Despite the widespread clinical use of dynamic susceptibility contrast (DSC) MRI, DSC-MRI methodology has not been standardized, hindering its utilization for response assessment in multicenter trials. Recently, the DSC-MRI Standardization Subcommittee of the Jumpstarting Brain Tumor Drug Development Coalition issued an updated consensus DSC-MRI protocol compatible with the standardized brain tumor imaging protocol (BTIP) for high-grade gliomas that is increasingly used in the clinical setting and is the default MRI protocol for the National Clinical Trials Network. After reviewing the basis for controversy over DSC-MRI protocols, this paper provides evidence-based best practices for clinical DSC-MRI as determined by the Committee, including pulse sequence (gradient echo vs spin echo), BTIP-compliant contrast agent dosing (preload and bolus), flip angle (FA), echo time (TE), and post-processing leakage correction. In summary, full-dose preload, full-dose bolus dosing using intermediate (60°) FA and field strength-dependent TE (40–50 ms at 1.5 T, 20–35 ms at 3 T) provides overall best accuracy and precision for cerebral blood volume estimates. When single-dose contrast agent usage is desired, no-preload, full-dose bolus dosing using low FA (30°) and field strength-dependent TE provides excellent performance, with reduced contrast agent usage and elimination of potential systematic errors introduced by variations in preload dose and incubation time.

BACKGROUND AND PURPOSE: Contrast extravasation in DSC-MRI potentiates inaccurate and imprecise estimates of glioma rCBV. We tested assertions that preload and postprocessing algorithms minimize this error by comparing Gd-rCBV using permutations of these 2 techniques with criterion standard rCBV using MION, an intravascular agent. MATERIALS AND METHODS: We imaged 7 Fisher rats with 9L gliosarcomas, by using 3T gradient-echo DSC-MRI with MION (2.0 mg Fe/kg) and staged injection of Gd-diethylene triamine pentaacetic acid: a 0.1-mmol/kg bolus provided no preload (P−) data and served as preload (P+) for a subsequent 0.2-mmol/kg bolus. We computed MION-rCBV (steady-state ΔR2*, tumor versus normal brain) and Gd-rCBV ΔR2* [t] integration) without (C−) and with (C+) postprocessing correction, thereby testing 4 correction permutations: P−C−, P−C+, P+C−, and P+C+. We tested whether each permutation reduced bias and variance of the Gd/MION rCBV differences by using generalized estimating equations and Fmax statistics (P < .05 significant). RESULTS: Gd-rCBV progressively better approximated MION-rCBV with increasing leakage correction. There was no statistically significant bias for the mean percentage deviation of Gd-rCBV from MION-rCBV for any correction permutation, but there was significantly reduced variance by using P+C− (22-fold), P−C+ (32-fold), and P+C+ (267-fold) compared with P−C−. P+C+ provided significant additional variance reduction compared with P+C− (12-fold) and P−C+ (8-fold). Linear regression of Gd-rCBV versus MION-rCBV revealed P+C+ to have the closest slope and intercept compared with the ideal, substantially better than P+C−. CONCLUSIONS: Preload and postprocessing correction significantly reduced the variance of Gd-rCBV estimates, and bias reduction approached significance. Postprocessing correction provide significant benefit beyond preload alone.

OBJECTIVE. The purpose of this study was to determine whether the preoperative MRI findings of enhanced diffusivity, macrocyst content, and internal hemorrhage in pituitary macroadenomas are predictive of successful transsphenoidal hypophysectomy.MATERIALS AND METHODS. We retrospectively reviewed the preoperative and postoperative sella protocol MR images of 28 patients who underwent transsphenoidal hypophysectomy for chiasm-compressing macroadenoma. Chiasmatic decompression defined surgical success. Two neuroradiologists differentiated nonsolid (macrocystic and macrohemorrhagic) from solid tumors, computed apparent diffusion coefficient (ADC) and T2-weighted signal intensity normalized to pons in solid tumors, and measured change in tumor height. A neuropathologist graded reticulin content in tumor specimens. Categorical and dichotomous variables were examined with the chi-square or Fisher's exact test; continuous-scale data were analyzed with the Student's t test, analysis of variance, or linear regress...

Objective: For patients with high-grade glioma on clinical trials it is important to accurately assess time of disease progression. However, differentiation between pseudoprogression (PsP) and progressive disease (PD) is unreliable with standard magnetic resonance imaging (MRI) techniques. Dynamic susceptibility contrast perfusion MRI (DSC-MRI) can measure relative cerebral blood volume (rCBV) and may help distinguish PsP from PD. Methods: A subset of patients with high-grade glioma on a phase II clinical trial with temozolomide, paclitaxel poliglumex, and concurrent radiation were assessed. Nine patients (3 grade III, 6 grade IV), with a total of 19 enhancing lesions demonstrating progressive enhancement (≥25% increase from nadir) on postchemoradiation conventional contrast-enhanced MRI, had serial DSC-MRI. Mean leakage-corrected rCBV within enhancing lesions was computed for all postchemoradiation time points. Results: Of the 19 progressively enhancing lesions, 10 were classified as PsP and 9 as PD by biopsy/surgery or serial enhancement patterns during interval follow-up MRI. Mean rCBV at initial progressive enhancement did not differ significantly between PsP and PD (2.35 vs. 2.17; P=0.67). However, change in rCBV at first subsequent follow-up (−0.84 vs. 0.84; P=0.001) and the overall linear trend in rCBV after initial progressive enhancement (negative vs. positive slope; P=0.04) differed significantly between PsP and PD. Conclusions: Longitudinal trends in rCBV may be more useful than absolute rCBV in distinguishing PsP from PD in chemoradiation-treated high-grade gliomas with DSC-MRI. Further studies of DSC-MRI in high-grade glioma as a potential technique for distinguishing PsP from PD are indicated.

A deep learning model was developed to accurately detect large vessel occlusions at multiphase CT angiography imaging examinations. Background Large vessel occlusion (LVO) stroke is one of the most time-sensitive diagnoses in medicine and requires emergent endovascular therapy to reduce morbidity and mortality. Leveraging recen...