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

Technological innovations in genomics and related fields have facilitated large sequencing efforts, supported new biological discoveries in cancer, and spawned an era of liquid biopsy biomarkers. Despite these advances, precision oncology has practical constraints, partly related to cancer's biological diversity and spatial and temporal complexity. Advanced imaging technologies are being developed to address some of the current limitations in early detection, treatment selection and planning, drug delivery, and therapeutic response, as well as difficulties posed by drug resistance, drug toxicity, disease monitoring, and metastatic evolution. We discuss key areas of advanced imaging for improving cancer outcomes and survival. Finally, we discuss practical challenges to the broader adoption of precision imaging in the clinic and the need for a robust translational infrastructure.

•Single-step QSM susceptibility values were the most correlated with iron.•Single-step QSM better distinguished premanifest HD subjects from healthy controls.•COSMOS-trained QSM values were most similar to COSMOS but less correlated with iron. 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.

Corticobasal degeneration (CBD), progressive supranuclear palsy (PSP) and a subset of frontotemporal dementia (FTD) are neurodegenerative disorders characterized by tau inclusions in neurons and glia (tauopathies). Although clinical, pathological and genetic evidence suggests overlapping pathobiology between CBD, PSP, and FTD, the relationship between these disorders is still not well understood. Using summary statistics (odds ratios and p values) from large genome-wide association studies (total n  = 14,286 cases and controls) and recently established genetic methods, we investigated the genetic overlap between CBD and PSP and CBD and FTD. We found up to 800-fold enrichment of genetic risk in CBD across different levels of significance for PSP or FTD. In addition to NSF (tagging the MAPT H1 haplotype), we observed that SNPs in or near MOBP , CXCR4 , EGFR, and GLDC showed significant genetic overlap between CBD and PSP, whereas only SNPs tagging the MAPT haplotype overlapped between CBD and FTD. The risk alleles of the shared SNPs were associated with expression changes in cis -genes. Evaluating transcriptome levels across adult human brains, we found a unique neuroanatomic gene expression signature for each of the five overlapping gene loci (omnibus ANOVA p  < 2.0 × 10 −16 ). Functionally, we found that these shared risk genes were associated with protein interaction and gene co-expression networks and showed enrichment for several neurodevelopmental pathways. Our findings suggest: (1) novel genetic overlap between CBD and PSP beyond the MAPT locus; (2) strong ties between CBD and FTD through the MAPT clade, and (3) unique combinations of overlapping genes that may, in part, influence selective regional or neuronal vulnerability observed in specific tauopathies.

Improved understanding of how the human brain is \"wired\" on a macroscale may now be possible due to the emerging field of MRI connectomics. However, mapping the rapidly developing infant brain networks poses challenges. In this study, we applied an automated template-free \"baby connectome\" framework using diffusion MRI to non-invasively map the structural brain networks in subjects of different ages, including premature neonates, term-born neonates, six-month-old infants, and adults. We observed increasing brain network integration and decreasing segregation with age in term-born subjects. We also explored how the equal area nodes can be grouped into modules without any prior anatomical information--an important step toward a fully network-driven registration and analysis of brain connectivity.

The urgency for climate action is recognised by international government and healthcare organisations, including the United Nations (UN) and World Health Organisation (WHO). Climate change, biodiversity loss, and pollution negatively impact all life on earth. All populations are impacted but not equally; the most vulnerable are at highest risk, an inequity further exacerbated by differences in access to healthcare globally. The delivery of healthcare exacerbates the planetary health crisis through greenhouse gas emissions, largely due to combustion of fossil fuels for medical equipment production and operation, creation of medical and non-medical waste, and contamination of water supplies. As representatives of radiology societies from across the globe who work closely with industry, and both governmental and non-governmental leaders in multiple capacities, we advocate together for urgent, impactful, and measurable changes to the way we deliver care by further engaging our members, policymakers, industry partners, and our patients. Simultaneous challenges including global health disparities, resource allocation, and access to care must inform these efforts. Climate literacy should be increasingly added to radiology training programmes. More research is required to understand and measure the environmental impact of radiological services and inform mitigation, adaptation and monitoring efforts. Deeper collaboration with industry partners is necessary to support innovations in the supply chain, energy utilization, and circular economy. Many solutions have been proposed and are already available, but we must understand and address barriers to implementation of current and future sustainable innovations. Finally, there is a compelling need to partner with patients, to ensure that trust in the excellence of clinical care is maintained during the transition to sustainable radiology. By fostering a culture of global cooperation and rapid sharing of solutions amongst the broader imaging community, we can transform radiological practice to mitigate its environmental impact, adapt and develop resilience to current and future climate and environmental threats, and simultaneously improve access to care.

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. 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. 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. 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 BACKGROUND: Arteriovenous malformation (AVM) patients present in 4 ways relative to hemorrhage: (1) unruptured, without a history or radiographic evidence of old hemorrhage (EOOH); (2) silent hemorrhage, without a bleeding history but with EOOH; (3) ruptured, with acute bleeding but without EOOH; and (4) reruptured, with acute bleeding and EOOH. OBJECTIVE: We hypothesized that characteristics and outcomes in the unrecognized group of silent hemorrhage patients may differ from those of unruptured patients. METHODS: Two hundred forty-two patients operated-on since 1997 were categorized by hemorrhage status and hemosiderin positivity in this cohort study: unruptured (group 1), silent hemorrhage (group 2), and ruptured/reruptured (group 3/4). Group 3/4 was combined because hemosiderin cannot distinguish acute hemorrhage from older silent hemorrhage. RESULTS: Hemosiderin was found in 45% of specimens. Seventy-five patients (31.0%) had unruptured AVMs, 30 (12.4%) had silent hemorrhage, and 137 (56.6%) had ruptured/reruptured AVMs. Deep drainage, posterior fossa location, preoperative modified Rankin Scale (mRS) score, outcome, and macrophage score were different across groups. Only the macrophage score was different between the groups without clinical hemorrhage. Outcomes were better in silent hemorrhage patients than in those with frank rupture (mean mRS scores of 1.2 and 1.7, respectively). CONCLUSION: One-third of patients present with silent AVM hemorrhage. No clinical or anatomic features differentiate these patients from unruptured patients, except the presence of hemosiderin and macrophages. Silent hemorrhage can be diagnosed using magnetic resonance imaging with iron-sensitive imaging. Silent hemorrhage portends an aggressive natural history, and surgery halts progression to rerupture. Good final mRS outcomes and better outcomes than in those with frank rupture support surgery for silent hemorrhage patients, despite the findings of ARUBA.

Defining the structural and functional connectivity of the human brain (the human \"connectome\") is a basic challenge in neuroscience. Recently, techniques for noninvasively characterizing structural connectivity networks in the adult brain have been developed using diffusion and high-resolution anatomic MRI. The purpose of this study was to establish a framework for assessing structural connectivity in the newborn brain at any stage of development and to show how network properties can be derived in a clinical cohort of six-month old infants sustaining perinatal hypoxic ischemic encephalopathy (HIE). Two different anatomically unconstrained parcellation schemes were proposed and the resulting network metrics were correlated with neurological outcome at 6 months. Elimination and correction of unreliable data, automated parcellation of the cortical surface, and assembling the large-scale baby connectome allowed an unbiased study of the network properties of the newborn brain using graph theoretic analysis. In the application to infants with HIE, a trend to declining brain network integration and segregation was observed with increasing neuromotor deficit scores.

MRI connectomics methods treat the brain as a network and provide new information about its organization, efficiency, and mechanisms of disruption. The most commonly used method of defining network nodes is to register the brain to a standardized anatomical atlas based on the Brodmann areas. This approach is limited by inter-subject variability and can be especially problematic in the context of brain maturation or neuroplasticity (cerebral reorganization after brain damage). In this study, we combined different image processing and network theory methods and created a novel approach that enables atlas-free construction and connection-wise comparison of diffusion MRI-based brain networks. We illustrated the proposed approach in three age groups: neonates, 6-month-old infants, and adults. First, we explored a data-driven method of determining the optimal number of equal-area nodes based on the assumption that all cortical areas of the brain are connected and, thus, no part of the brain is structurally isolated. Second, to enable a connection-wise comparison, alignment to a \"reference brain\" was performed in the network domain within each group using a matrix alignment algorithm with simulated annealing. The correlation coefficients after pair-wise network alignment ranged from 0.6102 to 0.6673. To test the method's reproducibility, one subject from the 6-month-old group and one from the adult group were scanned twice, resulting in correlation coefficients of 0.7443 and 0.7037, respectively. While being less than 1 due to parcellation and noise, statistically, these values were significantly higher than inter-subject values. Rotation of the parcellation largely explained the variability. Through the abstraction from anatomy, the developed framework allows for a fully network-driven analysis of structural MRI connectomes and can be applied to subjects at any stage of development and with substantial differences in cortical anatomy.