Background and Purpose—: If magnetic resonance imaging (MRI) is to compete with computed tomography for evaluation of patients with acute ischemic stroke, there is a need for further improvements in acquisition speed. Methods—: Inclusion criteria for this prospective, single institutional study were symptoms of acute ischemic stroke within 24 hours onset, National Institutes of Health Stroke Scale ≥3, and absence of MRI contraindications. A combination of echo-planar imaging (EPI) and a parallel acquisition technique were used on a 3T magnetic resonance (MR) scanner to accelerate the acquisition time. Image analysis was performed independently by 2 neuroradiologists. Results—: A total of 62 patients met inclusion criteria. A repeat MRI scan was performed in 22 patients resulting in a total of 84 MRIs available for analysis. Diagnostic image quality was achieved in 100% of diffusion-weighted imaging, 100% EPI-fluid attenuation inversion recovery imaging, 98% EPI-gradient recalled echo, 90% neck MR angiography and 96% of brain MR angiography, and 94% of dynamic susceptibility contrast perfusion scans with interobserver agreements ( k ) ranging from 0.64 to 0.84. Fifty-nine patients (95%) had acute infarction. There was good interobserver agreement for EPI-fluid attenuation inversion recovery imaging findings ( k =0.78; 95% confidence interval, 0.66–0.87) and for detection of mismatch classification using dynamic susceptibility contrast-Tmax ( k =0.92; 95% confidence interval, 0.87–0.94). Thirteen acute intracranial hemorrhages were detected on EPI-gradient recalled echo by both observers. A total of 68 and 72 segmental arterial stenoses were detected on contrast-enhanced MR angiography of the neck and brain with k =0.93, 95% confidence interval, 0.84 to 0.96 and 0.87, 95% confidence interval, 0.80 to 0.90, respectively. Conclusions—: A 6-minute multimodal MR protocol with good diagnostic quality is feasible for the evaluation of patients with acute ischemic stroke and can result in significant reduction in scan time rivaling that of the multimodal computed tomographic protocol.

Arterial spin labeling is an emerging noninvasive MRI technique for assessing cerebral perfusion that uses magnetically labeled arterial water protons in blood as an endogenous diffusible tracer. Arterial spin labeling (ASL) is an emerging noninvasive MRI technique for assessing cerebral perfusion. An important advantage of ASL perfusion is the lack of a requirement for an exogenous tracer....

OBJECTIVE. Differentiation of radiation necrosis (RN) from recurrent tumor (RT) in treated patients with glioblastoma remains a diagnostic challenge. The purpose of this study is to evaluate the diagnostic performance of multiparametric MRI in distinguishing RN from RT in patients with glioblastoma, with the use of a combination of MR perfusion and diffusion parameters. MATERIALS AND METHODS. Patients with glioblastoma who had a new enhancing mass develop after completing standard treatment were retrospectively evaluated. Apparent diffusion coefficient (ADC), volume transfer constant (Ktrans), and relative cerebral blood volume (rCBV) values were calculated from the MR images on which the enhancing lesions first appeared. Repeated measure of analysis, logistic regression, and ROC analysis were performed. RESULTS. Of a total of 70 patients evaluated, 46 (34 with RT and 12 with RN) met our inclusion criteria. Patients with RT had significantly higher mean rCBV (p < 0.001) and Ktrans (p = 0.006) values and l...

BACKGROUND AND PURPOSE: The hypervascular nature of parathyroid adenomas can be explored by proper dynamic imaging to narrow the target lesions for surgical exploration. The purpose of this study was to establish MR perfusion characteristics of parathyroid adenomas to differentiate them from their mimics, such as subjacent thyroid tissue and cervical lymph nodes. MATERIALS AND METHODS: Preoperative high-spatial and -temporal resolution dynamic 4D contrast-enhanced MR imaging in 30 patients with surgically proved parathyroid adenomas was evaluated retrospectively. Using coregistered images, we placed ROIs over the parathyroid adenoma, thyroid gland, and a cervical lymph node (jugulodigastric) to obtain peak enhancement, time-to-peak, wash-in, and washout in each patient. Data were analyzed by logistic regression and analysis of variance. Receiver operating characteristic analysis was performed to determine the optimal parameters for determination of parathyroid adenomas versus thyroid tissue and cervical lymph nodes. RESULTS: Parathyroid adenomas showed significantly (P < .05) faster time-to-peak, higher wash-in, and higher washout compared with cervical lymph nodes and significantly (P < .05) higher peak enhancement, faster time-to-peak, higher wash-in, and higher washout compared with thyroid tissue. Logistic regression analysis indicated significant contribution from time-to-peak (P = .02), wash-in (P = .03), and washout (P = .008) for differentiation of parathyroid adenomas from thyroid and cervical lymph nodes. Using receiver operating characteristic analysis, we obtained the best diagnostic accuracy from a combination of time-to-peak/wash-in/washout in the differentiation of parathyroid adenomas versus lymph nodes (area under the curve, 0.96; sensitivity/specificity, 88%/90%) and in distinguishing parathyroid adenomas versus thyroid tissue (area under the curve, 0.96; sensitivity/specificity, 91%/95%). CONCLUSIONS: Dynamic 4D contrast-enhanced MR imaging can be used to exploit the hypervascular nature of parathyroid adenomas. Multiparametric MR perfusion can distinguish parathyroid adenomas from subjacent thyroid tissue or lymph nodes with diagnostic accuracies of 96%.

In patients with gliomas, isocitrate dehydrogenase 1 (IDH1) mutation status has been studied as a prognostic indicator. Recent advances in machine learning (ML) have demonstrated promise in utilizing radiomic features to study disease processes in the brain. We investigate whether ML analysis of multiparametric radiomic features from preoperative Magnetic Resonance Imaging (MRI) can predict IDH1 mutation status in patients with glioma. This retrospective study included patients with glioma with known IDH1 status and preoperative MRI. Radiomic features were extracted from Fluid-Attenuated Inversion Recovery (FLAIR) and Diffusion-Weighted-Imaging (DWI). The dataset was split into training, validation, and testing sets by stratified sampling. Synthetic Minority Oversampling Technique (SMOTE) was applied to the training sets. eXtreme Gradient Boosting (XGBoost) classifiers were trained, and the hyperparameters were tuned. Receiver operating characteristic curve (ROC), accuracy, and f1-scores were collected. A total of 100 patients (age: 55 ± 15, M/F 60/40); with IDH1 mutant (n = 22) and IDH1 wildtype (n = 78) were included. The best performance was seen with a DWI-trained XGBoost model, which achieved ROC with Area Under the Curve (AUC) of 0.97, accuracy of 0.90, and f1-score of 0.75 on the test set. The FLAIR-trained XGBoost model achieved ROC with AUC of 0.95, accuracy of 0.90, f1-score of 0.75 on the test set. A model that was trained on combined FLAIR-DWI radiomic features did not provide incremental accuracy. The results show that a XGBoost classifier using multiparametric radiomic features derived from preoperative MRI can predict IDH1 mutation status with > 90% accuracy.