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Background and Purpose Methods Results Conclusions Neuromix is a fast, motion robust multi‐contrast sequence capable of providing all diagnostic contrasts in ∼3.5 minutes. However, more evaluation is needed across the various contrasts compared to gold standard, optimized sequences routinely used in the clinic. The goal of this study was to prospectively determine how NeuroMix performs in the clinical setting compared to routine clinical MRI.NeuroMix and routine clinical MRI sequences were acquired on a 3 Tesla clinical scanner for 39 patients clinically indicated for brain MRI. Three radiologists were asked to assess the diagnostic confidence of NeuroMix compared to the routine MRI using a series of questions. Signal‐to‐noise and contrast‐to‐noise ratios (SNR and CNR) were assessed for NeuroMix. Fleiss’ free‐marginal multirater kappa was calculated for the qualitative assessment performed by the radiologists.Radiologists were comfortable substituting or reading some of the NeuroMix sequences in place of the corresponding conventional sequence for some contrasts, including diffusion‐weighted imaging, single‐shot T2, and susceptibility‐weighted imaging. The image quality, SNR, and CNR allowed the radiologists to visualize anatomy and pathology on NeuroMix images. There was no significant difference between coefficient of variation for the apparent diffusion coefficient maps (p = .084).Analysis revealed both positives and some pitfalls of NeuroMix. However, these results indicate Neuromix as having the capability to be a backup sequence in case artifacts are present in routine sequences, or potentially a replacement for some contrasts altogether. [ABSTRACT FROM AUTHOR]

In accelerated MRI, the robust artificial-neural-network for k-space interpolation (RAKI) method is an attractive learning-based reconstruction that does not require additional training data. This study was focused on obtaining high quality MR images from regular under-sampled multi-coil k-space data using a high-pass filtered RAKI (HP-RAKI) reconstruction without any extra training data. MRI scan from human subjects was under-sampled with a regular pattern using skipped phase encoding and a fully sampled k-space center. A high-pass (HP) filter was applied in k-space to reduce image support to facilitate linear prediction. The HP filtered k-space center was used to train the RAKI network without any extra training data. The unacquired k-space data can be predicted from a trained RAKI network with optimized parameters. Final reconstruction was obtained after performing an inverse HP filtering for the predicted k-space data. This HP-RAKI method can be extended to corresponding residual structure (HP-rRAKI). HP-RAKI was compared with GRAPPA, HP-GRAPPA, RAKI and MW-RAKI algorithms, and HP-rRAKI was compared with corresponding residual extensions, including rRAKI and MW-rRAKI, all qualitatively and quantitatively using visual inspection and such metrics as SSIM and PSNR. HP-RAKI and HP-rRAKI were found to be effective in reconstructing MR images even at high acceleration factors. HP-RAKI and HP-rRAKI compared favorably with other algorithms. Using high-pass filtered central k-space data for training, HP-RAKI offers higher reconstruction quality for regularly under-sampled multi-coil k-space data without any extra training data. It has shown promising capabilities for fast MRI applications, especially those lacking fully sampled training data.

Abstract Background Abbreviated breast MRI (FAST MRI) is being introduced into clinical practice to screen women with mammographically dense breasts or with a personal history of breast cancer. This study aimed to optimise diagnostic accuracy through the adaptation of interpretation-training. Methods A FAST MRI interpretation-training programme (short presentations and guided hands-on workstation teaching) was adapted to provide additional training during the assessment task (interpretation of an enriched dataset of 125 FAST MRI scans) by giving readers feedback about the true outcome of each scan immediately after each scan was interpreted (formative assessment). Reader interaction with the FAST MRI scans used developed software (RiViewer) that recorded reader opinions and reading times for each scan. The training programme was additionally adapted for remote e-learning delivery. Study design Prospective, blinded interpretation of an enriched dataset by multiple readers. Results 43 mammogram readers completed the training, 22 who interpreted breast MRI in their clinical role (Group 1) and 21 who did not (Group 2). Overall sensitivity was 83% (95%CI 81–84%; 1994/2408), specificity 94% (95%CI 93–94%; 7806/8338), readers’ agreement with the true outcome kappa = 0.75 (95%CI 0.74–0.77) and diagnostic odds ratio = 70.67 (95%CI 61.59–81.09). Group 1 readers showed similar sensitivity (84%) to Group 2 (82% p = 0.14), but slightly higher specificity (94% v. 93%, p = 0.001). Concordance with the ground truth increased significantly with the number of FAST MRI scans read through the formative assessment task (p = 0.002) but by differing amounts depending on whether or not a reader had previously attended FAST MRI training (interaction p = 0.02). Concordance with the ground truth was significantly associated with reading batch size (p = 0.02), tending to worsen when more than 50 scans were read per batch. Group 1 took a median of 56 seconds (range 8–47,466) to interpret each FAST MRI scan compared with 78 (14–22,830, p

Despite its superior soft tissue contrast and non-invasive nature, MRI requires long scan times due to its intrinsic signal acquisition principles, a main drawback which technological advancements in MRI have been focused on. In particular, scan time reduction is a natural requirement in neuroimaging due to detailed structures requiring high resolution imaging and often volumetric (3D) acquisitions, and numerous studies have recently attempted to harness deep learning (DL) technology in enabling scan time reduction and image quality improvement. Various DL-based image reconstruction products allow for additional scan time reduction on top of existing accelerated acquisition methods without compromising the image quality. [ABSTRACT FROM AUTHOR]

Background: Abbreviated breast MRI (FAST MRI) is being introduced into clinical practice to screen women with mammographically dense breasts or with a personal history of breast cancer. This study aimed to optimise diagnostic accuracy through the adaptation of interpretation-training. Methods: A FAST MRI interpretation-training programme (short presentations and guided hands-on workstation teaching) was adapted to provide additional training during the assessment task (interpretation of an enriched dataset of 125 FAST MRI scans) by giving readers feedback about the true outcome of each scan immediately after each scan was interpreted (formative assessment). Reader interaction with the FAST MRI scans used developed software (RiViewer) that recorded reader opinions and reading times for each scan. The training programme was additionally adapted for remote e-learning delivery. Study design: Prospective, blinded interpretation of an enriched dataset by multiple readers. Results: 43 mammogram readers completed the training, 22 who interpreted breast MRI in their clinical role (Group 1) and 21 who did not (Group 2). Overall sensitivity was 83% (95%CI 81–84%; 1994/2408), specificity 94% (95%CI 93–94%; 7806/8338), readers' agreement with the true outcome kappa = 0.75 (95%CI 0.74–0.77) and diagnostic odds ratio = 70.67 (95%CI 61.59–81.09). Group 1 readers showed similar sensitivity (84%) to Group 2 (82% p = 0.14), but slightly higher specificity (94% v. 93%, p = 0.001). Concordance with the ground truth increased significantly with the number of FAST MRI scans read through the formative assessment task (p = 0.002) but by differing amounts depending on whether or not a reader had previously attended FAST MRI training (interaction p = 0.02). Concordance with the ground truth was significantly associated with reading batch size (p = 0.02), tending to worsen when more than 50 scans were read per batch. Group 1 took a median of 56 seconds (range 8–47,466) to interpret each FAST MRI scan compared with 78 (14–22,830, p < 0.0001) for Group 2. Conclusions: Provision of immediate feedback to mammogram readers during the assessment test set reading task increased specificity for FAST MRI interpretation and achieved high diagnostic accuracy. Optimal reading-batch size for FAST MRI was 50 reads per batch. Trial registration (25/09/2019): ISRCTN16624917. [ABSTRACT FROM AUTHOR]

Purpose: To develop a model‐based deep neural network for high‐quality image reconstruction of undersampled multi‐coil CEST data. Theory and Methods: Inspired by the variational network (VN), the CEST image reconstruction equation is unrolled into a deep neural network (CEST‐VN) with a k‐space data‐sharing block that takes advantage of the inherent redundancy in adjacent CEST frames and 3D spatial–frequential convolution kernels that exploit correlations in the x‐ω domain. Additionally, a new pipeline based on multiple‐pool Bloch–McConnell simulations is devised to synthesize multi‐coil CEST data from publicly available anatomical MRI data. The proposed network is trained on simulated data with a CEST‐specific loss function that jointly measures the structural and CEST contrast. The performance of CEST‐VN was evaluated on four healthy volunteers and five brain tumor patients using retrospectively or prospectively undersampled data with various acceleration factors, and then compared with other conventional and state‐of‐the‐art reconstruction methods. Results: The proposed CEST‐VN method generated high‐quality CEST source images and amide proton transfer‐weighted maps in healthy and brain tumor subjects, consistently outperforming GRAPPA, blind compressed sensing, and the original VN. With the acceleration factors increasing from 3 to 6, CEST‐VN with the same hyperparameters yielded similar and accurate reconstruction without apparent loss of details or increase of artifacts. The ablation studies confirmed the effectiveness of the CEST‐specific loss function and data‐sharing block used. Conclusions: The proposed CEST‐VN method can offer high‐quality CEST source images and amide proton transfer‐weighted maps from highly undersampled multi‐coil data by integrating the deep learning prior and multi‐coil sensitivity encoding model. [ABSTRACT FROM AUTHOR]

ABSTRACT: Background: Pediatric elbow trauma presents a diagnostic challenge for clinicians. Radiographs may be insufficient in assessing the radiocapitellar alignment and advanced imaging or invasive procedures such as an intraoperative arthrogram may be indicated. The purpose of this study is to describe the application and utility of fast magnetic resonance imaging (MRI) in the diagnosis and management of traumatic elbow injuries in pediatric patients. Methods: We reviewed pediatric patients who underwent a fast MRI of the elbow between 2014 and 2022. A fast MRI was defined as an MRI using rapid sequences accelerated conventional sequences, or fewer conventional sequences than a standard exam. Imaging for chronic or nontraumatic injuries was excluded. We identified patient demographics, MRI scan times, and clinical course. Results: Twenty-three patients (8 females and 15 males) with fast elbow MRI scans were identified. The mean age was 5 years (range 2-12 years, SD 2 years). All patients had radiographs and 4 had CT scans prior to MRI. The average scanning time was 14 minutes (SD 6 minutes). Only one patient required anesthesia. Twenty of the 23 patients had a fracture identified on elbow radiographs and 8 were Monteggia fracture-dislocations. Nine of these patients demonstrated subluxation of the radiocapitellar joint requiring operative management in 8 patients, and one additional patient underwent surgery for displaced radial neck fracture. Three patients sustained an elbow dislocation without fracture identified on radiographs; of these, one had persistent radiocapitellar subluxation on MRI requiring surgical management. A total of 13 patients (56.5%) did not require operative management based on fast MRI findings. Outcome data were available for 22 patients with a mean follow-up was 23.6 months. One patient had residual pain and 8 had some range of motion limitation at the final follow-up. All patients had reduced elbow joints on follow-up radiographs. Conclusions: Fast MRIs can assist with the clinical evaluation of acute elbow injury in pediatric patients with equivocal radiographic findings and particularly in those with possible radiocapitellar subluxation/dislocation. This novel imaging technique has the potential to change clinical management, decrease the need for unnecessary procedures, and prevent complications of missed injuries. Key Concepts: (1) Fast MRIs allow for high-resolution imaging of structures in skeletally immature patients without the need for ionizing radiation and anesthesia. (2) Radiographs alone may be insufficient for identifying radiocapitellar subluxation in pediatric patients. (3) Confirmation of anatomic alignment with fast MRI can obviate the need for invasive procedures such as joint arthrogram in patients who sustain traumatic elbow injuries. Level of Evidence: IV

Compressed sensing is commonly concerned with optimizing the image quality after a partial undersampling of the measurable k -space to accelerate MRI. In this article, we propose to change the focus from the quality of the reconstructed image to the quality of the downstream image analysis outcome. Specifically, we propose to optimize the patterns according to how well a sought-after pathology could be detected or localized in the reconstructed images. We find the optimal undersampling patterns in k -space that maximize target value functions of interest in commonplace medical vision problems (reconstruction, segmentation, and classification) and propose a new iterative gradient sampling routine universally suitable for these tasks. We validate the proposed MRI acceleration paradigm on three classical medical datasets, demonstrating a noticeable improvement of the target metrics at the high acceleration factors (for the segmentation problem at ×16 acceleration, we report up to 12% improvement in Dice score over the other undersampling patterns). • New paradigm for accelerating MRI, where k-space is undersampled intelligently for the benefit of downstream image analysis. • In this paradigm, the learned pattern can ruin the look of the image, while boosting the target metrics. • Our iterative gradient sampling (IGS) algorithm optimizes undersampling patterns for specific medical vision tasks and applications (cardiac, neurological, and orthopedic utility confirmed). • Noticeable improvement to target value metrics compared to the other undersampling patterns, such as equispaced or central masks. • The method proves especially instrumental at the highest acceleration factors when good image quality is impossible to obtain. [ABSTRACT FROM AUTHOR]

Magnetic Resonance Imaging (MRI) is an essential medical imaging modality that provides excellent soft-tissue contrast and high-resolution images of the human body, allowing us to understand detailed information on morphology, structural integrity, and physiologic processes. However, MRI exams usually require lengthy acquisition times. Methods such as parallel MRI and Compressive Sensing (CS) have significantly reduced the MRI acquisition time by acquiring less data through undersampling k-space. The state-of-the-art of fast MRI has recently been redefined by integrating Deep Learning (DL) models with these undersampled approaches. This Systematic Literature Review (SLR) comprehensively analyzes deep MRI reconstruction models, emphasizing the key elements of recently proposed methods and highlighting their strengths and weaknesses. This SLR involves searching and selecting relevant studies from various databases, including Web of Science and Scopus, followed by a rigorous screening and data extraction process using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. It focuses on various techniques, such as residual learning, image representation using encoders and decoders, data-consistency layers, unrolled networks, learned activations, attention modules, plug-and-play priors, diffusion models, and Bayesian methods. This SLR also discusses the use of loss functions and training with adversarial networks to enhance deep MRI reconstruction methods. Moreover, we explore various MRI reconstruction applications, including non-Cartesian reconstruction, super-resolution, dynamic MRI, joint learning of reconstruction with coil sensitivity and sampling, quantitative mapping, and MR fingerprinting. This paper also addresses research questions, provides insights for future directions, and emphasizes robust generalization and artifact handling. Therefore, this SLR serves as a valuable resource for advancing fast MRI, guiding research and development efforts of MRI reconstruction for better image quality and faster data acquisition. [ABSTRACT FROM AUTHOR]

Purpose: Computed tomography (CT) is still used in the imaging diagnosis of acute appendicitis in children at many hospitals. We implemented an ultrasound (US) and fast magnetic resonance imaging (MRI) pathway for suspected appendicitis at our institution with the goal of reducing radiation exposure in children. Methods: All children (< 18 years old) who underwent appendectomy between January 2011 and July 2021 were reviewed. Data were collected on all imaging studies performed. In December 2015, we initiated an imaging pathway for suspected acute appendicitis. US was the initial imaging study, and a rapid protocol MRI was performed if US was equivocal. Those could not tolerate MRI underwent CT. We evaluated the difference in percentage of patients who underwent CT before and after pathway initiation. Results: 554 patients who underwent appendectomy did not have prior imaging studies on presentation to our hospital and were included in analysis. After initiating the pathway, the use of abdominal US increased from 87% (220 of 254) to 97% (291 of 300, p < 0.0001) and the use of MRI increased by 100% (0 MRIs pre-protocol, 90 of 300 patients post-protocol, p < 0.0001). CT utilization decreased significantly from 32% (82 of 254) to 2% (6 of 300, p < 0.0001). Conclusion: Embracing a new US and rapid MRI pathway to evaluate pediatric patients with suspected acute appendicitis resulted in significant reduction in CT utilization and therefore radiation exposure. [ABSTRACT FROM AUTHOR]

One of the main challenges to obtain a high throughput in the MRI process is a slow signal acquisition. This process could be improved using a parallel imaging technique, where fewer raw data with multiple radio frequency (RF) coils are acquired simultaneously to reconstruct a final MR image. Nowadays, all multi-coil MRI machines have a parallel imaging technique for the image reconstruction. However, the parallel imaging still cannot accelerate sufficiently to reduce the overall acquisition time. In another way, this paper proposes a solution relying on a deep convolution neural network (CNN) to generate high-quality reconstruction MR images with higher acceleration factors. The proposed method, called StrainNet, performs the reconstructions by encoding the under-sampled data (i.e., for the speeding up process) into high-level features. Then the important part of the network, called Strainer, is applied to discard irrelevance information, and decodes remaining features back to reconstruct MR images. The proposed network could be trained end-to-end with a newly presented loss function, Dual-Domain Loss (DDL), combining both spatial and frequency losses. The experimental results are based on the fastMRI dataset and show that StrainNet outperforms the competing methods for both 4- and 8-fold accelerations.

Abstract Background Abbreviated breast MRI (abMRI) is being introduced in breast screening trials and clinical practice, particularly for women with dense breasts. Upscaling abMRI provision requires the workforce of mammogram readers to learn to effectively interpret abMRI. The purpose of this study was to examine the diagnostic accuracy of mammogram readers to interpret abMRI after a single day of standardised small-group training and to compare diagnostic performance of mammogram readers experienced in full-protocol breast MRI (fpMRI) interpretation (Group 1) with that of those without fpMRI interpretation experience (Group 2). Methods Mammogram readers were recruited from six NHS Breast Screening Programme sites. Small-group hands-on workstation training was provided, with subsequent prospective, independent, blinded interpretation of an enriched dataset with known outcome. A simplified form of abMRI (first post-contrast subtracted images (FAST MRI), displayed as maximum-intensity projection (MIP) and subtracted slice stack) was used. Per-breast and per-lesion diagnostic accuracy analysis was undertaken, with comparison across groups, and double-reading simulation of a consecutive screening subset. Results 37 readers (Group 1: 17, Group 2: 20) completed the reading task of 125 scans (250 breasts) (total = 9250 reads). Overall sensitivity was 86% (95% confidence interval (CI) 84–87%; 1776/2072) and specificity 86% (95%CI 85–86%; 6140/7178). Group 1 showed significantly higher sensitivity (843/952; 89%; 95%CI 86–91%) and higher specificity (2957/3298; 90%; 95%CI 89–91%) than Group 2 (sensitivity = 83%; 95%CI 81–85% (933/1120) p < 0.0001; specificity = 82%; 95%CI 81–83% (3183/3880) p < 0.0001). Inter-reader agreement was higher for Group 1 (kappa = 0.73; 95%CI 0.68–0.79) than for Group 2 (kappa = 0.51; 95%CI 0.45–0.56). Specificity improved for Group 2, from the first 55 cases (81%) to the remaining 70 (83%) (p = 0.02) but not for Group 1 (90–89% p = 0.44), whereas sensitivity remained consistent for both Group 1 (88–89%) and Group 2 (83–84%). Conclusions Single-day abMRI interpretation training for mammogram readers achieved an overall diagnostic performance within benchmarks published for fpMRI but was insufficient for diagnostic accuracy of mammogram readers new to breast MRI to match that of experienced fpMRI readers. Novice MRI reader performance improved during the reading task, suggesting that additional training could further narrow this performance gap.

• Accelerated MRI reconstruction was improved by using an HP-RAKI algorithm. • A high-pass (HP) filter reduced image support to facilitate linear prediction. • Normal contrast was restored by using an inverse HP filtering. • HP-RAKI method was extended to corresponding residual structure. • High quality reconstruction was achieved without any extra training data. In accelerated MRI, the robust artificial-neural-network for k-space interpolation (RAKI) method is an attractive learning-based reconstruction that does not require additional training data. This study was focused on obtaining high quality MR images from regular under-sampled multi-coil k-space data using a high-pass filtered RAKI (HP-RAKI) reconstruction without any extra training data. MRI scan from human subjects was under-sampled with a regular pattern using skipped phase encoding and a fully sampled k-space center. A high-pass (HP) filter was applied in k-space to reduce image support to facilitate linear prediction. The HP filtered k-space center was used to train the RAKI network without any extra training data. The unacquired k-space data can be predicted from a trained RAKI network with optimized parameters. Final reconstruction was obtained after performing an inverse HP filtering for the predicted k-space data. This HP-RAKI method can be extended to corresponding residual structure (HP-rRAKI). HP-RAKI was compared with GRAPPA, HP-GRAPPA, RAKI and MW-RAKI algorithms, and HP-rRAKI was compared with corresponding residual extensions, including rRAKI and MW-rRAKI, all qualitatively and quantitatively using visual inspection and such metrics as SSIM and PSNR. HP-RAKI and HP-rRAKI were found to be effective in reconstructing MR images even at high acceleration factors. HP-RAKI and HP-rRAKI compared favorably with other algorithms. Using high-pass filtered central k-space data for training, HP-RAKI offers higher reconstruction quality for regularly under-sampled multi-coil k-space data without any extra training data. It has shown promising capabilities for fast MRI applications, especially those lacking fully sampled training data. [ABSTRACT FROM AUTHOR]

• HASTE DL sequence can reduce the acquisition time of pancreas MRI. • HASTE DL with enhanced denoising can improve the image quality for evaluating pancreatic cyst. • Variability in pancreatic cyst size measurement can be reduced using HASTE DL. To assess the feasibility of the single-shot turbo spin echo sequence using deep learning-based reconstruction (DLR) (HASTE DL) with enhanced denoising for pancreas MRI. Patients who underwent pancreas MRI from March to April 2021 were included. Four T2-weighted images (non-FS conventional HASTE vs. HASTE DL with enhanced denoising and FS HASTE DL with enhanced denoising vs. HASTE DL) were acquired. Two readers independently assessed the image quality parameters of the two non-FS image sets using a 4-point Likert scale. The signal-to-noise ratio (SNR) of the cystic lesions and pancreatic parenchyma and the contrast-to-noise ratio between the cystic lesion and pancreatic parenchyma were calculated for all four image sets. The size of the largest cystic lesion and the diameter of pancreatic duct were measured. A total of 63 patients were included, 48 (76.2 %) of whom had 136 pancreatic cystic lesion(s). The acquisition times of conventional HASTE and HASTE DL were 69 and 18 sec, respectively. All image quality parameters except artifacts for reader 2 were significantly better for HASTE DL with enhanced denoising. Those images also received scores for overall image quality that were significantly higher than those for the conventional HASTE (3.26 ± 0.54 vs. 2.47 ± 0.56, p < 0.001). The SNR of the pancreatic cystic lesion and pancreatic parenchyma was significantly higher in the HASTE DL with enhanced denoising (p < 0.001 for both). Inter-reader variability for measuring the pancreatic cyst size (ICC, 0.999 vs. 0.995; 95 % LoA, −0.13481 to 0.14743 vs. −0.24097 to 0.27404) and duct diameter (ICC, 0.994 vs. 0.969; 95 % LoA, −0.11684 to 0.36026 vs. −0.45544 to 0.44664) was lower in HASTE DL with enhanced denoising than in the conventional HASTE. HASTE DL with enhanced denoising could be useful for reducing the acquisition time of pancreas MRI while improving the image quality for the evaluation of pancreatic cystic lesions. [ABSTRACT FROM AUTHOR]

Background and Purpose: Neuromix is a fast, motion robust multi-contrast sequence capable of providing all diagnostic contrasts in ∼3.5 minutes. However, more evaluation is needed across the various contrasts compared to gold standard, optimized sequences routinely used in the clinic. The goal of this study was to prospectively determine how NeuroMix performs in the clinical setting compared to routine clinical MRI., Methods: NeuroMix and routine clinical MRI sequences were acquired on a 3 Tesla clinical scanner for 39 patients clinically indicated for brain MRI. Three radiologists were asked to assess the diagnostic confidence of NeuroMix compared to the routine MRI using a series of questions. Signal-to-noise and contrast-to-noise ratios (SNR and CNR) were assessed for NeuroMix. Fleiss' free-marginal multirater kappa was calculated for the qualitative assessment performed by the radiologists., Results: Radiologists were comfortable substituting or reading some of the NeuroMix sequences in place of the corresponding conventional sequence for some contrasts, including diffusion-weighted imaging, single-shot T 2 , and susceptibility-weighted imaging. The image quality, SNR, and CNR allowed the radiologists to visualize anatomy and pathology on NeuroMix images. There was no significant difference between coefficient of variation for the apparent diffusion coefficient maps (p = .084)., Conclusions: Analysis revealed both positives and some pitfalls of NeuroMix. However, these results indicate Neuromix as having the capability to be a backup sequence in case artifacts are present in routine sequences, or potentially a replacement for some contrasts altogether., (© 2024 American Society of Neuroimaging.)

Objective. To develop a nonlinear, model-based parameter estimation method directly from incomplete measurements in k  -  w space for robust spectral analysis in highly accelerated chemical exchange saturation transfer (CEST) magnetic resonance imaging (MRI). Approach . A CEST-specific, separable nonlinear model, which describes spectral decomposition using multi-pool Lorentzian functions (conventional magnetization transfer (MT), direct saturation of water signals (DS), amide, amine, and nuclear Overhauser effect) derived from the steady-state Bloch McConnel equation, is incorporated into a measurement model in CEST MRI. Furthermore, signal drop in saturation transfer experiments is formulated by an additional, separable nonlinear spectral prior indicating that the symmetric z-spectra synthesized using conventional MT and DS always remain higher or equal to the whole z-spectra with all pools. Given the above considerations, linear and nonlinear parameters in the proposed method are estimated in an alternating fashion directly from highly incomplete measurements in k  -  w space by solving a constrained optimization problem with the physics-constrained spectral priors while imposing additional sparsity priors on spatial parameter maps. Main results. Compared with conventional methods, the proposed method yields clearer delineation of tumor-specific CEST maps without apparent artifact and noise. Significance. We successfully demonstrated the feasibility of the proposed method for CEST MRI with highly incomplete measurements thus enabling high-resolution whole brain CEST MRI in clinically reasonable imaging time., (© 2024 Institute of Physics and Engineering in Medicine. All rights, including for text and data mining, AI training, and similar technologies, are reserved.)

To assess the possibility of reducing the image acquisition time for diffusion-weighted whole-body imaging with background body signal suppression (DWIBS) by denoising with deep learning-based reconstruction (dDLR). Seventeen patients with prostate cancer who underwent DWIBS by 1.5 T magnetic resonance imaging with a number of excitations of 2 (NEX2) and 8 (NEX8) were prospectively enrolled. The NEX2 image data were processed by dDLR (dDLR-NEX2), and the NEX2, dDLR-NEX2, and NEX8 image data were analyzed. In qualitative analysis, two radiologists rated the perceived coarseness, conspicuity of metastatic lesions (lymph nodes and bone), and overall image quality. The contrast-to-noise ratios (CNRs), contrast ratios, and mean apparent diffusion coefficients (ADCs) of metastatic lesions were calculated in a quantitative analysis. The image acquisition time of NEX2 was 2.8 times shorter than that of NEX8 (3 min 30 s vs 9 min 48 s). The perceived coarseness and overall image quality scores reported by both readers were significantly higher for dDLR-NEX2 than for NEX2 (P = 0.005–0.040). There was no significant difference between dDLR-NEX2 and NEX8 in the qualitative analysis. The CNR of bone metastasis was significantly greater for dDLR-NEX2 than for NEX2 and NEX8 (P = 0.012 for both comparisons). The contrast ratios and mean ADCs were not significantly different among the three image types. dDLR improved the image quality of DWIBS with NEX2. In the context of lymph node and bone metastasis evaluation with DWIBS in patients with prostate cancer, dDLR-NEX2 has potential to be an alternative to NEX8 and reduce the image acquisition time. • Denoising with deep learning-based reconstruction (dDLR) is a new technique. • dDLR improved the image quality of whole-body diffusion-weighted imaging (DWI). • dDLR has potential to reduce the image acquisition time of whole-body DWI. [ABSTRACT FROM AUTHOR]

In clinical medicine, magnetic resonance imaging (MRI) is one of the most important tools for diagnosis, triage, prognosis, and treatment planning. However, MRI suffers from an inherent slow data acquisition process because data is collected sequentially in k-space. In recent years, most MRI reconstruction methods proposed in the literature focus on holistic image reconstruction rather than enhancing the edge information. This work steps aside this general trend by elaborating on the enhancement of edge information. Specifically, we introduce a novel parallel imaging coupled dual discriminator generative adversarial network (PIDD-GAN) for fast multi-channel MRI reconstruction by incorporating multi-view information. The dual discriminator design aims to improve the edge information in MRI reconstruction. One discriminator is used for holistic image reconstruction, whereas the other one is responsible for enhancing edge information. An improved U-Net with local and global residual learning is proposed for the generator. Frequency channel attention blocks (FCA Blocks) are embedded in the generator for incorporating attention mechanisms. Content loss is introduced to train the generator for better reconstruction quality. We performed comprehensive experiments on Calgary-Campinas public brain MR dataset and compared our method with state-of-the-art MRI reconstruction methods. Ablation studies of residual learning were conducted on the MICCAI13 dataset to validate the proposed modules. Results show that our PIDD-GAN provides high-quality reconstructed MR images, with well-preserved edge information. The time of single-image reconstruction is below 5ms, which meets the demand of faster processing. [ABSTRACT FROM AUTHOR]

Background: Abbreviated breast MRI (abMRI) is being introduced in breast screening trials and clinical practice, particularly for women with dense breasts. Upscaling abMRI provision requires the workforce of mammogram readers to learn to effectively interpret abMRI. The purpose of this study was to examine the diagnostic accuracy of mammogram readers to interpret abMRI after a single day of standardised small-group training and to compare diagnostic performance of mammogram readers experienced in full-protocol breast MRI (fpMRI) interpretation (Group 1) with that of those without fpMRI interpretation experience (Group 2).Methods: Mammogram readers were recruited from six NHS Breast Screening Programme sites. Small-group hands-on workstation training was provided, with subsequent prospective, independent, blinded interpretation of an enriched dataset with known outcome. A simplified form of abMRI (first post-contrast subtracted images (FAST MRI), displayed as maximum-intensity projection (MIP) and subtracted slice stack) was used. Per-breast and per-lesion diagnostic accuracy analysis was undertaken, with comparison across groups, and double-reading simulation of a consecutive screening subset.Results: 37 readers (Group 1: 17, Group 2: 20) completed the reading task of 125 scans (250 breasts) (total = 9250 reads). Overall sensitivity was 86% (95% confidence interval (CI) 84-87%; 1776/2072) and specificity 86% (95%CI 85-86%; 6140/7178). Group 1 showed significantly higher sensitivity (843/952; 89%; 95%CI 86-91%) and higher specificity (2957/3298; 90%; 95%CI 89-91%) than Group 2 (sensitivity = 83%; 95%CI 81-85% (933/1120) p < 0.0001; specificity = 82%; 95%CI 81-83% (3183/3880) p < 0.0001). Inter-reader agreement was higher for Group 1 (kappa = 0.73; 95%CI 0.68-0.79) than for Group 2 (kappa = 0.51; 95%CI 0.45-0.56). Specificity improved for Group 2, from the first 55 cases (81%) to the remaining 70 (83%) (p = 0.02) but not for Group 1 (90-89% p = 0.44), whereas sensitivity remained consistent for both Group 1 (88-89%) and Group 2 (83-84%).Conclusions: Single-day abMRI interpretation training for mammogram readers achieved an overall diagnostic performance within benchmarks published for fpMRI but was insufficient for diagnostic accuracy of mammogram readers new to breast MRI to match that of experienced fpMRI readers. Novice MRI reader performance improved during the reading task, suggesting that additional training could further narrow this performance gap. [ABSTRACT FROM AUTHOR]

Background: Deep learning‐based reconstruction (DLR) can potentially improve image quality by reduction of noise, thereby enabling fast acquisition of magnetic resonance imaging (MRI). However, a systematic evaluation of image quality and diagnostic performance of MRI using short acquisition time with DLR has rarely been investigated in men with prostate cancer. Purpose: To assess the image quality and diagnostic performance of MRI using short acquisition time with DLR for the evaluation of extraprostatic extension (EPE). Study Type: Retrospective. Population: One hundred and nine men. Field Strength/Sequence: 3 T; turbo spin echo T2‐weighted images (T2WI), echo‐planar diffusion‐weighted, and spoiled gradient echo dynamic contrast‐enhanced images. Assessment: To compare image quality, signal‐to‐noise ratio (SNR) and contrast‐to‐noise ratio (CNR) and subjective analysis using Likert scales on three T2WIs (MRI using conventional acquisition time, MRI using short acquisition time [fast MRI], and fast MRI with DLR) were performed. The diagnostic performance for EPE was evaluated by three independent readers. Statistical Tests: SNR, CNR, and image quality scores across the three imaging protocols were compared using Friedman tests. The diagnostic performance for EPE was assessed using the area under receiver operating characteristic curves (AUCs). P < 0.05 was considered statistically significant. Results: Fast MRI with DLR demonstrated significantly higher SNR (mean ± SD, 14.7 ± 6.8 vs. 8.8 ± 4.9) and CNR (mean ± SD, 6.5 ± 6.3 vs. 3.4 ± 3.6) values and higher image quality scores (median, 4.0 vs. 3.0 for three readers) than fast MRI. The AUCs for EPE were significantly higher with the use of DLR (0.86 vs. 0.75 for reader 2 and 0.82 vs. 0.73 for reader 3) compared with fast MRI, whereas differences were not significant for reader 1 (0.81 vs. 0.74; P = 0.09). Data Conclusion: DLR may be useful in reducing the acquisition time of prostate MRI without compromising image quality or diagnostic performance. Level of Evidence: 4 Technical Efficacy: Stage 3 [ABSTRACT FROM AUTHOR]

Compressed Sensing was recently proposed to reduce the long acquisition time of Magnetic Resonance Imaging by undersampling the signal frequency content and then algorithmically reconstructing the original image. We propose a way to significantly improve the above method by exploiting a deep neural network to tackle both problems of frequency sub-sampling and image reconstruction simultaneously, thanks to the introduction of a new loss function to drive the training and the addition of a post-processing non-neural stage. Furthermore, we highlight how some of the quantities along the processing chain can be used as a proxy of the quality of the recovered image, thus allowing a self-assessment of the whole technique. All improvements hinge on the possibility of identifying constraints to which the final image must obey and suitably enforce them. The effectiveness of our approach is tested on real-world MRI acquisitions from the fastMRI public database and achieves an appreciable improvement in Peak Signal-to-Noise Ratio with respect to the original CS-based proposal with speed-up factors 4 and 8. [ABSTRACT FROM AUTHOR]

Purpose of Review: This paper discusses personalized risk assessment and screening, incorporating genetic and radiomic breast cancer risk information. The paper further explores opportunities to incorporate genomic information into traditional risk assessment tools, to improve the discriminatory accuracy of these tools, and integrate the information into clinical decision-making. Recent Findings: We summarize screening guidelines, strategies for identifying high risk women and discuss clinical validation studies of polygenic risk scores (PRSs). We describe the options for supplemental imaging. Both sensitivity and specificity are improving in this area, and testing is more available. Summary: Personalized screening and chemoprevention will only be possible with improved risk estimates (including breast density and PRS), meaningful, consistent shared-decision making conversations, and widespread availability of modalities for supplemental imaging. Future directions include earlier genetic testing and further validation of the PRS, particularly in non-European populations, a focus on breast density and texture in risk estimation and management and education efforts around risk communication. [ABSTRACT FROM AUTHOR]

Purpose: Implement a fast, motion‐robust pulse sequence that acquires T1‐weighted, T2‐weighted, T2*‐weighted, T2 fluid‐attenuated inversion recovery, and DWI data in one run with only one prescription and one prescan. Methods: A software framework was developed that configures and runs several sequences in one main sequence. Based on that framework, the NeuroMix sequence was implemented, containing motion robust single‐shot sequences using EPI and fast spin echo (FSE) readouts (without EPI distortions). Optional multi‐shot sequences that provide better contrast, higher resolution, or isotropic resolution could also be run within the NeuroMix sequence. An optimized acquisition order was implemented that minimizes times where no data is acquired. Results: NeuroMix is customizable and takes between 1:20 and 4 min for a full brain scan. A comparison with the predecessor EPIMix revealed significant improvements for T2‐weighted and T2 fluid‐attenuated inversion recovery, while taking only 8 s longer for a similar configuration. The optional contrasts were less motion robust but offered a significant increase in quality, detail, and contrast. Initial clinical scans on 1 pediatric and 1 adult patient showed encouraging image quality. Conclusion: The single‐shot FSE readouts for T2‐weighted and T2 fluid‐attenuated inversion recovery and the optional multishot FSE and 3D‐EPI contrasts significantly increased diagnostic value compared with EPIMix, allowing NeuroMix to be considered as a standalone brain MRI application. [ABSTRACT FROM AUTHOR]

Purpose: MRI has become the tool of choice for brain imaging, providing unrivalled contrast between soft tissues, as well as a wealth of information about anatomy, function, and neurochemistry. Image quality, in terms of spatial resolution and noise, is strongly dependent on acquisition duration. A typical brain MRI scan may last several minutes, with total protocol duration often exceeding 30 minutes. Long scan duration leads to poor patient experience, long waiting time for appointments, and high costs. Therefore, shortening MRI scans is crucial. In this paper, we investigate the enhancement of low-resolution (LR) brain MRI scanning, to enable shorter acquisition times without compromising the diagnostic value of the images. Methods: We propose a novel fully convolutional neural enhancement approach. It is optimized for accelerated LR MRI acquisitions obtained by reducing the acquisition matrix size only along phase encoding direction. The network is trained to transform the LR acquisitions into corresponding high-resolution (HR) counterparts in an end-to-end manner. In contrast to previous neural-based MRI enhancement algorithms, such as DAGAN, the LR images used for training are real acquisitions rather than smoothed, downsampled versions of the HR images. Results: The proposed method is validated qualitatively and quantitatively for an acceleration factor of 4. Favourable comparison is demonstrated against the state-of-the-art DeblurGAN and DAGAN algorithms in terms of PSNR and SSIM scores. The result was further confirmed by an image quality rating experiment performed by four senior neuroradiologists. Conclusions: The proposed method may become a valuable tool for scan time reduction in brain MRI. In continuation of this research, the validation should be extended to larger datasets acquired for different imaging protocols, and considering several MRI machines produced by different vendors. [ABSTRACT FROM AUTHOR]

MRI reconstruction is one of the critical processes of MRI machines, along with the acquisition. Due to a slow processing time of signal acquiring, parallel imaging and reconstruction techniques are applied for acceleration. To accelerate the acquisition process, fewer raw data are sampled simultaneously with all RF coils acquisition. Then, the reconstruction uses under-sampled data from all RF coils to restore the final MR image that resembles the fully sampled MR image. These processes have been a traditional procedure inside the MRI system since the invention of the multi-coils MRI machine. This paper proposes the deep learning technique with a lightweight network. The deep neural network is capable of generating the high-quality reconstructed MR image with a high peak signal-to-noise ratio (PSNR). This also opens a high acceleration factor for MR data acquisition. The lightweight network is called Multi-Level Pooling Encoder–Decoder Net (MLPED Net). The proposed network outperforms the traditional encoder–decoder networks on 4-fold acceleration with a significant margin on every evaluation metric. The network can be trained end-to-end, and it is a lightweight structure that can reduce training time significantly. Experimental results are based on a publicly available MRI Knee dataset from the fastMRI competition.

Cine cardiac magnetic resonance imaging (MRI) is widely used for the diagnosis of cardiac diseases thanks to its ability to present cardiovascular features in excellent contrast. As compared to computed tomography (CT), MRI, however, requires a long scan time, which inevitably induces motion artifacts and causes patients’ discomfort. Thus, there has been a strong clinical motivation to develop techniques to reduce both the scan time and motion artifacts. Given its successful applications in other medical imaging tasks such as MRI super-resolution and CT metal artifact reduction, deep learning is a promising approach for cardiac MRI motion artifact reduction. In this paper, we propose a novel recurrent generative adversarial network model for cardiac MRI motion artifact reduction. This model utilizes bi-directional convolutional long short-term memory (ConvLSTM) and multi-scale convolutions to improve the performance of the proposed network, in which bi-directional ConvLSTMs handle long-range temporal features while multi-scale convolutions gather both local and global features. We demonstrate a decent generalizability of the proposed method thanks to the novel architecture of our deep network that captures the essential relationship of cardiovascular dynamics. Indeed, our extensive experiments show that our method achieves better image quality for cine cardiac MRI images than existing state-of-the-art methods. In addition, our method can generate reliable missing intermediate frames based on their adjacent frames, improving the temporal resolution of cine cardiac MRI sequences. [ABSTRACT FROM AUTHOR]

• Despite high demand, long MRI acquisition times lead to extended patient wait times. • CS-SuperRes reconstructions for low-resolution matches image quality of standard CS. • Using CS-SuperRes resulted in a 57% reduction in scan time. • 2D knee MRI protocol within 5 min while maintaining excellent image quality. • Enabling new ways for patients' logistics and workflow-management. To investigate the potential of combining Compressed Sensing (CS) and a newly developed AI-based super resolution reconstruction prototype consisting of a series of convolutional neural networks (CNN) for a complete five-minute 2D knee MRI protocol. In this prospective study, 20 volunteers were examined using a 3T-MRI-scanner (Ingenia Elition X, Philips). Similar to clinical practice, the protocol consists of a fat-saturated 2D-proton-density-sequence in coronal, sagittal and transversal orientation as well as a sagittal T1-weighted sequence. The sequences were acquired with two different resolutions (standard and low resolution) and the raw data reconstructed with two different reconstruction algorithms: a conventional Compressed SENSE (CS) and a new CNN-based algorithm for denoising and subsequently to interpolate and therewith increase the sharpness of the image (CS-SuperRes). Subjective image quality was evaluated by two blinded radiologists reviewing 8 criteria on a 5-point Likert scale and signal-to-noise ratio calculated as an objective parameter. The protocol reconstructed with CS-SuperRes received higher ratings than the time-equivalent CS reconstructions, statistically significant especially for low resolution acquisitions (e.g., overall image impression: 4.3 ± 0.4 vs. 3.4 ± 0.4, p < 0.05). CS-SuperRes reconstructions for the low resolution acquisition were comparable to traditional CS reconstructions with standard resolution for all parameters, achieving a scan time reduction from 11:01 min to 4:46 min (57 %) for the complete protocol (e.g. overall image impression: 4.3 ± 0.4 vs. 4.0 ± 0.5, p < 0.05). The newly-developed AI-based reconstruction algorithm CS-SuperRes allows to reduce scan time by 57% while maintaining unchanged image quality compared to the conventional CS reconstruction. [ABSTRACT FROM AUTHOR]

Abstract Background Rapid magnetic resonance imaging (MRI) protocols may be effective in the emergency department (ED) to evaluate nontraumatic neurologic complaints. We evaluate neuroimaging (rapid MRI [rMRI]), head computerized tomography [HCT], and full MRI) use following widespread implementation of rMRI protocols in a pediatric emergency department (ED). Methods We conducted a retrospective study in a tertiary care pediatric ED of encounters with neuroimaging during two 9-month periods: one prior to (control period) and one after generalized availability of 4 rMRI protocols (rMRI period). The primary outcome was differences in neuroimaging rates between the two periods. Secondary outcomes included ED process measures, unsuccessful imaging, and undetected pathology, with full MRI within 14 days as the reference standard. Results There were 1052 encounters with neuroimaging during the control and 1308 during the rMRI periods. Differences in neuroimaging between periods were 27.7% for rMRI (95% CI, 24.4, 31.0), − 21.5% for HCT (95% CI, − 25.5, − 17.5), and − 6.2% for full MRI (95% CI, − 9.3, − 3.1%.) Time to imaging (182 [IQR 138–255] versus 86 [IQR 52–137] minutes) as well as ED length of stay (396 [IQR 304–484] versus 257 [IQR 196–334] minutes) was longer for rMRI versus HCT (p

Deep learning methods for fast MRI have shown promise in reconstructing high-quality images from undersampled multi-coil k-space data, leading to reduced scan duration. However, existing methods encounter challenges related to limited receptive fields in dual-domain (k-space and image domains) reconstruction networks, rigid data consistency operations, and suboptimal refinement structures, which collectively restrict overall reconstruction performance. This study introduces a comprehensive framework that addresses these challenges and enhances MR image reconstruction quality. Firstly, we propose Faster Inverse Fourier Convolution (FasterIFC), a frequency domain convolutional operator that significantly expands the receptive field of k-space domain reconstruction networks. Expanding the information extraction range to the entire frequency spectrum according to the spectral convolution theorem in Fourier theory enables the network to easily utilize richer redundant long-range information from adjacent, symmetrical, and diagonal locations of multi-coil k-space data. Secondly, we introduce a novel softer Data Consistency (softerDC) layer, which achieves an enhanced balance between data consistency and smoothness. This layer facilitates the implementation of diverse data consistency strategies across distinct frequency positions, addressing the inflexibility observed in current methods. Finally, we present the Dual-Domain Faster Fourier Convolution Based Network (D2F2), which features a centrosymmetric dual-domain parallel structure based on FasterIFC. This architecture optimally leverages dual-domain data characteristics while substantially expanding the receptive field in both domains. Coupled with the softerDC layer, D2F2 demonstrates superior performance on the NYU fastMRI dataset at multiple acceleration factors, surpassing state-of-the-art methods in both quantitative and qualitative evaluations., Competing Interests: Declaration of competing interest None Declared., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

BACKGROUND. Abbreviated breast MRI (AB-MRI) achieves a higher cancer detection rate (CDR) than digital breast tomosynthesis when applied for baseline (i.e., first-round) supplemental screening of individuals with dense breasts. Limited literature has evaluated subsequent (i.e., sequential) AB-MRI screening rounds. OBJECTIVE. This study aimed to compare outcomes between baseline and subsequent rounds of screening AB-MRI in individuals with dense breasts who otherwise had an average risk for breast cancer. METHODS. This retrospective study included patients with dense breasts who otherwise had an average risk for breast cancer and underwent AB-MRI for supplemental screening between December 20, 2016, and May 10, 2023. The clinical interpretations and results of recommended biopsies for AB-MRI examinations were extracted from the EMR. Baseline and subsequent-round AB-MRI examinations were compared. RESULTS. The final sample included 2585 AB-MRI examinations (2007 baseline and 578 subsequent-round examinations) performed for supplemental screening of 2007 women (mean age, 57.1 years old) with dense breasts. Of 2007 baseline examinations, 1658 (82.6%) were assessed as BI-RADS category 1 or 2, 171 (8.5%) as BI-RADS category 3, and 178 (8.9%) as BI-RADS category 4 or 5. Of 578 subsequent-round examinations, 533 (92.2%) were assessed as BI-RADS category 1 or 2, 20 (3.5%) as BI-RADS category 3, and 25 (4.3%) as BI-RADS category 4 or 5 ( p < .001). The abnormal interpretation rate (AIR) was 17.4% (349/2007) for baseline examinations versus 7.8% (45/578) for subsequent-round examinations ( p < .001). For baseline examinations, PPV2 was 21.3% (38/178), PPV3 was 26.6% (38/143), and the CDR was 18.9 cancers per 1000 examinations (38/2007). For subsequent-round examinations, PPV2 was 28.0% (7/25) ( p = .45), PPV3 was 29.2% (7/24) ( p = .81), and the CDR was 12.1 cancers per 1000 examinations (7/578) ( p = .37). All 45 cancers diagnosed by baseline or subsequent-round AB-MRI were stage 0 or 1. Seven cancers diagnosed by subsequent-round AB-MRI had a mean interval of 872 ± 373 (SD) days since prior AB-MRI and node-negative status at surgical axillary evaluation; six had an invasive component, all measuring 1.2 cm or less. CONCLUSION. Subsequent rounds of AB-MRI screening of individuals with dense breasts had lower AIR than baseline examinations while maintaining a high CDR. All cancers detected by subsequent-round examinations were early-stage node-negative cancers. CLINICAL IMPACT. The findings support sequential AB-MRI for supplemental screening in individuals with dense breasts. Further investigations are warranted to optimize the screening interval.

Despite its superior soft tissue contrast and non-invasive nature, MRI requires long scan times due to its intrinsic signal acquisition principles, a main drawback which technological advancements in MRI have been focused on. In particular, scan time reduction is a natural requirement in neuroimaging due to detailed structures requiring high resolution imaging and often volumetric (3D) acquisitions, and numerous studies have recently attempted to harness deep learning (DL) technology in enabling scan time reduction and image quality improvement. Various DL-based image reconstruction products allow for additional scan time reduction on top of existing accelerated acquisition methods without compromising the image quality.

Functional MRI (fMRI) is an important tool for non-invasive studies of brain function. Over the past decade, multi-echo fMRI methods that sample multiple echo times has become popular with potential to improve quantification. While these acquisitions are typically performed with Cartesian trajectories, non-Cartesian trajectories, in particular spiral acquisitions, hold promise for denser sampling of echo times. However, such acquisitions require very high acceleration rates for sufficient spatiotemporal resolutions. In this work, we propose to use a physics-driven deep learning (PD-DL) reconstruction to accelerate multi-echo spiral fMRI by 10-fold. We modify a self-supervised learning algorithm for optimized training with non-Cartesian trajectories and use it to train the PD-DL network. Results show that the proposed self-supervised PD-DL reconstruction achieves high spatio-temporal resolution with meaningful BOLD analysis.

Electroencephalography (EEG) concurrently collected with functional magnetic resonance imaging (fMRI) is heavily distorted by the repetitive gradient coil switching during the fMRI acquisition. The performance of the typical template-based gradient artifact suppression method can be suboptimal because the artifact changes over time. Gradient artifact residuals also impede the subsequent suppression of ballistocardiography artifacts.Here we propose recording continuous EEG with temporally sparse fast fMRI (fast fMRI-EEG) to minimize the EEG artifacts caused by MRI gradient coil switching without significantly compromising the field-of-view and spatiotemporal resolution of fMRI. Using simultaneous multi-slice inverse imaging to achieve whole-brain fMRI with isotropic 5-mm resolution in 0.1 ​s, and performing these acquisitions once every 2 ​s, we have 95% of the duty cycle available to record EEG with substantially less gradient artifact. We found that the standard deviation of EEG signals over the entire acquisition period in fast fMRI-EEG was reduced to 54% of that in conventional concurrent echo-planar imaging (EPI) and EEG recordings (EPI-EEG) across participants. When measuring 15-Hz steady-state visual evoked potentials (SSVEPs), the baseline-normalized oscillatory neural response in fast fMRI-EEG was 2.5-fold of that in EPI-EEG. The functional MRI responses associated with the SSVEP delineated by EPI and fast fMRI were similar in the spatial distribution, the elicited waveform, and detection power. Sparsely interleaved fast fMRI-EEG provides high-quality EEG without substantially compromising the quality of fMRI in evoked response measurements, and has the potential utility for applications where the onset of the target stimulus cannot be precisely determined, such as epilepsy.

Compressed Sensing Magnetic Resonance Imaging (CS-MRI) could be considered a challenged task since it could be designed as an efficient technique for fast MRI acquisition which could be highly beneficial for several clinical routines. In fact, it could grant better scan quality by reducing motion artifacts amount as well as the contrast washout effect. It offers also the possibility to reduce the exploration cost and the patient's anxiety. Recently, Deep Learning Neuronal Network (DL) has been suggested in order to reconstruct MRI scans with conserving the structural details and improving parallel imaging-based fast MRI. In this paper, we propose Deep Convolutional Encoder-Decoder architecture for CS-MRI reconstruction. Such architecture bridges the gap between the non-learning techniques, using data from only one image, and approaches using large training data. The proposed approach is based on autoencoder architecture divided into two parts: an encoder and a decoder. The encoder as well as the decoder has essentially three convolutional blocks. The proposed architecture has been evaluated through two databases: Hammersmith dataset (for the normal scans) and MICCAI 2018 (for pathological MRI). Moreover, we extend our model to cope with noisy pathological MRI scans. The normalized mean square error (NMSE), the peak-to-noise ratio (PSNR), and the structural similarity index (SSIM) have been adopted as evaluation metrics in order to evaluate the proposed architecture performance and to make a comparative study with the state-of-the-art reconstruction algorithms. The higher PSNR and SSIM values as well as the lowest NMSE values could attest that the proposed architecture offers better reconstruction and preserves textural image details. Furthermore, the running time is about 0.8 s, which is suitable for real-time processing. Such results could encourage the neurologist to adopt it in their clinical routines. Graphical abstract. [ABSTRACT FROM AUTHOR]

Background: Rapid magnetic resonance imaging (MRI) protocols may be effective in the emergency department (ED) to evaluate nontraumatic neurologic complaints. We evaluate neuroimaging (rapid MRI [rMRI]), head computerized tomography [HCT], and full MRI) use following widespread implementation of rMRI protocols in a pediatric emergency department (ED).Methods: We conducted a retrospective study in a tertiary care pediatric ED of encounters with neuroimaging during two 9-month periods: one prior to (control period) and one after generalized availability of 4 rMRI protocols (rMRI period). The primary outcome was differences in neuroimaging rates between the two periods. Secondary outcomes included ED process measures, unsuccessful imaging, and undetected pathology, with full MRI within 14 days as the reference standard.Results: There were 1052 encounters with neuroimaging during the control and 1308 during the rMRI periods. Differences in neuroimaging between periods were 27.7% for rMRI (95% CI, 24.4, 31.0), - 21.5% for HCT (95% CI, - 25.5, - 17.5), and - 6.2% for full MRI (95% CI, - 9.3, - 3.1%.) Time to imaging (182 [IQR 138-255] versus 86 [IQR 52-137] minutes) as well as ED length of stay (396 [IQR 304-484] versus 257 [IQR 196-334] minutes) was longer for rMRI versus HCT (p < 0.01). Between the control and rMRI periods, there were differences in types of neuroimaging performed for patients with altered mental status, headache, seizure, shunt dysfunction, stroke, syncope, trauma, vomiting, infection, and other neurologic complaints (p < 0.05). rMRI studies were unsuccessful in 3.6% of studies versus 0.0% of HCTs (p < 0.01). The 22 unsuccessful rMRI studies were unsuccessful due to artifacts from dental hardware (n = 2) and patient motion (n = 20). None of the rMRI studies with full MRI follow-up imaging had undetected pathology; the false negative rate for the HCT exams was as high as 25%.Conclusions: After routine ED use of 4 rMRI protocols, there was a more than 20% decrease in HCT use without missed diagnoses. Time to neuroimaging and length of stay were longer for rMRI than HCT, with higher rates of unsuccessful imaging. Despite these limitations, rMRI may be an alternative to HCT for nontraumatic complaints in the ED. [ABSTRACT FROM AUTHOR]

Purpose: To optimize operations for a newly implemented abbreviated MR (AB-MR) breast cancer screening protocol, activity times were compared with the full-protocol examination.Methods: Activity times from 70 AB-MR and 736 full MR screening studies were analyzed. Total study time was measured from opening to closing examinations and expected scan time by summing the time of acquisition for each imaging series for either protocol. Actual scan time was obtained from DICOM headers. Total technologist activity time was obtained by subtracting expected scan time from total study time. Technologist activity time included both scan-related and non-scan-related activities.Results: The actual scan time for the AB protocol was 17.5 ± 0.5 min, compared with 28.8 ± 0.3 min (mean ± SE) for the full protocol (difference, 11.3 min; P < .0001). The total study time was 36.0 ± 3.2 min for AB-MRI and 49.7 ± 0.8 min for the full protocol (difference, 13.7 min; P < .0001), implying that the AB-MR protocol had only a 38% greater patient flow rate than the full protocol.Conclusions: The realized gains in patient flow rate were lower than expected based on scan times (65%) because of increased technologist activity time for the AB-MR protocol. [ABSTRACT FROM AUTHOR]

The purpose is to determine whether an abbreviated MRI protocol (ABMR) is ready to be used for breast cancer screening in an academic practice setting. Two hundred and fifty nine breast MRIs from 1/1/2012 to 6/30/2012 were retrospectively reviewed using ABMR (MIP, Pre‐contrastT1, single dynamic post‐contrastT1, and subtraction). Five breast radiologists (4‐28 year‐expr) participated in this reader study performed in two phases: Phase1 ‐ radiologist's privy to clinical history but not to comparison imaging. Phase2 ‐ radiologists provided comparison imaging. For phase1, studies were reviewed using three steps: (a) MIP only (positive/negative/intermediate); (b) ABMR (recall/no recall) and (c) With T2 (for changes in recommendations). Radiologist also recorded total time for interpretation. In Phase2 the MRIs coded as "recall" were re‐reviewed with available comparison studies, noting changes in final recommendation. The abnormal interpretation rates (AIRs) were calculated for phase1 and phase2 results with comparison to the original full protocol. Of the 259 patients (avg. age‐52 years; range 26‐78), there were seven cancers (three invasive, three DCIS and one breast lymphoma). Acquisition time for ABMR was 3 minutes, ABMR + T2‐8 minutes, and original full protocol 16 minutes. Average MIP was positive or indeterminate in 86% (6/7) and negative in 14% (1/7) cancers. The average AIR for MIP only was 20.8% (sens‐77.1%; spec‐80.8%. The AIR w/o comparisons was 25.6% (sens‐91.4%; spec‐ 76.2%); however the average AIR decreased in phase 2 with comparisons to 13.7% (sens‐91.4%; spec‐88.5%). The AIR of the original full protocol read was 16.2% (sens‐100%; spec‐85.7%). Addition of T2 changed assessment in only 3% (1.2%‐6.5%). Avg. read time for ABMR including T2 was 2.5 minutes (1.6‐4.0 minutes). ABMR is reliable for breast cancer screening, with acceptable interpretation time and acceptable AIR. [ABSTRACT FROM AUTHOR]

Study Design: Diagnostic accuracy.Objective: The purpose of this study was to determine if neural axis abnormalities in early onset scoliosis (EOS) patients can be reliably detected with limited magnetic resonance imaging (MRI) sequences (sagittal T1, sagittal T2).Summary Of Background Data: MRIs are often performed in EOS patients as studies have shown there are neural axis abnormalities in up to 40% of this population. MRIs are expensive, lengthy, and often require general anesthesia. In young children prolonged or repeated exposure to general anesthesia may be associated with neurocognitive damage.Methods: A retrospective review of consecutive EOS patients from February to December 2017 who received an MRI of the cervical, thoracic, and lumbar spine was conducted. MRI images were reviewed for neural axis abnormalities. Two sequences (sagittal T1, sagittal T2) of these previously reviewed MRIs were read at a separate time by an attending pediatric neuroradiologist. The imaging findings from these two select sequences were then compared with the prior radiology report based on all of the standard MRI sequences.Results: Fifty patients met criteria. Ten patients (20%) had neural axis abnormalities detected by the full MRI. All of these neural axis abnormalities were detected on the combination of sagittal T1 + sagittal T2 images. Standard MRIs lasted 66 ± 20 minutes and patients required 90 ± 22 minutes of anesthesia. Sagittal T1 + sagittal T2 sequences lasted 21 ± 7 minutes (P < 0.0001).Conclusion: Limited sequence MRIs with sagittal T1 and T2 sequences for EOS patients had 100% sensitivity for the detection of neural axis abnormalities and would allow for a 68% reduction in the length of MRI and significant reduction in anesthesia time.Level Of Evidence: 3. [ABSTRACT FROM AUTHOR]

Compressed sensing magnetic resonance imaging (CS-MRI) enables fast acquisition, which is highly desirable for numerous clinical applications. This can not only reduce the scanning cost and ease patient burden, but also potentially reduce motion artefacts and the effect of contrast washout, thus yielding better image quality. Different from parallel imaging-based fast MRI, which utilizes multiple coils to simultaneously receive MR signals, CS-MRI breaks the Nyquist–Shannon sampling barrier to reconstruct MRI images with much less required raw data. This paper provides a deep learning-based strategy for reconstruction of CS-MRI, and bridges a substantial gap between conventional non-learning methods working only on data from a single image, and prior knowledge from large training data sets. In particular, a novel conditional Generative Adversarial Networks-based model (DAGAN)-based model is proposed to reconstruct CS-MRI. In our DAGAN architecture, we have designed a refinement learning method to stabilize our U-Net based generator, which provides an end-to-end network to reduce aliasing artefacts. To better preserve texture and edges in the reconstruction, we have coupled the adversarial loss with an innovative content loss. In addition, we incorporate frequency-domain information to enforce similarity in both the image and frequency domains. We have performed comprehensive comparison studies with both conventional CS-MRI reconstruction methods and newly investigated deep learning approaches. Compared with these methods, our DAGAN method provides superior reconstruction with preserved perceptual image details. Furthermore, each image is reconstructed in about 5 ms, which is suitable for real-time processing. [ABSTRACT FROM AUTHOR]