Abstract
In this study, a fast data-driven optimization approach, named bias-accelerated subset selection (BASS), is proposed for learning efficacious sampling patterns (SPs) with the purpose of reducing scan time in large-dimensional parallel MRI. BASS is applicable when Cartesian fully-sampled k-space measurements of specific anatomy are available for training and the reconstruction method for undersampled measurements is specified; such information is used to define the efficacy of any SP for recovering the values at the non-sampled k-space points. BASS produces a sequence of SPs with the aim of finding one of a specified size with (near) optimal efficacy. BASS was tested with five reconstruction methods for parallel MRI based on low-rankness and sparsity that allow a free choice of the SP. Three datasets were used for testing, two of high-resolution brain images (text {T}_{2}-weighted images and, respectively, text {T}_{1rho }-weighted images) and another of knee images for quantitative mapping of the cartilage. The proposed approach has low computational cost and fast convergence; in the tested cases it obtained SPs up to 50 times faster than the currently best greedy approach. Reconstruction quality increased by up to 45% over that provided by variable density and Poisson disk SPs, for the same scan time. Optionally, the scan time can be nearly halved without loss of reconstruction quality. Quantitative MRI and prospective accelerated MRI results show improvements. Compared with greedy approaches, BASS rapidly learns effective SPs for various reconstruction methods, using larger SPs and larger datasets; enabling better selection of sampling-reconstruction pairs for specific MRI problems.
Highlights
Background and purposeFast magnetic resonance (MR) pulse sequences for measurements acquisition[1,2,14], parallel imaging (PI) using multichannel receive radio frequency a rrays[15,16,17], and C S3–6 are examples of advancements towards rapid MRI
bias-accelerated subset selection (BASS) and Pareto optimization algorithm for subset selection (POSS) can go on minimizing the cost function beyond the stopping point of LB-L finding even better sampling patterns (SPs)
It is important to clarify that the results shown for variable density, Poisson disk, combined variable density and Poisson disk, and adaptive SP are the best obtained among a parameter optimization process spending 50 epochs
Summary
Background and purposeFast magnetic resonance (MR) pulse sequences for measurements acquisition[1,2,14], parallel imaging (PI) using multichannel receive radio frequency a rrays[15,16,17], and C S3–6 are examples of advancements towards rapid MRI. PI uses multiple receivers with different spatial coil sensitivities to capture samples in p arallel[18], increasing the amount of measurements in the same scan time. CS relies on incoherent sampling and sparse reconstruction. The sparse signals spread almost uniformly in the sampling domain, and random-like patterns can be used to undersample the k-space[3,4,5,19,20]. Successful reconstructions with undersampled measurements, such as PI and CS, use prior knowledge about the true signal to remove the artifacts of undersampling, preserving most of the desired signal. Deep learning-based reconstructions have shown that undersampling artifacts can be separated from true signals by learning the parameters of a neural network from sampled d atasets[23,25,26]
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