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The perivascular space (PVS) is important to brain waste clearance and brain metabolic homeostasis. Enlarged PVS (ePVS) becomes visible on magnetic resonance imaging (MRI) and is best appreciated on T2-weighted (T2w) images. However, quantification of ePVS is challenging because standard-of-care T1-weighted (T1w) and T2w images are often obtained via two-dimensional (2D) acquisition, whereas accurate quantification of ePVS normally requires high-resolution volumetric three-dimensional (3D) T1w and T2w images. The purpose of this study was to investigate the use of a deep-learning-based super-resolution (SR) technique to improve ePVS quantification from 2D T2w images for application in patients with traumatic brain injury (TBI). We prospectively recruited 26 volunteers (age: 31 ± 12 years, 12 male/14 female) where both 2D T2w and 3D T2w images were acquired along with 3D T1w images to validate the ePVS quantification using SR T2w images. We then applied the SR method to retrospectively acquired 2D T2w images in 41 patients with chronic TBI (age: 41 ± 16 years, 32 male/9 female). ePVS volumes were automatically quantified within the whole-brain white matter and major brain lobes (temporal, parietal, frontal, occipital) in all subjects. Pittsburgh Sleep Quality Index (PSQI) scores were obtained on all patients with TBI. Compared with the silver standard (3D T2w), in the validation study, the SR T2w provided similar whole-brain white matter ePVS volume (r = 0.98, p < 0.0001), and similar age-related ePVS burden increase (r = 0.80, p < 0.0001). In the patient study, patients with TBI with poor sleep showed a higher age-related ePVS burden increase than those with good sleep. Sleep status is a significant interaction factor in the whole brain (p = 0.047) and the frontal lobe (p = 0.027). We demonstrate that images produced by SR of 2D T2w images can be automatically analyzed to produce results comparable to those obtained by 3D T2 volumes. Reliable age-related ePVS burden across the whole-brain white matter was observed in all subjects. Poor sleep, affecting the glymphatic function, may contribute to the accelerated increase of ePVS burden following TBI. [ABSTRACT FROM AUTHOR]

A central challenge of medical imaging studies is to extract biomarkers that characterize disease pathology or outcomes. Modern automated approaches have found tremendous success in high‐resolution, high‐quality magnetic resonance images. These methods, however, may not translate to low‐resolution images acquired on magnetic resonance imaging (MRI) scanners with lower magnetic field strength. In low‐resource settings where low‐field scanners are more common and there is a shortage of radiologists to manually interpret MRI scans, it is critical to develop automated methods that can augment or replace manual interpretation, while accommodating reduced image quality. We present a fully automated framework for translating radiological diagnostic criteria into image‐based biomarkers, inspired by a project in which children with cerebral malaria (CM) were imaged using low‐field 0.35 Tesla MRI. We integrate multiatlas label fusion, which leverages high‐resolution images from another sample as prior spatial information, with parametric Gaussian hidden Markov models based on image intensities, to create a robust method for determining ventricular cerebrospinal fluid volume. We also propose normalized image intensity and texture measurements to determine the loss of gray‐to‐white matter tissue differentiation and sulcal effacement. These integrated biomarkers have excellent classification performance for determining severe brain swelling due to CM. [ABSTRACT FROM AUTHOR]

Background: The etiology of idiopathic intracranial hypertension (IIH) is uncertain. Studies suggest the fundamental cause of the Chiari 1 malformation, a congenitally hypoplastic posterior fossa, may explain the genesis of IIH in some patients. Purpose: To assess the hypothesis that linear and volumetric measurements of the posterior fossa (PF) can be used as predictors of IIH. Material and Methods: A retrospective analysis of magnetic resonance imaging (MRI) studies on 27 patients with IIH and 14 matched controls was performed. A volumetric sagittal magnetization prepared rapid acquisition gradient echo sequence was used to derive 10 linear cephalometric measurements. Total intracranial and bony posterior fossa volumes (PFVs) were derived by manual segmentation. The ratio of PFV to total intracranial volume was calculated. Results: In total, 41 participants were included, all women. Participants with IIH had higher median body mass index (BMI). No significant differences in linear cephalometric measurements, total intracranial volumes, and PFVs between the groups were identified. Linear measurements were not predictive of volumetric measurements. However, on multivariate logistic regression analysis, the likelihood of IIH decreased significantly per unit increase in relative PFV (odds ratio [OR]=3.66 × 10−50; 95% confidence interval [CI]=1.39 × 10−108 to 1.22 × 10−5; P = 0.04). Conversely, the likelihood of IIH increased per unit BMI increase (OR=1.19; 95% CI=1.04–1.47; P = 0.02). Conclusion: MRI-based volumetric measurements imply that PF alterations may be partly responsible for the development of IIH and Chiari 1 malformations. Symptoms of IIH may arise due to an interplay between these and metabolic, hormonal, or other factors. [ABSTRACT FROM AUTHOR]