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This study aimed to develop a rapid, 1 mm3 isotropic resolution, whole-brain MRI technique for automatic lesion segmentation and multi-parametric mapping without using contrast by continuously applying balanced steady-state free precession with inversion pulses throughout incomplete inversion recovery in a single 6 min scan. Modified k-means clustering was performed for automatic brain tissue and lesion segmentation using distinct signal evolutions that contained mixed T1/T2/magnetization transfer properties. Multi-compartment modeling was used to derive quantitative multi-parametric maps for tissue characterization. Fourteen patients with contrast-enhancing gliomas were scanned with this sequence prior to the injection of a contrast agent, and their segmented lesions were compared to conventionally defined manual segmentations of T2-hyperintense and contrast-enhancing lesions. Simultaneous T1, T2, and macromolecular proton fraction maps were generated and compared to conventional 2D T1 and T2 mapping and myelination water fraction mapping acquired with MAGiC. The lesion volumes defined with the new method were comparable to the manual segmentations (r = 0.70, p < 0.01; t-test p > 0.05). The T1, T2, and macromolecular proton fraction mapping values of the whole brain were comparable to the reference values and could distinguish different brain tissues and lesion types (p < 0.05), including infiltrating tumor regions within the T2-lesion. Highly efficient, whole-brain, multi-contrast imaging facilitated automatic lesion segmentation and quantitative multi-parametric mapping without contrast, highlighting its potential value in the clinic when gadolinium is contraindicated.

This study aimed to implement a multimodal 1H/HP-13C imaging protocol to augment the serial monitoring of patients with glioma, while simultaneously pursuing methods for improving the robustness of HP-13C metabolic data. A total of 100 1H/HP [1-13C]-pyruvate MR examinations (104 HP-13C datasets) were acquired from 42 patients according to the comprehensive multimodal glioma imaging protocol. Serial data coverage, accuracy of frequency reference, and acquisition delay were evaluated using a mixed-effects model to account for multiple exams per patient. Serial atlas-based HP-13C MRI demonstrated consistency in volumetric coverage measured by inter-exam dice coefficients (0.977 ± 0.008, mean ± SD; four patients/11 exams). The atlas-derived prescription provided significantly improved data quality compared to manually prescribed acquisitions (n = 26/78; p = 0.04). The water-based method for referencing [1-13C]-pyruvate center frequency significantly reduced off-resonance excitation relative to the coil-embedded [13C]-urea phantom (4.1 ± 3.7 Hz vs. 9.9 ± 10.7 Hz; p = 0.0007). Significantly improved capture of tracer inflow was achieved with the 2-s versus 5-s HP-13C MRI acquisition delay (p = 0.007). This study demonstrated the implementation of a comprehensive multimodal 1H/HP-13C MR protocol emphasizing the monitoring of steady-state/dynamic metabolism in patients with glioma.

Automated quantification of data acquired as part of an MRI exam requires identification of the specific acquisition of relevance to a particular analysis. This motivates the development of methods capable of reliably classifying MRI acquisitions according to their nominal contrast type, e.g., T1 weighted, T1 post-contrast, T2 weighted, T2-weighted FLAIR, proton-density weighted. Prior studies have investigated using imaging-based methods and DICOM metadata-based methods with success on cohorts of patients acquired as part of a clinical trial. This study compares the performance of these methods on heterogeneous clinical datasets acquired with many different scanners from many institutions. RF and CNN models were trained on metadata and pixel data, respectively. A combined RF model incorporated CNN logits from the pixel-based model together with metadata. Four cohorts were used for model development and evaluation: MS research (n = 11,106 series), MS clinical (n = 3244 series), glioma research (n = 612 series, test/validation only), and ADNI PTSD (n = 477 series, training only). Together, these cohorts represent a broad range of acquisition contexts (scanners, sequences, institutions) and subject pathologies. Pixel-based CNN and combined models achieved accuracies between 97 and 98% on the clinical MS cohort. Validation/test accuracies with the glioma cohort were 99.7% (metadata only) and 98.4 (CNN). Accurate and generalizable classification of MRI acquisition contrast types was demonstrated. Such methods are important for enabling automated data selection in high-throughput and big-data image analysis applications.

BackgroundDynamic hyperpolarized (HP)-13C MRI has enabled real-time, non-invasive assessment of Warburg-related metabolic dysregulation in glioma using a [1-13C]pyruvate tracer that undergoes conversion to [1-13C]lactate and [13C]bicarbonate. Using a multi-parametric 1H/HP-13C imaging approach, we investigated dynamic and steady-state metabolism, together with physiological parameters, in high-grade gliomas to characterize active tumor.MethodsMulti-parametric 1H/HP-13C MRI data were acquired from fifteen patients with progressive/treatment-naïve glioblastoma [prog/TN GBM, IDH-wildtype (n = 11)], progressive astrocytoma, IDH-mutant, grade 4 (G4AIDH+, n = 2) and GBM manifesting treatment effects (n = 2). Voxel-wise regional analysis of the cohort with prog/TN GBM assessed imaging heterogeneity across contrast-enhancing/non-enhancing lesions (CEL/NEL) and normal-appearing white matter (NAWM) using a mixed effects model. To enable cross-nucleus parameter association, normalized perfusion, diffusion, and dynamic/steady-state (HP-13C/spectroscopic) metabolic data were collectively examined at the 13C resolution. Prog/TN GBM were similarly compared against progressive G4AIDH+ and treatment effects.ResultsRegional analysis of Prog/TN GBM metabolism revealed statistically significant heterogeneity in 1H choline-to-N-acetylaspartate index (CNI)max, [1-13C]lactate, modified [1-13C]lactate-to-[1-13C]pyruvate ratio (CELval > NELval > NAWMval); [1-13C]lactate-to-[13C]bicarbonate ratio (CELval > NELval/NAWMval); and 1H-lactate (CELval/NELval > NAWMundetected). Significant associations were found between normalized perfusion (cerebral blood volume, nCBV; peak height, nPH) and levels of [1-13C]pyruvate and [1-13C]lactate, as well as between CNImax and levels of [1-13C]pyruvate, [1-13C]lactate and modified ratio. GBM, by comparison to G4AIDH+, displayed lower perfusion %-recovery and modeled rate constants for [1-13C]pyruvate-to-[1-13C]lactate conversion (kPL), and higher 1H-lactate and [1-13C]pyruvate levels, while having higher nCBV, %-recovery, kPL, [1-13C]pyruvate-to-[1-13C]lactate and modified ratios relative to treatment effects.ConclusionsGBM consistently displayed aberrant, Warburg-related metabolism and regional heterogeneity detectable by novel HP-13C/1H imaging techniques.

AimNeuroinflammation plays a key role in both the pathogenesis and the progression of cerebral cavernous malformations (CCM). Flutriciclamide ([18F]GE-180) is a translocator protein (TSPO) targeting positron emission tomography (PET) tracer, developed for imaging neuroinflammation. The objectives of this study were to describe characteristics of flutriciclamide uptake in different brain tissue regions in CCM patients compared to controls, and to evaluate flutriciclamide uptake and iron deposition within CCM lesions.Materials and methodsFive patients with CCM and six controls underwent a 60 or 90 min continuous PET/MRI scan following 315 ± 68.9 MBq flutriciclamide administration. Standardized uptake value (SUV) and standardized uptake value ratio (SUVr) were obtained using the striatum as a pseudo-reference. Quantitative susceptibility maps (QSM) were used to define the location of the vascular malformation and calculate the amount of iron deposition in each lesion.ResultsIncreased flutriciclamide uptake was observed in all CCM lesions. The temporal pole demonstrated the highest radiotracer uptake; the paracentral lobule, cuneus and hippocampus exhibited moderate uptake; while the striatum had the lowest uptake, with average SUVs of 0.66, 0.55, 0.63, 0.55, and 0.33 for patient with CCM and 0.57, 0.50, 0.48, 0.42, and 0.32 for controls, respectively. Regional SUVr showed similar trends. The average SUV and QSM values in CCM lesions were 0.58 ± 0.23 g/ml and 0.30 ± 0.10 ppm. SUVs and QSM were positively correlated in CCM lesions (r = 0.53, p = 0.03).ConclusionThe distribution of flutriciclamide ([18F]GE-180) in the human brain and CCM lesions demonstrated the potential of this TSPO PET tracer as a marker of neuroinflammation that may be relevant for characterizing CCM disease progression along with QSM.

Quantitative susceptibility mapping (QSM) has the potential for being a biomarker for various diseases because of its ability to measure tissue susceptibility related to iron deposition, myelin, and hemorrhage from the phase signal of a T2 *-weighted MRI. Despite its promise as a quantitative marker, QSM is faced with many challenges, including its dependence on preprocessing of the raw phase data, the relatively weak tissue signal, and the inherently ill posed relationship between the magnetic dipole and measured phase. The goal of this study was to evaluate the effects of background field removal and dipole inversion algorithms on noise characteristics, image uniformity, and structural contrast for cerebral microbleed (CMB) quantification at both 3T and 7T. We selected four widely used background phase removal and five dipole field inversion algorithms for QSM and applied them to volunteers and patients with CMBs, who were scanned at two different field strengths, with ground truth QSM reference calculated using multiple orientation scans. 7T MRI provided QSM images with lower noise than did 3T MRI. QSIP and VSHARP + iLSQR achieved the highest white matter homogeneity and vein contrast, with QSIP also providing the highest CMB contrast. Compared with ground truth COSMOS QSM images, overall good correlations between susceptibility values of dipole inversion algorithms and the COSMOS reference were observed in basal ganglia regions, with VSHARP + iLSQR achieving the susceptibility values most similar to COSMOS across all regions. This study can provide guidance for selecting the most appropriate QSM processing pipeline based on the application of interest and scanner field strength.

BackgroundDiagnostic classification of diffuse gliomas now requires an assessment of molecular features, often including IDH-mutation and 1p19q-codeletion status. Because genetic testing requires an invasive process, an alternative noninvasive approach is attractive, particularly if resection is not recommended. The goal of this study was to evaluate the effects of training strategy and incorporation of biologically relevant images on predicting genetic subtypes with deep learning.MethodsOur dataset consisted of 384 patients with newly diagnosed gliomas who underwent preoperative MRI with standard anatomical and diffusion-weighted imaging, and 147 patients from an external cohort with anatomical imaging. Using tissue samples acquired during surgery, each glioma was classified into IDH-wildtype (IDHwt), IDH-mutant/1p19q-noncodeleted (IDHmut-intact), and IDH-mutant/1p19q-codeleted (IDHmut-codel) subgroups. After optimizing training parameters, top performing convolutional neural network (CNN) classifiers were trained, validated, and tested using combinations of anatomical and diffusion MRI with either a 3-class or tiered structure. Generalization to an external cohort was assessed using anatomical imaging models.ResultsThe best model used a 3-class CNN containing diffusion-weighted imaging as an input, achieving 85.7% (95% CI: [77.1, 100]) overall test accuracy and correctly classifying 95.2%, 88.9%, 60.0% of the IDHwt, IDHmut-intact, and IDHmut-codel tumors. In general, 3-class models outperformed tiered approaches by 13.5%-17.5%, and models that included diffusion-weighted imaging were 5%-8.8% more accurate than those that used only anatomical imaging.ConclusionTraining a classifier to predict both IDH-mutation and 1p19q-codeletion status outperformed a tiered structure that first predicted IDH-mutation, then 1p19q-codeletion. Including apparent diffusion coefficient (ADC), a surrogate marker of cellularity, more accurately captured differences between subgroups.

BackgroundMonitoring lower-grade gliomas (LrGGs) for disease progression is made difficult by the limits of anatomical MRI to distinguish treatment related tissue changes from tumor progression. MR spectroscopic imaging (MRSI) offers additional metabolic information that can help address these challenges. The goal of this study was to compare longitudinal changes in multiparametric MRI, including diffusion weighted imaging, perfusion imaging, and 3D MRSI, for LrGG patients who progressed at the final time-point and those who remained clinically stable.MethodsForty-one patients with LrGG who were clinically stable were longitudinally assessed for progression. Changes in anatomical, diffusion, perfusion and MRSI data were acquired and compared between patients who remained clinically stable and those who progressed.ResultsThirty-one patients remained stable, and 10 patients progressed. Over the study period, progressed patients had a significantly greater increase in normalized choline, choline-to-N-acetylaspartic acid index (CNI), normalized creatine, and creatine-to-N-acetylaspartic acid index (CRNI), than stable patients. CRNI was significantly associated with progression status and WHO type. Progressed astrocytoma patients had greater increases in CRNI than stable astrocytoma patients.ConclusionsLrGG patients in surveillance with tumors that progressed had significantly increasing choline and creatine metabolite signals on MRSI, with a trend of increasing T2 FLAIR volumes, compared to LrGG patients who remained stable. These data show that MRSI can be used in conjunction with anatomical imaging studies to gain a clearer picture of LrGG progression, especially in the setting of clinical ambiguity.

BackgroundCognitive impairment and cerebral microbleeds (CMBs) are long-term side-effects of cranial radiation therapy (RT). Previously we showed that memory function is disrupted in young patients and that the rate of cognitive decline correlates with CMB development. However, vascular injury alone cannot explain RT-induced cognitive decline. Here we use resting-state functional MRI (rsfMRI) to further investigate the complex mechanisms underlying memory impairment after RT.MethodsNineteen young patients previously treated with or without focal or whole-brain RT for a brain tumor underwent cognitive testing followed by 7T rsfMRI and susceptibility-weighted imaging for CMB detection. Global brain modularity and efficiency, and rsfMRI signal variability within the dorsal attention, salience, and frontoparietal networks were computed. We evaluated whether MR metrics could distinguish age- and sex-matched controls (N = 19) from patients and differentiate patients based on RT exposure and aggressiveness. We also related MR metrics with memory performance, CMB burden, and risk factors for cognitive decline after RT.ResultsCompared to controls, patients exhibited widespread hyperconnectivity, similar modularity, and significantly increased efficiency (p < 0.001) and network variability (p < 0.001). The most abnormal values were detected in patients treated with high dose whole-brain RT, having supratentorial tumors, and who did not undergo RT but had hydrocephalus. MR metrics and memory performance were correlated (R = 0.34-0.53), though MR metrics were more strongly related to risk factors for cognitive worsening and CMB burden with evidence of functional recovery.ConclusionsMR metrics describing brain connectivity and variability represent promising candidate imaging biomarkers for monitoring of long-term cognitive side-effects after RT.

BackgroundReal-time metabolic conversion of intravenously-injected hyperpolarized [1-13C]pyruvate to [1-13C]lactate and [13C]bicarbonate in the brain can be measured using dynamic hyperpolarized carbon-13 (HP-13C) MRI. However, voxel-wise evaluation of metabolism in patients with glioma is challenged by the limited signal-to-noise ratio (SNR) of downstream 13C metabolites, especially within lesions. The purpose of this study was to evaluate the ability of higher-order singular value decomposition (HOSVD) denoising methods to enhance dynamic HP [1-13C]pyruvate MRI data acquired from patients with glioma.MethodsDynamic HP-13C MRI were acquired from 14 patients with glioma. The effects of two HOSVD denoising techniques, tensor rank truncation-image enhancement (TRI) and global-local HOSVD (GL-HOSVD), on the SNR and kinetic modeling were analyzed in [1-13C]lactate data with simulated noise that matched the levels of [13C]bicarbonate signals. Both methods were then evaluated in patient data based on their ability to improve [1-13C]pyruvate, [1-13C]lactate and [13C]bicarbonate SNR. The effects of denoising on voxel-wise kinetic modeling of kPL and kPB was also evaluated. The number of voxels with reliable kinetic modeling of pyruvate-to-lactate (kPL) and pyruvate-to-bicarbonate (kPB) conversion rates within regions of interest (ROIs) before and after denoising was then compared.ResultsBoth denoising methods improved metabolite SNR and regional signal coverage. In patient data, the average increase in peak dynamic metabolite SNR was 2-fold using TRI and 4-5 folds using GL-HOSVD denoising compared to acquired data. Denoising reduced kPL modeling errors from a native average of 23% to 16% (TRI) and 15% (GL-HOSVD); and kPB error from 42% to 34% (TRI) and 37% (GL-HOSVD) (values were averaged voxelwise over all datasets). In contrast-enhancing lesions, the average number of voxels demonstrating within-tolerance kPL modeling error relative to the total voxels increased from 48% in the original data to 84% (TRI) and 90% (GL-HOSVD), while the number of voxels showing within-tolerance kPB modeling error increased from 0% to 15% (TRI) and 8% (GL-HOSVD).ConclusionPost-processing denoising methods significantly improved the SNR of dynamic HP-13C imaging data, resulting in a greater number of voxels satisfying minimum SNR criteria and maximum kinetic modeling errors in tumor lesions. This enhancement can aid in the voxel-wise analysis of HP-13C data and thereby improve monitoring of metabolic changes in patients with glioma following treatment.

BackgroundNeurocognitive deficits in pediatric cancer survivors occur frequently; however, individual outcomes are unpredictable. We investigate clinical, genetic, and imaging predictors of neurocognition in pediatric cancer survivors, with a focus on survivors of central nervous system (CNS) tumors exposed to radiation.MethodsOne hundred eighteen patients with benign or malignant cancers (median diagnosis age: 7; 32% embryonal CNS tumors) were selected from an existing multi-institutional cohort (RadART Pro) if they had: 1) neurocognitive evaluation; 2) available DNA; 3) standard imaging. Utilizing RadART Pro, we collected clinical history, genomic sequencing, CNS imaging, and neurocognitive outcomes. We performed single nucleotide polymorphism (SNP) genotyping for candidate genes associated with neurocognition: COMT, BDNF, KIBRA, APOE, KLOTHO. Longitudinal neurocognitive testing were performed using validated computer-based CogState batteries. The imaging cohort was made of patients with available iron-sensitive (n = 28) and/or T2 FLAIR (n = 41) sequences. Cerebral microbleeds (CMB) were identified using a semi-automated algorithm. Volume of T2 FLAIR white matter lesions (WML) was measured using an automated method based on a convolutional neural network. Summary statistics were performed for patient characteristics, neurocognitive assessments, and imaging. Linear mixed effects and hierarchical models assessed patient characteristics and SNP relationship with neurocognition over time. Nested case-control analysis was performed to compare candidate gene carriers to non-carriers.ResultsCMB presence at baseline correlated with worse performance in 3 of 7 domains, including executive function. Higher baseline WML volumes correlated with worse performance in executive function and verbal learning. No candidate gene reliably predicted neurocognitive outcomes; however, APOE ϵ4 carriers trended toward worse neurocognitive function over time compared to other candidate genes and carried the highest odds of low neurocognitive performance across all domains (odds ratio 2.85, P=0.002). Hydrocephalus and seizures at diagnosis were the clinical characteristics most frequently associated with worse performance in neurocognitive domains (5 of 7 domains). Overall, executive function and verbal learning were the most frequently negatively impacted neurocognitive domains.ConclusionPresence of CMB, APOE ϵ4 carrier status, hydrocephalus, and seizures correlate with worse neurocognitive outcomes in pediatric cancer survivors, enriched with CNS tumors exposed to radiation. Ongoing research is underway to verify trends in larger cohorts.

Background and purposeBrain cavernous angiomas with symptomatic hemorrhage (CASH) have a high risk of neurological disability from recurrent bleeding. Systematic assessment of baseline features and multisite validation of novel magnetic resonance imaging biomarkers are needed to optimize clinical trial design aimed at novel pharmacotherapies in CASH.MethodsThis prospective, multicenter, observational cohort study included adults with unresected, adjudicated brain CASH within the prior year. Six US sites screened and enrolled patients starting August 2018. Baseline demographics, clinical and imaging features, functional status (modified Rankin Scale and National Institutes of Health Stroke Scale), and patient quality of life outcomes (Patient-Reported Outcomes Measurement Information System-29 and EuroQol-5D) were summarized using descriptive statistics. Patient-Reported Outcomes Measurement Information System-29 scores were standardized against a reference population (mean 50, SD 10), and one-sample t test was performed for each domain. A subgroup underwent harmonized magnetic resonance imaging assessment of lesional iron content with quantitative susceptibility mapping and vascular permeability with dynamic contrast-enhanced quantitative perfusion.ResultsAs of May 2020, 849 patients were screened and 110 CASH cases enrolled (13% prevalence of trial eligible cases). The average age at consent was 46±16 years, 53% were female, 41% were familial, and 43% were brainstem lesions. At enrollment, ≥90% of the cohort had independent functional outcome (modified Rankin Scale score ≤2 and National Institutes of Health Stroke Scale score 30% of patients (EuroQol-5D). Patients had significantly worse Patient-Reported Outcomes Measurement Information System-29 scores for anxiety (P=0.007), but better depression (P=0.002) and social satisfaction scores (P=0.012) compared with the general reference population. Mean baseline quantitative susceptibility mapping and permeability of CASH lesion were 0.45±0.17 ppm and 0.39±0.31 mL/100 g per minute, respectively, which were similar to historical CASH cases and consistent across sites.ConclusionsThese baseline features will aid investigators in patient stratification and determining the most appropriate outcome measures for clinical trials of emerging pharmacotherapies in CASH.

Abstract INTRODUCTION Mutations in isocitrate dehydrogenase (IDH) have been investigated as a prognostic biomarker in glioma. The presence of the IDH mutation (IDHm) is associated with 2-hydroxyglutarate (2HG) production and inhibition of glutamate synthesis (McBrayer, Cell 2018). Hyperpolarized carbon-13 (HP-13C) MRI enables dynamic measurements of in-vivo metabolism using a [2-13C]pyruvate labeled probe that undergoes conversion to [2-13C]lactate and [5-13C]glutamate. Here, we present HP [2-13C]pyruvate data from healthy volunteers and patients with IDHm diffuse glioma. Due to its intrinsic low signal-to-noise ratio (SNR), we demonstrate the ability of post-processing denoising to improve its utility and aid in detection of metabolic changes associated with IDHm. METHODS Dynamic HP 13C data were acquired following intravenous injection of [2-13C]pyruvate from five healthy volunteers and one patient with IDHm grade III astrocytoma. A novel multi-resolution frequency specific multislice EPI sequence was used to obtain [2-13C]pyruvate, [5-13C]glutamate, and downfield and upfield [2-13C]lactate signals (3s temporal resolution, pyruvate/lactate/glutamate spatial resolutions = 0.75x0.75cm2/ 2.25x2.25cm2/ 2.25x2.25cm2, 5 slices 3cm thick). Following phase correction, patch-based tensor decomposition denoising was applied to metabolite images. Metabolite differences between normal-appearing white matter (NAWM) and T2 lesion were examined for the patient data. RESULTS HP [2-13C]pyruvate imaging is able to simultaneously probe glycolytic ([2-13C]lactate) and oxidative ([5-13C]glutamate) metabolism. Denoised pyruvate/lactate/glutamate signals achieved a 4-9/3-6/3-7 fold increase in SNR. T2 lesion exhibited decreased glutamate-to-pyruvate and glutamate-to-lactate AUC ratios versus contralateral NAWM (p< 0.018, p < 1.5e-5), consistent with IDH mutant status. CONCLUSION We successfully demonstrated the feasibility of applying variable resolution HP [2-13C]pyruvate metabolic imaging to detect IDHm specific metabolism. This technique addresses a major hurdle in HP 13C MRI by improving SNR while permitting robust metabolism quantification. Future studies will optimize methods for acquiring and processing data to evaluate further data acquired from IDHm glioma patients. Supported by NIH T32 CA151022, P01 CA118816, and NICO.

PurposeAlthough radiation therapy (RT) is a common treatment for pediatric brain tumors, it is associated with detrimental long-term effects such as impaired cognition, vascular injury, and increased stroke risk. This study aimed to develop metrics that describe vascular injury and relate them to the presence of cerebral microbleeds (CMBs) and cognitive performance scores.MethodsTwenty-five young adult survivors of pediatric brain tumors treated with either whole-brain (n = 12), whole-ventricular (n = 7), or no RT (n = 6) underwent 7T MRI and neurocognitive testing. Simultaneously acquired MR angiography and susceptibility-weighted images were used to segment CMBs and vessels and quantify their radii and volume.ResultsPatients treated with whole-brain RT had significantly lower arterial volumes (p = 0.003) and a higher proportion of smaller vessels (p = 0.003) compared to the whole-ventricular RT and non-irradiated control patients. Normalized arterial volume decreased with increasing CMB count (R = - 0.66, p = 0.003), and decreasing trends were observed with time since RT and at longitudinal follow-up. Global cognition and verbal memory significantly decreased with smaller normalized arterial volume (p ≤ 0.05).ConclusionsArterial volume is reduced with increasing CMB presence and is influenced by the total brain volume exposed to radiation. This work highlights the potential use of vascular-derived metrics as non-invasive markers of treatment-induced injury and cognitive impairment in pediatric brain tumor patients.

BackgroundRadiation therapy (RT) is essential to the management of many brain tumors, but has been known to lead to cognitive decline and vascular injury in the form of cerebral microbleeds (CMBs).PurposeIn a subset of children, adolescents, and young adults recruited from a larger trial investigating arteriopathy and stroke risk after RT, we evaluated the prevalence of CMBs after RT, examined risk factors for CMBs and cognitive impairment, and related their longitudinal development to cognitive performance changes.MethodsTwenty-five patients (mean 17 years, range: 10-25 years) underwent 7-Tesla MRI and cognitive assessment. Nineteen patients were treated with whole-brain or focal RT 1-month to 20-years prior, while 6 non-irradiated patients with posterior-fossa tumors served as controls. CMBs were detected on 7T susceptibility-weighted imaging (SWI) using semi-automated software, a first use in this population.ResultsCMB detection sensitivity with 7T SWI was higher than previously reported at lower field strengths, with one or more CMBs detected in 100% of patients treated with RT at least 1-year prior. CMBs were localized to dose-targeted brain volumes with risk factors including whole-brain RT (p = 0.05), a higher RT dose (p = 0.01), increasing time since RT (p = 0.03), and younger age during RT (p = 0.01). Apart from RT dose, these factors were associated with impaired memory performance. Follow-up data in a subset of patients revealed a proportional increase in CMB count with worsening verbal memory performance (r = -0.85, p = 0.03).ConclusionsTreatment with RT during youth is associated with the chronic development of CMBs that evolve with memory impairment over time.

Abstract OBJECTIVE To identify genetic predictors of neurocognition, CMBs, brain volume, and WM changes in pediatric brain tumor survivors. METHODS Patients were selected from an existing cohort (RadART) if they had: 1) at least one neurocognitive evaluation using computer-based CogState; 2) available DNA; 3) standard imaging. Candidate gene or genome-wide genotyping was performed on all patients. CMBs were identified using a semi-automated algorithm developed in MATLAB. Volume of T2/FLAIR WM signal abnormality was measured using a semi-automated method based on a convolutional neural network. Brain volume and cortical thickness were measured using FreeSurfer volumetric analysis. Logistic and linear regression were done to compare phenotypes with candidate genotypes. Genome-wide efficient mixed-model analysis was done to compare neurocognition and CMBs. Gene set analysis was done using https://fuma.ctglab.nl/. RESULTS APOE4 was a candidate variant associated with non-lobar, larger volume CMBs (p

Abstract INTRODUCTION Detecting radiological response or resistance to treatment in patients with GBM is difficult with conventional MRI. In response to this challenge, hyperpolarized carbon-13 (HP-13C) MRI techniques were developed to probe real-time [1-13C]pyruvate metabolism. METHODS Dynamic HP-13C MRI was acquired pre-operatively from 6 patientswith recurrent GBM following intravenous injection of HP [1-13C]pyruvate. Five were confirmed with tumor progression and one had treatment effects without progression. Frequency-selective echo-planar imaging (8 slices, 3s temporal resolution, 3.38 cm3 spatial resolution, 60s acquisition) captured [1-13C]pyruvate metabolism to [1-13C]lactate and [1-13C]bicarbonate in the brain. Proton imaging included 3-D FLAIR, T1-weighted post-Gd IRSPGR, and spectroscopy. Carbon-13 voxels with non-enhancing lesion (NEL) or contrast-enhancing lesion (CEL) were identified for subsequent analysis. Temporally-summed HP-13C metabolite data within the CEL and NEL were evaluated using the pyruvate-to-lactate ratio; a modified ratio that takes into account vascular contributions of pyruvate; and parameter percentile ranks over the entire brain. Proton spectroscopy data were processed to obtain choline-to-NAA index (CNI) maps, which provide z-scores of relative tissue abnormality. RESULTS All of the anatomic lesions displayed abnormal CNI with maximum values of 3.22-6.35. The 5 patients with CEL lesions demonstrated 87th– 98thpercentile levels of pyruvate in the brain; and 95th-100thpercentile levels of lactate in 4 progressed patients and 60thpercentile in the patient presenting with treatment effects. For the patient with an exclusively non-enhancing lesion, percentile levels of pyruvate and lactate were 66thand 88thin the brain, respectively. The mean+/-SD percentile of the lactate-to-pyruvate and modified ratios were 75+/-22, 86+/-23 and 60+/-3, 71+/-12 in the progressed and non-progressed patients, respectively. CONCLUSION These data importantly demonstrate aberrant [1-13C]pyruvate metabolism in patients with GBM in both contrast-enhancing and non-enhancing lesions. Ongoing studies will further characterize the utility of HP imaging markers.

Abstract OBJECTIVE To identify genetic predictors of neurocognition, CMBs, brain volume, and WM changes in pediatric brain tumor survivors. METHODS Patients were selected from an existing cohort (RadART) if they had: 1) at least one neurocognitive evaluation using computer-based CogState; 2) available DNA; 3) standard imaging. Candidate gene or genome-wide genotyping was performed on all patients. CMBs were identified using a semi-automated algorithm developed in MATLAB. Volume of T2/FLAIR WM signal abnormality was measured using a semi-automated method based on a convolutional neural network. Brain volume and cortical thickness were measured using FreeSurfer volumetric analysis. Logistic and linear regression were done to compare phenotypes with candidate genotypes. Genome-wide efficient mixed-model analysis was done to compare neurocognition and CMBs. Gene set analysis was done using https://fuma.ctglab.nl/. RESULTS APOE4 was a candidate variant associated with non-lobar, larger volume CMBs (p< 0.05). At the GWAS-level (n=225), specific genes trended with visual memory, psychomotor function, and CMB count (p< 5x10-8). Using gene set analyses, there were gene set trends seen with CMB count and psychomotor function. Small sample size and low mutant allele frequency limited reliability of these findings. Preliminary volumetric analysis show reduced volume within the right parietal, medial occipital and inferior temporal lobes with increased cortical thickness in the left occipital and medial parietal lobe in patients carrying the ApoE4 allele. WM signal assessments are ongoing. CONCLUSION Genetic markers may be associated with neurocognition, CMBs, brain volume and WM changes in pediatric brain tumor survivors; however, larger cohorts are needed to confirm specific gene relevance.

Quantitative susceptibility mapping (QSM) is a powerful MRI technique that has shown great potential in quantifying tissue susceptibility in numerous neurological disorders. However, the intrinsic ill-posed dipole inversion problem greatly affects the accuracy of the susceptibility map. We propose QSMGAN: a 3D deep convolutional neural network approach based on a 3D U-Net architecture with increased receptive field of the input phase compared to the output and further refined the network using the WGAN with gradient penalty training strategy. Our method generates accurate QSM maps from single orientation phase maps efficiently and performs significantly better than traditional non-learning-based dipole inversion algorithms. The generalization capability was verified by applying the algorithm to an unseen pathology--brain tumor patients with radiation-induced cerebral microbleeds.

ObjectivesTreatment-induced lesions represent a great challenge in neuro-oncology. The aims of this study were (i) to characterize treatment induced lesions in glioblastoma patients treated with chemoradiotherapy and heat-shock protein (HSP) vaccine and (ii) to evaluate the diagnostic accuracy of diffusion weighted imaging for differentiation between treatment-induced lesions and tumor progression.MethodsTwenty-seven patients with newly diagnosed glioblastoma treated with HSP vaccine and chemoradiotherapy were included. Serial magnetic resonance imaging evaluation was performed to detect treatment-induced lesions and assess their growth. Quantitative analysis of the apparent diffusion coefficient (ADC) was performed to discriminate treatment-induced lesions from tumor progression. Mann-Whitney U-test and receiver operating characteristic (ROC) curves were used for analysis.ResultsThirty-three percent of patients developed treatment-induced lesions. Five treatment-related lesions appeared between end of radiotherapy and the first vaccine administration; 4 lesions within the first 4 months from vaccine initiation and 1 at 3.5 years. Three patients with pathology proven treatment-induced lesions showed a biphasic growth pattern progressed shortly after. ADC ratio between the peripheral enhancing rim and central necrosis showed an accuracy of 0.84 (95% CI 0.63-1) for differentiation between progression and treatment-induced lesions.ConclusionOur findings do not support the iRANO recommendation of a 6-month time window in which progressive disease should not be declared after immunotherapy initiation. A biphasic growth pattern of pathologically proven treatment-induced lesions was associated with a dismal prognosis. The presence of lower ADC values in the central necrotic portion of the lesions compared to the enhancing rim shows high specificity for detection of treatment-induced lesions.

Purpose:Precise quantification of cerebral arteries can help with differentiation and prognostication of cerebrovascular disease. Existing image processing and segmentation algorithms for magnetic resonance angiography (MRA) are limited to the analysis of either 2D maximum intensity projection images or the entire 3D volume. The goal of this study was to develop a fully automated, hybrid 2D-3D method for robust segmentation of arteries and accurate quantification of vessel radii using MRA at varying projection thicknesses. Methods:A novel algorithm that employs an adaptive Frangi filter for segmentation of vessels followed by estimation of vessel radii is presented. The method was evaluated on MRA datasets and corresponding manual segmentations from three healthy subjects for various projection thicknesses. In addition, the vessel metrics were computed in four additional subjects. Three synthetically generated angiographic datasets resembling brain vasculature were also evaluated under different noise levels. Dice similarity coefficient, Jaccard Index, F-score, and concordance correlation coefficient were used to measure the segmentation accuracy of manual versus automatic segmentation. Results:Our new adaptive filter rendered accurate representations of vessels, maintained accurate vessel radii, and corresponded better to manual segmentation at different projection thicknesses than prior methods. Validation with synthetic datasets under low contrast and noisy conditions revealed accurate quantification of vessels without distortions. Conclusion:We have demonstrated a method for automatic segmentation of vascular trees and the subsequent generation of a vessel radii map. This novel technique can be applied to analyze arterial structures in healthy and diseased populations and improve the characterization of vascular integrity.

BackgroundHyperpolarized carbon-13 (HP-13C) MRI is a non-invasive imaging technique for probing brain metabolism, which may improve clinical cancer surveillance. This work aimed to characterize the consistency of serial HP-13C imaging in patients undergoing treatment for brain tumors and determine whether there is evidence of aberrant metabolism in the tumor lesion compared to normal-appearing tissue.MethodsSerial dynamic HP [1-13C]pyruvate MRI was performed on 3 healthy volunteers (6 total examinations) and 5 patients (21 total examinations) with diffuse infiltrating glioma during their course of treatment, using a frequency-selective echo-planar imaging (EPI) sequence. HP-13C imaging at routine clinical timepoints overlapped treatment, including radiotherapy (RT), temozolomide (TMZ) chemotherapy, and anti-angiogenic/investigational agents. Apparent rate constants for [1-13C]pyruvate conversion to [1-13C]lactate (kPL) and [13C]bicarbonate (kPB) were simultaneously quantified based on an inputless kinetic model within normal-appearing white matter (NAWM) and anatomic lesions defined from 1H MRI. The inter/intra-subject consistency of kPL-NAWM and kPB-NAWM was measured in terms of the coefficient of variation (CV).ResultsWhen excluding scans following anti-angiogenic therapy, patient values of kPL-NAWM and kPB-NAWM were 0.020 s-1 ± 23.8% and 0.0058 s-1 ± 27.7% (mean ± CV) across 17 HP-13C MRIs, with intra-patient serial kPL-NAWM/kPB-NAWM CVs ranging 6.8-16.6%/10.6-40.7%. In 4/5 patients, these values (0.018 s-1 ± 13.4% and 0.0058 s-1 ± 24.4%; n = 13) were more similar to those from healthy volunteers (0.018 s-1 ± 5.0% and 0.0043 s-1 ± 12.6%; n = 6) (mean ± CV). The anti-angiogenic agent bevacizumab was associated with global elevations in apparent rate constants, with maximum kPL-NAWM in 2 patients reaching 0.047 ± 0.001 and 0.047 ± 0.003 s-1 (±model error). In 3 patients with progressive disease, anatomic lesions showed elevated kPL relative to kPL-NAWM of 0.024 ± 0.001 s-1 (±model error) in the absence of gadolinium enhancement, and 0.032 ± 0.008, 0.040 ± 0.003 and 0.041 ± 0.009 s-1 with gadolinium enhancement. The lesion kPB in patients was reduced to unquantifiable values compared to kPB-NAWM.ConclusionSerial measures of HP [1-13C]pyruvate metabolism displayed consistency in the NAWM of healthy volunteers and patients. Both kPL and kPB were globally elevated following bevacizumab treatment, while progressive disease demonstrated elevated kPL in gadolinium-enhancing and non-enhancing lesions. Larger prospective studies with homogeneous patient populations are planned to evaluate metabolic changes following treatment.

This paper was published inadvertently as open access. It has been corrected online.

Cerebral microbleeds, which are small focal hemorrhages in the brain that are prevalent in many diseases, are gaining increasing attention due to their potential as surrogate markers of disease burden, clinical outcomes, and delayed effects of therapy. Manual detection is laborious and automatic detection and labeling of these lesions is challenging using traditional algorithms. Inspired by recent successes of deep convolutional neural networks in computer vision, we developed a 3D deep residual network that can distinguish true microbleeds from false positive mimics of a previously developed technique based on traditional algorithms. A dataset of 73 patients with radiation-induced cerebral microbleeds scanned at 7 T with susceptibility-weighted imaging was used to train and evaluate our model. With the resulting network, we maintained 95% of the true microbleeds in 12 test patients and the average number of false positives was reduced by 89%, achieving a detection precision of 71.9%, higher than existing published methods. The likelihood score predicted by the network was also evaluated by comparing to a neuroradiologist's rating, and good correlation was observed.

BackgroundAlthough radiation therapy (RT) contributes to survival benefit in many brain tumor patients, it has also been associated with long-term brain injury. Cerebral microbleeds (CMBs) represent an important manifestation of radiation-related injury.PurposeTo characterize the change in size and number of CMBs over time and to evaluate their relationship to white matter structural integrity as measured using diffusion MRI indices.Study typeLongitudinal, retrospective, human cohort.PopulationIn all, 113 brain tumor patients including patients treated with focal RT (n = 91, 80.5%) and a subset of nonirradiated controls (n = 22, 19.5%).Field strength/sequenceSingle and multiecho susceptibility-weighted imaging (SWI) and multiband, shell, and direction diffusion tensor imaging (DTI) at 7 T.AssessmentPatients were scanned either once or serially. CMBs were detected and quantified on SWI images using a semiautomated approach. Local and global fractional anisotropy (FA) were measured from DTI data for a subset of 35 patients.Statistical testsPotential risk factors for CMB development were determined by multivariate linear regression and using linear mixed-effect models. Longitudinal FA was quantitatively and qualitatively evaluated for trends.ResultsAll patients scanned at 1 or more years post-RT had CMBs. A history of multiple surgical resections was a risk factor for development of CMBs. The total number and volume of CMBs increased by 18% and 11% per year, respectively, although individual CMBs decreased in volume over time. Simultaneous to these microvascular changes, FA decreased by a median of 6.5% per year. While the majority of nonirradiated controls had no CMBs, four control patients presented with fewer than five CMBs.Data conclusionIdentifying patients who are at the greatest risk for CMB development, with its likely associated long-term cognitive impairment, is an important step towards developing and piloting preventative and/or rehabilitative measures for patients undergoing RT.Level of evidence3 Technical Efficacy: Stage 4 J. Magn. Reson. Imaging 2019;50:868-877.

The goal of this study was to find the most robust algorithm for a phase-sensitive coil combination of 3D single-cycle and lactate-edited, multi-channel H-1 point-resolved spectroscopy (PRESS) localized echo planar spectroscopic imaging (EPSI) data for clinical applications in the brain. Data were acquired over 5-10 minutes at 3T using 8- or 32-channel array coils. Peak referencing with residual water and N-acetyl-aspartate, first-point phasing, generalized least squared (GLS) and whitened singular-value decomposition (WSVD) combination algorithms were evaluated relative to unsuppressed water with data from a phantom, six volunteers and 55 patients with brain tumors. Comparison metrics were signal-to-noise ratio, coefficient of variance and percent signal increase. Where residual water was present, using it as a reference peak for phasing and weighting factors from an imaging calibration scan gave the best overall performance. Greater improvement was seen for large selected volumes (>720 cm3 ) and for the 32-channel array (25%) compared with the 8-channel array (19%). Applying voxel-by-voxel phase corrections produced a larger increase in performance for the 32- versus 8-channel coil. We conclude that, for clinically relevant 3D H-1 PRESS localized EPSI studies, the most robust technique employed individual phase maps generated from high residual water and individual amplitude maps generated from calibration scans.

Background and purposeWith extensive research efforts in place to address the clinical relevance of cerebral microbleeds (CMBs), there remains a need for fast and accurate methods to detect and quantify CMB burden. Although some computer-aided detection algorithms have been proposed in the literature with high sensitivity, their specificity remains consistently poor. More sophisticated machine learning methods appear to be promising in their ability to minimize false positives (FP) through high-level feature extraction and the discrimination of hard-mimics. To achieve superior performance, these methods require sizable amounts of precisely labelled training data. Here we present a user-guided tool for semi-automated CMB detection and volume segmentation, offering high specificity for routine use and FP labelling capabilities to ease and expedite the process of generating labelled training data.Materials and methodsExisting computer-aided detection methods reported by our group were extended to include fully-automated segmentation and user-guided CMB classification with FP labelling. The algorithm's performance was evaluated on a test set of ten patients exhibiting radiotherapy-induced CMBs on MR images.ResultsThe initial algorithm's base sensitivity was maintained at 86.7%. FP's were reduced to inter-rater variations and segmentation results were in 98% agreement with ground truth labelling. There was an approximate 5-fold reduction in the time users spent evaluating CMB burden with the algorithm versus without computer aid. The Intra-class Correlation Coefficient for inter-rater agreement was 0.97 CI[0.92,0.99].ConclusionsThis development serves as a valuable tool for routine evaluation of CMB burden and data labelling to improve CMB classification with machine learning. The algorithm is available to the public on GitHub (https://github.com/LupoLab-UCSF/CMB_labeler).

BackgroundActivation of the phosphatidylinositol 3-kinase (PI3K)/mammalian target of rapamycin (mTOR) pathway is common in patients with low-grade gliomas (LGGs), but agents that inhibit this pathway, including mTOR inhibitors, have not been studied in this population.MethodsFifty-eight patients with pathologic evidence of recurrence after they had initially been diagnosed with World Health Organization (WHO) grade II gliomas were enrolled into a prospective phase 2 clinical trial and received daily everolimus (RAD001) for 1 year or until progression. Tissue at the time of enrollment was analyzed for markers of PI3K/mTOR pathway activation. Thirty-eight patients underwent serial multiparametric magnetic resonance imaging, with the tumor volume and the perfusion metrics (the fractional blood volume [fBV] for capillary density and the transfer coefficient [Kps ] for vascular permeability) measured during treatment. The primary endpoint was progression-free survival at 6 months (PFS-6) in patients with WHO II disease at enrollment.ResultsFor patients with WHO II gliomas at enrollment, the PFS-6 rate was 84%, and this met the primary endpoint (P

BackgroundCranial irradiation is associated with long-term cognitive changes. Cerebral microbleeds (CMBs) have been identified on susceptibility-weighted MRI (SWI) in patients who have received prior cranial radiation, and serve as radiographic markers for microvascular injury thought to contribute to late cognitive decline. The relationship between CMB formation and radiation dose has not previously been quantified.MethodsSWI was performed on 13 patients with stable WHO grade III-IV gliomas between 2 and 4 years after chemoradiotherapy to 60 Gy. The median age at the time of treatment was 41 years (range 25 - 74 years). CMBs were identified as discrete foci of susceptibility on SWI that did not correspond to vessels. CMB density for low (45 Gy) dose regions was computed.ResultsTwelve of 13 patients exhibited CMBs. The number of CMBs was significantly higher for late (>3 years from treatment) compared to early (

BackgroundAlthough the contrast-enhancing (CE) lesion on T1-weighted MR images is widely used as a surrogate for glioblastoma (GBM), there are also non-enhancing regions of infiltrative tumor within the T2-weighted lesion, which elude radiologic detection. Because non-enhancing GBM (Enh-) challenges clinical patient management as latent disease, this study sought to characterize ex vivo metabolic profiles from Enh- and CE GBM (Enh+) samples, alongside histological and in vivo MR parameters, to assist in defining criteria for estimating total tumor burden.MethodsFifty-six patients with newly diagnosed GBM received a multi-parametric pre-surgical MR examination. Targets for obtaining image-guided tissue samples were defined based on in vivo parameters that were suspicious for tumor. The actual location from where tissue samples were obtained was recorded, and half of each sample was analyzed for histopathology while the other half was scanned using HR-MAS spectroscopy.ResultsThe Enh+ and Enh- tumor samples demonstrated comparable mitotic activity, but also significant heterogeneity in microvascular morphology. Ex vivo spectroscopic parameters indicated similar levels of total choline and N-acetylaspartate between these contrast-based radiographic subtypes of GBM, and characteristic differences in the levels of myo-inositol, creatine/phosphocreatine, and phosphoethanolamine. Analysis of in vivo parameters at the sample locations were consistent with histological and ex vivo metabolic data.ConclusionsThe similarity between ex vivo levels of choline and NAA, and between in vivo levels of choline, NAA and nADC in Enh+ and Enh- tumor, indicate that these parameters can be used in defining non-invasive metrics of total tumor burden for patients with GBM.

Abstract PURPOSE 7 Tesla (7T) MRI scanners can provide novel information to improve the characterization of gliomas. However, patients who receive 7T scans must undergo separate clinical evaluations at lower field strengths due to a lack of clinical validation of 7T anatomical imaging. The purpose of this study was to develop a robust volumetric anatomical imaging protocol for the clinical evaluation of patients with glioma at 7T and compare lesion definition to standard 3T imaging. METHODS 3D T2-weighted, T2 FLAIR, and T1-weighted sequences at 7T were optimized to match the contrast, resolution, and scan time of corresponding clinical sequences at 3T. Ten patients with contrast-enhancing glioma (grades II-IV) were scanned with a protocol consisting of pre-contrast anatomical imaging and post-contrast T1-weighted imaging at both field strengths, with the 7T scan occurring in between the pre- and post-contrast 3T imaging. A half-dose of contrast was used at 7T, with an additional half-dose given immediately afterwards at 3T to provide similar lesion contrast given the 2.3-fold difference in field strength. Metrics for comparison between field strengths included volumes of T2 and contrast-enhancing lesions, and Likert-scale ratings (1-5) of lesion definition by a neuro-radiologist. RESULTS T2 and contrast-enhancing lesion volumes were not significantly different between field strengths, despite the trend in larger enhancing lesion volumes at 3T, which was expected given the protocol design. 7T T2-weighted and post-contrast T1-weighted images received on average half-point higher Likert-scale ratings than corresponding 3T images, whereas the opposite trend was observed with the T2 FLAIR and pre-contrast T1-weighted images. CONCLUSION Our pilot study suggests that clinical assessment of contrast-enhancing and T2-hyperintense lesions in glioma is feasible at 7T, which would obviate the need for two scans, allowing patients to take advantage of the increased sensitivity in metabolic and physiologic imaging available at 7T and ultimately improve patient care.

Abstract INTRODUCTION While radiation therapy (RT) remains a standard practice in the upfront treatment of high-grade gliomas and low-grade gliomas at the time of recurrence, it is often associated with long-term effects including vascular injury and cognitive decline. The former typically manifests as size-varying hemosiderin deposits in the brain called cerebral microbleeds (CMBs), and can be accompanied by changes in the surrounding white-matter (WM) microstructure. As the median survival of lower grade gliomas can be 10 years or longer with current treatment strategies, minimizing the deficits incurred through treatment becomes especially important. Prior studies have shown increases in CMB formation over time that were dependent on RT dose and were not observed in patients treated only with chemotherapy. The goal of this study was to characterize the evolution of CMBs in a much larger cohort with serial scans and relate their changes over time to alterations in WM structure. METHODS 125 patients (age:46 ± 12) were scanned on a 7-Tesla GE scanner either once or serially for 2-8 scans. The cohort included patients treated with RT for a glioma 2 months to 27 years prior, and a subset of nonirradiated control patients. Susceptibility-weighted vascular imaging and multi-directional, multi-shell diffusion-tensor imaging were acquired. Characteristics of RT-induced CMBs were evaluated within- and across patients, correlated with clinical and treatment parameters, and related to global and local WM changes. RESULTS Total CMB burden increased over time and was more severe for larger irradiated volumes and frontal lobe tumors. WM fractional anisotropy and diffusivity changes initially decreased as a function of CMB burden before improving approximately 6 years post-treatment. CONCLUSIONS Although there was initially a direct relationship between RT-induced vasculature injury and degradation of WM microstructure, changes in WM structure appeared to be reversible over time, whereas CMBs, potential markers of vascular injury, persisted.

PurposeFamilial cerebral cavernous malformation (CCM) patients present with multiple lesions that can grow both in number and size over time and are reliably detected on susceptibility-weighted imaging (SWI). Manual counting of lesions is arduous and subject to high variability. We aimed to develop an automated algorithm for counting CCM microbleeds (lesions

PurposeDissolution dynamic nuclear polarization (DNP) enables the acquisition of 13 C magnetic resonance data with a high sensitivity. Recently, metabolically inactive hyperpolarized 13 C-labeled compounds have shown to be potentially useful for perfusion imaging. The purpose of this study was to validate hyperpolarized perfusion imaging methods by comparing with conventional gadolinium (Gd)-based perfusion MRI techniques and pathology.MethodsDynamic 13 C data using metabolically inactive hyperpolarized bis-1,1-(hydroxymethyl)-[1-13 C]cyclopropane-d8 (HMCP) were obtained from an orthotopic human glioblastoma (GBM) model for the characterization of tumor perfusion and compared with standard Gd-based dynamic susceptibility contrast (DSC) MRI data and immunohistochemical analysis from resected brains.ResultsDistinct HMCP perfusion characteristics were observed within the GBM tumors compared with contralateral normal brain tissue. The perfusion parameters obtained from the hyperpolarized HMCP data in tumor were strongly correlated with normalized peak height measured from the DSC images. The results from immunohistochemical analysis supported these findings by showing a high level of vascular staining for tumor that exhibited high levels of hyperpolarized HMCP signal.ConclusionThe results from this study have demonstrated that hyperpolarized HMCP data can be used as an indicator of tumor perfusion in an orthotopic xenograft model for GBM. Magn Reson Med 77:841-847, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

BackgroundA specific form of small-vessel vasculopathy-cerebral microbleeds (CMBs)-has been linked to various types of dementia in adults. We assessed the incidence of CMBs and their association with neurocognitive function in pediatric brain tumor survivors.MethodsIn a multi-institutional cohort of 149 pediatric brain tumor patients who received cranial radiation therapy (CRT) between 1987 and 2014 at age

Although fully automated volumetric approaches for monitoring brain tumor response have many advantages, most available deep learning models are optimized for highly curated, multi-contrast MRI from newly diagnosed gliomas, which are not representative of post-treatment cases in the clinic. Improving segmentation for treated patients is critical to accurately tracking changes in response to therapy. We investigated mixing data from newly diagnosed (n = 208) and treated (n = 221) gliomas in training, applying transfer learning (TL) from pre- to post-treatment imaging domains, and incorporating spatial regularization for T2-lesion segmentation using only T2 FLAIR images as input to improve generalization post-treatment. These approaches were evaluated on 24 patients suspected of progression who had received prior treatment. Including 26% of treated patients in training improved performance by 13.9%, and including more treated and untreated patients resulted in minimal changes. Fine-tuning with treated glioma improved sensitivity compared to data mixing by 2.5% (p < 0.05), and spatial regularization further improved performance when used with TL by 95th HD, Dice, and sensitivity (6.8%, 0.8%, 2.2%; p < 0.05). While training with ≥60 treated patients yielded the majority of performance gain, TL and spatial regularization further improved T2-lesion segmentation to treated gliomas using a single MR contrast and minimal processing, demonstrating clinical utility in response assessment. [ABSTRACT FROM AUTHOR]

BackgroundRadiotherapy (RT) is an integral component in managing patients with glioma, but the damage it may cause to healthy brain tissue and quality of life is of concern. Susceptibility-weighted imaging (SWI) is highly sensitive to the detection of microbleeds that occur years after RT. This study's goals were to characterize the evolution of radiation-induced microbleeds in normal-appearing brain and determine whether the administration of an anti-angiogenic agent altered this process.MethodsSerial high-resolution SWI was acquired on 17 patients with high-grade glioma between 8 months and 4.5 years posttreatment with RT and adjuvant chemotherapy. Nine of these patients were also treated with the anti-angiogenic agent enzastaurin. Microbleeds were identified as discrete foci of susceptibility not corresponding to vessels, tumor, or postoperative infarct, and counted in normal-appearing brain. Analysis of covariance was performed to compare slopes of regression of individual patients' microbleed counts over time, Wilcoxon rank-sum tests examined significant differences in rates of microbleed formation between groups, and linear and quadratic mixed-effects models were employed.ResultsThe number of microbleeds increased with time for all patients, with initial onset occurring at 8-22 months. No microbleeds disappeared once formed. The average rate of microbleed formation significantly increased after 2 years post-RT (P < .001). Patients receiving anti-angiogenic therapy exhibited fewer microbleeds overall (P < .05) and a significant reduction in initial rate of microbleed appearance (P = .01).ConclusionsWe have demonstrated a dramatic increase in microbleed formation after 2 years post-RT that was decelerated by the concomitant administration of anti-angiogenic therapy, which may aid in determining brain regions susceptible to RT.