The study population contains 145 patients who were prospectively recruited for coronary CT angiography (CCTA) and fundoscopy. This study first examined the association between retinal vascular changes and the Coronary Artery Disease Reporting and Data System (CAD-RADS) as assessed on CCTA. Then, we developed a graph neural network (GNN) model for predicting the CAD-RADS as a proxy for coronary artery disease. The CCTA scans were stratified by CAD-RADS scores by expert readers, and the vascular biomarkers were extracted from their fundus images. Association analyses of CAD-RADS scores were performed with patient characteristics, retinal diseases, and quantitative vascular biomarkers. Finally, a GNN model was constructed for the task of predicting the CAD-RADS score compared to traditional machine learning (ML) models. The experimental results showed that a few retinal vascular biomarkers were significantly associated with adverse CAD-RADS scores, which were mainly pertaining to arterial width, arterial angle, venous angle, and fractal dimensions. Additionally, the GNN model achieved a sensitivity, specificity, accuracy and area under the curve of 0.711, 0.697, 0.704 and 0.739, respectively. This performance outperformed the same evaluation metrics obtained from the traditional ML models (p < 0.05). The data suggested that retinal vasculature could be a potential biomarker for atherosclerosis in the coronary artery and that the GNN model could be utilized for accurate prediction.

Purpose: To demonstrate magnetization transfer (MT) effects with low specific absorption rate (SAR) on ultra‐low‐field (ULF) MRI.Methods: MT imaging was implemented by using sinc‐modulated RF pulse train (SPT) modules to provide bilateral off‐resonance irradiation. They were incorporated into 3D gradient echo (GRE) and fast spin echo (FSE) protocols on a shielding‐free 0.055T head scanner. MT effects were first verified using phantoms. Brain MT imaging was conducted in both healthy subjects and patients.Results: MT effects were clearly observed in phantoms using six SPT modules with total flip angle 3600° at central primary saturation bands of approximate offset ±786 Hz, even in the presence of large relative B₀ inhomogeneity. For brain, strong MT effects were observed in gray matter, white matter, and muscle in 3D GRE and FSE imaging using six and sixteen SPT modules with total flip angle 3600° and 9600°, respectively. Fat, cerebrospinal fluid, and blood exhibited relatively weak MT effects. MT preparation enhanced tissue contrasts in T2‐weighted and FLAIR‐like images, and improved brain lesion delineation. The estimated MT SAR was 0.0024 and 0.0008 W/kg for two protocols, respectively, which is far below the US Food and Drug Administration (FDA) limit of 3.0 W/kg.Conclusion: Robust MT effects can be readily obtained at ULF with extremely low SAR, despite poor relative B₀ homogeneity in ppm. This unique advantage enables flexible MT pulse design and implementation on low‐cost ULF MRI platforms to achieve strong MT effects in brain and beyond, potentially augmenting their clinical utility in the future.

Objective: The high cost and limited accessibility of MRI scanners remain significant barriers to their broader use in clinical settings. This study aims to demonstrate the feasibility of balanced steady-state free precession (bSSFP) imaging at ultra-low-field (ULF) on a highly simplified and low-cost 0.05 Tesla whole-body MRI scanner. Methods: Experiments were conducted using a newly developed 0.05 Tesla MRI scanner that employed a permanent magnet without the need for magnetic or radiofrequency shielding. We optimized the bSSFP protocol for imaging the brain, spine, chest, abdomen, pelvis, and knee in healthy volunteers. We also examined the dependency of tissue contrast on the excitation flip angle. Results: The bSSFP protocols demonstrated reasonable image quality at 0.05 Tesla, allowing visualization of various anatomical structures. The protocols provided a spatial resolution of 2 × 2 × 6 mm³ with approximately 5 minutes of scan time per protocol. Good soft tissue contrasts were shown, facilitating the identification of major tissue types within each structure. Although bSSFP exhibited predominantly T2/T1 contrast, it could be adjusted to some extent by varying the flip angle. Conclusion: The bSSFP sequence is particularly effective for imaging at ULF due to the substantially decreased tissue T1 values. This study demonstrates that imaging various anatomical structures with bSSFP at 0.05 Tesla is efficient and feasible. Significance: Such bSSFP protocol benefits from ULF and can provide superior soft tissue contrasts compared to CT and ultrasound. This ULF bSSFP approach may offer a cost-effective alternative for imaging soft tissues in clinical settings lacking traditional MRI access.

Background: Chronic diseases are closely linked to alterations in body composition, yet there is a need for reliable biomarkers to assess disease risk and progression. This study aimed to develop and validate a biological age indicator based on body composition derived from dual-energy X-ray absorptiometry (DXA) scans, offering a novel approach to evaluating health status and predicting disease outcomes. Methods: A deep learning model was trained on a reference population from the UK Biobank to estimate body composition biological age (BCBA). The model’s performance was assessed across various groups, including individuals with typical and atypical body composition, those with pre-existing diseases, and those who developed diseases after DXA imaging. Key metrics such as c-index were employed to examine BCBA’s diagnostic and prognostic potential for type 2 diabetes, major adverse cardiovascular events (MACE), atherosclerotic cardiovascular disease (ASCVD), and hypertension. Results: Here we show that BCBA strongly correlates with chronic disease diagnoses and risk prediction. BCBA demonstrated significant associations with type 2 diabetes (odds ratio 1.08 for females and 1.04 for males, p < 0.0005), MACE (odds ratio 1.10 for females and 1.11 for males, p < 0.0005), ASCVD (odds ratio 1.07 for females and 1.10 for males, p < 0.0005), and hypertension (odds ratio 1.06 for females and 1.04 for males, p < 0.0005). It outperformed standard cardiovascular risk profiles in predicting MACE and ASCVD. Conclusions: BCBA is a promising biomarker for assessing chronic disease risk and progression, with potential to improve clinical decision-making. Its integration into routine health assessments could aid early disease detection and personalised interventions.

Different human calf muscles have specialized functions, leading to inherent metabolic differences. Imaging the metabolic and compositional properties of calf muscles is important for evaluating muscle health. This study aims to characterize the metabolic and compositional heterogeneity of calf muscle by chemical exchange saturation transfer (CEST) magnetic resonance imaging (MRI) at 3 T. CEST MRI was performed on 14 healthy volunteers. CEST contrasts (amide, PCr, rNOE, and MT) were extracted from segmented muscles, and used for correlation analysis with age, fat content, BMI, and muscular strength. Distinct CEST signatures were observed between deep stabilizer and superficial powerhouse muscles. Significant correlations (e.g., rNOE vs. strength) revealed the impact of adiposity on muscle physiology. This study demonstrates the feasibility of CEST MRI for noninvasively characterizing muscle‐specific metabolic and compositional alterations, revealing distinct signatures between functionally distinct muscle groups and significant associations with adiposity and body composition.