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Myelination-related MR signal changes in white matter are helpful for assessing normal development in infants and children. A rule-based myelination evaluation workflow regarding signal changes on T1-weighted images (T1WIs) and T2-weighted images (T2WIs) has been widely used in radiology. This study aimed to simulate a rule-based workflow using a stacked deep learning model and evaluate age estimation accuracy.The age estimation system involved two stacked neural networks: a target network-to extract five myelination-related images from the whole brain, and an age estimation network from extracted T1- and T2WIs separately. A dataset was constructed from 119 children aged below 2 years with two MRI systems. A four-fold cross-validation method was adopted. The correlation coefficient (CC), mean absolute error (MAE), and root mean squared error (RMSE) of the corrected chronological age of full-term birth, as well as the mean difference and the upper and lower limits of 95% agreement, were measured. Generalization performance was assessed using datasets acquired from different MR images. Age estimation was performed in Sturge-Weber syndrome (SWS) cases.There was a strong correlation between estimated age and corrected chronological age (MAE: 0.98 months; RMSE: 1.27 months; and CC: 0.99). The mean difference and standard deviation (SD) were -0.15 and 1.26, respectively, and the upper and lower limits of 95% agreement were 2.33 and -2.63 months. Regarding generalization performance, the performance values on the external dataset were MAE of 1.85 months, RMSE of 2.59 months, and CC of 0.93. Among 13 SWS cases, 7 exceeded the limits of 95% agreement, and a proportional bias of age estimation based on myelination acceleration was exhibited below 12 months of age (P = 0.03).Stacked deep learning models automated the rule-based workflow in radiology and achieved highly accurate age estimation in infants and children up to 2 years of age.

The present study describes a model-based approach for correcting off-resonance in the context of double half-echo k-space acquisitions. This technique employs center-out readouts in forward and reverse directions to reduce echo-times but is sensitive to off-resonance, which manifests as pixel shifts in both directions. Demodulating the k-space signal with a constant off-resonance term per slice removes pixel shifts and results in a marked reduction in blurring. Phantom and in vivo datasets are demonstrated from low bandwidth sodium imaging.

Purpose There remains no consensus normal-appearing white matter (NAWM) normalization method to compute normalized relative cerebral blood volume (nrCBV) and apparent diffusion coefficient (nADC) in brain tumors. This reader study explored nrCBV and nADC differences using different NAWM normalization methods. Methods Thirty-five newly diagnosed glioma patients were studied. For each patient, two readers created four NAWM regions of interests: (1) a single plane in the centrum semiovale (CSOp), (2) 3 spheres in the centrum semiovale (CSOs), (3) a single plane in the slice of the tumor center (TUMp), and (4) 3 spheres in the slice of the tumor center (TUMs). Readers repeated NAWM segmentations 1 month later. Differences in nrCBV and nADC of the FLAIR hyperintense tumor, inter-/intra-reader variability, and time to segment NAWM were assessed. As a validation step, the diagnostic performance of each method for IDH-status prediction was evaluated. Results Both readers obtained significantly different nrCBV (P < .001), nADC (P < .001), and time to segment NAWM (P < .001) between the four normalization methods. nrCBV and nADC were significantly different between CSO and TUM methods, but not between planar and spherical methods in the same NAWM region. Broadly, CSO methods were quicker than TUM methods, and spherical methods were quicker than planar methods. For all normalization techniques, inter-reader reproducibility and intra-reader repeatability were excellent (intraclass correlation coefficient > 0.9), and the IDH-status predictive performance remained similar. Conclusion The selected NAWM region significantly impacts nrCBV and nADC values. CSO methods, particularly CSOs, may be preferred because of time reduction, similar reader variability, and similar diagnostic performance compared to TUM methods.

Relaxation times and morphological information are fundamental magnetic resonance imaging-derived metrics of the human brain that reflect the status of the underlying tissue. Magnetic resonance fingerprinting (MRF) enables simultaneous acquisition of T1 and T2 maps inherently aligned to the anatomy, allowing whole-brain relaxometry and morphometry in a single scan. In this study, we revealed the feasibility of 3D MRF for simultaneous brain structure-wise morphometry and relaxometry. Comprehensive test–retest scan analyses using five 1.5-T and three 3.0-T systems from a single vendor including different scanner types across 3 institutions demonstrated that 3D MRF-derived morphological information and relaxation times are highly repeatable at both 1.5 T and 3.0 T. Regional cortical thickness and subcortical volume values showed high agreement and low bias across different field strengths. The ability to acquire a set of regional T1, T2, thickness, and volume measurements of neuroanatomical structures with high repeatability and reproducibility facilitates the ability of longitudinal multicenter imaging studies to quantitatively monitor changes associated with underlying pathologies, disease progression, and treatments.

Diffusion tensor imaging (DTI) has been established its usefulness in evaluating normal-appearing white matter (NAWM) and other lesions that are difficult to evaluate with routine clinical MRI in the evaluation of the brain and spinal cord lesions in multiple sclerosis (MS), a demyelinating disease. With the recent advances in the software and hardware of MRI systems, increasingly complex and sophisticated MRI and analysis methods, such as q-space imaging, diffusional kurtosis imaging, neurite orientation dispersion and density imaging, white matter tract integrity, and multiple diffusion encoding, referred to as advanced diffusion MRI, have been proposed. These are capable of capturing in vivo microstructural changes in the brain and spinal cord in normal and pathological states in greater detail than DTI.This paper reviews the current status of recent advanced diffusion MRI for assessing MS in vivo as part of an issue celebrating two decades of magnetic resonance in medical sciences (MRMS), an official journal of the Japanese Society of Magnetic Resonance in Medicine.

Oscillating-gradient spin-echo sequences enable the measurement of diffusion weighting with a short diffusion time and can provide indications of internal structures. We report two cases of brain abscess in which the apparent diffusion coefficient (ADC) values appear higher at short diffusion times in comparison with those at long diffusion times. Diffusion time dependence of the ADC in brain abscesses suggests not only substrate viscosity but also restricted diffusion due to the structure within the lesions.

The volumes of intracranial tissues of 40 healthy volunteers acquired from 0.3- and 3-T scanners were compared using intraclass correlation coefficients, correlation analyses, and Bland-Altman analyses. We found high intraclass correlation coefficients, high Pearson's correlation coefficients, and low percentage biases in all tissues and most of the brain regions, although small differences were observed in some areas. These findings may support the validity of brain volumetry with low-field magnetic resonance imaging.

Non-enhancing (NE) infiltrating tumor cells beyond the contrast-enhancing (CE) bulk of tumor are potential propagators of recurrence after gross total resection of high-grade glioma. We leveraged single-nucleus RNA-sequencing to compare prospectively identified biopsy specimens from CE and NE regions. Recurrent gliomas recapitulate the previously reported glioma cellular states of primary gliomas. Tumor cells in NE regions are enriched in oligodendrocyte progenitor cell-like and neural progenitor cell-like cellular states, while CE regions are enriched for astrocyte-like and mesenchymal-like states. These NE glioma cells have similar copy number alterations and proportions of proliferative and putative glioma stem cells relative to CE regions. Tumor cells in NE regions exhibit upregulation of genes previously associated with lower grade gliomas. Cell-, gene-, and pathway-level analyses of the tumor microenvironment in the NE region reveal relative downregulation of tumor-mediated neovascularization and cell-mediated immune response, but increased glioma-to-neuron interactions. This comprehensive analysis illustrates differing tumor and non-tumor landscapes of CE and NE regions in high-grade gliomas, highlighting the NE region as an area harboring likely initiators of recurrence in a pro-tumor microenvironment and identifying possible targets for future design of NE-specific adjuvant therapy.

We hypothesize that myelin is more susceptible to damage over time than axons. We investigated the association between the estimated duration from the onset of multiple sclerosis (MS) plaques and myelin- and axon-related quantitative synthetic magnetic resonance imaging (SyMRI) and neurite orientation dispersion and density imaging (NODDI) metrics. We analyzed 31 patients with MS with 73 newly appeared plaques. Simple linear regression analysis was performed to assess the association between the estimated duration from the onset of plaques and quantitative MRI metrics. These metrics included the myelin volume fraction (MVF), axon volume fraction, and g-ratio in plaque and normal-appearing white matter. MS plaques with a longer estimated duration from onset were significantly correlated with a lower MVF (slope = − 0.0070, R2 = 0.0970), higher g-ratio (slope = 0.0078, R2 = 0.0842) (all P values

Low-field magnetic resonance imaging (MRI) systems have seen a renaissance recently due to improvements in technology (both hardware and software). Originally, the performance of low-field MRI systems was rated lower than their actual clinical usefulness, and they were viewed as low-cost but poorly performing systems. However, various applications similar to high-field MRI systems (1.5 T and 3 T) have gradually become possible, culminating with high-performance low-field MRI systems and their adaptations now being proposed that have unique advantages over high-field MRI systems in various aspects. This review article describes the physical characteristics of low-field MRI systems and presents both their advantages and disadvantages for clinical use (past to present), along with their cutting-edge clinical applications.

The association of overall survival (OS) with tumor burden, including contrast enhanced (CE) volume on CE T1-weighted images, fluid-attenuated inversion recovery (FLAIR) hyperintense volume, and 3, 4-dihydroxy-6-[18F]-fluoro-L-phenylalanine (FDOPA) hypermetabolic volume, in isocitrate dehydrogenase (IDH) wild-type gliomas remains unclear. This study aimed to assess the association between biological tumor burden in pre- and post-operative status and OS in IDH wild-type gliomas, and evaluated which volume was the best predictor of OS. Thirty-four patients with treatment-naive IDH wild-type gliomas (WHO grade II 6, III 15, IV 13) were retrospectively included. Three pre-operative tumor regions of interest (ROIs) were segmented based on the CE, FLAIR hyperintense, and FDOPA hypermetabolic regions. Resected ROIs were segmented from the post-operative images. Residual CE, FLAIR hyperintense, and FDOPA hypermetabolic ROIs were created by subtracting resected ROIs from pre-operative ROIs. Cox regression analysis was conducted to investigate the association of OS with the volume of each ROI, and Akaike information criterion was used to assess the fitness. Residual CE volume had a significant association with OS [hazard ratio (HR) = 1.26, p = 0.039], but this effect disappeared when controlling for tumor grade. Residual FDOPA hypermetabolic volume best fit the regression model and was significantly associated with OS (HR = 1.18, p = 0.008), even when controlling for tumor grade. FLAIR hyperintense volume showed no significant association with OS. Residual FDOPA hypermetabolic burden predicted OS for IDH wild-type gliomas, regardless of the tumor grade. Furthermore, removing hypermetabolic and CE regions may improve the prognosis.

There remains no consensus normal-appearing white matter (NAWM) normalization method to compute normalized relative cerebral blood volume (nrCBV) and apparent diffusion coefficient (nADC) in brain tumors. This reader study explored nrCBV and nADC differences using different NAWM normalization methods.Thirty-five newly diagnosed glioma patients were studied. For each patient, two readers created four NAWM regions of interests: (1) a single plane in the centrum semiovale (CSOp), (2) 3 spheres in the centrum semiovale (CSOs), (3) a single plane in the slice of the tumor center (TUMp), and (4) 3 spheres in the slice of the tumor center (TUMs). Readers repeated NAWM segmentations 1 month later. Differences in nrCBV and nADC of the FLAIR hyperintense tumor, inter-/intra-reader variability, and time to segment NAWM were assessed. As a validation step, the diagnostic performance of each method for IDH-status prediction was evaluated.Both readers obtained significantly different nrCBV (P.001), nADC (P.001), and time to segment NAWM (P.001) between the four normalization methods. nrCBV and nADC were significantly different between CSO and TUM methods, but not between planar and spherical methods in the same NAWM region. Broadly, CSO methods were quicker than TUM methods, and spherical methods were quicker than planar methods. For all normalization techniques, inter-reader reproducibility and intra-reader repeatability were excellent (intraclass correlation coefficient0.9), and the IDH-status predictive performance remained similar.The selected NAWM region significantly impacts nrCBV and nADC values. CSO methods, particularly CSOs, may be preferred because of time reduction, similar reader variability, and similar diagnostic performance compared to TUM methods.

Oscillating gradient spin-echo (OGSE) sequences provide access to short diffusion times and may provide insight into micro-scale internal structures of pathologic lesions based on an analysis of changes in diffusivity with differing diffusion times. We hypothesized that changes in diffusivity acquired with a shorter diffusion time may permit elucidation of properties related to the internal structure of extra-axial brain tumors. This study aimed to investigate the utility of changes in diffusivity between short and long diffusion times for characterizing extra-axial brain tumors. In total, 12 patients with meningothelial meningiomas, 13 patients with acoustic neuromas, and 11 patients with pituitary adenomas were scanned with a 3 T magnetic resonance imaging (MRI) scanner with diffusion-weighted imaging (DWI) using OGSE and pulsed gradient spin-echo (PGSE) (effective diffusion times [Δ

Purpose To quantify the radiation dose distribution and lesion morphometry (shape) at baseline, prior to chemoradiation, and at the time of radiographic recurrence in patients with glioblastoma (GBM). Methods The IMRT dose distribution, location of the center of mass, sphericity, and solidity of the contrast enhancing tumor at baseline and the time of tumor recurrence was quantified in 48 IDH wild-type GBM who underwent postoperative IMRT (2 Gy daily for total of 60 Gy) with concomitant and adjuvant temozolomide. Results Average radiation dose within enhancing tumor at baseline and recurrence was ≥ 60 Gy. Centroid location of the enhancing tumor shifted an average of 11.3 mm at the time of recurrence with respect to pre-IMRT location. A positive correlation was observed between change in centroid location and PFS in MGMT methylated patients (P = 0.0007) and Cox multivariate regression confirmed centroid distance from baseline was associated with PFS when accounting for clinical factors (P = 0.0189). Lesion solidity was higher at recurrence compared to baseline (P = 0.0118). Tumors that progressed > 12 weeks after IMRT were significantly more spherical (P = 0.0094). Conclusion Most GBMs recur local within therapeutic IMRT doses; however, tumors with longer PFS occurred further from the original tumor location and were more solid and/or nodular.

Background Diffusion MRI estimates of the apparent diffusion coefficient (ADC) have been shown to be useful in predicting treatment response in patients with glioblastoma (GBM), with ADC elevations indicating tumor cell death. We aimed to investigate whether the ADC values measured before and after treatment with immune checkpoint inhibitors (ICIs) and the changes in these ADC values could predict overall survival (OS) in patients with recurrent IDH wild-type GBM. Methods Forty-four patients who met the following inclusion criteria were included in this retrospective study: (i) diagnosed with recurrent IDH wild-type GBM and treated with either pembrolizumab or nivolumab and (ii) availability of diffusion data on pre- and post-ICI MRI. Tumor volume and the median relative ADC (rADC) with respect to the normal-appearing white matter within the enhancing tumor were calculated. Results Median OS among all patients was 8.1 months (range, 1.0–22.5 months). Log-rank test revealed that higher post-treatment rADC was associated with a significantly longer OS (median, 10.3 months for rADC ≥ 1.63 versus 6.1 months for rADC < 1.63; P = .02), whereas tumor volume, pretreatment rADC, and changes in rADC after treatment were not significantly associated with OS. Cox regression analysis revealed that post-treatment rADC significantly influenced OS (P = .02, univariate analysis), even after controlling for age and sex (P =.01, multivariate analysis), and additionally controlling for surgery after ICI treatment (P = .045, multivariate analysis). Conclusions Elevated post-treatment rADC may be an early imaging biomarker for OS benefits in GBM patients receiving ICI treatment.

Multiple sclerosis and neuromyelitis optica spectrum disorders are both neuroinflammatory diseases and have overlapping clinical manifestations. We developed a convolutional neural network model that differentiates between the two based on magnetic resonance imaging data. Thirty-five patients with relapsing-remitting multiple sclerosis and eighteen age-, sex-, disease duration-, and Expanded Disease Status Scale-matched patients with anti-aquaporin-4 antibody-positive neuromyelitis optica spectrum disorders were included in this study. All patients were scanned on a 3-T scanner using a multi-dynamic multi-echo sequence that simultaneously measures R1 and R2 relaxation rates and proton density. R1, R2, and proton density maps were analyzed using our convolutional neural network model. To avoid overfitting on a small dataset, we aimed to separate features of images into those specific to an image and those common to the group, based on SqueezeNet. We used only common features for classification. Leave-one-out cross validation was performed to evaluate the performance of the model. The area under the receiver operating characteristic curve of the developed convolutional neural network model for differentiating between the two disorders was 0.859. The sensitivity to multiple sclerosis and neuromyelitis optica spectrum disorders, and accuracy were 80.0%, 83.3%, and 81.1%, respectively. In conclusion, we developed a convolutional neural network model that differentiates between multiple sclerosis and neuromyelitis optica spectrum disorders, and which is designed to avoid overfitting on small training datasets. Our proposed algorithm may facilitate a differential diagnosis of these diseases in clinical practice.

Purpose : To assess the association between hypometabolic extension of 18F-fluorodeoxyglucose positron emission tomography and diffusion tensor imaging indices, including mean diffusivity (MD) and fractional anisotropy (FA), in hippocampal sclerosis (HS). Methods : Thirty-six unilateral HS were retrospectively selected and stratified into two groups: broad and localized hypometabolic groups (hypometabolism beyond [n = 26] and within the temporal lobe [n = 10]). Forty-one pairs of gray matter (GM) regions of interest (ROIs) were segmented using FreeSurfer software. The GM ROIs were applied to MD maps, and median MD values within each ROI were compared between hemispheres ipsilateral and contralateral to HS using a mixed effect model. Tract-Based Spatial Statistics (TBSS) was used to evaluate FA of white matter (WM) tracts between hemispheres ipsilateral and contralateral to HS. Disease laterality was controlled for. Results : The MD values in the thalamus, caudate, hippocampus, amygdala, superior frontal gyrus, middle and inferior temporal gyrus, temporal pole, and isthmus cingulate gyrus were significantly higher in the HS side than the contralateral side for the broad hypometabolic group. Those in the amygdala and superior temporal sulcus were significantly higher in the HS side than the contralateral side for the localized group. The TBSS analyses showed significantly decreased FA in the WM tracts of the temporal and frontal lobes for the broad hypometabolic group, while no tracts showed significant differences for the localized group. Conclusion : The hypometabolic extension for HS was associated with the abnormalities of MD and FA in GM and WM, respectively, with more widespread microstructural alterations for broad hypometabolic HS.

Although tumor localization and 3,4-dihydroxy-6-18F-fluoro-l-phenylalanine (FDOPA) uptake may have an association, preferential tumor localization in relation to FDOPA uptake is yet to be investigated in lower-grade gliomas (LGGs). This study aimed to identify differences in the frequency of tumor localization between FDOPA hypometabolic and hypermetabolic LGGs using a probabilistic radiographic atlas. Fifty-one patients with newly diagnosed LGG (WHO grade II, 29; III, 22; isocitrate dehydrogenase wild-type, 21; mutant 1p19q non-codeleted,16; mutant codeleted, 14) who underwent FDOPA positron emission tomography (PET) were retrospectively selected. Semiautomated tumor segmentation on FLAIR was performed. Patients with LGGs were separated into two groups (FDOPA hypometabolic and hypermetabolic LGGs) according to the normalized maximum standardized uptake value of FDOPA PET (a threshold of the uptake in the striatum) within the segmented regions. Spatial normalization procedures to build a 3D MRI-based atlas using each segmented region were validated by an analysis of differential involvement statistical mapping. Superimposition of regions of interest showed a high number of hypometabolic LGGs localized in the frontal lobe, while a high number of hypermetabolic LGGs was localized in the insula, putamen, and temporal lobe. The statistical mapping revealed that hypometabolic LGGs occurred more frequently in the superior frontal gyrus (close to the supplementary motor area), while hypermetabolic LGGs occurred more frequently in the insula. Radiographic atlases revealed preferential frontal lobe localization for FDOPA hypometabolic LGGs, which may be associated with relatively early detection.

To evaluate the relationship between abnormal findings on abdomino-pelvic CT and adverse events in oncologic patients treated with lenvatinib, and their relationship with treatment planning. This single institutional retrospective study included 58 patients with unresectable hepatocellular carcinoma or unresectable thyroid carcinoma (mean age ± standard deviation 69.6 ± 10.0 years; range 39–84 years; 48 men) who underwent CT between October 2016 and July 2020. Two radiologists who were blinded to clinical information including the presence or absence of diarrhea evaluated the imaging findings, including the presence/absence of enteritis in each intestinal segment. Gastrointestinal adverse events (diarrhea, decreased appetite, nausea, and vomiting) and other drug-induced adverse events requiring treatment or follow-up during lenvatinib treatment were also investigated. The frequency of these adverse events was compared between the patients with and without enteritis using Fisher’s exact test or the Mann–Whitney U test. Enteritis was found on CT in the majority (33/58 [56.9%]) of the patients, and most of them (25/33 [75.8%]) showed duodenojejunitis. The frequency of gastrointestinal adverse events (28/33 [84.8%] vs. 13/25 [56.0%], p = 0.009), diarrhea (20/33 [60.6%] vs. 3/25 [12.0%], p

Purpose The aim of this study was to investigate whether the detectability of brain volume change in voxel-based morphometry (VBM) with gray matter images is affected by mild white matter lesions (MWLs). Materials and methods Three-dimensional T1-weighted images (3D-T1WIs) of 11 healthy subjects were obtained using a 3T MR scanner. We initially created 3D-T1WIs with focal cortical atrophy simulated cortical atrophy in left amygdala (type A) and the left medial frontal lobe (type B) from control 3D-T1WIs. Next, the following three types of MWL images were created: type A + 1L and type B + 1L images, only one white matter lesion; type A + 4L and type B + 4L images, four white matter lesions at distant positions; and type A + 4L* and type B + 4L* images, four white matter lesions at clustered positions. Comparisons between the control group and the other groups were performed with VBM using segmented gray matter images. Results The gray matter volume was significantly lower in the type A group than in the control group, and similar results were observed in the type A + 1L, type A + 4L, and type A + 4L* groups. Additionally, the gray matter volume was significantly lower in the type B group than in the control group, and similar results were observed in the type B + 1L, type B + 4L, and type B + 4L* groups, but the cluster size in type B + 4L* was smaller than that in type B. Conclusion Our study showed that the detectability of brain volume change in VBM with gray matter images was not decreased by MWLs as lacunar infarctions. Therefore, we think that group comparisons with VBM should be analyzed by groups including and excluding subjects with MWLs, respectively.

INTRODUCTION Sodium MRI has been recently explored in brain tumors to potentially reveal complementary information with proton MRI about the tumor microenvironment. However, there remains limited investigation combining sodium and quantitative proton MRI techniques. This pilot study explored the associations of sodium MRI with diffusion-, pH-, and hypoxia-weighted MRI biomarkers in gliomas. METHODS Eleven patients with gliomas (n=8 IDH-wild-type; n=3 IDH-mutant) who obtained sodium, anatomical, diffusion-, pH-, and hypoxia-weighted MRI were studied. All images were obtained on a 3T Siemens Prisma with a dedicated multi-nuclear head coil. Regions-of-interest were segmented on the contrast-enhancing (CE) (n=10), FLAIR (n=11), and necrotic regions (n=9). Sodium values were normalized to the mean values of the eyes. Median sodium, apparent diffusion coefficient (ADC), ADC(L) from bimodal histogram analyses, MTRasym (acidity), and R2’ (hypoxia) values were obtained. RESULTS There was increased sodium in necrosis compared to FLAIR regions (mean difference=0.24; P/i>< 0.05) and compared to CE regions (mean difference=0.17; P/i>=0.07). There were significant positive correlations between sodium and ADC in CE (R=0.94; P/i>< 0.0001) and FLAIR regions (ρ=0.77; P/i>< 0.01), but not in necrosis (R=0.44; P/i>=0.24). There was also a significant positive correlation with ADC(L) in CE regions (R=0.90; P< 0.001) but not in FLAIR regions (ρ=0.50; P/i>=0.12). Furthermore, there was a significant positive correlation between sodium and MTRasym in necrosis (R=0.74; P/i>< 0.05) and a positive trend in CE regions (R=0.55; P=0.10). There was also a negative trend between sodium and R2’ in FLAIR regions (ρ=-0.58; P/i>=0.11). CONCLUSIONS Because ADC and ADC(L) are inversely related to cell density, increased sodium in gliomas may reflect increased extracellular space stemming from lower cell density, as sodium concentration is higher extracellularly. Positive associations between sodium and acidity as well as negative associations between sodium and hypoxia may reflect alterations in coupled sodium-pH and sodium-hypoxia homeostasis in gliomas.

In recently published work, we demonstrated that acute inhibition of the EGFR – the most frequently altered oncogene in GBM – can rapidly reduce glycolysis in a subset of GBM. To quantify this effect, we propose a new combination metabolic MR-PET imaging biomarker that combines 18F-FDG PET, DSC perfusion MRI, diffusion MRI, and pH- and oxygen-sensitive amine CEST-SAGE-EPI. Specifically, this “glycolytic index” or GI, can be defined as elevated glucose uptake (18F-FDG standard uptake value), elevated tumor acidity (MTRasym@3ppm), and lower oxygen utilization (relative cerebral metabolic rate of oxygen, rCMRO2, defined as R2’ x rCBF/rCBV from oxygen-sensitive SAGE-EPI and DSC perfusion), normalized to cell density (using ADC from diffusion MRI). In the current study, we explored the use of Tagrisso® (Osimertinib or AZD9291), which has very high brain to plasma ratios (> 1) and has proven effective in CNS metastasis of EGFR mutant lung cancer, as a potential treatment for EGFR amplified recurrent GBM. We have conducted a clinical trial (NCT03732352) to determine whether we observe similar metabolic imaging changes in EGFR mutated or amplified recurrent glioblastoma patients after treatment with osimertinib. Consistent with prior in vitro and in vivo results, treated patients exhibited a reduced glycolytic flux as indicated by reduction in 18F-FDG PET uptake (-3% change in SUV), tumor acidity (-19% change in MTRasym@3ppm) on pH-weighted CEST MRI, and glycolytic index (GI) (-25% change) in EGFR amplified recurrent GBM within 24 hours of treatment. Preliminary data suggests early change in GI correlates with and predicts both PFS (Pearson correlation, R2=0.3463, P=0.0441; Log-rank, P=0.0467) and OS (R2=0.6368, P=0.0019; Log-rank, P=0.0192). These preliminary data establish the scientific premise that a “glycolytic index” created using 18F-FDG PET, DSC perfusion, diffusion MRI, and pH- and oxygen-weighted amine CEST-SAGE-EPI may be an important tool for quantifying and visualizing glycolytic flux in GBM.