The assessment of vascular anatomy and functions using magnetic resonance imaging (MRI) is critical for medical diagnosis, whereas the commonly used low‐field MRI system (≤3 T) suffers from low spatial resolution. Ultrahigh field (UHF) MRI (≥7 T), with significantly improved resolution and signal‐to‐noise ratio, shows great potential to provide high‐resolution vasculature images. However, practical applications of UHF MRI technology for vascular imaging are currently limited by the low sensitivity and accuracy of single‐mode (T1 or T2) contrast agents. Herein, a UHF‐tailored T1–T2 dual‐mode iron oxide nanoparticle‐based contrast agent (UDIOC) with extremely small core size and ultracompact hydrophilic surface modification, exhibiting dually enhanced T1–T2 contrast effect under the 7 T magnetic field, is reported. The UDIOC enables clear visualization of microvasculature as small as ≈140 µm in diameter under UHF MRI, extending the detection limit of the 7 T MR angiography. Moreover, by virtue of high‐resolution UHF MRI and a simple double‐checking process, UDIOC‐based dual‐mode dynamic contrast‐enhanced MRI is successfully applied to detect tumor vascular permeability with extremely high sensitivity and accuracy, providing a novel paradigm for the precise medical diagnosis of vascular‐related diseases.

Quantitative physiological parameters can be obtained from nonlinear pharmacokinetic models, such as the extended Tofts (eTofts) model, applied to dynamic contrast‐enhanced magnetic resonance imaging (DCE‐MRI). However, the computation of such nonlinear models is time consuming. The aim of this study was to develop a convolutional neural network (CNN) for accelerating the computation of fitting eTofts model without sacrificing agreement with conventional nonlinear‐least‐square (NLLS) fitting. This was a retrospective study, which included 13 patients with brain glioma for training (75%) and validation (25%), and 11 patients (three glioma, four brain metastases, and four lymphoma) for testing. CAIPIRINHA‐Dixon‐TWIST DCE‐MRI and double flip angle T1 map acquired at 3 T were used. A CNN with both local pathway and global pathway modules was designed to estimate the eTofts model parameters, the volume transfer constant (Ktrans), blood volume fraction (vp), and volume fraction of extracellular extravascular space (ve), from DCE‐MRI data of tumor and normal‐appearing voxels. The CNN was trained on mixed dataset consisting of synthetic and patient data. The CNN result and computation speed were compared with NLLS fitting. The robustness to noise variations and generalization to brain metastases and lymphoma data were also evaluated. Statistical tests used were Student's t test on mean absolute error, concordance correlation coefficient (CCC), and normalized root mean squared error. Including global pathway modules in the CNN and training the network with mixed data significantly (p < 0.05) improved the CNN performance. Compared with NLLS fitting, CNN yields an average CCC greater than 0.986 for Ktrans, greater than 0.965 for vp, and greater than 0.948 for ve. The CNN accelerated computation speed approximately 2000 times compared to NLLS, showed robustness to noise (signal‐to‐noise ratio >34.42 dB), and had no significant (p > 0.21) difference applied to brain metastases and lymphoma data. In conclusion, the proposed CNN to estimate eTofts parameters showed comparable result as NLLS fitting while significantly reducing the computation time. Level of Evidence 3 Technical Efficacy Stage 1

No abstract

Advanced age, accompanied by impaired glymphatic function, is a key risk factor for many neurodegenerative diseases. To study age-related differences in the human glymphatic system, we measured the influx and efflux activities of the glymphatic system via two non-invasive diffusion magnetic resonance imaging (MRI) methods, ultra-long echo time and low-b diffusion tensor imaging (DTIlow–b) measuring the subarachnoid space (SAS) flow along the middle cerebral artery and DTI analysis along the perivascular space (DTI-ALPS) along medullary veins in 22 healthy volunteers (aged 21–75 years). We first evaluated the circadian rhythm dependence of the glymphatic activity by repeating the MRI measurements at five time points from 8:00 to 23:00 and found no time-of-day dependence in the awake state under the current sensitivity of MRI measurements. Further test–retest analysis demonstrated high repeatability of both diffusion MRI measurements, suggesting their reliability. Additionally, the influx rate of the glymphatic system was significantly higher in participants aged &gt;45 years than in participants aged 21–38, while the efflux rate was significantly lower in those aged &gt;45 years. The mismatched influx and efflux activities in the glymphatic system might be due to age-related changes in arterial pulsation and aquaporin-4 polarization.