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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]

Simple Summary: Standard clinical brain tumor magnetic resonance imaging (MRI) exams require contrast injection and multiple structural MRI sequences, acquired individually in 30 min. This study developed a single 6 min sequence and automatic processing strategy for multi-contrast whole-brain imaging to achieve the lesion detection of gliomas without the use of a contrast agent, which has the potential to significantly improve clinical imaging workflows and provide substantial benefits to patients for which gadolinium is contraindicated. Automatically segmented tumor lesions from fourteen patients with contrast-enhancing gliomas were comparable to manually defined lesions from conventional T2-FLAIR (Fluid-Attenuated Inversion Recovery) and T1-post-contrast imaging with contrast administration. The T2-hyperintensity lesion could be further separated into two components, likely demarcating a more infiltrative tumor region within the edema. Multi-parametric mapping based on multi-compartment modeling allowed for quantitative lesion characterization. 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. [ABSTRACT FROM AUTHOR]

The article discusses the ethical considerations surrounding MRI research in human subjects in the era of precision medicine. It emphasizes the importance of respecting the diversity of human subjects, including factors such as biological variables, socio-economic status, cultural heritage, and life experience. The article also highlights the need for beneficence, ensuring that research is beneficial and safe for both current and future patients. Additionally, the article addresses the concept of justice, emphasizing the importance of global collaboration and respecting cultural and regulatory differences when establishing ethical standards. The authors suggest that the International Society for Magnetic Resonance in Medicine (ISMRM) can play a significant role in promoting and facilitating ethical research in human subjects. [Extracted from the article]

Simple Summary: Dynamic hyperpolarized carbon-13 (HP-13C) MRI is a novel molecular imaging technique that allows for real-time in vivo imaging of glycolysis and oxidative phosphorylation using [1-13C]pyruvate as a non-radioactive and non-toxic metabolic probe. While HP-13C MRI has recently demonstrated the potential to capture Warburg-related metabolic dysregulation in patients with progressive/treatment-naïve high-grade gliomas, further improvement in methodologies is still necessary to help monitor disease status and evaluate early predictors of therapeutic response. To this end, a multimodal 1H/HP-13C MRI protocol was implemented in this study, which incorporated advanced brain tumor MR acquisitions and improved HP-13C metabolic imaging methodologies for the serial monitoring of patients with glioma over the course of clinical treatment. 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. [ABSTRACT FROM AUTHOR]

Background: Although susceptibility‐weighted imaging (SWI) is the gold standard for visualizing cerebral microbleeds (CMBs) in the brain, the required phase data are not always available clinically. Having a postprocessing tool for generating SWI contrast from T2*‐weighted magnitude images is therefore advantageous. Purpose: To create synthetic SWI images from clinical T2*‐weighted magnitude images using deep learning and evaluate the resulting images in terms of similarity to conventional SWI images and ability to detect radiation‐associated CMBs. Study Type: Retrospective. Population: A total of 145 adults (87 males/58 females; 43.9 years old) with radiation‐associated CMBs were used to train (16,093 patches/121 patients), validate (484 patches/4 patients), and test (2420 patches/20 patients) our networks. Field Strength/Sequence: 3D T2*‐weighted, gradient‐echo acquired at 3 T. Assessment: Structural similarity index (SSIM), peak signal‐to‐noise‐ratio (PSNR), normalized mean‐squared‐error (nMSE), CMB counts, and line profiles were compared among magnitude, original SWI, and synthetic SWI images. Three blinded raters (J.E.V.M., M.A.M., B.B. with 8‐, 6‐, and 4‐years of experience, respectively) independently rated and classified test‐set images. Statistical Tests: Kruskall–Wallis and Wilcoxon signed‐rank tests were used to compare SSIM, PSNR, nMSE, and CMB counts among magnitude, original SWI, and predicted synthetic SWI images. Intraclass correlation assessed interrater variability. P values <0.005 were considered statistically significant. Results: SSIM values of the predicted vs. original SWI (0.972, 0.995, 0.9864) were statistically significantly higher than that of the magnitude vs. original SWI (0.970, 0.994, 0.9861) for whole brain, vascular structures, and brain tissue regions, respectively; 67% (19/28) CMBs detected on original SWI images were also detected on the predicted SWI, whereas only 10 (36%) were detected on magnitude images. Overall image quality was similar between the synthetic and original SWI images, with less artifacts on the former. Conclusions: This study demonstrated that deep learning can increase the susceptibility contrast present in neurovasculature and CMBs on T2*‐weighted magnitude images, without residual susceptibility‐induced artifacts. This may be useful for more accurately estimating CMB burden from magnitude images alone. Evidence Level: 3. Technical Efficacy: Stage 2. [ABSTRACT FROM AUTHOR]

Purpose: While 3D MR spectroscopic imaging (MRSI) provides valuable spatial metabolic information, one of the hurdles for clinical translation is its interpretation, with voxel-wise quality control (QC) as an essential and the most time-consuming step. This work evaluates the accuracy of machine learning (ML) models for automated QC filtering of individual spectra from 3D healthy control and patient datasets. Methods: A total of 53 3D MRSI datasets from prior studies (30 neurological diseases, 13 brain tumors, and 10 healthy controls) were included in the study. Three ML models were evaluated: a random forest classifier (RF), a convolutional neural network (CNN), and an inception CNN (ICNN) along with two hybrid models: CNN + RF, ICNN + RF. QC labels used for training were determined manually through consensus of two MRSI experts. Normalized and cropped real-valued spectra was used as input. A cross-validation approach was used to separate datasets into training/validation/testing sets of aggregated voxels. Results: All models achieved a minimum AUC of 0.964 and accuracy of 0.910. In datasets from neurological disease and controls, the CNN model produced the highest AUC (0.982), while the RF model achieved the highest AUC in patients with brain tumors (0.976). Within tumor lesions, which typically exhibit abnormal metabolism, the CNN AUC was 0.973 while that of the RF was 0.969. Data quality inference times were on the order of seconds for an entire 3D dataset, offering drastic time reduction compared to manual labeling. Conclusion: ML methods accurately and rapidly performed automated QC. Results in tumors highlights the applicability to a variety of metabolic conditions. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Background: Cognitive 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. Methods: Nineteen 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. Results: Compared 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. Conclusions: MR metrics describing brain connectivity and variability represent promising candidate imaging biomarkers for monitoring of long-term cognitive side-effects after RT. [ABSTRACT FROM AUTHOR]

This article discusses the ethical considerations of using preclinical models in imaging research. It highlights the challenges of replicating preclinical research in human studies and the need for validation and best practices. The article also explores the selection of animal models, such as rodents, nonhuman primates, and large animals, and the efforts to improve their translational value. It emphasizes the importance of international collaborations and the need for harmonizing ethical guidelines across different countries. The article concludes by emphasizing the significance of preclinical models in understanding and treating human diseases and the importance of unified guidelines while respecting cultural and institutional norms. [Extracted from the article]

Background: Neurocognitive 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. Methods: One 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. Results: CMB 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. Conclusion: Presence 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The article discusses the relationship between cognitive decline and whole-brain radiation therapy (WBRT) in patients with brain metastases. The absence of established biomarkers makes it challenging to evaluate and predict radiation-induced brain changes associated with cognitive decline. The study presents a novel imaging-based prediction model using neurite orientation dispersion and density imaging (NODDI) measurements to assess cognitive impairment in patients treated with WBRT. The findings provide insights into vulnerable neuroanatomical structures and offer a promising clinical tool for monitoring and evaluating treatment effects. However, further research and validation are needed for the translation of this imaging marker into clinical practice. [Extracted from the article]

Purpose: Although 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. Methods: Twenty-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. Results: Patients 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). Conclusions: Arterial 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. [ABSTRACT FROM AUTHOR]

Although combined spin‐ and gradient‐echo (SAGE) dynamic susceptibility‐contrast (DSC) MRI can provide perfusion quantification that is sensitive to both macrovessels and microvessels while correcting for T1‐shortening effects, spatial coverage is often limited in order to maintain a high temporal resolution for DSC quantification. In this work, we combined a SAGE echo‐planar imaging (EPI) sequence with simultaneous multi‐slice (SMS) excitation and blipped controlled aliasing in parallel imaging (blipped CAIPI) at 3 T to achieve both high temporal resolution and whole brain coverage. Two protocols using this sequence with multi‐band (MB) acceleration factors of 2 and 3 were evaluated in 20 patients with treated gliomas to determine the optimal scan parameters for clinical use. ΔR2*(t) and ΔR2(t) curves were derived to calculate dynamic signal‐to‐noise ratio (dSNR), ΔR2*‐ and ΔR2‐based relative cerebral blood volume (rCBV), and mean vessel diameter (mVD) for each voxel. The resulting SAGE DSC images acquired using MB acceleration of 3 versus 2 appeared visually similar in terms of image distortion and contrast. The difference in the mean dSNR from normal‐appearing white matter (NAWM) and that in the mean dSNR between NAWM and normal‐appearing gray matter were not statistically significant between the two protocols. ΔR2*‐ and ΔR2‐rCBV maps and mVD maps provided unique contrast and spatial heterogeneity within tumors. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Objectives: Treatment-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. Methods: Twenty-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. Results: Thirty-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. Conclusion: Our 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. [ABSTRACT FROM AUTHOR]

Purpose: This study demonstrated a magnetic resonance (MR) signal multitask learning method for three‐dimensional (3D) simultaneous segmentation and relaxometry of human brain tissues. Materials and Methods: A 3D inversion‐prepared balanced steady‐state free precession sequence was used for acquiring in vivo multicontrast brain images. The deep neural network contained three residual blocks, and each block had 8 fully connected layers with sigmoid activation, layer norm, and 256 neurons in each layer. Online‐synthesized MR signal evolutions and labels were used to train the neural network batch‐by‐batch. Empirically defined ranges of T1 and T2 values for the normal gray matter, white matter, and cerebrospinal fluid (CSF) were used as the prior knowledge. MRI brain experiments were performed on three healthy volunteers. The mean and standard deviation for the T1 and T2 values in vivo were reported and compared to literature values. Additional animal (N = 6) and prostate patient (N = 1) experiments were performed to compare the estimated T1 and T2 values with those from gold standard methods and to demonstrate clinical applications of the proposed method. Results: In animal validation experiment, the differences/errors (mean difference ± standard deviation of difference) between the T1 and T2 values estimated from the proposed method and the ground truth were 113 ± 486 and 154 ± 512 ms for T1, and 5 ± 33 and 7 ± 41 ms for T2, respectively. In healthy volunteer experiments (N = 3), whole brain segmentation and relaxometry were finished within ~ 5 s. The estimated apparent T1 and T2 maps were in accordance with known brain anatomy, and not affected by coil sensitivity variation. Gray matter, white matter, and CSF were successfully segmented. The deep neural network can also generate synthetic T1‐ and T2‐weighted images. Conclusion: The proposed multitask learning method can directly generate brain apparent T1 and T2 maps, as well as synthetic T1‐ and T2‐weighted images, in conjunction with segmentation of gray matter, white matter, and CSF. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Background: Although 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.Purpose: To 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 Type: Longitudinal, retrospective, human cohort.Population: In 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/sequence: Single and multiecho susceptibility-weighted imaging (SWI) and multiband, shell, and direction diffusion tensor imaging (DTI) at 7 T.Assessment: Patients 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 Tests: Potential 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.Results: All 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 Conclusion: Identifying 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 Evidence: 3 Technical Efficacy: Stage 4 J. Magn. Reson. Imaging 2019;50:868-877. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this study was to compare C-13 imaging parameters with hyperpolarized [1-13C]pyruvate with conventional gadolinium (Gd)-based perfusion weighted imaging using an orthotopic xenograft model of human glioblastoma multiforme (GBM).Procedures: C-13 3D magnetic resonance spectroscopic imaging (MRSI) data were obtained from 14 tumor-bearing rats after the injection of hyperpolarized [1-13C]pyruvate at a 3T scanner. Dynamic susceptibility contrast (DSC) perfusion-weighted MR images were obtained following intravenous administration of Gd-DTPA. Normalized lactate, pyruvate, total carbon, and lactate to pyruvate ratio from C-13 MRSI data were compared with normalized peak height and percent recovery of ΔR2* curve from the DSC images in the voxels containing tumor using a Pearson's linear correlation.Results: Normalized peak height from DSC imaging showed substantial correlations with normalized lactate (r = 0.6, p = 0.02) and total carbon (r = 0.6, p = 0.02) from hyperpolarized C-13 MRSI data.Conclusions: Since the peak height in the ΔR2* curve from DSC data is related to the extent of blood volume, these hyperpolarized C-13 imaging parameters may be used to assess blood volume in rodent intracranial xenograft models of GBM. [ABSTRACT FROM AUTHOR]

Background: Activation 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.Methods: Fifty-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.Results: For patients with WHO II gliomas at enrollment, the PFS-6 rate was 84%, and this met the primary endpoint (P < .001 for an improvement from the historical rate of 17%). Evidence of PI3K/mTOR activation by immunohistochemistry for phosphorylated ribosomal S6Ser240/244 (p-S6Ser240/244 ) was associated with worse progression-free survival (PFS; hazard ratio [HR], 3.03; P = .004) and overall survival (HR, 12.7; P = .01). Tumor perfusion decreased after 6 months (median decrease in fBV, 15%; P = .03; median decrease in Kps , 12%; P = .09), with greater decreases associated with improved PFS (HR for each 10% fBV decrease, 0.71; P = .01; HR for each 10% Kps decrease, 0.82; P = .04).Conclusions: Patients with recurrent LGGs demonstrated a high degree of disease stability during treatment with everolimus. PI3K/mTOR activation, as measured by immunohistochemistry for p-S6, was associated with a worse prognosis. Tumor vascular changes were observed that were consistent with the antiangiogenic effects of mTOR inhibitors. These results support further study of everolimus for LGGs. Cancer 2017;123:4631-4639. © 2017 American Cancer Society. [ABSTRACT FROM AUTHOR]

Despite the longstanding role of radiation in cancer treatment and the presence of advanced, high-resolution imaging techniques, delineation of voxels at-risk for progression remains purely a geometric expansion of anatomic images, missing subclinical disease at risk for recurrence while treating potentially uninvolved tissue and increasing toxicity. This remains despite the modern ability to precisely shape radiation fields. A striking example of this is the treatment of glioblastoma, a highly infiltrative tumor that may benefit from accurate identification of subclinical disease. In this study, we hypothesize that parameters from physiologic and metabolic magnetic resonance imaging (MRI) at diagnosis could predict the likelihood of voxel progression at radiographic recurrence in glioblastoma by identifying voxel characteristics that indicate subclinical disease. Integrating dosimetry can reveal its effect on voxel outcome, enabling risk-adapted voxel dosing. As a system example, 24 patients with glioblastoma treated with radiotherapy, temozolomide and an anti-angiogenic agent were analyzed. Pretreatment median apparent diffusion coefficient (ADC), fractional anisotropy (FA), relative cerebral blood volume (rCBV), vessel leakage (percentage recovery), choline-to-NAA index (CNI) and dose of voxels in the T2 nonenhancing lesion (NEL), T1 post-contrast enhancing lesion (CEL) or normal-appearing volume (NAV) of brain, were calculated for voxels that progressed [NAV→NEL, CEL (N = 8,765)] and compared against those that remained stable [NAV→NAV (N = 98,665)]. Voxels that progressed (NAV→NEL) had significantly different ( P < 0.01) ADC (860), FA (0.36) and CNI (0.67) versus stable voxels (804, 0.43 and 0.05, respectively), indicating increased cell turnover, edema and decreased directionality, consistent with subclinical disease. NAV→CEL voxels were more abnormal (1,014, 0.28, 2.67, respectively) and leakier (percentage recovery = 70). A predictive model identified areas of recurrence, demonstrating that elevated CNI potentiates abnormal diffusion, even far (>2 cm) from the tumor and dose escalation >45 Gy has diminishing benefits. Integrating advanced MRI with dosimetry can identify at voxels at risk for progression and may allow voxel-level risk-adapted dose escalation to subclinical disease while sparing normal tissue. When combined with modern planning software, this technique may enable risk-adapted radiotherapy in any disease site with multimodal imaging. [ABSTRACT FROM AUTHOR]

Background: Cranial 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.Methods: SWI 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 (<30 Gy), median (30-45 Gy), and high (>45 Gy) dose regions was computed.Results: Twelve of 13 patients exhibited CMBs. The number of CMBs was significantly higher for late (>3 years from treatment) compared to early (<3 years) timepoints (early median 6 CMBs; late median 27 CMBs; p = 0.001), and there were proportionally more CMBs at lower doses for late scans (p = 0.006). 88% of all CMBs were observed in regions receiving at least 30 Gy, but the CMB density within medium and high dose regions was not significantly different (p = 0.33 and p = 0.9, respectively, for early and late time points).Conclusions: CMBs predominantly form in regions receiving at least 30 Gy, but form in lower dose regions with longer follow-up. We do not observe a clear dose-response relationship at doses above 30 Gy. These findings provide important information to assess the risk of late microvascular sequelae from cranial irradiation. [ABSTRACT FROM AUTHOR]

Purpose Dissolution dynamic nuclear polarization (DNP) enables the acquisition of 13C magnetic resonance data with a high sensitivity. Recently, metabolically inactive hyperpolarized 13C-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. Methods Dynamic 13C data using metabolically inactive hyperpolarized bis-1,1-(hydroxymethyl)-[1-13C]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. Results Distinct 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. Conclusion The 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 [ABSTRACT FROM AUTHOR]

This review is focused on describing the use of magnetic resonance ( MR) spectroscopy for metabolic imaging of brain tumors. We will first review the MR metabolic imaging findings generated from preclinical models, focusing primarily on in vivo studies, and will then describe the use of metabolic imaging in the clinical setting. We will address relatively well-established 1 H MRS approaches, as well as 31 P MRS, 13 C MRS and emerging hyperpolarized 13 C MRS methodologies, and will describe the use of metabolic imaging for understanding the basic biology of glioma as well as for improving the characterization and monitoring of brain tumors in the clinic. [ABSTRACT FROM AUTHOR]

Background: The purpose of this study was to implement and evaluate the utility of a multi-echo sequence at 7 Tesla (T) for simultaneous time-of-flight (TOF) MR-angiography (MRA) and susceptibility-weighted imaging (SWI) of radiation-induced cerebral microbleeds (CMBs), intracranial arteries, and veins. Methods: A four-echo gradient-echo sequence was implemented on a 7T scanner. The first echo was used to create TOF-MRA images, while the remaining echoes were combined to visualize CMBs and veins on SWI images. The sequence was evaluated on eight brain tumor patients with known radiation-induced CMBs. Single-echo images were also acquired to visually and quantitatively compare the contrast-to-noise ratio (CNR) of small- and intermediate-vessels between acquisitions. The number of CMBs detected with each acquisition was also quantified. Statistical significance was determined using a Wilcoxon signed-rank test. Results: Compared with the single-echo sequences, the CNR of small and intermediate arteries increased 7.6% (P < 0.03) and 9.5% (P = 0.06), respectively, while the CNR of small and intermediate veins were not statistically different between sequences (P = 0.95 and P = 0.46, respectively). However, these differences were not discernible by visual inspection. Also the multi-echo sequence detected 18.3% more CMBs (P < 0.008) due to higher slice resolution. Conclusion: The proposed 7T multi-echo sequence depicts arteries, veins, and CMBs on a single image to facilitate quantitative evaluation of radiation-induced vascular injury. [ABSTRACT FROM AUTHOR]

Background The purpose of this study was to evaluate the feasibility of using a short echo time, three-dimensional H-1 magnetic resonance spectroscopic imaging (MRSI) sequence at 7 Tesla (T) to assess the metabolic signature of lesions for patients with glioma. Methods Twenty-nine patients with glioma were studied. MRSI data were obtained using CHESS water suppression, spectrally selective adiabatic inversion-recovery pulses and automatically prescribed outer-volume-suppression for lipid suppression, and spin echo slice selection (echo time = 30 ms). An interleaved flyback echo-planar trajectory was applied to shorten the total acquisition time (∼10 min). Relative metabolite ratios were estimated in tumor and in normal-appearing white and gray matter (NAWM, GM). Results Levels of glutamine, myo-inositol, glycine, and glutathione relative to total creatine (tCr) were significantly increased in the T2 lesions for all tumor grades compared with those in the NAWM ( P < 0.05), while N-acetyl aspartate to tCr were significantly decreased ( P < 0.05). In grade 2 gliomas, level of total choline-containing-compounds to tCr was significantly increased ( P = 0.0137), while glutamate to tCr was significantly reduced ( P = 0.0012). Conclusion The improved sensitivity of MRSI and the increased number of metabolites that can be evaluated using 7T MR scanners is of interest for evaluating patients with glioma. This study has successfully demonstrated the application of a short-echo spin-echo MRSI sequence to detect characteristic differences in regions of tumor versus normal appearing brain. J. Magn. Reson. Imaging 2015;41:1332-1341. © 2014 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

Purpose To evaluate which common post-processing method applied to gradient-echo DSC-MRI data, acquired with a single gadolinium injection and low flip-angle, most accurately reflects microvascular histopathology for patients with de novo, treatment-naive glioblastoma multiforme (GBM). Materials and Methods Seventy-two tissue samples were collected from 35 patients with treatment-naive GBM. Sample locations were co-registered to preoperative gradient-echo dynamic susceptibility contrast (DSC) MRI acquired with 35° flip-angle and 0.1 mmol/kg gadolinium. Estimates of blood volume and leakiness at each sample location were calculated using four common postprocessing methods (leakage-corrected nonlinear gamma-variate, non-parametric, scaled MR-signal, and unscaled MR-signal). Tissue sample microvascular morphology was characterized using Factor VIII immunohistochemical analysis. A random-effects regression model, adjusted for repeated measures and contrast-enhancement (CE), identified whether MR parameter estimates significantly predicted IHC findings. Results Elevated blood volume estimates from nonlinear and non-parametric methods significantly predicted increased microvascular hyperplasia. Abnormal microvasculature existed beyond the CE-lesion and was significantly reflected by increased blood volume from nonlinear, non-parametric, and scaled MR-signal analysis. Conclusion This study provides histopathological support for both non-parametric and nonlinear post-processing of low flip-angle DSC-MRI for characterizing microvascular hyperplasia within GBM. Non-parametric analysis with a single gadolinium injection may be a particularly useful strategy clinically, as it requires less computational expense and limits gadolinium exposure. J. Magn. Reson. Imaging 2013;38:388-400. © 2013 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

The article offers information on a research that analyzes the effect of 7T magnetic resonance imaging technique in the diagnosis of multiple sclerosis (MS). It is mentioned that five patients with MS were serially scanned for around 2.5 years at 7T using high resolution gradient-echo sequence. It was observed that ring phase lesions remained unchanged over 2.5 years which contrasts the notion that acute activated iron-rich macrophages reside in such lesions.

Purpose To investigate changes in image and dynamic signal-to-noise ratios (SNRs) of the ΔR2* curve, as well as magnetic susceptibility-induced artifacts between a standard quadrature head coil and an eight-channel phased-array coil with and without sensitivity-encoding (SENSE) at 3T, compared to the current clinical standard head coil acquisition at 1.5T. Materials and Methods Dynamic susceptibility contrast (DSC) perfusion MRI was performed on 80 brain tumor patients using a gradient-echo, echo-planar imaging (EPI) sequence. Image and dynamic SNR were compared between 1.5T and 3T field strengths, a quadrature and eight-channel phased-array coil, and a conventional vs. partially parallel EPI acquisition with SENSE reconstruction. The amount of geometric distortion and signal dropout was quantified and compared between conventional and SENSE EPI acquisitions within the same exam at 3T. Results An initial 2.6-fold elevation in dynamic SNR was observed in normal-appearing white matter when doubling the field strength ( P < 0.001), with an additional 1.7-fold increase found when employing an eight-channel phased-array coil ( P < 0.002). Compared to the standard 3T eight-channel coil acquisition, the implementation of SENSE reduced the number of voxels experiencing large anterior shifts in the phase-encode direction, lowered the volume of signal dropout by 2.0-11.5%, and allowed a 1.4-fold increase in slice coverage, while only decreasing the dynamic SNR by 22%. Conclusion SENSE EPI at 3T yielded a significant improvement in dynamic SNR over the 1.5T acquisitions. A significant reduction in magnetic susceptibility-induced artifacts was achieved with SENSE EPI compared to the standard EPI eight-channel coil acquisition at 3T. J. Magn. Reson. Imaging 2006. © 2006 Wiley-Liss, Inc. [ABSTRACT FROM AUTHOR]

This paper was published inadvertently as open access. It has been corrected online. [ABSTRACT FROM AUTHOR]