Standardized reporting for Head CT Scans in patients suspected of traumatic brain injury (TBI): An international expert endeavor.

BACKGROUND AND PURPOSE: Traumatic brain injury (TBI) is a major source of health loss and disability worldwide. Accurate and timely diagnosis of TBI is critical for appropriate treatment and management of the condition. Neuroimaging plays a crucial role in the diagnosis and characterization of TBI. Computed tomography (CT) is the first-line diagnostic imaging modality typically utilized in patients with suspected acute mild, moderate and severe TBI. Radiology reports play a crucial role in the diagnostic process, providing critical information about the location and extent of brain injury, as well as factors that could prevent secondary injury. However, the complexity and variability of radiology reports can make it challenging for healthcare providers to extract the necessary information for diagnosis and treatment planning. METHODS/RESULTS/CONCLUSION: In this article, we report the efforts of an international group of TBI imaging experts to develop a clinical radiology report template for CT scans obtained in patients suspected of TBI and consisting of fourteen different subdivisions (CT technique, mechanism of injury or clinical history, presence of scalp injuries, fractures, potential vascular injuries, potential injuries involving the extra-axial spaces, brain parenchymal injuries, potential injuries involving the cerebrospinal fluid spaces and the ventricular system, mass effect, secondary injuries, prior or coexisting pathology).

Accelerated MRI reconstructions via variational network and feature domain learning.

We introduce three architecture modifications to enhance the performance of the end-to-end (E2E) variational network (VarNet) for undersampled MRI reconstructions. We first implemented the Feature VarNet, which propagates information throughout the cascades of the network in an N-channel feature-space instead of a 2-channel feature-space. Then, we add an attention layer that utilizes the spatial locations of Cartesian undersampling artifacts to further improve performance. Lastly, we combined the Feature and E2E VarNets into the Feature-Image (FI) VarNet, to facilitate cross-domain learning and boost accuracy. Reconstructions were evaluated on the fastMRI dataset using standard metrics and clinical scoring by three neuroradiologists. Feature and FI VarNets outperformed the E2E VarNet for 4 × , 5 × and 8 × Cartesian undersampling in all studied metrics. FI VarNet secured second place in the public fastMRI leaderboard for 4 × Cartesian undersampling, outperforming all open-source models in the leaderboard. Radiologists rated FI VarNet brain reconstructions with higher quality and sharpness than the E2E VarNet reconstructions. FI VarNet excelled in preserving anatomical details, including blood vessels, whereas E2E VarNet discarded or blurred them in some cases. The proposed FI VarNet enhances the reconstruction quality of undersampled MRI and could enable clinically acceptable reconstructions at higher acceleration factors than currently possible.

Deep learning with diffusion MRI as in vivo microscope reveals sex-related differences in human white matter microstructure.

Biological sex is a crucial variable in neuroscience studies where sex differences have been documented across cognitive functions and neuropsychiatric disorders. While gross statistical differences have been previously documented in macroscopic brain structure such as cortical thickness or region size, less is understood about sex-related cellular-level microstructural differences which could provide insight into brain health and disease. Studying these microstructural differences between men and women paves the way for understanding brain disorders and diseases that manifest differently in different sexes. Diffusion MRI is an important in vivo, non-invasive methodology that provides a window into brain tissue microstructure. Our study develops multiple end-to-end classification models that accurately estimates the sex of a subject using volumetric diffusion MRI data and uses these models to identify white matter regions that differ the most between men and women. 471 male and 560 female healthy subjects (age range, 22-37 years) from the Human Connectome Project are included. Fractional anisotropy, mean diffusivity and mean kurtosis are used to capture brain tissue microstructure characteristics. Diffusion parametric maps are registered to a standard template to reduce bias that can arise from macroscopic anatomical differences like brain size and contour. This study employ three major model architectures: 2D convolutional neural networks, 3D convolutional neural networks and Vision Transformer (with self-supervised pretraining). Our results show that all 3 models achieve high sex classification performance (test AUC 0.92-0.98) across all diffusion metrics indicating definitive differences in white matter tissue microstructure between males and females. We further use complementary model architectures to inform about the pattern of detected microstructural differences and the influence of short-range versus long-range interactions. Occlusion analysis together with Wilcoxon signed-rank test is used to determine which white matter regions contribute most to sex classification. The results indicate that sex-related differences manifest in both local features as well as global features / longer-distance interactions of tissue microstructure. Our highly consistent findings across models provides new insight supporting differences between male and female brain cellular-level tissue organization particularly in the central white matter.

FastMRI Prostate: A public, biparametric MRI dataset to advance machine learning for prostate cancer imaging.

Magnetic resonance imaging (MRI) has experienced remarkable advancements in the integration of artificial intelligence (AI) for image acquisition and reconstruction. The availability of raw k-space data is crucial for training AI models in such tasks, but public MRI datasets are mostly restricted to DICOM images only. To address this limitation, the fastMRI initiative released brain and knee k-space datasets, which have since seen vigorous use. In May 2023, fastMRI was expanded to include biparametric (T2- and diffusion-weighted) prostate MRI data from a clinical population. Biparametric MRI plays a vital role in the diagnosis and management of prostate cancer. Advances in imaging methods, such as reconstructing under-sampled data from accelerated acquisitions, can improve cost-effectiveness and accessibility of prostate MRI. Raw k-space data, reconstructed images and slice, volume and exam level annotations for likelihood of prostate cancer are provided in this dataset for 47468 slices corresponding to 1560 volumes from 312 patients. This dataset facilitates AI and algorithm development for prostate image reconstruction, with the ultimate goal of enhancing prostate cancer diagnosis.

A Comprehensive and Broad Approach to Resting-State Functional Connectivity in Adult Patients with Mild Traumatic Brain Injury.

BACKGROUND AND PURPOSE: Several recent works using resting-state fMRI suggest possible alterations of resting-state functional connectivity after mild traumatic brain injury. However, the literature is plagued by various analysis approaches and small study cohorts, resulting in an inconsistent array of reported findings. In this study, we aimed to investigate differences in whole-brain resting-state functional connectivity between adult patients with mild traumatic brain injury within 1 month of injury and healthy control subjects using several comprehensive resting-state functional connectivity measurement methods and analyses. MATERIALS AND METHODS: A total of 123 subjects (72 patients with mild traumatic brain injury and 51 healthy controls) were included. A standard fMRI preprocessing pipeline was used. ROI/seed-based analyses were conducted using 4 standard brain parcellation methods, and the independent component analysis method was applied to measure resting-state functional connectivity. The fractional amplitude of low-frequency fluctuations was also measured. Group comparisons were performed on all measurements with appropriate whole-brain multilevel statistical analysis and correction. RESULTS: There were no significant differences in age, sex, education, and hand preference between groups as well as no significant correlation between all measurements and these potential confounders. We found that each resting-state functional connectivity measurement revealed various regions or connections that were different between groups. However, after we corrected for multiple comparisons, the results showed no statistically significant differences between groups in terms of resting-state functional connectivity across methods and analyses. CONCLUSIONS: Although previous studies point to multiple regions and networks as possible mild traumatic brain injury biomarkers, this study shows that the effect of mild injury on brain resting-state functional connectivity has not survived after rigorous statistical correction. A further study using subject-level connectivity analyses may be necessary due to both subtle and variable effects of mild traumatic brain injury on brain functional connectivity across individuals.

Functional Connectivity Changes on Resting-State fMRI after Mild Traumatic Brain Injury: A Systematic Review.

BACKGROUND: Mild traumatic brain injury is theorized to cause widespread functional changes to the brain. Resting-state fMRI may be able to measure functional connectivity changes after traumatic brain injury, but resting-state fMRI studies are heterogeneous, using numerous techniques to study ROIs across various resting-state networks. PURPOSE: We systematically reviewed the literature to ascertain whether adult patients who have experienced mild traumatic brain injury show consistent functional connectivity changes on resting-state -fMRI, compared with healthy patients. DATA SOURCES: We used 5 databases (PubMed, EMBASE, Cochrane Central, Scopus, Web of Science). STUDY SELECTION: Five databases (PubMed, EMBASE, Cochrane Central, Scopus, and Web of Science) were searched for research published since 2010. Search strategies used keywords of "functional MR imaging" and "mild traumatic brain injury" as well as related terms. All results were screened at the abstract and title levels by 4 reviewers according to predefined inclusion and exclusion criteria. For full-text inclusion, each study was evaluated independently by 2 reviewers, with discordant screening settled by consensus. DATA ANALYSIS: Data regarding article characteristics, cohort demographics, fMRI scan parameters, data analysis processing software, atlas used, data characteristics, and statistical analysis information were extracted. DATA SYNTHESIS: Across 66 studies, 80 areas were analyzed 239 times for at least 1 time point, most commonly using independent component analysis. The most analyzed areas and networks were the whole brain, the default mode network, and the salience network. Reported functional connectivity changes varied, though there may be a slight trend toward decreased whole-brain functional connectivity within 1 month of traumatic brain injury and there may be differences based on the time since injury. LIMITATIONS: Studies of military, sports-related traumatic brain injury, and pediatric patients were excluded. Due to the high number of relevant studies and data heterogeneity, we could not be as granular in the analysis as we would have liked. CONCLUSIONS: Reported functional connectivity changes varied, even within the same region and network, at least partially reflecting differences in technical parameters, preprocessing software, and analysis methods as well as probable differences in individual injury. There is a need for novel rs-fMRI techniques that better capture subject-specific functional connectivity changes.

Callosal Interhemispheric Communication in Mild Traumatic Brain Injury: A Mediation Analysis on WM Microstructure Effects.

BACKGROUND AND PURPOSE: Because the corpus callosum connects the left and right hemispheres and a variety of WM bundles across the brain in complex ways, damage to the neighboring WM microstructure may specifically disrupt interhemispheric communication through the corpus callosum following mild traumatic brain injury. Here we use a mediation framework to investigate how callosal interhemispheric communication is affected by WM microstructure in mild traumatic brain injury. MATERIALS AND METHODS: Multishell diffusion MR imaging was performed on 23 patients with mild traumatic brain injury within 1 month of injury and 17 healthy controls, deriving 11 diffusion metrics, including DTI, diffusional kurtosis imaging, and compartment-specific standard model parameters. Interhemispheric processing speed was assessed using the interhemispheric speed of processing task (IHSPT) by measuring the latency between word presentation to the 2 hemivisual fields and oral word articulation. Mediation analysis was performed to assess the indirect effect of neighboring WM microstructures on the relationship between the corpus callosum and IHSPT performance. In addition, we conducted a univariate correlation analysis to investigate the direct association between callosal microstructures and IHSPT performance as well as a multivariate regression analysis to jointly evaluate both callosal and neighboring WM microstructures in association with IHSPT scores for each group. RESULTS: Several significant mediators in the relationships between callosal microstructure and IHSPT performance were found in healthy controls. However, patients with mild traumatic brain injury appeared to lose such normal associations when microstructural changes occurred compared with healthy controls. CONCLUSIONS: This study investigates the effects of neighboring WM microstructure on callosal interhemispheric communication in healthy controls and patients with mild traumatic brain injury, highlighting that neighboring noncallosal WM microstructures are involved in callosal interhemispheric communication and information transfer. Further longitudinal studies may provide insight into the temporal dynamics of interhemispheric recovery following mild traumatic brain injury.

Assessing the Performance of Artificial Intelligence Models: Insights from the American Society of Functional Neuroradiology Artificial Intelligence Competition.

BACKGROUND AND PURPOSE: Artificial intelligence models in radiology are frequently developed and validated using data sets from a single institution and are rarely tested on independent, external data sets, raising questions about their generalizability and applicability in clinical practice. The American Society of Functional Neuroradiology (ASFNR) organized a multicenter artificial intelligence competition to evaluate the proficiency of developed models in identifying various pathologies on NCCT, assessing age-based normality and estimating medical urgency. MATERIALS AND METHODS: In total, 1201 anonymized, full-head NCCT clinical scans from 5 institutions were pooled to form the data set. The data set encompassed studies with normal findings as well as those with pathologies, including acute ischemic stroke, intracranial hemorrhage, traumatic brain injury, and mass effect (detection of these, task 1). NCCTs were also assessed to determine if findings were consistent with expected brain changes for the patient's age (task 2: age-based normality assessment) and to identify any abnormalities requiring immediate medical attention (task 3: evaluation of findings for urgent intervention). Five neuroradiologists labeled each NCCT, with consensus interpretations serving as the ground truth. The competition was announced online, inviting academic institutions and companies. Independent central analysis assessed the performance of each model. Accuracy, sensitivity, specificity, positive and negative predictive values, and receiver operating characteristic (ROC) curves were generated for each artificial intelligence model, along with the area under the ROC curve. RESULTS: Four teams processed 1177 studies. The median age of patients was 62 years, with an interquartile range of 33 years. Nineteen teams from various academic institutions registered for the competition. Of these, 4 teams submitted their final results. No commercial entities participated in the competition. For task 1, areas under the ROC curve ranged from 0.49 to 0.59. For task 2, two teams completed the task with area under the ROC curve values of 0.57 and 0.52. For task 3, teams had little-to-no agreement with the ground truth. CONCLUSIONS: To assess the performance of artificial intelligence models in real-world clinical scenarios, we analyzed their performance in the ASFNR Artificial Intelligence Competition. The first ASFNR Competition underscored the gap between expectation and reality; and the models largely fell short in their assessments. As the integration of artificial intelligence tools into clinical workflows increases, neuroradiologists must carefully recognize the capabilities, constraints, and consistency of these technologies. Before institutions adopt these algorithms, thorough validation is essential to ensure acceptable levels of performance in clinical settings.

Assessment of Precision and Accuracy of Brain White Matter Microstructure using Combined Diffusion MRI and Relaxometry.

Joint modeling of diffusion and relaxation has seen growing interest due to its potential to provide complementary information about tissue microstructure. For brain white matter, we designed an optimal diffusion-relaxometry MRI protocol that samples multiple b-values, B-tensor shapes, and echo times (TE). This variable-TE protocol (27 min) has as subsets a fixed-TE protocol (15 min) and a 2-shell dMRI protocol (7 min), both characterizing diffusion only. We assessed the sensitivity, specificity and reproducibility of these protocols with synthetic experiments and in six healthy volunteers. Compared with the fixed-TE protocol, the variable-TE protocol enables estimation of free water fractions while also capturing compartmental T2 relaxation times. Jointly measuring diffusion and relaxation offers increased sensitivity and specificity to microstructure parameters in brain white matter with voxelwise coefficients of variation below 10%.

Patient Engagement in Neuroradiology: A Narrative Review and Case Studies.

The field of patient engagement in radiology is evolving and offers ample opportunities for neuroradiologists to become involved. The patient journey can serve as a model that inspires patient engagement initiatives. The patient journey in radiology may be viewed in 5 stages: 1) awareness that an imaging test is needed, 2) considering having a specific imaging test, 3) access to imaging, 4) imaging service delivery, and 5) ongoing care. Here, we describe patient engagement opportunities based on literature review and paired with case studies by practicing neuroradiologists.

Resolution enhancement, noise suppression, and joint T2* decay estimation in dual-echo sodium-23 MR imaging using anatomically guided reconstruction.

PURPOSE: Sodium MRI is challenging because of the low tissue concentration of the 23 Na nucleus and its extremely fast biexponential transverse relaxation rate. In this article, we present an iterative reconstruction framework using dual-echo 23 Na data and exploiting anatomical prior information (AGR) from high-resolution, low-noise, 1 H MR images. This framework enables the estimation and modeling of the spatially varying signal decay due to transverse relaxation during readout (AGRdm), which leads to images of better resolution and reduced noise resulting in improved quantification of the reconstructed 23 Na images. METHODS: The proposed framework was evaluated using reconstructions of 30 noise realizations of realistic simulations of dual echo twisted projection imaging (TPI) 23 Na data. Moreover, three dual echo 23 Na TPI brain datasets of healthy controls acquired on a 3T Siemens Prisma system were reconstructed using conventional reconstruction, AGR and AGRdm. RESULTS: Our simulations show that compared to conventional reconstructions, AGR and AGRdm show improved bias-noise characteristics in several regions of the brain. Moreover, AGR and AGRdm images show more anatomical detail and less noise in the reconstructions of the experimental data sets. Compared to AGR and the conventional reconstruction, AGRdm shows higher contrast in the sodium concentration ratio between gray and white matter and between gray matter and the brain stem. CONCLUSION: AGR and AGRdm generate 23 Na images with high resolution, high levels of anatomical detail, and low levels of noise, potentially enabling high-quality 23 Na MR imaging at 3T.

Microstructurally Informed Subject-Specific Parcellation of the Corpus Callosum using Axonal Water Fraction.

The corpus callosum (CC) is the most important interhemispheric white matter (WM) structure composed of several anatomically and functionally distinct WM tracts. Resolving these tracts is a challenge since the callosum appears relatively homogenous in conventional structural imaging. Commonly used callosal parcellation methods such as the Hofer/Frahm scheme rely on rigid geometric guidelines to separate the substructures that are limited to consider individual variation. Here we present a novel subject-specific and microstructurally-informed method for callosal parcellation based on axonal water fraction (ƒ) known as a diffusion metric reflective of axon caliber and density. We studied 30 healthy subjects from the Human Connectome Project (HCP) dataset with multi-shell diffusion MRI. The biophysical parameter ƒ was derived from compartment-specific WM modeling. Inflection points were identified where there were concavity changes in ƒ across the CC to delineate callosal subregions. We observed relatively higher ƒ in anterior and posterior areas consisting of a greater number of small diameter fibers and lower ƒ in posterior body areas of the CC consisting of a greater number of large diameter fibers. Based on degree of change in ƒ along the callosum, seven callosal subregions can be consistently delineated for each individual. We observe that ƒ can capture differences in underlying tissue microstructures and seven subregions can be identified across CC. Therefore, this method provides microstructurally informed callosal parcellation in a subject-specific way, allowing for more accurate analysis in the corpus callosum.

Mapping tissue microstructure of brain white matter in vivo in health and disease using diffusion MRI.

Diffusion magnetic resonance imaging offers unique in vivo sensitivity to tissue microstructure in brain white matter, which undergoes significant changes during development and is compromised in virtually every neurological disorder. Yet, the challenge is to develop biomarkers that are specific to micrometer-scale cellular features in a human MRI scan of a few minutes. Here we quantify the sensitivity and specificity of a multicompartment diffusion modeling framework to the density, orientation and integrity of axons. We demonstrate that using a machine learning based estimator, our biophysical model captures the morphological changes of axons in early development, acute ischemia and multiple sclerosis (total N=821). The methodology of microstructure mapping is widely applicable in clinical settings and in large imaging consortium data to study development, aging and pathology.

Identifying relevant diffusion MRI microstructure biomarkers relating to exposure to repeated head impacts in contact sport athletes.

PURPOSE: Repeated head impacts (RHI) without concussion may cause long-term sequelae. A growing array of diffusion MRI metrics exist, both empiric and modeled and it is hard to know which are potentially important biomarkers. Common conventional statistical methods fail to consider interactions between metrics and rely on group-level comparisons. This study uses a classification pipeline as a means towards identifying important diffusion metrics associated with subconcussive RHI. METHODS: 36 collegiate contact sport athletes and 45 non-contact sport controls from FITBIR CARE were included. Regional/whole brain WM statistics were computed from 7 diffusion metrics. Wrapper-based feature selection was applied to 5 classifiers representing a range of learning capacities. Best 2 classifiers were interpreted to identify the most RHI-related diffusion metrics. RESULTS: Mean diffusivity (MD) and mean kurtosis (MK) are found to be the most important metrics for discriminating between athletes with and without RHI exposure history. Regional features outperformed global statistics. Linear approaches outperformed non-linear approaches with good generalizability (test AUC 0.80-0.81). CONCLUSION: Feature selection and classification identifies diffusion metrics that characterize subconcussive RHI. Linear classifiers yield the best performance and mean diffusion, tissue microstructure complexity, and radial extra-axonal compartment diffusion (MD, MK, De,⊥) are found to be the most influential metrics. This work provides proof of concept that applying such approach to small, multidimensional dataset can be successful given attention to optimizing learning capacity without overfitting and serves an example of methods that lead to better understanding of the myriad of diffusion metrics as they relate to injury and disease.

Thalamocortical coherence predicts persistent postconcussive symptoms.

The pathogenetic mechanism of persistent post-concussive symptoms (PCS) following concussion remains unclear. Thalamic damage is known to play a role in PCS prolongation while the evidence and biomarkers that trigger persistent PCS have never been elucidated. We collected longitudinal neuroimaging and behavior data from patients and rodents after concussion, complemented with rodents' histological staining data, to unravel the early biomarkers of persistent PCS. Diffusion tensor imaging (DTI) were acquired to investigated the thalamic damage, while quantitative thalamocortical coherence was derived through resting-state functional MRI for evaluating thalamocortical functioning and predicting long-term behavioral outcome. Patients with prolonged symptoms showed abnormal DTI-derived indices at the boundaries of bilateral thalami (peri-thalamic regions). Both patients and rats with persistent symptoms demonstrated enhanced thalamocortical coherence between different thalamocortical circuits, which disrupted thalamocortical multifunctionality. In rodents, the persistent DTI abnormalities were validated in thalamic reticular nucleus (TRN) through immunohistochemistry, and correlated with enhanced thalamocortical coherence. Strong predictive power of these coherence biomarkers for long-term PCS was also validated using another patient cohort. Postconcussive events may begin with persistent TRN injury, followed by disrupted thalamocortical coherence and prolonged PCS. Functional MRI-based coherence measures can be surrogate biomarkers for early prediction of long-term PCS.

FastMRI Prostate: A Publicly Available, Biparametric MRI Dataset to Advance Machine Learning for Prostate Cancer Imaging.

The fastMRI brain and knee dataset has enabled significant advances in exploring reconstruction methods for improving speed and image quality for Magnetic Resonance Imaging (MRI) via novel, clinically relevant reconstruction approaches. In this study, we describe the April 2023 expansion of the fastMRI dataset to include biparametric prostate MRI data acquired on a clinical population. The dataset consists of raw k-space and reconstructed images for T2-weighted and diffusion-weighted sequences along with slice-level labels that indicate the presence and grade of prostate cancer. As has been the case with fastMRI, increasing accessibility to raw prostate MRI data will further facilitate research in MR image reconstruction and evaluation with the larger goal of improving the utility of MRI for prostate cancer detection and evaluation. The dataset is available at https://fastmri.med.nyu.edu.

Deep multi-task learning and random forest for series classification by pulse sequence type and orientation.

PURPOSE: Increasingly complex MRI studies and variable series naming conventions reveal limitations of rule-based image routing, especially in health systems with multiple scanners and sites. Accurate methods to identify series based on image content would aid post-processing and PACS viewing. Recent deep/machine learning efforts classify 5-8 basic brain MR sequences. We present an ensemble model combining a convolutional neural network and a random forest classifier to differentiate 25 brain sequences and image orientation. METHODS: Series were grouped by descriptions into 25 sequences and 4 orientations. Dataset A, obtained from our institution, was divided into training (16,828 studies; 48,512 series; 112,028 images), validation (4746 studies; 16,612 series; 26,222 images) and test sets (6348 studies; 58,705 series; 3,314,018 images). Dataset B, obtained from a separate hospital, was used for out-of-domain external validation (1252 studies; 2150 series; 234,944 images). We developed an ensemble model combining a 2D convolutional neural network with a custom multi-task learning architecture and random forest classifier trained on DICOM metadata to classify sequence and orientation by series. RESULTS: The neural network, random forest, and ensemble achieved 95%, 97%, and 98% overall sequence accuracy on dataset A, and 98%, 99%, and 99% accuracy on dataset B, respectively. All models achieved > 99% orientation accuracy on both datasets. CONCLUSION: The ensemble model for series identification accommodates the complexity of brain MRI studies in state-of-the-art clinical practice. Expanding on previous work demonstrating proof-of-concept, our approach is more comprehensive with greater sequence diversity and orientation classification.

Exploring the Acceleration Limits of Deep Learning Variational Network-based Two-dimensional Brain MRI.

Purpose: To explore the limits of deep learning-based brain MRI reconstruction and identify useful acceleration ranges for general-purpose imaging and potential screening. Materials and Methods: In this retrospective study conducted from 2019 through 2021, a model was trained for reconstruction on 5847 brain MR images. Performance was evaluated across a wide range of accelerations (up to 100-fold along a single phase-encoded direction for two-dimensional [2D] sections) on the fastMRI test set collected at New York University, consisting of 558 image volumes. In a sample of 69 volumes, reconstructions were classified by radiologists for identification of two clinical thresholds: (a) general-purpose diagnostic imaging and (b) potential use in a screening protocol. A Monte Carlo procedure was developed to estimate reconstruction error with only undersampled data. The model was evaluated on both in-domain and out-of-domain data. The 95% CIs were calculated using the percentile bootstrap method. Results: Radiologists rated 100% of 69 volumes as having sufficient image quality for general-purpose imaging at up to 4× acceleration and 65 of 69 volumes (94%) as having sufficient image quality for screening at up to 14× acceleration. The Monte Carlo procedure estimated ground truth peak signal-to-noise ratio and mean squared error with coefficients of determination greater than 0.5 at 2× to 20× acceleration levels. Out-of-distribution experiments demonstrated the model's ability to produce images substantially distinct from the training set, even at 100× acceleration. Conclusion: For 2D brain images using deep learning-based reconstruction, maximum acceleration for potential screening was three to four times higher than that for diagnostic general-purpose imaging.Keywords: MRI Reconstruction, High Acceleration, Deep Learning, Screening, Out of Distribution Supplemental material is available for this article. © RSNA, 2022.