Cerebrovascular reactivity (CVR), the ability of cerebral vessels to dilate or constrict, has been shown to provide valuable information in the diagnosis and treatment evaluation of patients with various cerebrovascular conditions. CVR mapping is typically performed using hypercapnic gas inhalation as a vasoactive challenge while collecting BOLD images, but the inherent need of gas inhalation and the associated apparatus setup present a practical obstacle in applying it in routine clinical use. Therefore, we aimed to develop a new method to map CVR using resting-state BOLD data without the need of gas inhalation. This approach exploits the natural variation in respiration and measures its influence on BOLD MRI signal. In this work, we first identified a surrogate of the arterial CO2 fluctuation during spontaneous breathing from the global BOLD signal. Second, we tested the feasibility and reproducibility of the proposed approach to use the above-mentioned surrogate as a regressor to estimate voxel-wise CVR. Third, we validated the “resting-state CVR map” with a conventional CVR map obtained with hypercapnic gas inhalation in healthy volunteers. Finally, we tested the utility of this new approach in detecting abnormal CVR in a group of patients with Moyamoya disease, and again validated the results using the conventional gas inhalation method. Our results showed that global BOLD signal fluctuation in the frequency range of 0.02–0.04 Hz contains the most prominent contribution from natural variation in arterial CO2. The CVR map calculated using this signal as a regressor is reproducible across runs (ICC=0.91±0.06), and manifests a strong spatial correlation with results measured with a conventional hypercapnia-based method in healthy subjects (r=0.88, p<0.001). We also found that resting-state CVR was able to identify vasodilatory deficit in patients with steno-occlusive disease, the spatial pattern of which matches that obtained using the conventional gas method (r=0.71±0.18). These results suggest that CVR obtained with resting-state BOLD may be a useful alternative in detecting vascular deficits in clinical applications when gas challenge is not feasible.

Purpose Assessment of brain hemodynamics without exogenous contrast agents is of increasing importance in clinical applications. This study aims to develop an MR perfusion technique that can provide non-contrast and multi-parametric estimation of hemodynamic markers. Methods We devised an Arterial-Spin-Labeling (ASL) method based on the principle of MR Fingerprinting (MRF), referred to as MRF-ASL. By taking advantage of the rich information contained in MRF sequence, up to seven hemodynamic parameters can be estimated concomitantly. Feasibility demonstration, flip angle optimization, comparison with Look-Locker ASL, reproducibility test, sensitivity to hypercapnia challenge, and initial clinical application in an intracranial steno-occlusive process, Moyamoya disease, were performed to evaluate this technique. Results MRF-ASL provided estimation of up to seven parameters, including B1+, tissue T1, cerebral blood flow (CBF), tissue bolus-arrival-time (BAT), pass-through arterial BAT, pass-through blood volume, and pass-through blood travel time. Coefficients-of-variation (CoV) of the estimated parameters ranged from 0.2% to 9.6%. Hypercapnia resulted in an increase in CBF by 57.7%, and a decrease in BAT by 13.7% and 24.8% in tissue and vessels, respectively. Patients with Moyamoya disease showed diminished CBF and lengthened BAT that could not be detected with regular ASL. Conclusion MRF-ASL is a promising technique for non-contrast, multi-parametric perfusion assessment.

Purpose To determine the feasibility of radiomic (computer extracted texture) features in differentiating radiation necrosis (RN) from recurrent brain tumors on routine MRI (Gadolinium (Gd)-T1w, T2w, FLAIR). Methods A retrospective study of brain tumor MRI obtained after 9-months (or later) post-radio-chemotherapy was collected from two institutions. In total, 58 patient studies were analyzed, consisting of a training (N = 43) cohort from one institution and an independent test (N = 15) cohort from another, with surgical histologic findings confirmed by an experienced neuropathologist at the respective institutions. Brain lesions on MRI were manually annotated by an expert neuro-radiologist. A set of radiomic features was extracted for every lesion on each MRI sequence: Gd-T1w, T2w, FLAIR. Feature selection was employed to identify the top 5 most discriminating features for every MRI sequence on the training cohort. These features were then evaluated on the test cohort via a support vector machine (SVM) classifier. The classification performance was compared against diagnostic reads by two expert neuro-radiologists, who had access to the same MRI sequences (Gd-T1w, T2w, and FLAIR) as the classifier. Results On the training cohort, the area under the receiver operating characteristic curve (AUC) was highest for FLAIR with 0.79, 95% CI [0.77, 0.81] for primary (N =22), and 0.79, 95% CI [0.75, 0.83], for metastatic subgroups (N = 21). Of the 15 studies in the holdout cohort, the SVM classifier identified 12 of 15 studies correctly, while neuro-radiologist 1 diagnosed 7 of 15, and neuro-radiologist 2 diagnosed 8 of 15 studies correctly, respectively. Discussion Our preliminary results appear to suggest that radiomic features may provide complementary diagnostic information on routine MRI sequences that may improve distinction of RN from recurrence, both for primary and metastatic brain tumors.

Background Longitudinal measurement of glioma burden with MRI is the basis for treatment response assessment. In this study, we developed a deep learning algorithm that automatically segments abnormal fluid attenuated inversion recovery (FLAIR) hyperintensity and contrast-enhancing tumor, quantitating tumor volumes as well as the product of maximum bidimensional diameters according to the Response Assessment in Neuro-Oncology (RANO) criteria (AutoRANO). Methods Two cohorts of patients were used for this study. One consisted of 843 preoperative MRIs from 843 patients with low- or high-grade gliomas from 4 institutions and the second consisted of 713 longitudinal postoperative MRI visits from 54 patients with newly diagnosed glioblastomas (each with 2 pretreatment “baseline” MRIs) from 1 institution. Results The automatically generated FLAIR hyperintensity volume, contrast-enhancing tumor volume, and AutoRANO were highly repeatable for the double-baseline visits, with an intraclass correlation coefficient (ICC) of 0.986, 0.991, and 0.977, respectively, on the cohort of postoperative GBM patients. Furthermore, there was high agreement between manually and automatically measured tumor volumes, with ICC values of 0.915, 0.924, and 0.965 for preoperative FLAIR hyperintensity, postoperative FLAIR hyperintensity, and postoperative contrast-enhancing tumor volumes, respectively. Lastly, the ICCs for comparing manually and automatically derived longitudinal changes in tumor burden were 0.917, 0.966, and 0.850 for FLAIR hyperintensity volume, contrast-enhancing tumor volume, and RANO measures, respectively. Conclusions Our automated algorithm demonstrates potential utility for evaluating tumor burden in complex posttreatment settings, although further validation in multicenter clinical trials will be needed prior to widespread implementation.