Influence of arterial transit time delays on the differentiation between tumor progression and pseudoprogression in glioblastoma by arterial spin labeling magnetic resonance imaging.

Arterial spin labeling (ASL) and dynamic susceptibility contrast (DSC) magnetic resonance imaging (MRI) have shown potential for differentiating tumor progression from pseudoprogression. For pseudocontinuous ASL with a single postlabeling delay, the presence of delayed arterial transit times (ATTs) could affect the evaluation of ASL-MRI perfusion data. In this study, the influence of ATT artifacts on the perfusion assessment and differentiation between tumor progression and pseudoprogression were studied. This study comprised 66 adult patients (mean age 60 ± 13 years; 40 males) with a histologically confirmed glioblastoma who received postoperative radio (chemo)therapy. ASL-MRI and DSC-MRI scans were acquired at 3 months postradiotherapy as part of the standard clinical routine. These scans were visually scored regarding (i) the severity of ATT artifacts (%) on the ASL-MRI scans only, scored by two neuroradiologists; (ii) perfusion of the enhancing tumor lesion; and (iii) radiological evaluation of tumor progression versus pseudoprogression by one neuroradiologist. The final outcome was based on combined clinical and radiological follow-up until 9 months postradiotherapy. ATT artifacts were identified in all patients based on the mean scores of two raters. A significant difference between the radiological evaluation of ASL-MRI and DSC-MRI was observed only for ASL images with moderate ATT severity (30%-65%). The perfusion assessment showed ASL-MRI tending more towards hyperperfusion than DSC-MRI in the case of moderate ATT artifacts. In addition, there was a significant difference between the prediction of tumor progression with ASL-MRI and the final outcome in the case of severe ATT artifacts (McNemar test, p = 0.041). Despite using ASL imaging parameters close to the recommended settings, ATT artifacts frequently occur in patients with treated brain tumors. Those artifacts could hinder the radiological evaluation of ASL-MRI data and the detection of true disease progression, potentially affecting treatment decisions for patients with glioblastoma.

Identification of Distinct Brain MRI Phenotypes and Their Association With Long-Term Dementia Risk in Community-Dwelling Older Adults.

BACKGROUND AND OBJECTIVES: Individual brain MRI markers only show at best a modest association with long-term occurrence of dementia. Therefore, it is challenging to accurately identify individuals at increased risk for dementia. We aimed to identify different brain MRI phenotypes by hierarchical clustering analysis based on combined neurovascular and neurodegenerative brain MRI markers and to determine the long-term dementia risk within the brain MRI phenotype subgroups. METHODS: Hierarchical clustering analysis based on 32 combined neurovascular and neurodegenerative brain MRI markers in community-dwelling individuals of the Age-Gene/Environment Susceptibility Reykjavik Study was applied to identify brain MRI phenotypes. A Cox proportional hazards regression model was used to determine the long-term risk for dementia per subgroup. RESULTS: We included 3,056 participants and identified 15 subgroups with distinct brain MRI phenotypes. The phenotypes ranged from limited burden, mostly irregular white matter hyperintensity (WMH) shape and cerebral atrophy, mostly irregularly WMHs and microbleeds, mostly cortical infarcts and atrophy, mostly irregularly shaped WMH and cerebral atrophy to multiburden subgroups. Each subgroup showed different long-term risks for dementia (min-max range hazard ratios [HRs] 1.01-6.18; mean time to follow-up 9.9 ± 2.6 years); especially the brain MRI phenotype with mainly WMHs and atrophy showed a large increased risk (HR 6.18, 95% CI 3.37-11.32). DISCUSSION: Distinct brain MRI phenotypes can be identified in community-dwelling older adults. Our results indicate that distinct brain MRI phenotypes are related to varying long-term risks of developing dementia. Brain MRI phenotypes may in the future assist in an improved understanding of the structural correlates of dementia predisposition.

Investigation of metabolite correlates of CEST in the human brain at 7 T.

Metabolite-weighted chemical exchange saturation transfer MRI can be used to indirectly image metabolites such as creatine and glutamate. This study aims to further explore the contrast of CEST at 2 ppm in the human brain at 7T and investigate the metabolite correlates of CEST at 2 ppm via correlations with magnetic resonance spectroscopy (MRS). Simulations were performed to establish the optimal acquisition parameters, such as total saturation time (tsat) and B1 root mean squared (B1rms) for CEST at 2 ppm in the human brain. Parameters were validated via in vitro phantom studies at 7T using concentrations, pH and temperature comparable to what is found in the human brain. Finally, 10 healthy volunteers were scanned at 7T for comparison with MRS. Our results show that the optimal parameters to acquire CEST at 2 ppm images are: B1rms = 2.14 µT & tsat = 1500 ms, respectively. Comparison with MRS showed no significant correlation between CEST at 2 ppm and total Creatine measured by MRS (R = 0.19; p-value = 0.273). However, a significant correlation was found between CEST at 2 ppm and Glu (R = 0.39; p-value = 0.033), indicating the broad Glutamate-weighted CEST as the main measurable contributor to CEST at 2 ppm. We identified and confirmed optimal CEST at 2 ppm sequence parameters and validated CEST at 2 ppm measurements in a controlled in vitro environment. Our findings suggest that glutamate is a substantial contributor to the CEST at 2 ppm contrast observed in the human brain, whereas the creatine contribution to CEST at 2 ppm in the brain did not show a measurable contribution.

MRI phenotypes of glioblastomas early after treatment are suggestive of overall patient survival.

Background: Distinguishing true tumor progression (TP) from treatment-induced abnormalities (eg, pseudo-progression (PP) after radiotherapy) on conventional MRI scans remains challenging in patients with a glioblastoma. We aimed to establish brain MRI phenotypes of glioblastomas early after treatment by combined analysis of structural and perfusion tumor characteristics and assessed the relation with recurrence rate and overall survival time. Methods: Structural and perfusion MR images of 67 patients at 3 months post-radiotherapy were visually scored by a neuroradiologist. In total 23 parameters were predefined and used for hierarchical clustering analysis. Progression status was assessed based on the clinical course of each patient 9 months after radiotherapy (or latest available). Multivariable Cox regression models were used to determine the association between the phenotypes, recurrence rate, and overall survival. Results: We established 4 subgroups with significantly different tumor MRI characteristics, representing distinct MRI phenotypes of glioblastomas: TP and PP rates did not differ significantly between subgroups. Regression analysis showed that patients in subgroup 1 (characterized by having mostly small and ellipsoid nodular enhancing lesions with some hyper-perfusion) had a significant association with increased mortality at 9 months (HR: 2.6 (CI: 1.1-6.3); P = .03) with a median survival time of 13 months (compared to 22 months of subgroup 2). Conclusions: Our study suggests that distinct MRI phenotypes of glioblastomas at 3 months post-radiotherapy can be indicative of overall survival, but does not aid in differentiating TP from PP. The early prognostic information our method provides might in the future be informative for prognostication of glioblastoma patients.

Advanced MR Techniques for Preoperative Glioma Characterization: Part 2.

Preoperative clinical MRI protocols for gliomas, brain tumors with dismal outcomes due to their infiltrative properties, still rely on conventional structural MRI, which does not deliver information on tumor genotype and is limited in the delineation of diffuse gliomas. The GliMR COST action wants to raise awareness about the state of the art of advanced MRI techniques in gliomas and their possible clinical translation. This review describes current methods, limits, and applications of advanced MRI for the preoperative assessment of glioma, summarizing the level of clinical validation of different techniques. In this second part, we review magnetic resonance spectroscopy (MRS), chemical exchange saturation transfer (CEST), susceptibility-weighted imaging (SWI), MRI-PET, MR elastography (MRE), and MR-based radiomics applications. The first part of this review addresses dynamic susceptibility contrast (DSC) and dynamic contrast-enhanced (DCE) MRI, arterial spin labeling (ASL), diffusion-weighted MRI, vessel imaging, and magnetic resonance fingerprinting (MRF). EVIDENCE LEVEL: 3. TECHNICAL EFFICACY: Stage 2.

Advanced MR Techniques for Preoperative Glioma Characterization: Part 1.

Preoperative clinical magnetic resonance imaging (MRI) protocols for gliomas, brain tumors with dismal outcomes due to their infiltrative properties, still rely on conventional structural MRI, which does not deliver information on tumor genotype and is limited in the delineation of diffuse gliomas. The GliMR COST action wants to raise awareness about the state of the art of advanced MRI techniques in gliomas and their possible clinical translation or lack thereof. This review describes current methods, limits, and applications of advanced MRI for the preoperative assessment of glioma, summarizing the level of clinical validation of different techniques. In this first part, we discuss dynamic susceptibility contrast and dynamic contrast-enhanced MRI, arterial spin labeling, diffusion-weighted MRI, vessel imaging, and magnetic resonance fingerprinting. The second part of this review addresses magnetic resonance spectroscopy, chemical exchange saturation transfer, susceptibility-weighted imaging, MRI-PET, MR elastography, and MR-based radiomics applications. Evidence Level: 3 Technical Efficacy: Stage 2.

The use of variable delay multipulse chemical exchange saturation transfer for separately assessing different CEST pools in the human brain at 7T.

PURPOSE: Current challenges of in vivo CEST imaging include overlapping signals from different pools. The overlap arises from closely resonating pools and/or the broad magnetization transfer contrast (MTC) from macromolecules. This study aimed to evaluate the feasibility of variable delay multipulse (VDMP) CEST to separately assess solute pools with different chemical exchange rates in the human brain in vivo, while mitigating the MTC. METHODS: VDMP saturation buildup curves were simulated for amines, amides, and relayed nuclear Overhauser effect. VDMP data were acquired from glutamate and bovine serum albumin phantoms, and from six healthy volunteers at 7T. For the in vivo data, MTC removal was performed via a three-pool Lorentzian fitting. Different B1 amplitudes and mixing times were used to evaluate CEST pools with different exchange rates. RESULTS: The results show the importance of removing MTC when applying VDMP in vivo and the influence of B1 for distinguishing different pools. Finally, the optimal B1 and mixing times to effectively saturate slow- and fast-exchanging components are also reported. Slow-exchanging amides and rNOE components could be distinguished when using B1 = 1 µT and tmix = 10 ms and 40 ms, respectively. Fast-exchanging components reached the highest saturation when using a B1 = 2.8 µT and tmix = 0 ms. CONCLUSION: VDMP is a powerful CEST-editing tool, exploiting chemical exchange-rate differences. After MTC removal, it allows separate assessment of slow- and fast-exchanging solute pools in in vivo human brain.