Recommended Resting-State fMRI Acquisition and Preprocessing Steps for Preoperative Mapping of Language and Motor and Visual Areas in Adult and Pediatric Patients with Brain Tumors and Epilepsy.

Resting-state (rs) fMRI has been shown to be useful for preoperative mapping of functional areas in patients with brain tumors and epilepsy. However, its lack of standardization limits its widespread use and hinders multicenter collaboration. The American Society of Functional Neuroradiology, American Society of Pediatric Neuroradiology, and the American Society of Neuroradiology Functional and Diffusion MR Imaging Study Group recommend specific rs-fMRI acquisition approaches and preprocessing steps that will further support rs-fMRI for future clinical use. A task force with expertise in fMRI from multiple institutions provided recommendations on the rs-fMRI steps needed for mapping of language, motor, and visual areas in adult and pediatric patients with brain tumor and epilepsy. These were based on an extensive literature review and expert consensus.Following rs-fMRI acquisition parameters are recommended: minimum 6-minute acquisition time; scan with eyes open with fixation; obtain rs-fMRI before both task-based fMRI and contrast administration; temporal resolution of ≤2 seconds; scanner field strength of 3T or higher. The following rs-fMRI preprocessing steps and parameters are recommended: motion correction (seed-based correlation analysis [SBC], independent component analysis [ICA]); despiking (SBC); volume censoring (SBC, ICA); nuisance regression of CSF and white matter signals (SBC); head motion regression (SBC, ICA); bandpass filtering (SBC, ICA); and spatial smoothing with a kernel size that is twice the effective voxel size (SBC, ICA).The consensus recommendations put forth for rs-fMRI acquisition and preprocessing steps will aid in standardization of practice and guide rs-fMRI program development across institutions. Standardized rs-fMRI protocols and processing pipelines are essential for multicenter trials and to implement rs-fMRI as part of standard clinical practice.

Ethical Considerations and Fairness in the Use of Artificial Intelligence for Neuroradiology.

In this review, concepts of algorithmic bias and fairness are defined qualitatively and mathematically. Illustrative examples are given of what can go wrong when unintended bias or unfairness in algorithmic development occurs. The importance of explainability, accountability, and transparency with respect to artificial intelligence algorithm development and clinical deployment is discussed. These are grounded in the concept of "primum no nocere" (first, do no harm). Steps to mitigate unfairness and bias in task definition, data collection, model definition, training, testing, deployment, and feedback are provided. Discussions on the implementation of fairness criteria that maximize benefit and minimize unfairness and harm to neuroradiology patients will be provided, including suggestions for neuroradiologists to consider as artificial intelligence algorithms gain acceptance into neuroradiology practice and become incorporated into routine clinical workflow.

Trends of Diversity in Neuroradiology Trainees in United States 2015-2022.

Radiology has historically not been a very diverse field. Many steps have been taken in the past decade to increase diversity in the field and make it more inclusive. This study shows the relative trends specifically in neuroradiology trainees, and the need for reassessment and further steps to increase diversity.

Deep Learning-Generated Synthetic MR Imaging STIR Spine Images Are Superior in Image Quality and Diagnostically Equivalent to Conventional STIR: A Multicenter, Multireader Trial.

BACKGROUND AND PURPOSE: Deep learning image reconstruction allows faster MR imaging acquisitions while matching or exceeding the standard of care and can create synthetic images from existing data sets. This multicenter, multireader spine study evaluated the performance of synthetically created STIR compared with acquired STIR. MATERIALS AND METHODS: From a multicenter, multiscanner data base of 328 clinical cases, a nonreader neuroradiologist randomly selected 110 spine MR imaging studies in 93 patients (sagittal T1, T2, and STIR) and classified them into 5 categories of disease and healthy. A DICOM-based deep learning application generated a synthetically created STIR series from the sagittal T1 and T2 images. Five radiologists (3 neuroradiologists, 1 musculoskeletal radiologist, and 1 general radiologist) rated the STIR quality and classified disease pathology (study 1, n = 80). They then assessed the presence or absence of findings typically evaluated with STIR in patients with trauma (study 2, n = 30). The readers evaluated studies with either acquired STIR or synthetically created STIR in a blinded and randomized fashion with a 1-month washout period. The interchangeability of acquired STIR and synthetically created STIR was assessed using a noninferiority threshold of 10%. RESULTS: For classification, there was a decrease in interreader agreement expected by randomly introducing synthetically created STIR of 3.23%. For trauma, there was an overall increase in interreader agreement by +1.9%. The lower bound of confidence for both exceeded the noninferiority threshold, indicating interchangeability of synthetically created STIR with acquired STIR. Both the Wilcoxon signed-rank and t tests showed higher image-quality scores for synthetically created STIR over acquired STIR (P < .0001). CONCLUSIONS: Synthetically created STIR spine MR images were diagnostically interchangeable with acquired STIR, while providing significantly higher image quality, suggesting routine clinical practice potential.

Critical Appraisal of Artificial Intelligence-Enabled Imaging Tools Using the Levels of Evidence System.

Clinical adoption of an artificial intelligence-enabled imaging tool requires critical appraisal of its life cycle from development to implementation by using a systematic, standardized, and objective approach that can verify both its technical and clinical efficacy. Toward this concerted effort, the ASFNR/ASNR Artificial Intelligence Workshop Technology Working Group is proposing a hierarchal evaluation system based on the quality, type, and amount of scientific evidence that the artificial intelligence-enabled tool can demonstrate for each component of its life cycle. The current proposal is modeled after the levels of evidence in medicine, with the uppermost level of the hierarchy showing the strongest evidence for potential impact on patient care and health care outcomes. The intended goal of establishing an evidence-based evaluation system is to encourage transparency, foster an understanding of the creation of artificial intelligence tools and the artificial intelligence decision-making process, and to report the relevant data on the efficacy of artificial intelligence tools that are developed. The proposed system is an essential step in working toward a more formalized, clinically validated, and regulated framework for the safe and effective deployment of artificial intelligence imaging applications that will be used in clinical practice.

Malpractice Litigation Related to Diagnosis and Treatment of Intracranial Aneurysms.

BACKGROUND AND PURPOSE: Approaches to management of intracranial aneurysms are inconsistent, in part due to apprehension relating to potential malpractice claims. The purpose of this article was to review the causes of action underlying medical malpractice lawsuits related to the diagnosis and management of intracranial aneurysms and to identify the factors associated and their outcomes. MATERIALS AND METHODS: We consulted 2 large legal databases in the United States to search for cases in which there were jury awards and settlements related to the diagnosis and management of patients with intracranial aneurysms in the United States. Files were screened to include only those cases in which the cause of action involved negligence in the diagnosis and management of a patient with an intracranial aneurysm. RESULTS: Between 2000 and 2020, two hundred eighty-seven published case summaries were identified, of which 133 were eligible for inclusion in the analysis. Radiologists constituted 16% of 159 physicians sued in these lawsuits. Failure to diagnose was the most common medical malpractice claim referenced (100/133 cases), with the most common subgroups being "failure to include cerebral aneurysm as a differential and thus perform adequate work-up" (30 cases), and "failure to correctly interpret aneurysm evidence on CT or MR imaging" (16 cases). Only 6 of these 16 cases were adjudicated at trial, with 2 decided in favor of the plaintiff (awarded $4,000,000 and $43,000,000, respectively). CONCLUSIONS: Incorrect interpretation of imaging is relatively infrequent as a cause of malpractice litigation compared with failure to diagnose aneurysms in the clinical setting by neurosurgeons, emergency physicians, and primary care providers.

ACR White Paper on Magnetoencephalography and Magnetic Source Imaging: A Report from the ACR Commission on Neuroradiology.

Magnetoencephalography, the extracranial detection of tiny magnetic fields emanating from intracranial electrical activity of neurons, and its source modeling relation, magnetic source imaging, represent a powerful functional neuroimaging technique, able to detect and localize both spontaneous and evoked activity of the brain in health and disease. Recent years have seen an increased utilization of this technique for both clinical practice and research, in the United States and worldwide. This report summarizes current thinking, presents recommendations for clinical implementation, and offers an outlook for emerging new clinical indications.

Survey of the American Society of Neuroradiology Membership on the Use and Value of Extracranial Carotid Vessel Wall MRI.

BACKGROUND AND PURPOSE: Extracranial vessel wall MRI (EC-VWI) contributes to vasculopathy characterization. This survey study investigated EC-VWI adoption by American Society of Neuroradiology (ASNR) members and indications and barriers to implementation. MATERIALS AND METHODS: The ASNR Vessel Wall Imaging Study Group survey on EC-VWI use, frequency, applications, MR imaging systems and field strength used, protocol development approaches, vendor engagement, reasons for not using EC-VWI, ordering provider interest, and impact on clinical care was distributed to the ASNR membership between April 2, 2019, to August 30, 2019. RESULTS: There were 532 responses; 79 were excluded due to minimal, incomplete response and 42 due to redundant institutional responses, leaving 411 responses. Twenty-six percent indicated that their institution performed EC-VWI, with 66.3% performing it ≤1-2 times per month, most frequently on 3T MR imaging, with most using combined 3D and 2D protocols. Protocols most commonly included pre- and postcontrast T1-weighted imaging, TOF-MRA, and contrast-enhanced MRA. Inflammatory vasculopathy (63.3%), plaque vulnerability assessments (61.1%), intraplaque hemorrhage (61.1%), and dissection-detection/characterization (51.1%) were the most frequent applications. For those not performing EC-VWI, the reasons were a lack of ordering provider interest (63.9%), lack of radiologist time/interest (47.5%) or technical support (41.4%) for protocol development, and limited interpretation experience (44.9%) and knowledge of clinical applications (43.7%). Reasons given by 46.9% were that no providers approached radiology with interest in EC-VWI. If barriers were overcome, 51.1% of those not performing EC-VWI indicated they would perform it, and 40.6% were unsure; 48.6% did not think that EC-VWI had impacted patient management at their institution. CONCLUSIONS: Only 26% of neuroradiology groups performed EC-VWI, most commonly due to limited clinician interest. Improved provider and radiologist education, protocols, processing techniques, technical support, and validation trials could increase adoption.

Detection of Early Ischemic Changes with Virtual Noncontrast Dual-Energy CT in Acute Ischemic Stroke: A Noninferiority Analysis.

BACKGROUND AND PURPOSE: Dual-energy virtual NCCT has the potential to replace conventional NCCT to detect early ischemic changes in acute ischemic stroke. In this study, we evaluated whether virtual NCCT is noninferior compared with standard linearly blended NCCT, a surrogate of conventional NCCT, regarding the detection of early ischemic changes with ASPECTS. MATERIALS AND METHODS: Adult patients who presented with suspected acute ischemic stroke and who underwent dual-energy NCCT and CTA and brain MR imaging within 48 hours were included. Standard linearly blended images were reconstructed to match a conventional NCCT. Virtual NCCT images were reconstructed from CTA. ASPECTS was evaluated on conventional NCCT, virtual NCCT, and DWI, which served as the reference standard. Agreement between CT assessments and the reference standard was evaluated with the Lin concordance correlation coefficient. Noninferiority was assessed with bootstrapped estimates of the differences in ASPECTS between conventional and virtual NCCT with 95% CIs. RESULTS: Of the 193 included patients, 100 patients (52%) had ischemia on DWI. Compared with the reference standard, the ASPECTS concordance correlation coefficient for conventional and virtual NCCT was 0.23 (95% CI, 0.15-0.32) and 0.44 (95% CI, 0.33-0.53), respectively. The difference in the concordance correlation coefficient between virtual and conventional NCCT was 0.20 (95% CI, 0.01-0.39) and did not cross the prespecified noninferiority margin of -0.10. CONCLUSIONS: Dual-energy virtual NCCT is noninferior compared with conventional NCCT for the detection of early ischemic changes with ASPECTS.

Impact Analysis of Different CT Configurations of Carotid Artery Plaque Calcifications on Cerebrovascular Events.

BACKGROUND AND PURPOSE: CT is considered the standard reference both for quantification and characterization of carotid artery calcifications. Our aim was to investigate the relationship among different types of calcium configurations detected with CT within the plaque with a novel classification and to investigate the prevalence of cerebrovascular events. MATERIALS AND METHODS: Seven hundred ninety patients (men = 332; mean age, 69.7 [SD, 13] years; 508 symptomatic for cerebrovascular symptoms and 282 asymptomatic) who underwent computed tomography of the carotid arteries were retrospectively included in this institutional review board-approved study. The plaque was classified into 6 types according to the different types of calcium configurations as the following: type 1, complete absence of calcification within the plaque; type 2, intimal or superficial calcifications; type 3, deep or bulky calcifications; type 4, adventitial calcifications with internal soft plaque of <2 mm thickness; type 5, mixed patterns with intimal and bulky calcifications; and type 6, positive rim sign. RESULTS: The highest prevalence of cerebrovascular events was observed for type 6, for which 89 of the 99 cases were symptomatic. Type 6 plaque had the highest degree of correlation with TIA, stroke, symptoms, and ipsilateral infarct for both sides with a higher prevalence in younger patients. The frequency of symptoms observed by configuration type significantly differed between right and left plaques, with symptoms observed more frequently in type 6 calcification on the right side (50/53; 94%) than on the left side (39/46; 85%, P < .001). CONCLUSIONS: We propose a novel carotid artery plaque configuration classification that is associated with the prevalence of cerebrovascular events. If confirmed in longitudinal analysis, this classification could be used to stratify the risk of occurrence of ischemic events.

Roadmap Consensus on Carotid Artery Plaque Imaging and Impact on Therapy Strategies and Guidelines: An International, Multispecialty, Expert Review and Position Statement.

Current guidelines for primary and secondary prevention of stroke in patients with carotid atherosclerosis are based on the quantification of the degree of stenosis and symptom status. Recent publications have demonstrated that plaque morphology and composition, independent of the degree of stenosis, are important in the risk stratification of carotid atherosclerotic disease. This finding raises the question as to whether current guidelines are adequate or if they should be updated with new evidence, including imaging for plaque phenotyping, risk stratification, and clinical decision-making in addition to the degree of stenosis. To further this discussion, this roadmap consensus article defines the limits of luminal imaging and highlights the current evidence supporting the role of plaque imaging. Furthermore, we identify gaps in current knowledge and suggest steps to generate high-quality evidence, to add relevant information to guidelines currently based on the quantification of stenosis.

Prediction of Clinical Outcome in Patients with Large-Vessel Acute Ischemic Stroke: Performance of Machine Learning versus SPAN-100.

BACKGROUND AND PURPOSE: Traditional statistical models and pretreatment scoring systems have been used to predict the outcome for acute ischemic stroke patients (AIS). Our aim was to select the most relevant features in terms of outcome prediction on the basis of machine learning algorithms for patients with acute ischemic stroke and to compare the performance between multiple models and the Stroke Prognostication Using Age and National Institutes of Health Stroke Scale (SPAN-100) index model. MATERIALS AND METHODS: A retrospective multicenter cohort of 1431 patients with acute ischemic stroke was subdivided into recanalized and nonrecanalized patients. Extreme Gradient Boosting machine learning models were built to predict the mRS score at 90 days using clinical, imaging, combined, and best-performing features. Feature selection was performed using the relative weight and frequency of occurrence in the models. The model with the best performance was compared with the SPAN-100 index model using area under the receiver operating curve analysis. RESULTS: In 3 groups of patients, the baseline NIHSS was the most significant predictor of outcome among all the parameters, with relative weights of 0.36∼0.69; ischemic core volume on CTP ranked as the most important imaging biomarker with relative weights of 0.29∼0.47. The model with the best-performing features had a better performance than the other machine learning models. The area under the curve of the model with the best-performing features was higher than SPAN-100 model and reached statistical significance for the total (P < .05) and the nonrecanalized patients (P < .001). CONCLUSIONS: Machine learning-based feature selection can identify parameters with higher performance in outcome prediction. Machine learning models with the best-performing features, especially advanced CTP data, had superior performance of the recovery outcome prediction for patients with stroke at admission in comparison with SPAN-100.

Correlation between ASPECTS and Core Volume on CT Perfusion: Impact of Time since Stroke Onset and Presence of Large-Vessel Occlusion.

BACKGROUND AND PURPOSE: Both ASPECTS and core volume on CTP are used to estimate infarct volume in acute ischemic stroke. To evaluate the potential role of ASPECTS for acute endovascular treatment decisions, we studied the correlation between ASPECTS and CTP core, depending on the timing and the presence of large-vessel occlusion. MATERIALS AND METHODS: We retrospectively reviewed all MCA acute ischemic strokes with standardized reconstructions of CTP maps entered in the Acute STroke Registry and Analysis of Lausanne (ASTRAL) registry. Correlation between ASPECTS and CTP core was determined for early (<6 hours) versus late (6-24 hours) times from stroke onset and in the presence versus absence of large-vessel occlusion. We used correlation coefficients and adjusted multiple linear regression models. RESULTS: We included 1046 patients with a median age of 71.4 years (interquartile range, IQR = 59.8-79.4 years), an NIHSS score of 12 (IQR, 6-18), an ASPECTS of 9 (IQR, 7-10), and a CTP core of 13.6 mL (IQR, 0.6-52.8 mL). The overall correlation between ASPECTS and CTP core was moderate (ρ = -0.49, P < .01) but significantly stronger in the late-versus-early window (ρ = -0.56 and ρ = -0.48, respectively; P = .05) and in the presence versus absence of large-vessel occlusion (ρ = -0.40 and ρ = -0.20, respectively; P < .01). In the regression model, the independent association between ASPECTS and CTP core was confirmed and was twice as strong in late-arriving patients with large-vessel occlusion (ß = -0.21 per 10 mL; 95% CI, -0.27 to -0.15; P < .01) than in the overall population (ß = -0.10; 95% CI, -0.14 to -0.07; P < .01). CONCLUSIONS: In a large cohort of patients with acute ischemic stroke, we found a moderate correlation between ASPECTS and CTP core. However, this was stronger in patients with large-vessel occlusion and longer delay from stroke onset. Our results could support the use of ASPECTS as a surrogate marker of CTP core in late-arriving patients with acute ischemic stroke with large-vessel occlusion.

Artificial Intelligence in Neuroradiology: Current Status and Future Directions.

Fueled by new techniques, computational tools, and broader availability of imaging data, artificial intelligence has the potential to transform the practice of neuroradiology. The recent exponential increase in publications related to artificial intelligence and the central focus on artificial intelligence at recent professional and scientific radiology meetings underscores the importance. There is growing momentum behind leveraging artificial intelligence techniques to improve workflow and diagnosis and treatment and to enhance the value of quantitative imaging techniques. This article explores the reasons why neuroradiologists should care about the investments in new artificial intelligence applications, highlights current activities and the roles neuroradiologists are playing, and renders a few predictions regarding the near future of artificial intelligence in neuroradiology.

Hypoperfusion intensity ratio correlates with angiographic collaterals in acute ischaemic stroke with M1 occlusion.

BACKGROUND AND PURPOSE: Among patients with an acute ischaemic stroke secondary to large-vessel occlusion, the hypoperfusion intensity ratio (HIR) [time to maximum (TMax) > 10 volume/TMax > 6 volume] is a strong predictor of infarct growth. We studied the correlation between HIR and collaterals assessed with digital subtraction angiography (DSA) before thrombectomy. METHODS: Between January 2014 and March 2018, consecutive patients with an acute ischaemic stroke and an M1 middle cerebral artery (MCA) occlusion who underwent perfusion imaging and endovascular treatment at our center were screened. Ischaemic core (mL), HIR and perfusion mismatch (TMax > 6 s minus core volume) were assessed through magnetic resonance imaging or computed tomography perfusion. Collaterals were assessed on pre-intervention DSA using the American Society of Interventional and Therapeutic Neuroradiology/Society of Interventional Radiology (ASITN/SIR) scale. Baseline clinical and perfusion characteristics were compared between patients with good (ASITN/SIR score 3-4) and those with poor (ASITN/SIR score 0-2) DSA collaterals. Correlation between HIR and ASITN/SIR scores was evaluated using Pearson's correlation. Receiver operating characteristic analysis was performed to determine the optimal HIR threshold for the prediction of good DSA collaterals. RESULTS: A total of 98 patients were included; 49% (48/98) had good DSA collaterals and these patients had significantly smaller hypoperfusion volumes (TMax > 6 s, 89 vs. 125 mL; P = 0.007) and perfusion mismatch volumes (72 vs. 89 mL; P = 0.016). HIR was significantly correlated with DSA collaterals (-0.327; 95% confidence interval, -0.494 to -0.138; P = 0.01). An HIR cut-off of <0.4 best predicted good DSA collaterals with an odds ratio of 4.3 (95% confidence interval, 1.8-10.1) (sensitivity, 0.792; specificity, 0.560; area under curve, 0.708). CONCLUSION: The HIR is a robust indicator of angiographic collaterals and might be used as a surrogate of collateral assessment in patients undergoing magnetic resonance imaging. HIR <0.4 best predicted good DSA collaterals.

Focal Hypoperfusion in Acute Ischemic Stroke Perfusion CT: Clinical and Radiologic Predictors and Accuracy for Infarct Prediction.

BACKGROUND AND PURPOSE: Perfusion CT may improve the diagnostic performance of noncontrast CT in acute ischemic stroke. We assessed predictors of focal hypoperfusion in acute ischemic stroke and perfusion CT performance in predicting infarction on follow-up imaging. MATERIALS AND METHODS: Patients from the Acute STroke Registry and Analysis of Lausanne data base with acute ischemic stroke and perfusion CT were included. Clinical and radiologic data were collected. We identified predictors of focal hypoperfusion using multivariate analyses. RESULTS: From the 2216 patients with perfusion CT, 38.2% had an acute ischemic lesion on NCCT and 73.3% had focal hypoperfusion on perfusion CT. After we analyzed 104 covariates, high-admission NIHSS, visual field defect, aphasia, hemineglect, sensory deficits, and impaired consciousness were positively associated with focal hypoperfusion. Negative associations were pure posterior circulation, lacunar strokes, and anticoagulation. After integrating radiologic variables into the multivariate analyses, we found that visual field defect, sensory deficits, hemineglect, early ischemic changes on NCCT, anterior circulation, cardioembolic etiology, and arterial occlusion were positively associated with focal hypoperfusion, whereas increasing onset-to-CT delay, chronic vascular lesions, and lacunar etiology showed negative association. Sensitivity, specificity, and positive and negative predictive values of focal hypoperfusion on perfusion CT for infarct detection on follow-up MR imaging were 66.5%, 79.4%, 96.2%, and 22.8%, respectively, with an overall accuracy of 76.8%. CONCLUSIONS: Compared with NCCT, perfusion CT doubles the sensitivity in detecting acute ischemic stroke. Focal hypoperfusion is independently predicted by stroke severity, cortical clinical deficits, nonlacunar supratentorial strokes, and shorter onset-to-imaging delays. A high proportion of patients with focal hypoperfusion developed infarction on subsequent imaging, as did some patients without focal hypoperfusion, indicating the complementarity of perfusion CT and MR imaging in acute ischemic stroke.

A Review of Magnetic Particle Imaging and Perspectives on Neuroimaging.

Magnetic particle imaging is an emerging tomographic technique with the potential for simultaneous high-resolution, high-sensitivity, and real-time imaging. Magnetic particle imaging is based on the unique behavior of superparamagnetic iron oxide nanoparticles modeled by the Langevin theory, with the ability to track and quantify nanoparticle concentrations without tissue background noise. It is a promising new imaging technique for multiple applications, including vascular and perfusion imaging, oncology imaging, cell tracking, inflammation imaging, and trauma imaging. In particular, many neuroimaging applications may be enabled and enhanced with magnetic particle imaging. In this review, we will provide an overview of magnetic particle imaging principles and implementation, current applications, promising neuroimaging applications, and practical considerations.

Evaluation of Thick-Slab Overlapping MIP Images of Contrast-Enhanced 3D T1-Weighted CUBE for Detection of Intracranial Metastases: A Pilot Study for Comparison of Lesion Detection, Interpretation Time, and Sensitivity with Nonoverlapping CUBE MIP, CUBE, and Inversion-Recovery-Prepared Fast-Spoiled Gradient Recalled Brain Volume.

BACKGROUND AND PURPOSE: Early and accurate identification of cerebral metastases is important for prognostication and treatment planning although this process is often time consuming and labor intensive, especially with the hundreds of images associated with 3D volumetric imaging. This study aimed to evaluate the benefits of thick-slab overlapping MIPs constructed from contrast-enhanced T1-weighted CUBE (overlapping CUBE MIP) for the detection of brain metastases in comparison with traditional CUBE and inversion-recovery prepared fast-spoiled gradient recalled brain volume (IR-FSPGR-BRAVO) and nonoverlapping CUBE MIP. MATERIALS AND METHODS: A retrospective review of 48 patients with cerebral metastases was performed at our institution from June 2016 to October 2017. Brain MRIs, which were acquired on multiple 3T scanners, included gadolinium-enhanced T1-weighted IR-FSPGR-BRAVO and CUBE, with subsequent generation of nonoverlapping CUBE MIP and overlapping CUBE MIP. Two blinded radiologists identified the total number and location of metastases on each image type. The Cohen κ was used to determine interrater agreement. Sensitivity, interpretation time, and lesion contrast-to-noise ratio were assessed. RESULTS: Interrater agreement for identification of metastases was fair-to-moderate for all image types (κ = 0.222-0.598). The total number of metastases identified was not significantly different across the image types. Interpretation time for CUBE MIPs was significantly shorter than for CUBE and IR-FSPGR-BRAVO, saving at least 50 seconds per case on average (P < .001). The mean lesion contrast-to-noise ratio for both CUBE MIPs was higher than for IR-FSPGR-BRAVO. The mean contrast-to-noise ratio for small lesions (<4 mm) was lower for nonoverlapping CUBE MIP (1.55) than for overlapping CUBE MIP (2.35). For both readers, the sensitivity for lesion detection was high for all image types but highest for overlapping CUBE MIP and CUBE (0.93-0.97). CONCLUSIONS: This study suggests that the use of overlapping CUBE MIP or nonoverlapping CUBE MIP for the detection of brain metastases can reduce interpretation time without sacrificing sensitivity, though the contrast-to-noise ratio of lesions is highest for overlapping CUBE MIP.