Objectives Gadolinium-based contrast agents (GBCAs) have become an integral part in daily clinical decision making in the last 3 decades. However, there is a broad consensus that GBCAs should be exclusively used if no contrast-free magnetic resonance imaging (MRI) technique is available to reduce the amount of applied GBCAs in patients. In the current study, we investigate the possibility of predicting contrast enhancement from noncontrast multiparametric brain MRI scans using a deep-learning (DL) architecture. Materials and Methods A Bayesian DL architecture for the prediction of virtual contrast enhancement was developed using 10-channel multiparametric MRI data acquired before GBCA application. The model was quantitatively and qualitatively evaluated on 116 data sets from glioma patients and healthy subjects by comparing the virtual contrast enhancement maps to the ground truth contrast-enhanced T1-weighted imaging. Subjects were split in 3 different groups: enhancing tumors (n = 47), nonenhancing tumors (n = 39), and patients without pathologic changes (n = 30). The tumor regions were segmented for a detailed analysis of subregions. The influence of the different MRI sequences was determined. Results Quantitative results of the virtual contrast enhancement yielded a sensitivity of 91.8% and a specificity of 91.2%. T2-weighted imaging, followed by diffusion-weighted imaging, was the most influential sequence for the prediction of virtual contrast enhancement. Analysis of the whole brain showed a mean area under the curve of 0.969 ± 0.019, a peak signal-to-noise ratio of 22.967 ± 1.162 dB, and a structural similarity index of 0.872 ± 0.031. Enhancing and nonenhancing tumor subregions performed worse (except for the peak signal-to-noise ratio of the nonenhancing tumors). The qualitative evaluation by 2 raters using a 4-point Likert scale showed good to excellent (3–4) results for 91.5% of the enhancing and 92.3% of the nonenhancing gliomas. However, despite the good scores and ratings, there were visual deviations between the virtual contrast maps and the ground truth, including a more blurry, less nodular-like ring enhancement, few low-contrast false-positive enhancements of nonenhancing gliomas, and a tendency to omit smaller vessels. These “features” were also exploited by 2 trained radiologists when performing a Turing test, allowing them to discriminate between real and virtual contrast-enhanced images in 80% and 90% of the cases, respectively. Conclusions The introduced model for virtual gadolinium enhancement demonstrates a very good quantitative and qualitative performance. Future systematic studies in larger patient collectives with varying neurological disorders need to evaluate if the introduced virtual contrast enhancement might reduce GBCA exposure in clinical practice.

Background: The "glymphatic system" (GS), a brain-wide network of cerebrospinal fluid microcirculation, supplies a pathway through and out of the central nervous system (CNS); malfunction of the system is implicated in a variety of neurological disorders. In this exploratory study, we analyzed the potential of a new imaging approach that we coined delayed T2-weighted gadolinium-enhanced imaging to visualize the GS in vivo. Methods: Heavily T2-weighted fluid-attenuated inversion recovery (hT2w-FLAIR) magnetic resonance imaging was obtained before, and 3 hours and 24 hours after intravenous gadolinium-based contrast agent (GBCA) application in 33 neurologically healthy patients and 7 patients with an impaired blood-brain barrier (BBB) due to cerebral metastases. Signal intensity (SI) was determined in various cerebral fluid spaces, and white matter hyperintensities were quantified by applying the Fazekas scoring system. Findings: Delayed hT2w-FLAIR showed GBCA entry into the CNS via the choroid plexus and the ciliary body, with GBCA drainage along perineural sheaths of cranial nerves and along perivascular spaces of penetrating cortical arteries. In all patients and all sites, a significant SI increase was found for the 3 hours and 24 hours time points compared with baseline. Although no significant difference in SI was found between neurologically healthy patients and patients with an impaired BBB, a significant positive correlation between Fazekas scoring system and SI increase in the perivascular spaces 3 hours post injection was shown. Interpretation: Delayed T2-weighted gadolinium-enhanced imaging can visualize the GBCA pathway into and through the GS. Presence of GBCAs within the GS might be regarded as part of the natural excretion process and should not be mixed up with gadolinium deposition. Rather, the correlation found between deep white matter hyperintensities, an imaging sign of vascular dementia, and GS functioning demonstrated feasibility to exploit the pathway of GBCAs through the GS for diagnostic purposes.

PurposeTo prospectively investigate chemical exchange saturation transfer (CEST) MRI in glioblastoma patients as predictor of early tumor progression after first-line treatment.Experimental DesignTwenty previously untreated glioblastoma patients underwent CEST MRI employing a 7T whole-body scanner. Nuclear Overhauser effect (NOE) as well as amide proton transfer (APT) CEST signals were isolated using Lorentzian difference (LD) analysis and relaxation compensated by the apparent exchange-dependent relaxation rate (AREX) evaluation. Additionally, NOE-weighted asymmetric magnetic transfer ratio (MTRasym) and downfield-NOE-suppressed APT (dns-APT) were calculated. Patient response to consecutive treatment was determined according to the RANO criteria. Mean signal intensities of each contrast in the whole tumor area were compared between early-progressive and stable disease.ResultsPre-treatment tumor signal intensity differed significantly regarding responsiveness to first-line therapy in NOE-LD (p = 0.0001), NOE-weighted MTRasym (p = 0.0186) and dns-APT (p = 0.0328) contrasts. Hence, significant prediction of early progression was possible employing NOE-LD (AUC = 0.98, p = 0.0005), NOE-weighted MTRasym (AUC = 0.83, p = 0.0166) and dns-APT (AUC = 0.80, p = 0.0318). The NOE-LD provided the highest sensitivity (91%) and specificity (100%).ConclusionsCEST derived contrasts, particularly NOE-weighted imaging and dns-APT, yielded significant predictors of early progression after fist-line therapy in glioblastoma. Therefore, CEST MRI might be considered as non-invasive tool for customization of treatment in the future.

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The combined use of DSC and DCE MR perfusion may provide additional information of prognostic value for glioblastoma patient survival prediction. As K(trans) was not tightly coupled to CBV, both parameters may reflect different stages in the pathogenetic sequence of glioblastoma growth.

Highlights MRI derived total 23 Na concentration differs significantly in glioma subregions. Total 23 Na concentration could reflect IDH mutation status and tumor grade. 23 Na MRI yields potential non-invasive biomarkers for the treatment of gliomas.

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