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Image reconstruction is reformulated using a data-driven, supervised machine learning framework that allows a mapping between sensor and image domains to emerge from even noisy and undersampled data, improving accuracy and reducing image artefacts. Machine learning improves image reconstruction Reconstructing images from data, whether for medical or astronomical purposes, hinges on well-defined steps. The data sensor encodes an intermediate representation of the observed object, which is converted into an image by a mathematical operation known as the inversion of the encoding function. This inversion is often plagued by sensor imperfections and noise, requiring extra technique-specific steps to correct them. Here, Matthew Rosen and colleagues present a more unified framework termed 'automated transform by manifold approximation' (AUTOMAP). AUTOMAP tackles image reconstruction as a supervised learning task, which uses appropriate training data to link the sensor data to the output image. The authors implemented AUTOMAP with a deep neural network and tested its flexibility in learning how to reconstruct images for various magnetic resonance imaging acquisition strategies. AUTOMAP reduced artefacts and improved accuracy in images reconstructed from noisy and undersampled acquisitions. The authors expect their framework to apply to other imaging methods. Image reconstruction is essential for imaging applications across the physical and life sciences, including optical and radar systems, magnetic resonance imaging, X-ray computed tomography, positron emission tomography, ultrasound imaging and radio astronomy 1 , 2 , 3 . During image acquisition, the sensor encodes an intermediate representation of an object in the sensor domain, which is subsequently reconstructed into an image by an inversion of the encoding function. Image reconstruction is challenging because analytic knowledge of the exact inverse transform may not exist a priori , especially in the presence of sensor non-idealities and noise. Thus, the standard reconstruction approach involves approximating the inverse function with multiple ad hoc stages in a signal processing chain 4 , 5 , the composition of which depends on the details of each acquisition strategy, and often requires expert parameter tuning to optimize reconstruction performance. Here we present a unified framework for image reconstruction—automated transform by manifold approximation (AUTOMAP)—which recasts image reconstruction as a data-driven supervised learning task that allows a mapping between the sensor and the image domain to emerge from an appropriate corpus of training data. We implement AUTOMAP with a deep neural network and exhibit its flexibility in learning reconstruction transforms for various magnetic resonance imaging acquisition strategies, using the same network architecture and hyperparameters. We further demonstrate that manifold learning during training results in sparse representations of domain transforms along low-dimensional data manifolds, and observe superior immunity to noise and a reduction in reconstruction artefacts compared with conventional handcrafted reconstruction methods. In addition to improving the reconstruction performance of existing acquisition methodologies, we anticipate that AUTOMAP and other learned reconstruction approaches will accelerate the development of new acquisition strategies across imaging modalities.

Access to scanners for magnetic resonance imaging (MRI) is typically limited by cost and by infrastructure requirements. Here, we report the design and testing of a portable prototype scanner for brain MRI that uses a compact and lightweight permanent rare-earth magnet with a built-in readout field gradient. The 122-kg low-field (80 mT) magnet has a Halbach cylinder design that results in a minimal stray field and requires neither cryogenics nor external power. The built-in magnetic field gradient reduces the reliance on high-power gradient drivers, lowering the overall requirements for power and cooling, and reducing acoustic noise. Imperfections in the encoding fields are mitigated with a generalized iterative image reconstruction technique that leverages previous characterization of the field patterns. In healthy adult volunteers, the scanner can generate T1-weighted, T2-weighted and proton density-weighted brain images with a spatial resolution of 2.2 × 1.3 × 6.8 mm 3 . Future versions of the scanner could improve the accessibility of brain MRI at the point of care, particularly for critically ill patients. A portable prototype scanner for brain MRI that uses a compact and lightweight permanent rare-earth magnet with a built-in readout field gradient generates clinically relevant images of the brain, as shown in adult volunteers.

This project aims to characterize the impact of underlying noise distributions on diffusion-weighted imaging. The noise floor is a well-known problem for traditional magnitude-based diffusion-weighted MRI (dMRI) data, leading to biased diffusion model fits and inaccurate signal averaging. Here, we introduce a total-variation-based algorithm to eliminate shot-to-shot phase variations of complex-valued diffusion data with the intention to extract real-valued dMRI datasets. The obtained real-valued diffusion data are no longer superimposed by a noise floor but instead by a zero-mean Gaussian noise distribution, yielding dMRI data without signal bias. We acquired high-resolution dMRI data with strong diffusion weighting and, thus, low signal-to-noise ratio. Both the extracted real-valued and traditional magnitude data were compared regarding signal averaging, diffusion model fitting and accuracy in resolving crossing fibers. Our results clearly indicate that real-valued diffusion data enables idealized conditions for signal averaging. Furthermore, the proposed method enables unbiased use of widely employed linear least squares estimators for model fitting and demonstrates an increased sensitivity to detect secondary fiber directions with reduced angular error. The use of phase-corrected, real-valued data for dMRI will therefore help to clear the way for more detailed and accurate studies of white matter microstructure and structural connectivity on a fine scale. •We implemented a method to overcome the noise bias in dMRI.•Real-valued dMRI data are overlaid with Gaussian noise.•Real dMRI enables unbiased signal averaging and linear least squares model fits.•Increased diffusion-contrast and sensitivity to crossing fibers•More accurate fiber tracking results with reduced angular error

Objectives High-resolution post-contrast T1-weighted imaging is a workhorse sequence in the evaluation of neurological disorders. The T1-MPRAGE sequence has been widely adopted for the visualization of enhancing pathology in the brain. However, this three-dimensional (3D) acquisition is lengthy and prone to motion artifact, which often compromises diagnostic quality. The goal of this study was to compare a highly accelerated wave-controlled aliasing in parallel imaging (CAIPI) post-contrast 3D T1-MPRAGE sequence (Wave-T1-MPRAGE) with the standard 3D T1-MPRAGE sequence for visualizing enhancing lesions in brain imaging at 3 T. Methods This study included 80 patients undergoing contrast-enhanced brain MRI. The participants were scanned with a standard post-contrast T1-MPRAGE sequence (acceleration factor [R] = 2 using GRAPPA parallel imaging technique, acquisition time [TA] = 5 min 18 s) and a prototype post-contrast Wave-T1-MPRAGE sequence (R = 4, TA = 2 min 32 s). Two neuroradiologists performed a head-to-head evaluation of both sequences and rated the visualization of enhancement, sharpness, noise, motion artifacts, and overall diagnostic quality. A 15% noninferiority margin was used to test whether post-contrast Wave-T1-MPRAGE was noninferior to standard T1-MPRAGE. Inter-rater and intra-rater agreement were calculated. Quantitative assessment of CNR/SNR was performed. Results Wave-T1-MPRAGE was noninferior to standard T1-MPRAGE for delineating enhancing lesions with unanimous agreement in all cases between raters. Wave-T1-MPRAGE was noninferior in the perception of noise ( p < 0.001), motion artifact ( p < 0.001), and overall diagnostic quality ( p < 0.001). Conclusion High-accelerated post-contrast Wave-T1-MPRAGE enabled a two-fold reduction in acquisition time compared to the standard sequence with comparable performance for visualization of enhancing pathology and equivalent perception of noise, motion artifacts and overall diagnostic quality without loss of clinically important information. Key Points • Post-contrast wave-controlled aliasing in parallel imaging (CAIPI) T1-MPRAGE accelerated the acquisition of three-dimensional (3D) high-resolution post-contrast images by more than two-fold. • Post-contrast Wave-T1-MPRAGE was noninferior to standard T1-MPRAGE with unanimous agreement between reviewers (100% in 80 cases) for the visualization of intracranial enhancing lesions. • Wave-T1-MPRAGE was equivalent to the standard sequence in the perception of noise in 94% (75 of 80) of cases and was preferred in 16% (13 of 80) of cases for decreased motion artifact.

Objectives Wave-CAIPI (Controlled Aliasing in Parallel Imaging) enables dramatic reduction in acquisition time of 3D MRI sequences such as 3D susceptibility-weighted imaging (SWI) but has not been clinically evaluated at 1.5 T. We sought to compare highly accelerated Wave-CAIPI SWI (Wave-SWI) with two alternative standard sequences, conventional three-dimensional SWI and two-dimensional T2*-weighted Gradient-Echo (T2*w-GRE), in patients undergoing routine brain MRI at 1.5 T. Methods In this study, 172 patients undergoing 1.5 T brain MRI were scanned with a more commonly used susceptibility sequence (standard SWI or T2*w-GRE) and a highly accelerated Wave-SWI sequence. Two radiologists blinded to the acquisition technique scored each sequence for visualization of pathology, motion and signal dropout artifacts, image noise, visualization of normal anatomy (vessels and basal ganglia mineralization), and overall diagnostic quality. Superiority testing was performed to compare Wave-SWI to T2*w-GRE, and non-inferiority testing with 15% margin was performed to compare Wave-SWI to standard SWI. Results Wave-SWI performed superior in terms of visualization of pathology, signal dropout artifacts, visualization of normal anatomy, and overall image quality when compared to T2*w-GRE (all p < 0.001). Wave-SWI was non-inferior to standard SWI for visualization of normal anatomy and pathology, signal dropout artifacts, and overall image quality (all p < 0.001). Wave-SWI was superior to standard SWI for motion artifact ( p < 0.001), while both conventional susceptibility sequences were superior to Wave-SWI for image noise ( p < 0.001). Conclusions Wave-SWI can be performed in a 1.5 T clinical setting with robust performance and preservation of diagnostic quality. Key Points • Wave-SWI accelerated the acquisition of 3D high-resolution susceptibility images in 70% of the acquisition time of the conventional T2*GRE. • Wave-SWI performed superior to T2*w-GRE for visualization of pathology, signal dropout artifacts, and overall diagnostic image quality. • Wave-SWI was noninferior to standard SWI for visualization of normal anatomy and pathology, signal dropout artifacts, and overall diagnostic image quality.

BackgroundFast magnetic resonance imaging (MRI) sequences are advantageous in pediatric imaging as they can lessen child discomfort, decrease motion artifact and improve scanner availability.ObjectiveTo evaluate the feasibility of an ultrafast wave-CAIPI (controlled aliasing in parallel imaging) MP-RAGE (magnetization-prepared rapid gradient echo) sequence for brain imaging of awake pediatric patients.Materials and methodsEach MRI included a standard MP-RAGE sequence and an ultrafast wave-MP-RAGE sequence. Two neuroradiologists evaluated both sequences in terms of artifacts, noise, anatomical contrast and pathological contrast. A predefined 5-point scale was used by two independent pediatric neuroradiologists. A Wilcoxon signed-rank test was used to evaluate the difference between sequences for each variable.ResultsTwenty-four patients (14 males; mean age: 11.5±4.5 years, range: 1 month to 17.8 years) were included. Wave-CAIPI MP-RAGE provided a 77% reduction in scan time using a 32-channel coil and a 70% reduction using a 20-channel coil. Visualization of the pathology, artifacts and pathological enhancement (including parenchymal, leptomeningeal and dural enhancement) was not significantly different between standard MP-RAGE and wave-CAIPI MP-RAGE (all P>0.05). For central (P<0.001) and peripheral (P<0.001) noise, and the evaluation of the anatomical structures (P<0.001), the observers favored standard MP-RAGE over wave-CAIPI MP-RAGE.ConclusionUltrafast brain imaging with wave-CAIPI MP-RAGE is feasible in awake pediatric patients, providing a substantial reduction in scan time at a cost of subjectively increased image noise.

BackgroundSusceptibility-weighted imaging (SWI) is highly sensitive for intracranial hemorrhagic and mineralized lesions but is associated with long scan times. Wave controlled aliasing in parallel imaging (Wave-CAIPI) enables greater acceleration factors and might facilitate broader application of SWI, especially in motion-prone populations.ObjectiveTo compare highly accelerated Wave-CAIPI SWI to standard SWI in the non-sedated pediatric outpatient setting, with respect to the following variables: estimated scan time, image noise, artifacts, visualization of normal anatomy and visualization of pathology.Materials and methodsTwenty-eight children (11 girls, 17 boys; mean age ± standard deviation [SD] = 128.3±62 months) underwent 3-tesla (T) brain MRI, including standard three-dimensional (3-D) SWI sequence followed by a highly accelerated Wave-CAIPI SWI sequence for each subject. We rated all studies using a predefined 5-point scale and used the Wilcoxon signed rank test to assess the difference for each variable between sequences.ResultsWave-CAIPI SWI provided a 78% and 67% reduction in estimated scan time using the 32- and 20-channel coils, respectively, corresponding to estimated scan time reductions of 3.5 min and 3 min, respectively. All 28 children were imaged without anesthesia. Inter-reader agreement ranged from fair to substantial (k=0.67 for evaluation of pathology, 0.55 for anatomical contrast, 0.3 for central noise, and 0.71 for artifacts). Image noise was rated higher in the central brain with wave SWI (P<0.01), but not in the peripheral brain. There was no significant difference in the visualization of normal anatomical structures and visualization of pathology between the standard and wave SWI sequences (P=0.77 and P=0.79, respectively).ConclusionHighly accelerated Wave-CAIPI SWI of the brain can provide similar image quality to standard SWI, with estimated scan time reduction of 3–3.5 min depending on the radiofrequency coil used, with fewer motion artifacts, at a cost of mild but perceptibly increased noise in the central brain.

In this paper we draw on two stochastic optimization techniques, Simulated Annealing and Genetic Algorithm (SAGA), to create a hybrid to determine the optimal design of nonlinear Simulated Moving Bed (SMB) systems. A mathematical programming model based on the Standing Wave Design (SWD) offers a significant advantage in optimizing SMB systems. SAGA builds upon the strength of SA and GA to optimize the 16 variables of the mixed-integer nonlinear programming model for single- and multi-objective optimizations. The SAGA procedure is shown to be robust with computational time in minutes for single-objective optimization and in a few hours for a multi-objective optimization, which is comprised of more than one hundred points.

Access to scanners for magnetic resonance imaging (MRI) is typically limited by cost and by infrastructure requirements. Here, we report the design and testing of a portable prototype scanner for brain MRI that uses a compact and lightweight permanent rare-earth magnet with a built-in readout field gradient. The 122-kg low-field (80 mT) magnet uses has a Halbach-cylinder design that results in minimal stray field and requires neither cryogenics nor external power. The built-in magnetic-field gradient reduces the reliance on high-power gradient drivers, lowering the overall requirements for power and cooling, and reducing acoustic noise. Imperfections in the encoding fields are mitigated with a generalized iterative image-reconstruction technique that leverages prior characterization of the field patterns. In healthy adult volunteers, the scanner can generate T1-weighted, T2-weighted and proton-density-weighted brain images with a spatial resolution of 2.2 × 1.3 × 6.8 mm3. Future versions of the scanner could improve the accessibility of brain MRI at the point of care, particularly for critically ill patients.

T2-Shuffling reconstructs multiple sharp T2-weighted images from a single volumetric fast spin-echo (3D-FSE) scan. Wave-CAIPI is a parallel imaging technique that achieves good reconstruction at high accelerations through additional sinusoidal gradients that induce a voxel spreading effect in the readout direction to better take advantage of coil-sensitivity information. In this work, the Shuffling model in T2-Shuffling is augmented with wave-encoding to achieve higher acceleration capability. The resulting \"Wave-Shuffling\" approach is applied to 3D-FSE and Magnetization-Prepared Rapid Gradient-Echo (MPRAGE) to achieve rapid, 1 mm-isotropic resolution, time-resolved structural imaging.