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Purpose: Parallel imaging and compressed sensing reconstructions of large MRI datasets often have a prohibitive computational cost that bottlenecks clinical deployment, especially for three‐dimensional (3D) non‐Cartesian acquisitions. One common approach is to reduce the number of coil channels actively used during reconstruction as in coil compression. While effective for Cartesian imaging, coil compression inherently loses signal energy, producing shading artifacts that compromise image quality for 3D non‐Cartesian imaging. We propose coil sketching, a general and versatile method for computationally‐efficient iterative MR image reconstruction. Theory and Methods: We based our method on randomized sketching algorithms, a type of large‐scale optimization algorithms well established in the fields of machine learning and big data analysis. We adapt the sketching theory to the MRI reconstruction problem via a structured sketching matrix that, similar to coil compression, considers high‐energy virtual coils obtained from principal component analysis. But, unlike coil compression, it also considers random linear combinations of the remaining low‐energy coils, effectively leveraging information from all coils. Results: First, we performed ablation experiments to validate the sketching matrix design on both Cartesian and non‐Cartesian datasets. The resulting design yielded both improved computatioanal efficiency and preserved signal‐to‐noise ratio (SNR) as measured by the inverse g‐factor. Then, we verified the efficacy of our approach on high‐dimensional non‐Cartesian 3D cones datasets, where coil sketching yielded up to three‐fold faster reconstructions with equivalent image quality. Conclusion: Coil sketching is a general and versatile reconstruction framework for computationally fast and memory‐efficient reconstruction. [ABSTRACT FROM AUTHOR]

Purpose: To develop a method for building MRI reconstruction neural networks robust to changes in signal‐to‐noise ratio (SNR) and trainable with a limited number of fully sampled scans. Methods: We propose Noise2Recon, a consistency training method for SNR‐robust accelerated MRI reconstruction that can use both fully sampled (labeled) and undersampled (unlabeled) scans. Noise2Recon uses unlabeled data by enforcing consistency between model reconstructions of undersampled scans and their noise‐augmented counterparts. Noise2Recon was compared to compressed sensing and both supervised and self‐supervised deep learning baselines. Experiments were conducted using retrospectively accelerated data from the mridata three‐dimensional fast‐spin‐echo knee and two‐dimensional fastMRI brain datasets. All methods were evaluated in label‐limited settings and among out‐of‐distribution (OOD) shifts, including changes in SNR, acceleration factors, and datasets. An extensive ablation study was conducted to characterize the sensitivity of Noise2Recon to hyperparameter choices. Results: In label‐limited settings, Noise2Recon achieved better structural similarity, peak signal‐to‐noise ratio, and normalized‐RMS error than all baselines and matched performance of supervised models, which were trained with 14×$$ 14\times $$ more fully sampled scans. Noise2Recon outperformed all baselines, including state‐of‐the‐art fine‐tuning and augmentation techniques, among low‐SNR scans and when generalizing to OOD acceleration factors. Augmentation extent and loss weighting hyperparameters had negligible impact on Noise2Recon compared to supervised methods, which may indicate increased training stability. Conclusion: Noise2Recon is a label‐efficient reconstruction method that is robust to distribution shifts, such as changes in SNR, acceleration factors, and others, with limited or no fully sampled training data. [ABSTRACT FROM AUTHOR]

Purpose: To develop and validate a deep learning‐based reconstruction framework for highly accelerated two‐dimensional (2D) phase contrast (PC‐MRI) data with accurate and precise quantitative measurements. Methods: We propose a modified DL‐ESPIRiT reconstruction framework for 2D PC‐MRI, comprised of an unrolled neural network architecture with a Complex Difference estimation (CD‐DL). CD‐DL was trained on 155 fully sampled 2D PC‐MRI pediatric clinical datasets. The fully sampled data (n=29$$ n=29 $$) was retrospectively undersampled (6–11×$$ \times $$) and reconstructed using CD‐DL and a parallel imaging and compressed sensing method (PICS). Measurements of peak velocity and total flow were compared to determine the highest acceleration rate that provided accuracy and precision within ±5%$$ \pm 5\% $$. Feasibility of CD‐DL was demonstrated on prospectively undersampled datasets acquired in pediatric clinical patients (n=5$$ n=5 $$) and compared to traditional parallel imaging (PI) and PICS. Results: The retrospective evaluation showed that 9×$$ \times $$ accelerated 2D PC‐MRI images reconstructed with CD‐DL provided accuracy and precision (bias, [95%$$ \% $$ confidence intervals]) within ±5%$$ \pm 5\% $$. CD‐DL showed higher accuracy and precision compared to PICS for measurements of peak velocity (2.8%$$ \% $$ [−2.9$$ -2.9 $$, 4.5] vs. 3.9%$$ \% $$ [−11.0$$ -11.0 $$, 4.9]) and total flow (1.8%$$ \% $$ [−3.9$$ -3.9 $$, 3.4] vs. 2.9%$$ \% $$ [−7.1$$ -7.1 $$, 6.9]). The prospective feasibility study showed that CD‐DL provided higher accuracy and precision than PICS for measurements of peak velocity and total flow. Conclusion: In a retrospective evaluation, CD‐DL produced quantitative measurements of 2D PC‐MRI peak velocity and total flow with ≤5%$$ \le 5\% $$ error in both accuracy and precision for up to 9×$$ \times $$ acceleration. Clinical feasibility was demonstrated using a prospective clinical deployment of our 8×$$ \times $$ undersampled acquisition and CD‐DL reconstruction in a cohort of pediatric patients. [ABSTRACT FROM AUTHOR]

Artificial intelligence algorithms based on principles of deep learning (DL) have made a large impact on the acquisition, reconstruction, and interpretation of MRI data. Despite the large number of retrospective studies using DL, there are fewer applications of DL in the clinic on a routine basis. To address this large translational gap, we review the recent publications to determine three major use cases that DL can have in MRI, namely, that of model‐free image synthesis, model‐based image reconstruction, and image or pixel‐level classification. For each of these three areas, we provide a framework for important considerations that consist of appropriate model training paradigms, evaluation of model robustness, downstream clinical utility, opportunities for future advances, as well recommendations for best current practices. We draw inspiration for this framework from advances in computer vision in natural imaging as well as additional healthcare fields. We further emphasize the need for reproducibility of research studies through the sharing of datasets and software. Level of Evidence: 5 Technical Efficacy Stage: 2 [ABSTRACT FROM AUTHOR]

The image quality limitations of echo‐planar diffusion‐weighted imaging (DWI) are an obstacle to its widespread adoption in the breast. Steady‐state DWI is an alternative DWI method with more robust image quality but its contrast for imaging breast cancer is not well‐understood. The aim of this study was to develop and evaluate diffusion‐weighted double‐echo steady‐state imaging with a three‐dimensional cones trajectory (DW‐DESS‐Cones) as an alternative to conventional DWI for non‐contrast‐enhanced MRI in the breast. This prospective study included 28 women undergoing clinically indicated breast MRI and six asymptomatic volunteers. In vivo studies were performed at 3 T and included DW‐DESS‐Cones, DW‐DESS‐Cartesian, DWI, and CE‐MRI acquisitions. Phantom experiments (diffusion phantom, High Precision Devices) and simulations were performed to establish framework for contrast of DW‐DESS‐Cones in comparison to DWI in the breast. Motion artifacts of DW‐DESS‐Cones were measured with artifact‐to‐noise ratio in volunteers and patients. Lesion‐to‐fibroglandular tissue signal ratios were measured, lesions were categorized as hyperintense or hypointense, and an image quality observer study was performed in DW‐DESS‐Cones and DWI in patients. Effect of DW‐DESS‐Cones method on motion artifacts was tested by mixed‐effects generalized linear model. Effect of DW‐DESS‐Cones on signal in phantom was tested by quadratic regression. Correlation was calculated between DW‐DESS‐Cones and DWI lesion‐to‐fibroglandular tissue signal ratios. Inter‐observer agreement was assessed with Gwet's AC. Simulations predicted hyperintensity of lesions with DW‐DESS‐Cones but at a 3% to 67% lower degree than with DWI. Motion artifacts were reduced with DW‐DESS‐Cones versus DW‐DESS‐Cartesian (p < 0.05). Lesion‐to‐fibroglandular tissue signal ratios were not correlated between DW‐DESS‐Cones and DWI (r = 0.25, p = 0.38). Concordant hyperintensity/hypointensity was observed between DW‐DESS‐Cones and DWI in 11/14 lesions. DW‐DESS‐Cones improved sharpness, distortion, and overall image quality versus DWI. DW‐DESS‐Cones may be able to eliminate motion artifacts in the breast allowing for investigation of higher degrees of steady‐state diffusion weighting. Malignant breast lesions in DW‐DESS‐Cones demonstrated hyperintensity with respect to surrounding tissue without an injection of contrast. Level of Evidence: 2. Technical Efficacy Stage: 1. [ABSTRACT FROM AUTHOR]

Purpose: To enable motion‐robust, ungated, free‐breathing R2∗ mapping of hepatic iron overload in children with 3D multi‐echo UTE cones MRI. Methods: A golden‐ratio re‐ordered 3D multi‐echo UTE cones acquisition was developed with chemical‐shift encoding (CSE). Multi‐echo complex‐valued source images were reconstructed via gridding and coil combination, followed by confounder‐corrected R2∗ (=1/T2∗) mapping. A phantom containing 15 different concentrations of gadolinium solution (0–300 mM) was imaged at 3T. 3D multi‐echo UTE cones with an initial TE of 0.036 ms and Cartesian CSE‐MRI (IDEAL‐IQ) sequences were performed. With institutional review board approval, 85 subjects (81 pediatric patients with iron overload + 4 healthy volunteers) were imaged at 3T using 3D multi‐echo UTE cones with free breathing (FB cones), IDEAL‐IQ with breath holding (BH Cartesian), and free breathing (FB Cartesian). Overall image quality of R2∗ maps was scored by 2 blinded experts and compared by a Wilcoxon rank‐sum test. For each pediatric subject, the paired R2∗ maps were assessed to determine if a corresponding artifact‐free 15 mm region‐of‐interest (ROI) could be identified at a mid‐liver level on both images. Agreement between resulting R2∗ quantification from FB cones and BH/FB Cartesian was assessed with Bland‐Altman and linear correlation analyses. Results: ROI‐based regression analysis showed a linear relationship between gadolinium concentration and R2∗ in IDEAL‐IQ (y = 8.83x − 52.10, R2 = 0.995) as well as in cones (y = 9.19x − 64.16, R2 = 0.992). ROI‐based Bland‐Altman analysis showed that the mean difference (MD) was 0.15% and the SD was 5.78%. However, IDEAL‐IQ R2∗ measurements beyond 200 mM substantially deviated from a linear relationship for IDEAL‐IQ (y = 5.85x + 127.61, R2 = 0.827), as opposed to cones (y = 10.87x − 166.96, R2 = 0.984). In vivo, FB cones R2∗ had similar image quality with BH and FB Cartesian in 15 and 42 cases, respectively. FB cones R2∗ had better image quality scores than BH and FB Cartesian in 3 and 21 cases, respectively, where BH/FB Cartesian exhibited severe ghosting artifacts. ROI‐based Bland‐Altman analyses were 2.23% (MD) and 6.59% (SD) between FB cones and BH Cartesian and were 0.21% (MD) and 7.02% (SD) between FB cones and FB Cartesian, suggesting a good agreement between FB cones and BH (FB) Cartesian R2∗. Strong linear relationships were observed between BH Cartesian and FB cones (y = 1.00x + 1.07, R2 = 0.996) and FB Cartesian and FB cones (y = 0.98x + 1.68, R2 = 0.999). Conclusion: Golden‐ratio re‐ordered 3D multi‐echo UTE Cones MRI enabled motion‐robust, ungated, and free‐breathing R2∗ mapping of hepatic iron overload, with comparable R2∗ measurements and image quality to BH Cartesian, and better image quality than FB Cartesian. [ABSTRACT FROM AUTHOR]

Purpose: To propose a novel combined parallel imaging and deep learning‐based reconstruction framework for robust reconstruction of highly accelerated 2D cardiac cine MRI data. Methods: We propose DL‐ESPIRiT, an unrolled neural network architecture that utilizes an extended coil sensitivity model to address SENSE‐related field‐of‐view (FOV) limitations in previously proposed deep learning‐based reconstruction frameworks. Additionally, we propose a novel neural network design based on (2+1)D spatiotemporal convolutions to produce more accurate dynamic MRI reconstructions than conventional 3D convolutions. The network is trained on fully sampled 2D cardiac cine datasets collected from 11 healthy volunteers with IRB approval. DL‐ESPIRiT is compared against a state‐of‐the‐art parallel imaging and compressed sensing method known as l1‐ESPIRiT. The reconstruction accuracy of both methods is evaluated on retrospectively undersampled datasets (R = 12) with respect to standard image quality metrics as well as automatic deep learning‐based segmentations of left ventricular volumes. Feasibility of DL‐ESPIRiT is demonstrated on two prospectively undersampled datasets acquired in a single heartbeat per slice. Results: The (2+1)D DL‐ESPIRiT method produces higher fidelity image reconstructions when compared to l1‐ESPIRiT reconstructions with respect to standard image quality metrics (P <.001). As a result of improved image quality, segmentations made from (2+1)D DL‐ESPIRiT images are also more accurate than segmentations from l1‐ESPIRiT images. Conclusions: DL‐ESPIRiT synergistically combines a robust parallel imaging model and deep learning‐based priors to produce high‐fidelity reconstructions of retrospectively undersampled 2D cardiac cine data acquired with reduced FOV. Although a proof‐of‐concept is shown, further experiments are necessary to determine the efficacy of DL‐ESPIRiT in prospectively undersampled data. [ABSTRACT FROM AUTHOR]

Background: Quantitative T2 * MRI is the standard of care for the assessment of iron overload. However, patient motion corrupts T2 * estimates.Purpose: To develop and evaluate a motion-robust, simultaneous cardiac and liver T2 * imaging approach using non-Cartesian, rosette sampling and a model-based reconstruction as compared to clinical-standard Cartesian MRI.Study Type: Prospective.Phantom/population: Six ferumoxytol-containing phantoms (26-288 μg/mL). Eight healthy subjects and 18 patients referred for clinically indicated iron overload assessment.Field Strength/sequence: 1.5T, 2D Cartesian and rosette gradient echo (GRE) ASSESSMENT: GRE T2 * values were validated in ferumoxytol phantoms. In healthy subjects, test-retest and spatial coefficient of variation (CoV) analysis was performed during three breathing conditions. Cartesian and rosette T2 * were compared using correlation and Bland-Altman analysis. Images were rated by three experienced radiologists on a 5-point scale.Statistical Tests: Linear regression, analysis of variance (ANOVA), and paired Student's t-testing were used to compare reproducibility and variability metrics in Cartesian and rosette scans. The Wilcoxon rank test was used to assess reader score comparisons and reader reliability was measured using intraclass correlation analysis.Results: Rosette R2* (1/T2 *) was linearly correlated with ferumoxytol concentration (r2 = 1.00) and not significantly different than Cartesian values (P = 0.16). During breath-holding, ungated rosette liver and heart T2 * had lower spatial CoV (liver: 18.4 ± 9.3% Cartesian, 8.8% ± 3.4% rosette, P = 0.02, heart: 37.7% ± 14.3% Cartesian, 13.4% ± 1.7% rosette, P = 0.001) and higher-quality scores (liver: 3.3 [3.0-3.6] Cartesian, 4.7 [4.1-4.9] rosette, P = 0.005, heart: 3.0 [2.3-3] Cartesian, 4.5 [3.8-5.0] rosette, P = 0.005) compared to Cartesian values. During free-breathing and failed breath-holding, Cartesian images had very poor to average image quality with significant artifacts, whereas rosette remained very good, with minimal artifacts (P = 0.001).Data Conclusion: Rosette k-sampling with a model-based reconstruction offers a clinically useful motion-robust T2 * mapping approach for iron quantification. J. MAGN. RESON. IMAGING 2020;52:1688-1698. [ABSTRACT FROM AUTHOR]

Purpose: To develop a near‐silent and distortionless DWI (sd‐DWI) sequence using magnetization‐prepared rotating ultrafast imaging sequence. Methods: A rotating ultrafast imaging sequence was modified with driven‐equilibrium diffusion preparation, including eddy‐current compensation methods. To compensate for the T1 recovery during readout, a phase‐cycling method was used. Both compensation methods were validated in phantoms. The optimized sequence was compared with an EPI diffusion sequence for image distortion, contrast, ADC, and acoustic noise level in phantoms. The sequence was evaluated in 1 brain volunteer, 5 prostate volunteers, and 10 pediatric patients with joint diseases. Results: Combination of several eddy‐current compensation methods reduced the artifact to an acceptable level. Phase cycling reduced T1 recovery contamination during readout. In phantom scans, the optimized sequence generated similar image contrast to the EPI diffusion sequence, and ADC maps between the sequences were comparable; sd‐DWI had significantly lower acoustic noise (P <.05). In vivo brain scan showed reduced image distortion in sd‐DWI compared with the EPI diffusion, although residual motion artifact remains due to brain pulsation. The prostate scans showed that sd‐DWI can provide similar ADC compared with EPI diffusion, with no image distortion. Patient scans showed that the sequence can clearly depict joint lesions. Conclusion: An sd‐DWI sequence was developed and optimized. Compared with conventional EPI diffusion, sd‐DWI provided similar diffusion contrast, accurate ADC measurement, improved image quality, and minimal ambient scanning noise. The sequence showed the ability to obtain in vivo diffusion contrast in relatively motion‐free body regions, such as prostate and joint. [ABSTRACT FROM AUTHOR]

Purpose: To enable motion-robust, ungated, free-breathing R 2 ∗ mapping of hepatic iron overload in children with 3D multi-echo UTE cones MRI., Methods: A golden-ratio re-ordered 3D multi-echo UTE cones acquisition was developed with chemical-shift encoding (CSE). Multi-echo complex-valued source images were reconstructed via gridding and coil combination, followed by confounder-corrected R 2 ∗ (=1/ T 2 ∗ ) mapping. A phantom containing 15 different concentrations of gadolinium solution (0-300 mM) was imaged at 3T. 3D multi-echo UTE cones with an initial TE of 0.036 ms and Cartesian CSE-MRI (IDEAL-IQ) sequences were performed. With institutional review board approval, 85 subjects (81 pediatric patients with iron overload + 4 healthy volunteers) were imaged at 3T using 3D multi-echo UTE cones with free breathing (FB cones), IDEAL-IQ with breath holding (BH Cartesian), and free breathing (FB Cartesian). Overall image quality of R 2 ∗ maps was scored by 2 blinded experts and compared by a Wilcoxon rank-sum test. For each pediatric subject, the paired R 2 ∗ maps were assessed to determine if a corresponding artifact-free 15 mm region-of-interest (ROI) could be identified at a mid-liver level on both images. Agreement between resulting R 2 ∗ quantification from FB cones and BH/FB Cartesian was assessed with Bland-Altman and linear correlation analyses., Results: ROI-based regression analysis showed a linear relationship between gadolinium concentration and R 2 ∗ in IDEAL-IQ (y = 8.83x - 52.10, R 2 = 0.995) as well as in cones (y = 9.19x - 64.16, R 2 = 0.992). ROI-based Bland-Altman analysis showed that the mean difference (MD) was 0.15% and the SD was 5.78%. However, IDEAL-IQ R 2 ∗ measurements beyond 200 mM substantially deviated from a linear relationship for IDEAL-IQ (y = 5.85x + 127.61, R 2 = 0.827), as opposed to cones (y = 10.87x - 166.96, R 2 = 0.984). In vivo, FB cones R 2 ∗ had similar image quality with BH and FB Cartesian in 15 and 42 cases, respectively. FB cones R 2 ∗ had better image quality scores than BH and FB Cartesian in 3 and 21 cases, respectively, where BH/FB Cartesian exhibited severe ghosting artifacts. ROI-based Bland-Altman analyses were 2.23% (MD) and 6.59% (SD) between FB cones and BH Cartesian and were 0.21% (MD) and 7.02% (SD) between FB cones and FB Cartesian, suggesting a good agreement between FB cones and BH (FB) Cartesian R 2 ∗ . Strong linear relationships were observed between BH Cartesian and FB cones (y = 1.00x + 1.07, R 2 = 0.996) and FB Cartesian and FB cones (y = 0.98x + 1.68, R 2 = 0.999)., Conclusion: Golden-ratio re-ordered 3D multi-echo UTE Cones MRI enabled motion-robust, ungated, and free-breathing R 2 ∗ mapping of hepatic iron overload, with comparable R 2 ∗ measurements and image quality to BH Cartesian, and better image quality than FB Cartesian., (© 2021 International Society for Magnetic Resonance in Medicine.)

Background: Quantitative T 2 * MRI is the standard of care for the assessment of iron overload. However, patient motion corrupts T 2 * estimates., Purpose: To develop and evaluate a motion-robust, simultaneous cardiac and liver T 2 * imaging approach using non-Cartesian, rosette sampling and a model-based reconstruction as compared to clinical-standard Cartesian MRI., Study Type: Prospective., Phantom/population: Six ferumoxytol-containing phantoms (26-288 μg/mL). Eight healthy subjects and 18 patients referred for clinically indicated iron overload assessment., Field Strength/sequence: 1.5T, 2D Cartesian and rosette gradient echo (GRE) ASSESSMENT: GRE T 2 * values were validated in ferumoxytol phantoms. In healthy subjects, test-retest and spatial coefficient of variation (CoV) analysis was performed during three breathing conditions. Cartesian and rosette T 2 * were compared using correlation and Bland-Altman analysis. Images were rated by three experienced radiologists on a 5-point scale., Statistical Tests: Linear regression, analysis of variance (ANOVA), and paired Student's t-testing were used to compare reproducibility and variability metrics in Cartesian and rosette scans. The Wilcoxon rank test was used to assess reader score comparisons and reader reliability was measured using intraclass correlation analysis., Results: Rosette R2* (1/T 2 *) was linearly correlated with ferumoxytol concentration (r 2 = 1.00) and not significantly different than Cartesian values (P = 0.16). During breath-holding, ungated rosette liver and heart T 2 * had lower spatial CoV (liver: 18.4 ± 9.3% Cartesian, 8.8% ± 3.4% rosette, P = 0.02, heart: 37.7% ± 14.3% Cartesian, 13.4% ± 1.7% rosette, P = 0.001) and higher-quality scores (liver: 3.3 [3.0-3.6] Cartesian, 4.7 [4.1-4.9] rosette, P = 0.005, heart: 3.0 [2.3-3] Cartesian, 4.5 [3.8-5.0] rosette, P = 0.005) compared to Cartesian values. During free-breathing and failed breath-holding, Cartesian images had very poor to average image quality with significant artifacts, whereas rosette remained very good, with minimal artifacts (P = 0.001)., Data Conclusion: Rosette k-sampling with a model-based reconstruction offers a clinically useful motion-robust T 2 * mapping approach for iron quantification. J. MAGN. RESON. IMAGING 2020;52:1688-1698., (© 2020 International Society for Magnetic Resonance in Medicine.)

Magnetic Resonance Imaging (MRI) suffers from several artifacts, the most common of which are motion artifacts. These artifacts often yield images that are of non-diagnostic quality. To detect such artifacts, images are prospectively evaluated by experts for their diagnostic quality, which necessitates patient-revisits and rescans whenever non-diagnostic quality scans are encountered. This motivates the need to develop an automated framework capable of accessing medical image quality and detecting diagnostic and non-diagnostic images. In this paper, we explore several convolutional neural network-based frameworks for medical image quality assessment and investigate several challenges therein.