Among childhood cancer survivors, increased stroke risk after cranial radiation therapy may be caused by radiation-induced arteriopathy, but limited data exist to support this hypothesis. Herein, we assess the timing and presence of cerebral arteriopathy identified by magnetic resonance angiography (MRA) after cranial radiation therapy in childhood brain tumor survivors. In a cohort of 115 pediatric brain tumor survivors, we performed chart abstraction and prospective annual follow-up to assess the presence of large vessel cerebral arteriopathy by MRA. We identified 10 patients with cerebral arteriopathy. The cumulative incidence of arteriopathy 5 years post–cranial radiation therapy was 5.4% (CI 0.6%-10%) and 10 years was 16% (CI 4.6%-26%). One patient had an arterial ischemic stroke 2.4 years post–cranial radiation therapy in the distribution of a radiation-induced stenotic artery. We conclude that large vessel arteriopathies can occur within a few years of cranial radiation therapy and can become apparent on MRA in under a year.
Purpose: High-resolution vessel wall magnetic resonance imaging (VW-MRI) could provide a way to identify high risk arteriovenous malformation (AVM) features. We present the first pilot study of clinically unruptured AVMs evaluated by high-resolution VW-MRI. Methods: A retrospective review of clinically unruptured AVMs with VW-MRI between January 1, 2016 and December 31, 2018 was performed documenting the presence or absence of vessel wall “hyperintensity,” or enhancement, within the nidus as well as perivascular enhancement and evidence of old hemorrhage (EOOH). The extent of nidal vessel wall “hyperintensity” was approximated into five groups: 0, 1–25, 26–50, 51–75, and 76–100%. Results: Of the nine cases, eight demonstrated at least some degree of vessel wall nidus “hyperintensity.” Of those eight cases, four demonstrated greater than 50% of the nidus with hyperintensity at the vessel wall, and three cases had perivascular enhancement adjacent to nidal vessels. Although none of the subjects had prior clinical hemorrhage/AVM rupture, of the six patients with available susceptibility weighted imaging to assess for remote hemorrhage, only two had subtle siderosis to suggest prior sub-clinical bleeds. Conclusion: Vessel wall “enhancement” occurs in AVMs with no prior clinical rupture. Additional studies are needed to further investigate the implication of these findings.
We describe the design and results from the BraTS 2023 Intracranial Meningioma Segmentation Challenge. The BraTS Meningioma Challenge differed from prior BraTS Glioma challenges in that it focused on meningiomas, which are typically benign extra-axial tumors with diverse radiologic and anatomical presentation and a propensity for multiplicity. Nine participating teams each developed deep-learning automated segmentation models using image data from the largest multi-institutional systematically expert annotated multilabel multi-sequence meningioma MRI dataset to date, which included 1000 training set cases, 141 validation set cases, and 283 hidden test set cases. Each case included T2, T2/FLAIR, T1, and T1Gd brain MRI sequences with associated tumor compartment labels delineating enhancing tumor, non-enhancing tumor, and surrounding non-enhancing T2/FLAIR hyperintensity. Participant automated segmentation models were evaluated and ranked based on a scoring system evaluating lesion-wise metrics including dice similarity coefficient (DSC) and 95% Hausdorff Distance. The top ranked team had a lesion-wise median dice similarity coefficient (DSC) of 0.976, 0.976, and 0.964 for enhancing tumor, tumor core, and whole tumor, respectively and a corresponding average DSC of 0.899, 0.904, and 0.871, respectively. These results serve as state-of-the-art benchmarks for future pre-operative meningioma automated segmentation algorithms. Additionally, we found that 1286 of 1424 cases (90.3%) had at least 1 compartment voxel abutting the edge of the skull-stripped image edge, which requires further investigation into optimal pre-processing face anonymization steps.
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, T 1 ‐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
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.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.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.MR metrics describing brain connectivity and variability represent promising candidate imaging biomarkers for monitoring of long-term cognitive side-effects after RT.
Advanced IC's built with recent technology nodes take advantage of the process induced mechanical stress, which is used as one of the transistor performance boosters. Modulation of the stress level, experienced by silicon chip, has significant impact on its performance and reliability. Therefore, monitoring of this stress through wafer manufacturing and packaging process is of high importance. We have developed an in-die-embedded stress sensor, testable with standard product test that can with help measuring and monitoring stress level in the die. The sensor design was demonstrated for multiple advanced FinFET technology nodes (< 14nm). We have confirmed high sensitivity across process corners and temperature with consistent results between electrical wafer sort (EWS) and final test (FT). The results from the mechanical stress sensors indicate that the stress non-uniformity across the wafer is preserved through wafer dicing/thinning/packaging process. Statistical analysis of the sensor results enables detection of wafer patterns and outlier identification at EWS and subsequent FT after assembly enables detection of abnormal mechanical stress changes due to packaging. This mechanical stress sensor provides differentiated data for EWS, FT, and Burn-In (BI) to create product relevant screening specs for improved product reliability and can provide an early alarm for the product reliability risk due to effects such as delamination or cracks. This sensor has been implemented in the PDF Solutions' CV Core ® system which enables for in-field tracking and analyzing the sensor signals to detect and mitigate the potentially disastrous reliability failures.
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 Objective: Subjective tinnitus is an auditory phantom perceptual disorder without an objective biomarker. Fast and efficient diagnostic tools will advance clinical practice by detecting or confirming the condition, tracking change in severity, and monitoring treatment response. Motivated by evidence of subtle anatomical, morphological, or functional information in magnetic resonance images of the brain, we examine data-driven machine learning methods for joint tinnitus classification (tinnitus or no tinnitus) and tinnitus severity prediction. Approach: We propose a deep multi-task multimodal framework for tinnitus classification and severity prediction using structural MRI (sMRI) data. To leverage complementary information multimodal neuroimaging data, we integrate two modalities of three-dimensional sMRI—T1 weighted (T1w) and T2 weighted (T2w) images. To explore the key components in the MR images that drove task performance, we segment both T1w and T2w images into three different components—cerebrospinal fluid, grey matter and white matter, and evaluate performance of each segmented image. Main results: Results demonstrate that our multimodal framework capitalizes on the information across both modalities (T1w and T2w) for the joint task of tinnitus classification and severity prediction. Significance: Our model outperforms existing learning-based and conventional methods in terms of accuracy, sensitivity, specificity, and negative predictive value.
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