Growing evidence highlights the importance of body composition (BC), including bone, muscle, and adipose tissue (AT), as a critical biomarker for cardiometabolic risk stratification. However, conventional methods for quantifying BC components using medical images are hindered by labor-intensive workflows and limited anatomical coverage. This study developed BioCompNet—an end-to-end deep learning workflow that integrates dual-parametric magnetic resonance imaging (MRI) sequences (water/fat) with a hierarchical U-Net architecture to enable fully automated quantification of 15 biomechanically critical BC components. BioCompNet targets 10 abdominal compartments (vertebral bone, psoas muscles, core muscles, subcutaneous AT [SAT], superficial SAT, deep SAT, intraperitoneal AT, retroperitoneal AT, visceral AT, and intermuscular AT [IMAT]) and 5 thigh compartments (femur, muscle, SAT, IMAT, and vessels). The workflow was developed on 8,048 MRI slices from a community-based cohort ( n = 503) and independently validated on 240 MRI slices from a tertiary hospital ( n = 30). The model’s performance was benchmarked against expert annotations. On internal and external validation datasets, BioCompNet achieved average Dice similarity coefficients of 0.944 and 0.938 for abdominal compartments and 0.961 and 0.936 for thigh compartments, respectively. Excellent interreader reliability was observed (intraclass correlation coefficient ≥ 0.881) across all quantified features, and IMAT quantification showed a strong linear trend ( P _trend < 0.001) compared to physician-rated assessments. The workflow substantially reduced processing time from 128.8 ± 5.6 to 0.12 ± 0.001 min per case. By enabling rapid, accurate, and comprehensive volumetric analysis of BC components, BioCompNet establishes a scalable framework for precision cardiometabolic risk assessment and clinical decision support.

(1) Background: This study aimed to investigate the prevalence and risk factors for enlarged perivascular spaces (EPVS) in young adults from a neurology clinic-based cohort (≤45 years old) via unenhanced brain MRI. (2) Methods: A total of 931 young adults from a neurology clinic-based cohort who underwent unenhanced brain MRI between 1 January 2021 and 30 June 2021 were retrospectively included in this study. The EPVS were rated in the centrum semiovale (CSO-EPVS), basal ganglia (BG-EPVS), and midbrain (MB-EPVS) using a visual rating scale. The degrees of the CSO-EPVS, BG-EPVS, and MB-EPVS were all divided by a cutoff value of 1. Demographic factors, vascular risk factors, and symptoms were analyzed using the chi-square test and logistic regression to determine the risk factors of EPVS. (3) Results: The overall prevalence of EPVS was 99.8% (929/931). The CSO-EPVS, BG-EPVS, and MB-EPVS were predominantly scored as 1 (52.1%, 79.1%, and 58.3%, respectively). Logistic regression analysis identified age and hypertension as factors affecting the degrees of CSO-EPVS and BG-EPVS (p < 0.05). Hypertension (p < 0.001) and diabetes (p = 0.014) were revealed to be factors affecting the degree of BG-EPVS. Furthermore, patients with headache (OR = 1.807; p = 0.001) and dizziness (OR = 1.574; p = 0.025) were associated with MB-EPVS. (4) Conclusions: EPVS were frequently found in young adults and could be related to the symptoms. Age, hypertension, and diabetes were the risk factors for the severity of EPVS in the corresponding brain regions.

In the past few decades, brain diseases have taken a heavy toll on human health and social systems. Magnetic resonance imaging (MRI), photoacoustic imaging (PA), computed tomography (CT), and other imaging modes play important roles in disease prevention and treatment. However, the disadvantages of traditional imaging mode, such as long imaging time and large noise, limit the effective diagnosis of diseases, and reduce the precision treatment of diseases. The ever-growing applications of inorganic nanomaterials in biomedicine provide an exciting way to develop novel imaging systems. Moreover, these nanomaterials with special physicochemical characteristics can be modified by surface modification or combined with functional materials to improve targeting in different diseases of the brain to achieve accurate imaging of disease regions. This article reviews the potential applications of different types of inorganic nanomaterials in vivo imaging and in vitro detection of different brain disease models in recent years. In addition, the future trends, opportunities, and disadvantages of inorganic nanomaterials in the application of brain diseases are also discussed. Additionally, recommendations for improving the sensitivity and accuracy of inorganic nanomaterials in screening/diagnosis of brain diseases.

The impact of ischemic heart disease (IHD) on the brain glymphatic MRI indices and risk of Alzheimer's disease (AD) remains largely unclear. This study aimed to investigate the associations between IHD, brain glymphatic MRI indices and risk of AD. A total of 1385 non-dementia subjects (55.2 % male, mean age 73.53) were included. Diffusivity along the perivascular space (DTI-ALPS), free water (FW) and choroid plexus volume were used to reflect glymphatic function. The associations of IHD with MRI derived glymphatic indices, PET amyloid, tau and cognitive performance were explored by multiple regression analysis. IHD were tested as predictors of clinical progression using cox proportional hazards modeling. The mediation effect of MRI derived glymphatic indices on the relationship between IHD and cognitive changes was investigated. Individuals with IHD exhibited glymphatic dysfunction revealed by lower DTI-ALPS (p = 0.035), higher FW (p < 0.001), and higher choroid plexus volume (p = 0.019). IHD had poorer cognitive performance in MMSE (p = 0.022), ADNI-MEM (p = 0.001) and ADNI-MF (p = 0.006), and more amyloid deposition (p = 0.007). IHD had a higher diagnostic conversion risk (HR = 1.321, 95 % CI = 1.003-1.741). IHD was associated with longitudinal cognitive decline in all cognitive tests (p < 0.05 for all) and FW (β = 0.012, 95 % CI 0.001, 0.023, p = 0.038). FW demonstrated an indirect effect (β = -0.0009, 95 % CI: -0.0034, -0.0001) and mediated 13.85 % effect for the relationship between IHD and ADNI-EF decline. IHD is independently associated with AD risk, and brain glymphatic dysfunction may partially mediate this relationship.

The aim of this study was to evaluate multisequence 3.0-T MRI in the detection of severe traumatic brain injury (sTBI) and in predicting the outcome. 32 patients with sTBI were prospectively enrolled, and multisequence 3.0-T MRI was performed 4-8 weeks post injury. Quantitative data were recorded on each sequence. The ability to display the parenchymal lesions was compared with that of 64-slice spiral CT. The clinical and radiological results were correlated with the Glasgow Outcome Scale Extended scores 6 months after injury. 3.0-T MRI could display more lesions than CT, especially when the lesion was deeply located. The lesion volumes and diffuse axonal injury (DAI) scores were different between good and poor outcome groups on fluid attenuated inversion recovery (p < 0.05). The apparent diffusion coefficient (ADC) values of the splenium of the corpus callosum and brain stem were also different (p < 0.05). Patients with unfavourable outcome showed a significantly higher volume of haemorrhage on susceptibility-weighted imaging than those with favourable outcomes and had haemorrhages generally located more deeply. Logistic regression analysis revealed that the location of haemorrhage and the ADC values of the splenium of the corpus callosum were independent risk factors for poor outcome, with an overall predictive accuracy of 91.4%. The joint use of conventional and advanced sequences of 3.0-T MRI can comprehensively detect the pathological changes occurring after sTBI. Haemorrhagic and non-haemorrhagic DAIs in deep structures strongly suggest poor outcome. This article improves the understanding of advanced MRI sequences in the detection of patients with sTBI and prediction of prognosis.