even if stringent scan time limits are imposed. Diffusion spectrum imaging (DSI), an advanced acquisition strategy that allows for a high resolution of intra‐voxel microstructure, can be sufficiently accelerated by means of compressed sensing (CS) theory. CS theory describes a framework for the efficient collection of fewer samples of a data set than conventionally required followed by robust reconstruction to recover the full data set from sparse measurements. For an accurate recovery of DSI data, a suitable acquisition scheme for sparse q‐space sampling and the sensing and sparsifying bases for CS reconstruction need to be selected. In this work we explore three different types of q‐space undersampling schemes and two frameworks for CS reconstruction based on either Fourier or SHORE basis functions. After CS recovery, diffusion and microstructural parameters and orientational information are estimated from the reconstructed data by means of state‐of‐the‐art processing techniques for dMRI analysis. By means of simulation, diffusion phantom and in vivo DSI data, an isotropic distribution of q‐space samples was found to be optimal for sparse DSI. The CS reconstruction results indicate superior performance of Fourier‐based CS‐DSI compared to the SHORE‐based approach. Based on these findings we outline an experimental design for accelerated DSI and robust CS reconstruction of the sparse measurements that is suitable for the application within time‐limited studies.
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