Yanglei Wu

Background: White matter microstructure may be affected by cancer and its treatment, yet its association with subsequent cancer‐related cognitive impairment (CRCI) remains unclear. Purpose: To investigate whether baseline white matter diffusion metrics are associated with the time to CRCI onset and longitudinal cognitive trajectories. Study Type: Prospective longitudinal study. Population: A total of 230 female patients (49.4 ± 8.6 years) with histopathologically confirmed breast cancer (50 Stage II, 180 Stage III) with planned neoadjuvant chemotherapy before surgery. Field Strength/Sequence: A 3 T, multi‐shell SE ‐ EPI DWI . Assessment: Diffusion MRI was performed before chemotherapy (baseline). Neuropsychological assessments were carried out at baseline, after the first cycle of chemotherapy, after completion of chemotherapy but before surgery, and at 2–4 years after surgery. Diffusion tensor metrics (mean, radial, and axial diffusivity [MD, RD, and AD]), diffusion kurtosis metrics (axial and radial kurtosis [AK and RK]), and neurite orientation dispersion and density imaging metrics (orientation dispersion index [ODI], neurite density index [NDI]) were extracted from 25 white matter tracts, capturing both mean values and peak width. Statistical Tests: Cox regression and linear mixed‐effects models assessed associations with time to CRCI onset and cognitive trajectories, respectively. A p value < 0.05 was considered significant. Results: Higher ODI (anterior limb of the internal capsule [ALIC]: HR = 1.28), lower AD (ALIC: HR = 0.72, external capsule [EC]: HR = 0.73, superior fronto‐occipital fasciculus [SFO]: HR = 0.70), and greater peak width of AK (ALIC: HR = 1.29, EC: HR = 1.24, genu of the corpus callosum [GCC]: HR = 1.32) were associated with earlier CRCI onset. Lower FA, RK, and MK ( β = 0.13 to 0.27 × 10 ⁻³ ), higher MD, RD, ODI, and ISOVF ( β = −0.23 to −0.13 × 10 ⁻³ ), and higher peak width metrics ( β = −0.34 to −0.13 × 10 ⁻³ ) were associated with poorer longitudinal cognitive trajectories. Conclusions: Pretreatment white matter microstructure may help identify patients at greater risk of CRCI and poorer cognitive trajectories. Level of Evidence: 2. Technical Efficacy: Stage 3.

MRpro is an open‐source framework for MR image reconstruction and quantitative parameter estimation, with particular relevance to low‐field MRI. Built on PyTorch, it supports modern deep‐learning reconstructions and standardized data exchange through ISMRMRD for raw k‐space data and DICOM and NIfTI for reconstructed images and parameter maps. This facilitates integration into existing pipelines and the use of data from different devices. MRpro provides unified data structures for the consistent manipulation of MR datasets and associated metadata, together with a library of composable operators, proximable functionals, and optimization algorithms. The operator library includes a unified Fourier operator for all common trajectories and operators specifically developed for low‐field applications, such as a B‐correction operator. Together, these components underpin ready‐to‐use implementations of key reconstruction algorithms. For deep learning, MRpro includes data‐consistency layers, differentiable optimization layers, state‐of‐the‐art backbone networks, and access to public datasets to facilitate reproducibility. Automated quality control supports collaborative development. We demonstrate MRpro for automatic reconstruction, iterative SENSE, deep‐learning‐based reconstruction, and quantitative parameter estimation. For selected Cartesian and non‐Cartesian tasks, iterative SENSE reconstructions from MRpro differed from those obtained with BART, SigPy, and MRIReco by less than 3% relative RMSE. Further applications use public and simulated datasets and measured low‐field data acquired at 0.3 T, 0.6 T, and 47 mT. MRpro provides a reproducible and maintainable foundation for future MR imaging research.

Current aortic magnetic‐resonance‐elastography (MRE) infers wall stiffness indirectly from waveguide effects. This study aimed to evaluate the feasibility of abdominal aortic MRE for direct assessment of wall stiffness and its dependence on segmental region, patient position, and hemodynamic parameters. This prospective study examined 11 healthy young male volunteers using MRE to evaluate two anatomical segments of the abdominal aorta: the supraceliac (SC) and infrarenal (IR) segments. The volunteers underwent examinations in both the supine and prone positions. An ECG trigger delay was implemented for MRE to minimize the blood signal in the lumen. Three‐dimensional wavefields were acquired at three mechanical frequencies (70, 80, and 90 Hz), and shear wave speed (SWS) maps were reconstructed as a stiffness surrogate. In addition, blood flow‐velocity MRI was performed assess hemodynamic parameters. Test–retest examinations were performed in a subgroup to evaluate repeatability of aortic MRE. Aortic wall MRE was successful in all participants with good repeatability (ICC = 0.76). Mean wall SWS was higher in SC than IR segments at supine (1.47 ± 0.07 vs. 1.39 ± 0.13 m/s, p = 0.035) and prone positions (1.40 ± 0.09 vs. 1.33 ± 0.10 m/s, p = 0.046) with higher values in supine SC segments ( p = 0.019). Flow measurements revealed higher peak velocity and peak flow in SC than IR segments (all p < 0.001) with significant effects of examination position on peak velocity and cross‐sectional area in SC (all p < 0.05). In prone position, peak velocity correlated negatively with aorta stiffness (SC: r = −0.75, p = 0.008; IR: r = −0.69, p = 0.019). This study demonstrated abdominal aortic MRE for directly depicting wall stiffness with minimal blood contribution. Segment‐specific aortic wall stiffness was sensitive to circulatory loading and vascular distensibility. In a clinical context, high‐resolution multifrequency MRE could serve as an imaging marker for vascular health and abdominal aortic risk stratification.

BACKGROUND AND PURPOSE: Exploring the directionality of neural information in the brain is important for understanding brain mechanisms and neurodisease development. Granger causality analysis and the metabolic connectivity map can be used to investigate directional transmission of information between brain regions, but their differences in depicting functional effective connectivity are not clear. MATERIALS AND METHODS: Using the Monash rs-PET/MR imaging data set, we conducted Granger causality and metabolic connectivity map analyses of the dopamine reward circuit in the brain. The dopamine reward circuit is a well-known system consisting primarily of the bilateral orbital frontal cortex, caudate, nucleus accumbens, thalamus, and substantia nigra. We validated these circuit pathways using Granger causality and the metabolic connectivity map for identifying effective connectivities against a priori knowledge by testing the significance of directed pathways (P < .05, false discovery rate–corrected). RESULTS: We found 3 types of effective connectivities in the dopamine reward circuit: long-range, neighborhood, and symmetric. Granger causality analysis revealed long-range connections in the orbital frontal cortex–caudate and orbital frontal cortex–nucleus accumbens regions. Metabolic connectivity map analysis revealed neighborhood connections in the nucleus accumbens–caudate, substantia nigra–thalamus, and thalamus-caudate regions. Metabolic connectivity map analysis also found symmetric connections in each of the bilateral nucleus accumbens, caudate, thalamus, and orbital frontal cortex–caudate regions. Different patterns in directional networks of the dopamine reward circuit were revealed by Granger causality and metabolic connectivity map analyses. CONCLUSIONS: Granger causality analysis primarily identified bidirectional cortico-nucleus connections, while the metabolic connectivity map primarily identified direct connections among neighborhood and symmetric regions. The results of this study indicated that investigations of effective connectivities should use an appropriate analysis method depending on the purpose of the study.