The framework offers a robust and rapid method for estimating MK, with a protocol easily adapted on commercial scanners, as it requires only minimal modification of standard diffusion-weighting protocols. These properties make the method feasible in practically any clinical setting.

Purpose The clinical use of kurtosis imaging is impeded by long acquisitions and post-processing. Recently, estimation of mean kurtosis tensor W̅ and mean diffusivity (D̅) was made possible from 13 distinct DWI acquisitions (the 1-3-9 protocol) with simple post-processing. Here, we analyze the effects of noise and nonideal diffusion encoding, and propose a new correction strategy. We also present a 1-9-9 protocol with increased robustness to experimental imperfections and minimal additional scan time. This refinement does not affect computation time and also provides a fast estimate of fractional anisotropy (FA). Methods 1-3-9/1-9-9 data are acquired in rat and human brains, and estimates of D̅, FA, W̅ from human brains are compared to traditional estimates from an extensive DKI data set. Simulations are used to evaluate the influence of noise and diffusion encodings deviating from the scheme, and the performance of the correction strategy. Optimal b-values are determined from simulations and data. Results Accuracy and precision in D̅ and W̅ are comparable to nonlinear least squares estimation, and is improved with the 1-9-9 protocol. The compensation strategy vastly improves parameter estimation in non-ideal data. Conclusion The framework offers a robust and compact method for estimating several diffusion metrics. The protocol is easily implemented.

White matter tract integrity (WMTI) can characterize brain microstructure in areas with highly aligned fiber bundles. Several WMTI biomarkers have now been validated against microscopy and provided promising results in studies of brain development and aging, as well as in a number of brain disorders. Currently, WMTI is mostly used in dedicated animal studies and clinical studies of slowly progressing diseases but has not yet emerged as a routine clinical tool. To this end, a less data intensive experimental method would be beneficial by enabling high resolution validation studies, and ease clinical applications by speeding up data acquisition compared to typical diffusion kurtosis imaging (DKI) protocols utilized as part of WMTI imaging. Here, we evaluate WMTI based on recently introduced axially symmetric DKI which has lower data demand than conventional DKI. We compare WMTI parameters derived from conventional DKI to those calculated analytically from axially symmetric DKI. We employ numerical simulations, as well as data from fixed rat spinal cord (1 sample) and in vivo human (3 subjects) and rat brain (4 animals). Our analysis shows that analytical WMTI based on axially symmetric DKI with sparse data sets (19 images) produces WMTI metrics that correlate strongly with estimates based on traditional DKI data sets (60 images or more). We demonstrate the preclinical potential of the proposed WMTI technique in in vivo rat brain (300 μm isotropic resolution with whole brain coverage in a one hour acquisition). WMTI parameter estimates are subject to a duality leading to two solution branches dependent on a sign choice which is currently debated. Results from both of these branches are presented and discussed throughout our analysis. The proposed fast WMTI approach may be useful for preclinical research and e.g. clinical evaluation of patients with traumatic white matter injuries or symptoms of neurovascular or neuroinflammatory disorders.

BACKGROUND AND PURPOSE:Diffusional kurtosis imaging is an MR imaging technique that provides microstructural information in biologic systems. Its application in clinical studies, however, is hampered by long acquisition and postprocessing times. We evaluated a new and fast (2 minutes 46 seconds) diffusional kurtosis imaging method with regard to glioma grading, compared it with conventional diffusional kurtosis imaging, and compared the diagnostic accuracy of fast mean kurtosis (MKЈ) to that of the widely used mean diffusivity.

OBJECTIVE Mutations in the isocitrate dehydrogenase (IDH) genes are of proven diagnostic and prognostic significance for cerebral gliomas. The objective of this study was to evaluate the clinical feasibility of using a recently described method for determining IDH mutation status by using magnetic resonance spectroscopy (MRS) to detect the presence of 2-hydroxyglutarate (2HG), the metabolic product of the mutant IDH enzyme. METHODS By extending imaging time by 6 minutes, the authors were able to include a point-resolved spectroscopy (PRESS) MRS sequence in their routine glioma imaging protocol. In 30 of 35 patients for whom this revised protocol was used the lesions were subsequently diagnosed histologically as gliomas. Of the remaining 5 patients, 1 had a gangliocytoma, 1 had a primary CNS lymphoma, and 3 had nonneoplastic lesions. Immunohistochemistry and/or polymerase chain reaction were used to detect the presence of IDH mutations in the glioma tissue resected. RESULTS In vivo MRS for 2HG correctly identified the IDH mutational status in 88.6% of patients. The sensitivity and specificity was 89.5% and 81.3%, respectively, when using 2 mM 2HG as threshold to discriminate IDH-mutated from wildtype tumors. Two glioblastomas that had elevated 2HG levels did not have detectable IDH mutations, and in 2 IDH-mutated gliomas 2HG was not reliably detectable. CONCLUSIONS The noninvasive determination of the IDH mutation status of a presumed glioma by means of MRS may be incorporated into a routine diagnostic imaging protocol and can be used to obtain additional information for patient care.