PurposeThe expanded encoding model incorporates spatially- and time-varying field perturbations for correction during reconstruction. To date, these reconstructions have used the conjugate gradient method with early s...
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PurposeThe expanded encoding model incorporates spatially- and time-varying field perturbations for correction during reconstruction. To date, these reconstructions have used the conjugate gradient method with early stopping used as implicit regularization. However, this approach is likely suboptimal for low-SNR cases like diffusion or high-resolution MRI. Here, we investigate the extent that l1$$ {\ell}_1 $$-wavelet regularization, or equivalently compressed sensing (CS), combined with expandedencoding improves trade-offs between spatial resolution, readout time and SNR for single-shot spiral DWI at 7T. The reconstructions were performed using our open-source graphics processing unit-enabled reconstruction toolbox, "MatMRI," that allows inclusion of the different components of the expanded encoding model, with or without CS. MethodsIn vivo accelerated single-shot spirals were acquired with five acceleration factors (R) (2x-6x) and three in-plane spatial resolutions (1.5, 1.3, and 1.1 mm). From the in vivo reconstructions, we estimated diffusion tensors and computed fractional anisotropy maps. Then, simulations were used to quantitatively investigate and validate the impact of CS-based regularization on image quality when compared to a known ground truth. ResultsIn vivo reconstructions revealed improved image quality with retainment of small features when CS was used. Simulations showed that the joint use of the expanded encoding model and CS improves accuracy of image reconstructions (reduced mean-squared error) over the range of R investigated. ConclusionThe expanded encoding model and CS regularization are complementary tools for single-shot spiral diffusion MRI, which enables both higher spatial resolutions and higher R.
Developments in magnetic resonance imaging (MRI) in the last decades show a trend towards a growing number of array coils and an increasing use of a wide variety of sensors. Associated cabling and safety issues have b...
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Developments in magnetic resonance imaging (MRI) in the last decades show a trend towards a growing number of array coils and an increasing use of a wide variety of sensors. Associated cabling and safety issues have been addressed by moving data acquisition closer to the coil. However, with the increasing number of radio-frequency (RF) channels and trend towards higher acquisition duty-cycles, the data amount is growing, which poses challenges for throughput and data handling. As it is becoming a limitation, early compression and preprocessing is becoming ever more important. Additionally, sensors deliver diverse data, which require distinct and often low-latency processing for run-time updates of scanner operation. To address these challenges, we propose the transition to reconfigurable hardware with an application tailored assembly of interfaces and real-time processing resources. We present an integrated solution based on a system-on-chip (SoC), which offers sufficient throughput and hardware-based parallel processing power for very challenging applications. It is equipped with fiber-optical modules serving as versatile interfaces for modular systems with in-field operation. We demonstrate the utility of the platform on the example of concurrent imaging and field sensing with hardware-based coil compression and trajectory extraction. The preprocessed data are then used in expanded encoding model based image reconstruction of single-shot and segmented spirals as used in time-series and anatomical imaging respectively.
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