Automatic Brain Tissue and Lesion Segmentation and Multi-Parametric Mapping of Contrast-Enhancing Gliomas without the Injection of Contrast Agents: A Preliminary Study.

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.

Quantitative Assessment of Preanalytic Variables on Clinical Evaluation of PI3/AKT/mTOR Signaling Activity in Diffuse Glioma.

Biomarker-driven therapeutic clinical trials require the implementation of standardized, evidence-based practices for sample collection. In diffuse glioma, phosphatidylinositol 3 (PI3)-kinase/AKT/mTOR (PI3/AKT/mTOR) signaling is an attractive therapeutic target for which window-of-opportunity clinical trials could facilitate the identification of promising new agents. Yet, the relevant preanalytic variables and optimal tumor sampling methods necessary to measure pathway activity are unknown. To address this, we used a murine model for isocitrate dehydrogenase (IDH)-wildtype glioblastoma (GBM) and human tumor tissue, including IDH-wildtype GBM and IDH-mutant diffuse glioma. First, we determined the impact of delayed time-to-formalin fixation, or cold ischemia time (CIT), on the quantitative assessment of cellular expression of 6 phosphoproteins that are readouts of PI3K/AK/mTOR activity (phosphorylated-proline-rich Akt substrate of 40 kDa (p-PRAS40, T246), -mechanistic target of rapamycin (p-mTOR; S2448); -AKT (p-AKT, S473); -ribosomal protein S6 (p-RPS6, S240/244 and S235/236), and -eukaryotic initiation factor 4E-binding protein 1 (p-4EBP1, T37/46). With CITs ≥ 2 hours, typical of routine clinical handling, all had reduced or altered expression with p-RPS6 (S240/244) exhibiting relatively greater stability. A similar pattern was observed using patient tumor samples from the operating room with p-4EBP1 more sensitive to delayed fixation than p-RPS6 (S240/244). Many clinical trials utilize unstained slides for biomarker evaluation. Thus, we evaluated the impact of slide storage conditions on the detection of p-RPS6 (S240/244), p-4EBP1, and p-AKT. After 5 months, storage at -80°C was required to preserve the expression of p-4EBP1 and p-AKT, whereas p-RPS6 (240/244) expression was not stable regardless of storage temperature. Biomarker heterogeneity impacts optimal tumor sampling. Quantification of p-RPS6 (240/244) expression in multiple regionally distinct human tumor samples from 8 patients revealed significant intratumoral heterogeneity. Thus, the accurate assessment of PI3K/AKT/mTOR signaling in diffuse glioma must overcome intratumoral heterogeneity and multiple preanalytic factors, including time-to-formalin fixation, slide storage conditions, and phosphoprotein of interest.

Noninvasive Autopsy-Validated Tumor Probability Maps Identify Glioma Invasion Beyond Contrast Enhancement.

BACKGROUND AND OBJECTIVES: This study identified a clinically significant subset of patients with glioma with tumor outside of contrast enhancement present at autopsy and subsequently developed a method for detecting nonenhancing tumor using radio-pathomic mapping. We tested the hypothesis that autopsy-based radio-pathomic tumor probability maps would be able to noninvasively identify areas of infiltrative tumor beyond traditional imaging signatures. METHODS: A total of 159 tissue samples from 65 subjects were aligned to MRI acquired nearest to death for this retrospective study. Demographic and survival characteristics for patients with and without tumor beyond the contrast-enhancing margin were computed. An ensemble algorithm was used to predict pixelwise tumor presence from pathological annotations using segmented cellularity (Cell), extracellular fluid, and cytoplasm density as input (6 train/3 test subjects). A second level of ensemble algorithms was used to predict voxelwise Cell, extracellular fluid, and cytoplasm on the full data set (43 train/22 test subjects) using 5-by-5 voxel tiles from T1, T1 + C, fluid-attenuated inversion recovery, and apparent diffusion coefficient as input. The models were then combined to generate noninvasive whole brain maps of tumor probability. RESULTS: Tumor outside of contrast was identified in 41.5% of patients, who showed worse survival outcomes (hazard ratio = 3.90, P < .001). Tumor probability maps reliably tracked nonenhancing tumor on a range of local and external unseen data, identifying tumor outside of contrast in 69% of presurgical cases that also showed reduced survival outcomes (hazard ratio = 1.67, P = .027). CONCLUSION: This study developed a multistage model for mapping gliomas using autopsy tissue samples as ground truth, which was able to identify regions of tumor beyond traditional imaging signatures.

Glioblastoma evolution and heterogeneity from a 3D whole-tumor perspective.

Treatment failure for the lethal brain tumor glioblastoma (GBM) is attributed to intratumoral heterogeneity and tumor evolution. We utilized 3D neuronavigation during surgical resection to acquire samples representing the whole tumor mapped by 3D spatial coordinates. Integrative tissue and single-cell analysis revealed sources of genomic, epigenomic, and microenvironmental intratumoral heterogeneity and their spatial patterning. By distinguishing tumor-wide molecular features from those with regional specificity, we inferred GBM evolutionary trajectories from neurodevelopmental lineage origins and initiating events such as chromothripsis to emergence of genetic subclones and spatially restricted activation of differential tumor and microenvironmental programs in the core, periphery, and contrast-enhancing regions. Our work depicts GBM evolution and heterogeneity from a 3D whole-tumor perspective, highlights potential therapeutic targets that might circumvent heterogeneity-related failures, and establishes an interactive platform enabling 360° visualization and analysis of 3D spatial patterns for user-selected genes, programs, and other features across whole GBM tumors.

Advanced Hyperpolarized 13C Metabolic Imaging Protocol for Patients with Gliomas: A Comprehensive Multimodal MRI Approach.

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.

Altered Iron and Microstructure in Huntington's Disease Subcortical Nuclei: Insight From 7T MRI.

BACKGROUND: Pathophysiological changes of Huntington's disease (HD) can precede symptom onset by decades. Robust imaging biomarkers are needed to monitor HD progression, especially before the clinical onset. PURPOSE: To investigate iron dysregulation and microstructure alterations in subcortical regions as HD imaging biomarkers, and to associate such alterations with motor and cognitive impairments. STUDY TYPE: Prospective. POPULATION: Fourteen individuals with premanifest HD (38.0 ± 11.0 years, 9 females; far-from-onset N = 6, near-onset N = 8), 21 manifest HD patients (49.1 ± 12.1 years, 11 females), and 33 age-matched healthy controls (43.9 ± 12.2 years, 17 females). FIELD STRENGTH/SEQUENCE: 7 T, T1 -weighted imaging, quantitative susceptibility mapping, and diffusion tensor imaging. ASSESSMENT: Volume, susceptibility, fractional anisotropy (FA), and mean diffusivity (MD) within subcortical brain structures were compared across groups, used to establish HD classification models, and correlated to clinical measures and cognitive assessments. STATISTICAL TESTS: Generalized linear model, multivariate logistic regression, receiver operating characteristics with the area under the curve (AUC), and likelihood ratio test comparing a volumetric model to one that also includes susceptibility and diffusion metrics, Wilcoxon paired signed-rank test, and Pearson's correlation. A P-value <0.05 after Benjamini-Hochberg correction was considered statistically significant. RESULTS: Significantly higher striatal susceptibility and FA were found in premanifest and manifest HD preceding atrophy, even in far-from-onset premanifest HD compared to controls (putamen susceptibility: 0.027 ± 0.022 vs. 0.018 ± 0.013 ppm; FA: 0.358 ± 0.048 vs. 0.313 ± 0.039). The model with additional susceptibility, FA, and MD features showed higher AUC compared to volume features alone when differentiating premanifest HD from HC (0.83 vs. 0.66), and manifest from premanifest HD (0.94 vs. 0.83). Higher striatal susceptibility significantly correlated with cognitive deterioration in HD (executive function: r = -0.600; socioemotional function: r = -0.486). DATA CONCLUSION: 7 T MRI revealed iron dysregulation and microstructure alterations with HD progression, which could precede volume loss, provide added value to HD differentiation, and might be associated with cognitive changes. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY: Stage 2.

Trial Readiness of Cavernous Malformations With Symptomatic Hemorrhage, Part I: Event Rates and Clinical Outcome.

BACKGROUND: Cerebral cavernous malformation with symptomatic hemorrhage (SH) are targets for novel therapies. A multisite trial-readiness project (https://www.clinicaltrials.gov; Unique identifier: NCT03652181) aimed to identify clinical, imaging, and functional changes in these patients. METHODS: We enrolled adult cerebral cavernous malformation patients from 5 high-volume centers with SH within the prior year and no planned surgery. In addition to clinical and imaging review, we assessed baseline, 1- and 2-year National Institutes of Health Stroke Scale, modified Rankin Scale, European Quality of Life 5D-3 L, and patient-reported outcome-measurement information system, Version 2.0. SH and asymptomatic change rates were adjudicated. Changes in functional scores were assessed as a marker for hemorrhage. RESULTS: One hundred twenty-three, 102, and 69 patients completed baseline, 1- and 2-year clinical assessments, respectively. There were 21 SH during 178.3 patient years of follow-up (11.8% per patient year). At baseline, 62.6% and 95.1% of patients had a modified Rankin Scale score of 1 and National Institutes of Health Stroke Scale score of 0 to 4, respectively, which improved to 75.4% (P=0.03) and 100% (P=0.06) at 2 years. At baseline, 74.8% had at least one abnormal patient-reported outcome-measurement information system, Version 2.0 domain compared with 61.2% at 2 years (P=0.004). The most common abnormal European Quality of Life 5D-3 L domains were pain (48.7%), anxiety (41.5%), and participation in usual activities (41.4%). Patients with prospective SH were more likely than those without SH to display functional decline in sleep, fatigue, and social function patient-reported outcome-measurement information system, Version 2.0 domains at 2 years. Other score changes did not differ significantly between groups at 2 years. The sensitivity of scores as an SH marker remained poor at the time interval assessed. CONCLUSIONS: We report SH rate, functional, and patient-reported outcomes in trial-eligible cerebral cavernous malformation with SH patients. Functional outcomes and patient-reported outcomes generally improved over 2 years. No score change was highly sensitive or specific for SH and could not be used as a primary end point in a trial.

Trial Readiness of Cavernous Malformations With Symptomatic Hemorrhage, Part II: Biomarkers and Trial Modeling.

BACKGROUND: Quantitative susceptibility mapping (QSM) and dynamic contrast-enhanced quantitative perfusion (DCEQP) magnetic resonance imaging sequences assessing iron deposition and vascular permeability were previously correlated with new hemorrhage in cerebral cavernous malformations. We assessed their prospective changes in a multisite trial-readiness project. METHODS: Patients with cavernous malformation and symptomatic hemorrhage (SH) in the prior year, without prior or planned lesion resection or irradiation were enrolled. Mean QSM and DCEQP of the SH lesion were acquired at baseline and at 1- and 2-year follow-ups. Sensitivity and specificity of biomarker changes were analyzed in relation to predefined criteria for recurrent SH or asymptomatic change. Sample size calculations for hypothesized therapeutic effects were conducted. RESULTS: We logged 143 QSM and 130 DCEQP paired annual assessments. Annual QSM change was greater in cases with SH than in cases without SH (P=0.019). Annual QSM increase by ≥6% occurred in 7 of 7 cases (100%) with recurrent SH and in 7 of 10 cases (70%) with asymptomatic change during the same epoch and 3.82× more frequently than clinical events. DCEQP change had lower sensitivity for SH and asymptomatic change than QSM change and greater variance. A trial with the smallest sample size would detect a 30% difference in QSM annual change during 2 years of follow-up in 34 or 42 subjects (1 and 2 tailed, respectively); power, 0.8, α=0.05. CONCLUSIONS: Assessment of QSM change is feasible and sensitive to recurrent bleeding in cavernous malformations. Evaluation of an intervention on QSM percent change may be used as a time-averaged difference between 2 arms using a repeated measures analysis. DCEQP change is associated with lesser sensitivity and higher variability than QSM. These results are the basis of an application for certification by the US Food and Drug Administration of QSM as a biomarker of drug effect on bleeding in cavernous malformations. REGISTRATION: URL: https://www.clinicaltrials.gov; Unique identifier: NCT03652181.

Evaluation of deep learning models for quality control of MR spectra.

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.

Multi-parametric hyperpolarized 13C/1H imaging reveals Warburg-related metabolic dysfunction and associated regional heterogeneity in high-grade human gliomas.

BACKGROUND: Dynamic 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. METHODS: Multi-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. RESULTS: Regional 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. CONCLUSIONS: GBM consistently displayed aberrant, Warburg-related metabolism and regional heterogeneity detectable by novel HP-13C/1H imaging techniques.

Cavernous Angioma Symptomatic Hemorrhage (CASH) Trial Readiness II: Imaging Biomarkers and Trial Modeling.

Background: Quantitative susceptibility mapping (QSM) and dynamic contrast enhanced quantitative perfusion (DCEQP) MRI sequences assessing iron deposition and vascular permeability were previously correlated with new hemorrhage in cavernous angiomas. We assessed their prospective changes in cavernous angiomas with symptomatic hemorrhage (CASH) in a multisite trial readiness project ( clinicaltrials.gov NCT03652181 ). Methods: Patients with CASH in the prior year, without prior or planned lesion resection or irradiation were enrolled. Mean QSM and DCEQP of CASH lesion were acquired at baseline, and at 1- and 2-year follow-ups. Sensitivity and specificity of biomarker changes were analyzed in relation to predefined lesional symptomatic hemorrhage (SH) or asymptomatic change (AC). Sample size calculations for hypothesized therapeutic effects were conducted. Results: We logged 143 QSM and 130 DCEQP paired annual assessments. Annual QSM change was greater in cases with SH than in cases without SH (p= 0.019). Annual QSM increase by ≥ 6% occurred in 7 of 7 cases (100%) with recurrent SH and in 7 of 10 cases (70%) with AC during the same epoch, and 3.82 times more frequently than clinical events. DCEQP change had lower sensitivity for SH and AC than QSM change, and greater variance. A trial with smallest sample size would detect a 30% difference in QSM annual change in 34 or 42 subjects (one and two-tailed, respectively), power 0.8, alpha 0.05. Conclusions: Assessment of QSM change is feasible and sensitive to recurrent bleeding in CASH. Evaluation of an intervention on QSM percent change may be used as a time-averaged difference between 2 arms using a repeated measures analysis. DCEQP change is associated with lesser sensitivity and higher variability than QSM. These results are the basis of an application for certification by the U.S. F.D.A. of QSM as a biomarker of drug effect in CASH.

Evaluation of major depressive disorder using 7 Tesla phase sensitive neuroimaging before and after mindfulness-based cognitive therapy.

OBJECTIVE: We applied 7 Tesla phase sensitive imaging to evaluate the impact of brain iron levels on depression severity and cognitive function in individuals with major depressive disorder (MDD) treated with mindfulness-based cognitive therapy (MBCT). METHODS: Seventeen unmedicated MDD participants underwent MRI, evaluation of depression severity, and cognitive testing before and after receiving MBCT, compared to fourteen healthy controls (HC). Local field shift (LFS) values, measures of brain iron levels, were derived from phase images in the putamen, caudate, globus pallidus (GP), anterior cingulate cortex (ACC) and thalamus. RESULTS: Compared to the HC group, the MDD group had significantly lower baseline LFS (indicative of higher iron) in the left GP and left putamen and had a higher number of subjects with impairment in a test of information processing speed. In the MDD group, lower LFS values in the left and right ACC, right putamen, right GP, and right thalamus were significantly associated with depression severity; and lower LFS in the right GP was correlated with worse performance on measures of attention. All MBCT participants experienced depression relief. MBCT treatment also significantly improved executive function and attention. MBCT participants with lower baseline LFS values in the right caudate experienced significantly greater improvement in depression severity with treatment; and those with lower LFS values in the right ACC, right caudate, and right GB at baseline performed better on measures of verbal learning and memory after MBCT. CONCLUSIONS: Our study highlights the potential contribution of subtle differences in brain iron to MDD symptoms and their successful treatment.

A feasibility study for quantitative assessment of cerebrovascular malformations using flutriciclamide ([18F]GE-180) PET/MRI.

Aim: Neuroinflammation 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 methods: Five 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. Results: Increased 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). Conclusion: The 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.

DeepSWI: Using Deep Learning to Enhance Susceptibility Contrast on T2*-Weighted MRI.

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.

Comparison of quantitative susceptibility mapping methods for iron-sensitive susceptibility imaging at 7T: An evaluation in healthy subjects and patients with Huntington's disease.

Quantitative susceptibility mapping (QSM) is a promising tool for investigating iron dysregulation in neurodegenerative diseases, including Huntington's disease (HD). Many diverse methods have been proposed to generate accurate and robust QSM images. In this study, we evaluated the performance of different dipole inversion algorithms for iron-sensitive susceptibility imaging at 7T on healthy subjects of a large age range and patients with HD. We compared an iterative least-squares-based method (iLSQR), iterative methods that use regularization, single-step approaches, and deep learning-based techniques. Their performance was evaluated by comparing: (1) deviations from a multiple-orientation QSM reference; (2) visual appearance of QSM maps and the presence of artifacts; (3) susceptibility in subcortical brain regions with age; (4) regional brain susceptibility with published postmortem brain iron quantification; and (5) susceptibility in HD-affected basal ganglia regions between HD subjects and healthy controls. We found that single-step QSM methods with either total variation or total generalized variation constraints (SSTV/SSTGV) and the single-step deep learning method iQSM generally provided the best performance in terms of correlation with iron deposition and were better at differentiating between healthy controls and premanifest HD individuals, while deep learning QSM methods trained with multiple-orientation susceptibility data created QSM maps that were most similar to the multiple orientation reference and with the best visual scores.

Improving the Automatic Classification of Brain MRI Acquisition Contrast with Machine Learning.

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.

Longitudinal MR spectroscopy to detect progression in patients with lower-grade glioma in the surveillance phase.

Background: Monitoring 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. Methods: Forty-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. Results: Thirty-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. Conclusions: LrGG 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.