OBJECTIVE:To evaluate the diagnostic performance of a 3-min ultrafast brain MRI and MRA protocol for screening of acute ischemic stroke. METHODS:This study involved 67 adult patients who underwent ultrafast and reference MRI and MRA scans from September 2023 to June 2024 for stroke evaluation. Two readers independently assessed the ultrafast and reference MRI and MRA images in a masked and randomized manner for acute and chronic infarct and hemorrhage as well as large-vessel occlusion and severe stenosis. A 3-point Likert scale was used to evaluate diagnostic quality of the ultrafast sequences and Cohen's κ was used to assess interrater agreement. RESULTS:The ultrafast MRI and MRA protocol showed high diagnostic quality, with 98% of sequences rated as diagnostic. Raters showed perfect agreement in identifying acute infarcts, aneurysms, and vascular occlusions using both ultrafast and reference protocols and near-perfect agreement (>95%) for detecting acute hemorrhage and severe stenosis. For chronic conditions such as chronic infarction and chronic hemorrhage, there was substantial agreement with κ values ranging from 0.73 to 0.76. DISCUSSION:The screening ultrafast MRI and MRA protocol can effectively identify acute ischemic stroke and intracranial large-vessel occlusion with high diagnostic accuracy while significantly reducing acquisition time, making it suitable for initial stroke triage. Evaluation for chronic pathologies on the ultrafast protocol is inferior compared with standard MRI and MRA imaging.

We are thrilled to bring you the inaugural JACR Focus Issue on Teaching and Education. Radiology education is undergoing a profound transformation, guided by the fusion of technological advancements, societal shifts, and the evolving demands of health care. Within the pages of this focus issue, JACR presents a diverse collection of articles that collectively offer a panoramic view of the current state and future directions of radiology education and underscore the paramount importance of evidence-based education methods. We have included 15 articles that provide insight into understanding new teaching and learning techniques that improve comprehension and knowledge retention, how to address new challenges related to the coronavirus disease 2019 pandemic, how to maximize the use of artificial intelligence (AI) in the training and continuing medical education learning environments, how to enhance trainee performance with behavioral modification, and unintended consequences of eliminating United States Medical Licensing Examination Step 1 scores.

BACKGROUND:Substantial gaps exist in the neuroprognostication of cardiac arrest patients who remain comatose after the restoration of spontaneous circulation. Most studies focus on predicting survival, a measure confounded by the withdrawal of life-sustaining treatment decisions. Severe cerebral edema (SCE) may serve as an objective proximal imaging-based surrogate of neurologic injury. METHODS:We retrospectively analyzed data from 288 patients to automate SCE detection with machine learning (ML) and to test the hypothesis that the quantitative values produced by these algorithms (ML_SCE) can improve predictions of neurologic outcomes. Ground-truth SCE (GT_SCE) classification was based on radiology reports. RESULTS:The model attained a cross-validated testing accuracy of 87% [95% CI: 84%, 89%] for detecting SCE. Attention maps explaining SCE classification focused on cisternal regions (p < 0.05). Multivariable analyses showed that older age (p < 0.001), non-shockable initial cardiac rhythm (p = 0.004), and greater ML_SCE values (p < 0.001) were significant predictors of poor neurologic outcomes, with GT_SCE (p = 0.064) as a non-significant covariate. CONCLUSION:Our results support the feasibility of automated SCE detection. Future prospective studies with standardized neurologic assessments are needed to substantiate the utility of quantitative ML_SCE values to improve neuroprognostication.

Acute stroke imaging plays a vital and time-sensitive role in therapeutic decision-making. Current clinical workflows widely use computed tomography (CT) and magnetic resonance (MR) techniques including CT and MR perfusion to estimate the volume of ischemic penumbra at risk for infarction without acute intervention. The use of imaging techniques aimed toward evaluating the metabolic derangements underlying a developing infarct may provide additional information for differentiating the penumbra from benign oligemia and infarct core. The authors review several modalities of metabolic imaging including PET, hydrogen and oxygen spectroscopy, sodium MRI, and pH-weighted MRI.

Background and Aims: Treatment options for acute ischemic stroke (AIS) are uniquely dependent on the time of stroke onset. Intravenous thrombolysis must be administered within 4.5 hours of symptoms commencing, yet a subset of patients wake-up with symptoms and time of onset is unknown. Mismatch between fluid-attenuated inversion recovery (FLAIR) and diffusion-weighted imaging (DWI) MRI can be used to qualify wake-up strokes for thrombolysis. This has been shown to lead to improved functional outcomes in multicenter trials (WAKE-UP, MR-WITNESS). However, access to MRI is a barrier to differential diagnosis and subsequent treatment. Recent advances in portable, low-field MRI (LF-MRI) offer a solution to increase access to MRI technologies and circumvent the limitations of conventional systems. As such, in this pilot study, we sought to establish the utility of portable LF-MRI to identify DWI-FLAIR mismatch following AIS. Methods: Patients with a diagnosis of AIS presenting to Emergency Department or Intensive Care Unit of the Yale New Haven Hospital or Massachusetts General Hospital from January 2020 to June 2023 underwent DWI and FLAIR acquisition on a 0.064T MRI (Hyperfine Research Inc.) at <4.5 hours ( n =7), 4.5-6 hours ( n =7), and 6-48 hours ( n =110) since last known well (LKW). Conventional high-field (HF) MR images were acquired within 72 hours of LF-MRI. HF-DWI images were co-registered to LF-FLAIR and the hyperintense DWI stroke lesion segmented and superimposed on the FLAIR. The segmentation was mirrored to the contralateral hemisphere of the LF-FLAIR to establish a FLAIR signal intensity ratio (SIR). Results: The FLAIR SIR on images <4.5 hours since LKW was 0.98±0.08. The SIR for cases 4.5-6 hours was 1.34±0.33. Cases 6-48 hours produced a SIR of 1.56±0.36. The SIR at <4.5 hours was significantly less than that observed >6 hours since LKW ( p =0.03). Conclusion: This pilot study demonstrates that a 0.064T portable MRI is capable of distinguishing hyperacute stroke from later stroke onset using FLAIR SIR. Future research is needed in a larger cohort to determine if this imaging technology can be used to guide thrombolysis in the setting of wake-up stroke.

Background and Purpose Deep learning (DL) accelerated MR techniques have emerged as a promising approach to accelerate routine MR exams. While prior studies explored DL acceleration for specific lumbar MRI sequences, a gap remains in comprehending the impact of a fully DL-based MRI protocol on scan time and diagnostic quality for routine lumbar spine MRI. To address this, we assessed the image quality and diagnostic performance of a DL-accelerated lumbar spine MRI protocol in comparison to a conventional protocol. Methods We prospectively evaluated 36 consecutive outpatients undergoing non-contrast enhanced lumbar spine MRIs. Both protocols included sagittal T1, T2, STIR, and axial T2-weighted images. Two blinded neuroradiologists independently reviewed images for foraminal stenosis, spinal canal stenosis, nerve root compression, and facet arthropathy. Grading comparison employed the Wilcoxon signed rank test. For the head-to-head comparison, a 5-point Likert scale to assess image quality, considering artifacts, signal-to-noise ratio (SNR), anatomical structure visualization, and overall diagnostic quality. We applied a 15% noninferiority margin to determine whether the DL-accelerated protocol was noninferior. Results No significant differences existed between protocols when evaluating foraminal and spinal canal stenosis, nerve compression, or facet arthropathy (all p > .05). The DL-spine protocol was noninferior for overall diagnostic quality and visualization of the cord, CSF, intervertebral disc, and nerve roots. However, it exhibited reduced SNR and increased artifact perception. Interobserver reproducibility ranged from moderate to substantial (κ = 0.50–0.76). Conclusion Our study indicates that DL reconstruction in spine imaging effectively reduces acquisition times while maintaining comparable diagnostic quality to conventional MRI.

Background and aims: Portable, low-field (LF) MRI has the potential to improve access to expeditious, definitive brain imaging and facilitate diagnosis of acute stroke. Currently available diffusion-weighted imaging (DWI) protocols at LF are limited to a single diffusion direction due to acquisition duration. However, single-direction diffusion has reduced sensitivity for detecting acute ischemic infarcts, particularly small lesions residing in white matter tracts. The purpose of this study was to establish the feasibility of acquiring multi-direction DWI compared with single-direction counterparts on LF-MRI. Methods: Patients presenting with a diagnosis of acute ischemic stroke between July and September 2023 were eligible. Consented patients underwent DWI acquisition on a 0.064T LF-MRI (Mk1.9; Hyperfine Research Inc). Three diffusion directions (x, y, and z) were acquired with a b weighting of 900 s/mm 2 and a single acquisition with a b 0 s/mm 2 . The b 900 images were co-registered to the b 0, trace and apparent diffusion coefficient (ADC) maps calculated, and the final images interpolated at 1 mm 3 . Results: Ten patients presenting to the Massachusetts General Hospital with acute ischemic stroke were consented and imaged within 72 hours of last known well. The total acquisition time was 14 minutes, with all subjects able to tolerate the scan duration. Ischemic lesions as small as 0.1 mL were detectable on the LF-MRI (17.5 +/- 18.2 mL). An example of each diffusion direction individually, the combined trace, and corresponding ADC maps are shown in Figure 1, compared with conventional high-field (HF) diffusion images acquired within 30 minutes of the LF acquisition. Conclusion: Multi-direction DWI imaging is feasible on a 0.064T LF-MRI scanner. Our experience suggests further modifications to the pulse sequence and scanner configuration may facilitate a reduction in acquisition time, improve resolution, or both.

With an ever increasing focus on clinical productivity at the expense of education and research, many radiologists are facing a crisis as the academic work environment becomes more untenable. Simultaneously, physician burnout is on the rise, with a recent survey revealing that 54% of practicing radiologists are feeling burned out [1]. Although physician unions have been around since the 1970s, most practicing physicians remain ununionized. In contrast, as of 2023, approximately 15% of house staff at more than 60 hospitals are members of unions, some of which have been in existence since the 1930s [2].

BACKGROUND AND PURPOSE:The use of MR imaging in emergency settings has been limited by availability, long scan times, and sensitivity to motion. This study assessed the diagnostic performance of an ultrafast brain MR imaging protocol for evaluation of acute intracranial pathology in the emergency department and inpatient settings. MATERIALS AND METHODS:Sixty-six adult patients who underwent brain MR imaging in the emergency department and inpatient settings were included in the study. All patients underwent both the reference and the ultrafast brain MR protocols. Both brain MR imaging protocols consisted of T1-weighted, T2/T2*-weighted, FLAIR, and DWI sequences. The ultrafast MR images were reconstructed by using a machine-learning assisted framework. All images were reviewed by 2 blinded neuroradiologists. RESULTS:The average acquisition time was 2.1 minutes for the ultrafast brain MR protocol and 10 minutes for the reference brain MR protocol. There was 98.5% agreement on the main clinical diagnosis between the 2 protocols. In head-to-head comparison, the reference protocol was preferred in terms of image noise and geometric distortion (P < .05 for both). The ultrafast ms-EPI protocol was preferred over the reference protocol in terms of reduced motion artifacts (P < .01). Overall diagnostic quality was not significantly different between the 2 protocols (P > .05). CONCLUSIONS:The ultrafast brain MR imaging protocol provides high accuracy for evaluating acute pathology while only requiring a fraction of the scan time. Although there was greater image noise and geometric distortion on the ultrafast brain MR protocol images, there was significant reduction in motion artifacts with similar overall diagnostic quality between the 2 protocols.

OBJECTIVE:For stroke patients with unknown time of onset, mismatch between diffusion-weighted imaging (DWI) and fluid-attenuated inversion recovery (FLAIR) magnetic resonance imaging (MRI) can guide thrombolytic intervention. However, access to MRI for hyperacute stroke is limited. Here, we sought to evaluate whether a portable, low-field (LF)-MRI scanner can identify DWI-FLAIR mismatch in acute ischemic stroke. METHODS:Eligible patients with a diagnosis of acute ischemic stroke underwent LF-MRI acquisition on a 0.064-T scanner within 24 h of last known well. Qualitative and quantitative metrics were evaluated. Two trained assessors determined the visibility of stroke lesions on LF-FLAIR. An image coregistration pipeline was developed, and the LF-FLAIR signal intensity ratio (SIR) was derived. RESULTS:The study included 71 patients aged 71 ± 14 years and a National Institutes of Health Stroke Scale of 6 (interquartile range 3-14). The interobserver agreement for identifying visible FLAIR hyperintensities was high (κ = 0.85, 95% CI 0.70-0.99). Visual DWI-FLAIR mismatch had a 60% sensitivity and 82% specificity for stroke patients <4.5 h, with a negative predictive value of 93%. LF-FLAIR SIR had a mean value of 1.18 ± 0.18 <4.5 h, 1.24 ± 0.39 4.5-6 h, and 1.40 ± 0.23 >6 h of stroke onset. The optimal cut-point for LF-FLAIR SIR was 1.15, with 85% sensitivity and 70% specificity. A cut-point of 6.6 h was established for a FLAIR SIR <1.15, with an 89% sensitivity and 62% specificity. INTERPRETATION:A 0.064-T portable LF-MRI can identify DWI-FLAIR mismatch among patients with acute ischemic stroke. Future research is needed to prospectively validate thresholds and evaluate a role of LF-MRI in guiding thrombolysis among stroke patients with uncertain time of onset. ANN NEUROL 2024;96:321-331.

Portable, low-field magnetic resonance imaging (LF-MRI) of the brain may facilitate point-of-care assessment of patients with Alzheimer's disease (AD) in settings where conventional MRI cannot. However, image quality is limited by a lower signal-to-noise ratio. Here, we optimize LF-MRI acquisition and develop a freely available machine learning pipeline to quantify brain morphometry and white matter hyperintensities (WMH). We validate the pipeline and apply it to outpatients presenting with mild cognitive impairment or dementia due to AD. We find hippocampal volumes from <= 3 mm isotropic LF-MRI scans have agreement with conventional MRI and are more accurate than anisotropic counterparts. We also show WMH volume has agreement between manual segmentation and the automated pipeline. The increased availability and reduced cost of LF-MRI, in combination with our machine learning pipeline, has the potential to increase access to neuroimaging for dementia.

Early prediction of the recovery of consciousness in comatose cardiac arrest patients remains challenging. We prospectively studied task-relevant fMRI responses in 19 comatose cardiac arrest patients and five healthy controls to assess the fMRI's utility for neuroprognostication. Tasks involved instrumental music listening, forward and backward language listening, and motor imagery. Task-specific reference images were created from group-level fMRI responses from the healthy controls. Dice scores measured the overlap of individual subject-level fMRI responses with the reference images. Task-relevant responsiveness index (Rindex) was calculated as the maximum Dice score across the four tasks. Correlation analyses showed that increased Dice scores were significantly associated with arousal recovery (P < 0.05) and emergence from the minimally conscious state (EMCS) by one year (P < 0.001) for all tasks except motor imagery. Greater Rindex was significantly correlated with improved arousal recovery (P = 0.002) and consciousness (P = 0.001). For patients who survived to discharge (n = 6), the Rindex's sensitivity was 75% for predicting EMCS (n = 4). Task-based fMRI holds promise for detecting covert consciousness in comatose cardiac arrest patients, but further studies are needed to confirm these findings. Caution is necessary when interpreting the absence of task-relevant fMRI responses as a surrogate for inevitable poor neurological prognosis.

Background: Portable, low-field-strength (0.064-T) MRI has the potential to transform neuroimaging but is limited by low spatial resolution and low signal-to-noise ratio. Purpose: To implement a machine learning super-resolution algorithm that synthesizes higher spatial resolution images (1-mm isotropic) from lower resolution T1-weighted and T2-weighted portable brain MRI scans, making them amenable to automated quantitative morphometry.Materials and Methods: An external high-field-strength MRI data set (1-mm isotropic scans from the Open Access Series of Imaging Studies data set) and segmentations for 39 regions of interest (ROIs) in the brain were used to train a super-resolution convolu-tional neural network (CNN). Secondary analysis of an internal test set of 24 paired low-and high-field-strength clinical MRI scans in participants with neurologic symptoms was performed. These were part of a prospective observational study (August 2020 to December 2021) at Massachusetts General Hospital (exclusion criteria: inability to lay flat, body habitus preventing low-field -strength MRI, presence of MRI contraindications). Three well-established automated segmentation tools were applied to three sets of scans: high-field-strength (1.5-3 T, reference standard), low-field-strength (0.064 T), and synthetic high-field-strength images generated from the low-field-strength data with the CNN. Statistical significance of correlations was assessed with Student t tests. Correlation coefficients were compared with Steiger Z tests.Results: Eleven participants (mean age, 50 years +/- 14; seven men) had full cerebrum coverage in the images without motion arti-facts or large stroke lesion with distortion from mass effect. Direct segmentation of low-field-strength MRI yielded nonsignificant correlations with volumetric measurements from high field strength for most ROIs (P > .05). Correlations largely improved when segmenting the synthetic images: P values were less than .05 for all ROIs (eg, for the hippocampus [r = 0.85; P < .001], thalamus [r = 0.84; P = .001], and whole cerebrum [r = 0.92; P < .001]). Deviations from the model (z score maps) visually correlated with pathologic abnormalities.Conclusion: This work demonstrated proof-of-principle augmentation of portable MRI with a machine learning super-resolution algorithm, which yielded highly correlated brain morphometric measurements to real higher resolution images.(c) RSNA, 2022

The advent of portable, low-field MRI (LF-MRI) heralds new opportunities in neuroimaging. Low power requirements and transportability have enabled scanning outside the controlled environment of a conventional MRI suite, enhancing access to neuroimaging for indications that are not well suited to existing technologies. Maximizing the information extracted from the reduced signal-to-noise ratio of LF-MRI is crucial to developing clinically useful diagnostic images. Progress in electromagnetic noise cancellation and machine learning reconstruction algorithms from sparse k -space data as well as new approaches to image enhancement have now enabled these advancements. Coupling technological innovation with bedside imaging creates new prospects in visualizing the healthy brain and detecting acute and chronic pathological changes. Ongoing development of hardware, improvements in pulse sequences and image reconstruction, and validation of clinical utility will continue to accelerate this field. As further innovation occurs, portable LF-MRI will facilitate the democratization of MRI and create new applications not previously feasible with conventional systems.

Flow-related artifacts have been observed in highly accelerated T1-weighted contrast-enhanced wave-controlled aliasing in parallel imaging (CAIPI) magnetization-prepared rapid gradient-echo (MPRAGE) imaging and can lead to diagnostic uncertainty. We developed an optimized flow-mitigated Wave-CAIPI MPRAGE acquisition protocol to reduce these artifacts through testing in a custom-built flow phantom. In the phantom experiment, maximal flow artifact reduction was achieved with the combination of flow compensation gradients and radial reordered k -space acquisition and was included in the optimized sequence. Clinical evaluation of the optimized MPRAGE sequence was performed in 64 adult patients, who all underwent contrast-enhanced Wave-CAIPI MPRAGE imaging without flow-compensation and with optimized flow-compensation parameters. All images were evaluated for the presence of flow-related artifacts, signal-to-noise ratio (SNR), gray-white matter contrast, enhancing lesion contrast, and image sharpness on a 3-point Likert scale. In the 64 cases, the optimized flow mitigation protocol reduced flow-related artifacts in 89% and 94% of the cases for raters 1 and 2, respectively. SNR, gray-white matter contrast, enhancing lesion contrast, and image sharpness were rated as equivalent for standard and flow-mitigated Wave-CAIPI MPRAGE in all subjects. The optimized flow mitigation protocol successfully reduced the presence of flow-related artifacts in the majority of cases. Relevance statement As accelerated MRI using novel encoding schemes become increasingly adopted in clinical practice, our work highlights the need to recognize and develop strategies to minimize the presence of unexpected artifacts and reduction in image quality as potential compromises to achieving short scan times. Key points • Flow-mitigation technique led to an 89–94% decrease in flow-related artifacts. • Image quality, signal-to-noise ratio, enhancing lesion conspicuity, and image sharpness were preserved with the flow mitigation technique. • Flow mitigation reduced diagnostic uncertainty in cases where flow-related artifacts mimicked enhancing lesions. Graphical Abstract

Ischemic strokes in young adults are increasing in incidence and have emerged as a public health issue. The radiological features are not only diagnostic in identifying ischemic infarctions but also provide important clues in the investigation of the underlying causes or in the identification of risk factors. According to the different imaging patterns associated with ischemic stroke in young adults, the causes can be classified into 5 categories: cardioembolism, large vessel vasculopathy, small vessel vasculopathy, toxic-metabolic, and hypercoagulable disorders. The radiological features of each category and cause are described and summarized in this review.

Machine learning (ML) algorithms to detect critical findings on head CTs may expedite patient management. Most ML algorithms for diagnostic imaging analysis utilize dichotomous classifications to determine whether a specific abnormality is present. However, imaging findings may be indeterminate, and algorithmic inferences may have substantial uncertainty. We incorporated awareness of uncertainty into an ML algorithm that detects intracranial hemorrhage or other urgent intracranial abnormalities and evaluated prospectively identified, 1000 consecutive noncontrast head CTs assigned to Emergency Department Neuroradiology for interpretation. The algorithm classified the scans into high (IC+) and low (IC-) probabilities for intracranial hemorrhage or other urgent abnormalities. All other cases were designated as No Prediction (NP) by the algorithm. The positive predictive value for IC+ cases (N = 103) was 0.91 (CI: 0.84-0.96), and the negative predictive value for IC-cases (N = 729) was 0.94 (0.91-0.96). Admission, neurosurgical intervention, and 30-day mortality rates for IC+ was 75% (63-84), 35% (24-47), and 10% (4-20), compared to 43% (40-47), 4% (3-6), and 3% (2-5) for IC-. There were 168 NP cases, of which 32% had intracranial hemorrhage or other urgent abnormalities, 31% had artifacts and postoperative changes, and 29% had no abnormalities. An ML algorithm incorporating uncertainty classified most head CTs into clinically relevant groups with high predictive values and may help accelerate the management of patients with intracranial hemorrhage or other urgent intracranial abnormalities.

Purpose To evaluate the impact of magnetization transfer (MT) on brain tissue contrast in turbo-spin-echo (TSE) and EPI fluid-attenuated inversion recovery (FLAIR) images, and to optimize an MT-prepared EPI FLAIR pulse sequence to match the tissue contrast of a clinical reference TSE FLAIR protocol. Methods Five healthy volunteers underwent 3T brain MRI, including single slice TSE FLAIR, multi-slice TSE FLAIR, EPI FLAIR without MT-preparation, and MT-prepared EPI FLAIR with variations of the MT-preparation parameters, including number of preparation pulses, pulse amplitude, and resonance offset. Automated co-registration and gray matter (GM) versus white matter (WM) segmentation was performed using a T1-MPRAGE acquisition, and the GM versus WM signal intensity ratio (contrast ratio) was calculated for each FLAIR acquisition. Results Without MT preparation, EPI FLAIR showed poor tissue contrast (contrast ratio = 0.98), as did single slice TSE FLAIR. Multi-slice TSE FLAIR provided high tissue contrast (contrast ratio = 1.14). MT-prepared EPI FLAIR closely approximated the contrast of the multi-slice TSE FLAIR images for two combinations of the MT-preparation parameters (contrast ratio = 1.14). Optimized MT-prepared EPI FLAIR provided a 50% reduction in scan time compared to the reference TSE FLAIR acquisition. Conclusion Optimized MT-prepared EPI FLAIR provides comparable brain tissue contrast to the multi-slice TSE FLAIR images used in clinical practice.