Hardware and Software Setup for Quantitative 23Na Magnetic Resonance Imaging at 3T: A Phantom Study.

Magnetic resonance (MR) with sodium (23Na) is a noninvasive tool providing quantitative biochemical information regarding physiology, cellular metabolism, and viability, with the potential to extend MR beyond anatomical proton imaging. However, when using clinical scanners, the low detectable 23Na signal and the low 23Na gyromagnetic ratio require the design of dedicated radiofrequency (RF) coils tuned to the 23Na Larmor frequency and sequences, as well as the development of dedicated phantoms for testing the image quality, and an MR scanner with multinuclear spectroscopy (MNS) capabilities. In this work, we propose a hardware and software setup for evaluating the potential of 23Na magnetic resonance imaging (MRI) with a clinical scanner. In particular, the reliability of the proposed setup and the reproducibility of the measurements were verified by multiple acquisitions from a 3T MR scanner using a homebuilt RF volume coil and a dedicated sequence for the imaging of a phantom specifically designed for evaluating the accuracy of the technique. The final goal of this study is to propose a setup for standardizing clinical and research 23Na MRI protocols.

A Comprehensive Review of COVID-19-Related Olfactory Deficiency: Unraveling Associations with Neurocognitive Disorders and Magnetic Resonance Imaging Findings.

Olfactory dysfunction (OD) is one of the most common symptoms in COVID-19 patients and can impact patients' lives significantly. The aim of this review was to investigate the multifaceted impact of COVID-19 on the olfactory system and to provide an overview of magnetic resonance (MRI) findings and neurocognitive disorders in patients with COVID-19-related OD. Extensive searches were conducted across PubMed, Scopus, and Google Scholar until 5 December 2023. The included articles were 12 observational studies and 1 case report that assess structural changes in olfactory structures, highlighted through MRI, and 10 studies correlating the loss of smell with neurocognitive disorders or mood disorders in COVID-19 patients. MRI findings consistently indicate volumetric abnormalities, altered signal intensity of olfactory bulbs (OBs), and anomalies in the olfactory cortex among COVID-19 patients with persistent OD. The correlation between OD and neurocognitive deficits reveals associations with cognitive impairment, memory deficits, and persistent depressive symptoms. Treatment approaches, including olfactory training and pharmacological interventions, are discussed, emphasizing the need for sustained therapeutic interventions. This review points out several limitations in the current literature while exploring the intricate effects of COVID-19 on OD and its connection to cognitive deficits and mood disorders. The lack of objective olfactory measurements in some studies and potential validity issues in self-reports emphasize the need for cautious interpretation. Our research highlights the critical need for extensive studies with larger samples, proper controls, and objective measurements to deepen our understanding of COVID-19's long-term effects on neurological and olfactory dysfunctions.

Glioblastoma and Internal Carotid Artery Calcium Score: A Possible Novel Prognostic Partnership?

Purpose: Clinical evidence suggests an association between comorbidities and outcome in patients with glioblastoma (GBM). We hypothesised that the internal carotid artery (ICA) calcium score could represent a promising prognostic biomarker in a competing risk analysis in patients diagnosed with GBM. Methods: We validated the use of the ICA calcium score as a surrogate marker of the coronary calcium score in 32 patients with lung cancer. Subsequently, we assessed the impact of the ICA calcium score on overall survival in GBM patients treated with radio-chemotherapy. Results: We analysed 50 GBM patients. At the univariate analysis, methyl-guanine-methyltransferase gene (MGMT) promoter methylation (p = 0.048), gross total tumour resection (p = 0.017), and calcium score (p = 0.011) were significant prognostic predictors in patients with GBM. These three variables also maintained statistical significance in the multivariate analysis. Conclusions: the ICA calcium score could be a promising prognostic biomarker in GBM patients.

Cardiovascular Magnetic Resonance Reference Ranges From the Healthy Hearts Consortium.

BACKGROUND: The absence of population-stratified cardiovascular magnetic resonance (CMR) reference ranges from large cohorts is a major shortcoming for clinical care. OBJECTIVES: This paper provides age-, sex-, and ethnicity-specific CMR reference ranges for atrial and ventricular metrics from the Healthy Hearts Consortium, an international collaborative comprising 9,088 CMR studies from verified healthy individuals, covering the complete adult age spectrum across both sexes, and with the highest ethnic diversity reported to date. METHODS: CMR studies were analyzed using certified software with batch processing capability (cvi42, version 5.14 prototype, Circle Cardiovascular Imaging) by 2 expert readers. Three segmentation methods (smooth, papillary, anatomic) were used to contour the endocardial and epicardial borders of the ventricles and atria from long- and short-axis cine series. Clinically established ventricular and atrial metrics were extracted and stratified by age, sex, and ethnicity. Variations by segmentation method, scanner vendor, and magnet strength were examined. Reference ranges are reported as 95% prediction intervals. RESULTS: The sample included 4,452 (49.0%) men and 4,636 (51.0%) women with average age of 61.1 ± 12.9 years (range: 18-83 years). Among these, 7,424 (81.7%) were from White, 510 (5.6%) South Asian, 478 (5.3%) mixed/other, 341 (3.7%) Black, and 335 (3.7%) Chinese ethnicities. Images were acquired using 1.5-T (n = 8,779; 96.6%) and 3.0-T (n = 309; 3.4%) scanners from Siemens (n = 8,299; 91.3%), Philips (n = 498; 5.5%), and GE (n = 291, 3.2%). CONCLUSIONS: This work represents a resource with healthy CMR-derived volumetric reference ranges ready for clinical implementation.

Magnetic Resonance Imaging Characterization and Clinical Outcomes of Dilated and Arrhythmogenic Left Ventricular Cardiomyopathies.

BACKGROUND: Nondilated left ventricular cardiomyopathy (NDLVC) has been recently differentiated from dilated cardiomyopathy (DCM). A comprehensive characterization of these 2 entities using cardiac magnetic resonance (CMR) and genetic testing has never been performed. OBJECTIVES: This study sought to provide a thorough characterization and assess clinical outcomes in a large multicenter cohort of patients with DCM and NDLVC. METHODS: A total of 462 patients with DCM (227) or NDLVC (235) with CMR data from 4 different referral centers were retrospectively analyzed. The study endpoint was a composite of sudden cardiac death or major ventricular arrhythmias. RESULTS: In comparison to DCM, NDLVC had a higher prevalence of pathogenic or likely pathogenic variants of arrhythmogenic genes (40% vs 23%; P < 0.001), higher left ventricular (LV) systolic function (LV ejection fraction: 51% ± 12% vs 36% ± 15%; P < 0.001) and higher prevalence of free-wall late gadolinium enhancement (LGE) (27% vs 14%; P < 0.001). Conversely, DCM showed higher prevalence of pathogenic or likely pathogenic variants of nonarrhythmogenic genes (23% vs 12%; P = 0.002) and septal LGE (45% vs 32%; P = 0.004). Over a median follow-up of 81 months (Q1-Q3: 40-132 months), the study outcome occurred in 98 (21%) patients. LGE with septal location (HR: 1.929; 95% CI: 1.033-3.601; P = 0.039) was independently associated with the risk of sudden cardiac death or major ventricular arrhythmias together with LV dilatation, older age, advanced NYHA functional class, frequent ventricular ectopic activity, and nonsustained ventricular tachycardia. CONCLUSIONS: In a multicenter cohort of patients with DCM and NDLVC, septal LGE together with LV dilatation, age, advanced disease, and frequent and repetitive ventricular arrhythmias were powerful predictors of major arrhythmic events.

Arrhythmic Mitral Valve Prolapse Phenotype: An Unsupervised Machine Learning Analysis Using a Multicenter Cardiac MRI Registry.

Purpose To use unsupervised machine learning to identify phenotypic clusters with increased risk of arrhythmic mitral valve prolapse (MVP). Materials and Methods This retrospective study included patients with MVP without hemodynamically significant mitral regurgitation or left ventricular (LV) dysfunction undergoing late gadolinium enhancement (LGE) cardiac MRI between October 2007 and June 2020 in 15 European tertiary centers. The study end point was a composite of sustained ventricular tachycardia, (aborted) sudden cardiac death, or unexplained syncope. Unsupervised data-driven hierarchical k-mean algorithm was utilized to identify phenotypic clusters. The association between clusters and the study end point was assessed by Cox proportional hazards model. Results A total of 474 patients (mean age, 47 years ± 16 [SD]; 244 female, 230 male) with two phenotypic clusters were identified. Patients in cluster 2 (199 of 474, 42%) had more severe mitral valve degeneration (ie, bileaflet MVP and leaflet displacement), left and right heart chamber remodeling, and myocardial fibrosis as assessed with LGE cardiac MRI than those in cluster 1. Demographic and clinical features (ie, symptoms, arrhythmias at Holter monitoring) had negligible contribution in differentiating the two clusters. Compared with cluster 1, the risk of developing the study end point over a median follow-up of 39 months was significantly higher in cluster 2 patients (hazard ratio: 3.79 [95% CI: 1.19, 12.12], P = .02) after adjustment for LGE extent. Conclusion Among patients with MVP without significant mitral regurgitation or LV dysfunction, unsupervised machine learning enabled the identification of two phenotypic clusters with distinct arrhythmic outcomes based primarily on cardiac MRI features. These results encourage the use of in-depth imaging-based phenotyping for implementing arrhythmic risk prediction in MVP. Keywords: MR Imaging, Cardiac, Cardiac MRI, Mitral Valve Prolapse, Cluster Analysis, Ventricular Arrhythmia, Sudden Cardiac Death, Unsupervised Machine Learning Supplemental material is available for this article. © RSNA, 2024.

Functional magnetic resonance imaging in the evaluation of the elastic properties of ascending aortic aneurysm

Abstract Objective: To evaluate the aortic wall elasticity using the maximal rate of systolic distension (MRSD) and maximal rate of diastolic recoil (MRDR) and their correlation with the aortic size index (ASI). Methods: Forty-eight patients with thoracic aortic aneurysm were enrolled in this study. A standard magnetic resonance imaging (MRI) protocol was used to calculate MRSD and MRDR. Both MRSD and MRDR were expressed as percentile of maximal area/10-3 sec. ASI (maximal aortic diameter/body surface area) was calculated. A correlation between MRSD, MRDR, ASI, and the patient's age was performed using regression plot. Results: A significant correlation between MRSD (t=-4,36; r2=0.29; P≤0.0001), MRDR (t=3.92; r2=0.25; P=0.0003), and ASI (25±4.33 mm/m2; range 15,48-35,14 mm/m2) is observed. As ASI increases, aortic MRSD and MRDR decrease. Such inverse correlation between MRSD, MRDR, and ASI indicates increased stiffness of the ascending aorta. A significant correlation between the patient's age and the decrease in MRSD and MRDR is observed. Conclusion: MRSD and MRDR are significantly correlated with ASI and the patient's age. They seem to describe properly the increasing stiffness of aortas. These two new indexes provide a promising, accessible, and reproducible approach to evaluate the biomechanical property of the aorta.