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Blood–brain barrier (BBB) dysfunction is a hallmark of cerebral small vessel disease (cSVD). This study aimed to identify a mouse model that replicates BBB impairment and shares key cSVD risk factors. Transgenic db/db and LDLr−/−.Leiden mice, both prone to obesity and hypertension, were compared to C57BL/6J controls. BBB leakage was assessed using DCE-MRI and sodium fluorescein (NaFl); cerebral blood flow (CBF) by MRI. Dyslipidemia and vascular inflammation were measured by plasma tests. Tight junction integrity, endothelial dysfunction (glucose transporter 1, GLUT-1) and neuroinflammation were evaluated with immunohistochemistry and PCR. Both transgenic models developed an obese phenotype with hyperinsulinemia, but only LDLr−/−.Leiden mice showed human-like dyslipidemia. When fed a high-fat diet (HFD) or HFD plus cholesterol, LDLr−/−.Leiden mice showed reduced CBF, endothelial dysfunction (lowered GLUT-1), elevated vascular inflammation (ICAM-1, VCAM-1, S-selectin), and BBB leakage, as evidenced by DCE-MRI and NaFl, together with reduced ZO-1 and claudin-5 expression. Contrastingly, db/db mice showed endothelial dysfunction without BBB leakage. Neuroinflammation (IBA-1, GFAP) was observed only in LDLr−/−.Leiden groups, consistent with BBB disruption. These findings indicate that LDLr−/−.Leiden mice, but not db/db mice, are a promising translational model for studying BBB dysfunction in cSVD, offering insights into disease mechanisms and a platform for therapeutic development.

Abstract Dynamic contrast-enhanced MRI (DCE-MRI) is routinely included in the prostate MRI protocol for a long time; its role has been questioned. It provides rich spatial and temporal information. However, the contained information cannot be fully extracted in radiologists’ visual evaluation. More sophisticated computer algorithms are needed to extract the higher-order information. The purpose of this study was to apply a new deep learning algorithm, the bi-directional convolutional long short-term memory (CLSTM) network, and the radiomics analysis for differential diagnosis of PCa and benign prostatic hyperplasia (BPH). To systematically investigate the optimal amount of peritumoral tissue for improving diagnosis, a total of 9 ROIs were delineated by using 3 different methods. The results showed that bi-directional CLSTM with ± 20% region growing peritumoral ROI achieved the mean AUC of 0.89, better than the mean AUC of 0.84 by using the tumor alone without any peritumoral tissue (p = 0.25, not significant). For all 9 ROIs, deep learning had higher AUC than radiomics, but only reaching the significant difference for ± 20% region growing peritumoral ROI (0.89 vs. 0.79, p = 0.04). In conclusion, the kinetic information extracted from DCE-MRI using bi-directional CLSTM may provide helpful supplementary information for diagnosis of PCa.

Background Diffusion-weighted imaging (DWI) can increase breast MRI diagnostic specificity due to the tendency of malignancies to restrict diffusion. Diffusion tensor imaging (DTI) provides further information over conventional DWI regarding diffusion directionality and anisotropy. Our study evaluates DTI features of suspicious breast lesions detected on MRI to determine the added diagnostic value of DTI for breast imaging . Methods With IRB approval, we prospectively enrolled patients over a 3-year period who had suspicious (BI-RADS category 4 or 5) MRI-detected breast lesions with histopathological results. Patients underwent multiparametric 3 T MRI with dynamic contrast-enhanced (DCE) and DTI sequences. Clinical factors (age, menopausal status, breast density, clinical indication, background parenchymal enhancement) and DCE-MRI lesion parameters (size, type, presence of washout, BI-RADS category) were recorded prospectively by interpreting radiologists. DTI parameters (apparent diffusion coefficient [ADC], fractional anisotropy [FA], axial diffusivity [ λ 1 ], radial diffusivity [( λ 2  +  λ 3 )/2], and empirical difference [ λ 1  −  λ 3 ]) were measured retrospectively. Generalized estimating equations (GEE) and least absolute shrinkage and selection operator (LASSO) methods were used for univariate and multivariate logistic regression, respectively. Diagnostic performance was internally validated using the area under the curve (AUC) with bootstrap adjustment. Results The study included 238 suspicious breast lesions (95 malignant, 143 benign) in 194 women. In univariate analysis, lower ADC, axial diffusivity, and radial diffusivity were associated with malignancy (OR = 0.37–0.42 per 1-SD increase, p  < 0.001 for each), as was higher FA (OR = 1.45, p  = 0.007). In multivariate analysis, LASSO selected only ADC (OR = 0.41) as a predictor for a DTI-only model, while both ADC (OR = 0.41) and FA (OR = 0.88) were selected for a model combining clinical and imaging parameters. Post-hoc analysis revealed varying association of FA with malignancy depending on the lesion type. The combined model (AUC = 0.81) had a significantly better performance than Clinical/DCE-MRI-only (AUC = 0.76, p  < 0.001) and DTI-only (AUC = 0.75, p  = 0.002) models. Conclusions DTI significantly improves diagnostic performance in multivariate modeling. ADC is the most important diffusion parameter for distinguishing benign and malignant breast lesions, while anisotropy measures may help further characterize tumor microstructure and microenvironment.

Myocardial perfusion MRI is important for diagnosing coronary artery disease, but current clinical methods face challenges in balancing spatial resolution, temporal resolution, and slice coverage. Achieving broader slice coverage and higher temporal resolution is essential for accurately detecting abnormalities across different slice locations but remains difficult due to constraints in acquisition speed and heart rate variability. While techniques like parallel imaging and compressed sensing have significantly advanced perfusion imaging, they still suffer from noise amplification, residual artifacts, and potential temporal blurring due to the rapid transit of dynamic contrast vs. the temporal constraints of the reconstruction. This study introduces a conditional diffusion-based generative model for myocardial perfusion MRI super resolution, addressing the trade-offs between spatiotemporal resolution and slice coverage. We adapted Denoising Diffusion Probabilistic Models (DDPM) to enhance low-resolution perfusion images into high-resolution outputs without requiring temporal regularization. The forward diffusion process introduces Gaussian noise incrementally, while the reverse process employs a U-Net architecture to progressively denoise the images, conditioned on the low-resolution input image. We trained and validated the model on a retrospective dataset of dynamic contrast-enhanced (DCE) perfusion MRI, consisting of both stress and rest images from 47 patients with heart disease. Our results showed significant image quality improvements, with a 5.1% reduction in nRMSE, a 1.1% increase in PSNR, and a 2.2% boost in SSIM compared to GAN-based super-resolution method (  < 0.05 for all metrics using paired -test) in retrospective study. For the 9 prospective subjects, we achieved a total nominal acceleration of 8.5-fold across 5-6 slices through a combination of low-resolution acquisition and GRAPPA. PerfGen outperformed GAN-based approach in sharpness (4.36 ± 0.38 vs. 4.89 ± 0.22) and overall image quality (4.14 ± 0.28 vs. 4.89 ± 0.22), as assessed by two experts in a blinded evaluation (  < 0.05) in prospective study. This work demonstrates the capability of diffusion-based generative models in generating high-resolution myocardial perfusion MRI from conditional low-resolution images. This approach has shown the potentials to accelerate myocardial perfusion MRI while enhancing slice coverage and temporal resolution, offering a promising alternative to existing methods.

Advances in MRI techniques enable cerebral barrier transfer rates (K ) measurement in patients with vascular cognitive impairment and dementia (VCID). However, a consensus has not been reached on the dynamic contribution and importance of cerebral barrier abnormalities to the differential diagnosis of dementia subtypes. Our goal was to investigate the dynamics of blood-brain barrier (BBB) and blood-CSF barrier (BCSFB) K in patients with VCID longitudinally and determine the effect of aging. We studied subjects at two time points over two years; they were 65.5 years of age (SD = 15.94, M/F = 24/14) at the first visit. We studied 38 patients, 18 of whom had two visits. We calculated the BBB and BCSFB K with dynamic contrast-enhanced T1 MR, and we used H-MR spectroscopy to measure N-acetylaspartate (NAA) levels in the white matter as a marker of injury. In addition, we measured CSF levels of active-matrix metalloproteinase-3 (MMP3) as an inflammatory biomarker to aid in patient clustering. Longitudinal BBB measurements revealed variable dynamic behavior: after two years, the BBB K increased in 55% of patients and decreased in the remaining 45% unpredictably. We did not find a significant linear model of BBB K versus age for VCID. For healthy controls, the model was K = 0.0014 + 0.0002 × age, which was significant ( = 0.046). VCID patients showed a reduction in BCSFB K compared to healthy controls ( = 0.01). Combining NAA, CSF MMP3, and K in a clustering analysis separated patients into groups. These results suggest that BBB K in VCID is dynamic and BCSFB K reduced by age. By combining inflammatory biomarkers with BBB K data, it is possible to separate VCID patients into distinct groups with different underlying pathologies.

Background: The intratumoral heterogeneity of oxygen metabolism and angiogenesis are core hallmarks of glioma, unveiling that genetic aberrations associated with magnetic resonance imaging (MRI) phenotypes may aid in the diagnosis and treatment of glioma. Objective: To explore the predictability of MRI-based oxygen extraction fraction (OEF) mapping using cluster analysis of time evolution (CAT) for genetic profiling and glioma grading. Methods: Ninety-one patients with histopathologically confirmed glioma were examined with CAT for quantitative susceptibility mapping and quantitative blood oxygen level–dependent magnitude-based OEF mapping and dynamic contrast-enhanced (DCE) MRI. Imaging biomarkers, including oxygen metabolism (OEF) and angiogenesis [volume transfer constant, cerebral blood volume (CBV), and cerebral blood flow], were investigated to predict IDH mutation, O 6 -methylguanine-DNA-methyltransferase (MGMT) promoter methylation status, receptor tyrosine kinase (RTK) subgroup, and differentiation of glioblastoma (GBM) vs. lower-grade glioma (LGG). The corresponding DNA sequencing was also obtained. Results were compared with DCE-MRI using receiver operating characteristic (ROC) analysis. Results: IDH1-mutated LGGs exhibited significantly lower OEF and hypoperfusion than IDH wild-type tumors (all p < 0.01). OEF and perfusion metrics showed a tendency toward higher values in MGMT unmethylated GBM, but only OEF retained significance ( p = 0.01). Relative prevalence of RTK alterations was associated with increased OEF ( p = 0.003) and perfusion values ( p < 0.05). ROC analysis suggested OEF achieved best performance for IDH mutation detection [area under the curve (AUC) = 0.828]. None of the investigated parameters enabled prediction of MGMT status except OEF with a moderate AUC of 0.784. Predictive value for RTK subgroup was acceptable by using OEF (AUC = 0.764) and CBV (AUC = 0.754). OEF and perfusion metrics demonstrated excellent performance in glioma grading. Moreover, mutational landscape revealed hypoxia or angiogenesis-relevant gene signatures were associated with specific imaging phenotypes. Conclusion: CAT for MRI-based OEF mapping is a promising technology for oxygen measurement and along with perfusion MRI can predict genetic profiles and tumor grade in a non-invasive and clinically relevant manner. Clinical Impact: Physiological imaging provides an in vivo portrait of genetic alterations in glioma and offers a potential strategy for non-invasively selecting patients for individualized therapies.

Early identification of therapeutic response and tumor proliferative status is essential in nasopharyngeal carcinoma (NPC). Multiparametric MRI-combining IVIM and DCE-provides quantitative biomarkers reflecting tissue diffusion, perfusion, and vascular permeability. We evaluated whether pre-treatment IVIM- and DCE-derived parameters predict short-term response to induction chemotherapy plus concurrent chemoradiotherapy and whether they correlate with tumor proliferation (Ki-67). In this prospective study (n = 48; January 2021-January 2023), IVIM parameters (D, D*, f) and DCE parameters (K , K , V , V ) were quantified before treatment. Treatment response at 6 months was classified by RECIST 1.1 as complete response (CR) or non-CR. Ki-67 index was determined by immunohistochemistry (cutoff 50%). Group comparisons used t-tests or Mann-Whitney U tests; logistic regression identified independent predictors; ROC analysis evaluated diagnostic performance; Spearman correlation tested associations with Ki-67. Pre-treatment D was lower in the CR group (0.82 ± 0.12 vs. 0.92 ± 0.11 ×10 mm /s; = 0.007). K and K were higher in CR (0.95 ± 0.34 vs. 0.30 ± 0.31 min , = 0.014; 0.16 ± 0.09 vs. 0.11 ± 0.06 min , = 0.025). D was an independent predictor ( = 0.008). The combined model (D + K + K ) yielded AUC = 0.834 (sensitivity 90.0%; specificity 61.4%). Ki-67 correlated negatively with D (r = -0.329, = 0.022) and positively with V (r = 0.292, = 0.044). Multiparametric MRI can noninvasively predict short-term response and reflect proliferative status in NPC. Integrating IVIM-derived D with DCE-derived K and K improves early prediction of treatment efficacy; V may serve as an imaging surrogate for proliferation.

Osteoarthritis (OA) is a leading cause of disability worldwide, with its prevalence rising due to aging populations. Management ranges from conservative treatments such as weight management and pharmacologic therapy to surgical interventions such as total joint replacement. However, treating moderate knee OA remains challenging for patients unresponsive to conservative care but not yet surgical candidates. Genicular artery embolization (GAE) has emerged as a minimally invasive procedure targeting abnormal angiogenesis and inflammation in OA. This article explores GAE’s mechanism, patient-selection criteria, and effectiveness in pain reduction and functional improvement. Studies suggest that GAE has the potential to significantly improve pain and function in mild to moderate OA, with sustained benefits. Patient selection is crucial for optimal outcomes, with imaging playing a key role. While conventional MRI assesses structural damage, Dynamic Contrast-Enhanced MRI (DCE-MRI) offers superior insights by evaluating synovitis, quantifying cartilage degradation, and monitoring treatment response. Due to its strong correlation with pain scores and status as the best surrogate marker for inflammation in synovitis, DCE-MRI holds significant potential to enhance patient selection and treatment monitoring for GAE.

Introduction Breast cancer patients treated with neoadjuvant chemotherapy (NACT) are at risk of recurrence depending on clinicopathological characteristics. This preliminary study aimed to investigate the predictive performances of quantitative dynamic contrast-enhanced (DCE) MRI parameters, alone and in combination with clinicopathological variables, for prediction of recurrence in patients treated with NACT. Methods Forty-seven patients underwent pre- and post-NACT MRI exams including high spatiotemporal resolution DCE-MRI. The Shutter-Speed model was employed to perform pharmacokinetic analysis of the DCE-MRI data and estimate the K trans , v e , k ep , and τ i parameters. Univariable logistic regression was used to assess predictive accuracy for recurrence for each MRI metric, while Firth logistic regression was used to evaluate predictive performances for models with multi-clinicopathological variables and in combination with a single MRI metric or the first principal components of all MRI metrics. Results Pre- and post-NACT DCE-MRI parameters performed better than tumor size measurement in prediction of recurrence, whether alone or in combination with clinicopathological variables. Combining post-NACT K trans with residual cancer burden and age showed the best improvement in predictive performance with ROC AUC = 0.965. Conclusion Accurate prediction of recurrence pre- and/or post-NACT through integration of imaging markers and clinicopathological variables may help improve clinical decision making in adjusting NACT and/or adjuvant treatment regimens to reduce the risk of recurrence and improve survival outcome.

Background/Objectives: The Oncotype DX recurrence score (ODXRS) has emerged as an important tool for predicting recurrence risk and guiding treatment decisions in estrogen receptor-positive, human epidermal growth factor receptor 2-negative early-stage breast cancer. This review summarizes the current evidence on the clinical utility of the Oncotype DX RS and explores emerging research on potential imaging-based alternatives. The 21-gene assay provides a recurrence score that stratifies patients into low, intermediate, and high-risk groups, helping to identify patients who may benefit from adjuvant chemotherapy. Multiple validation studies have demonstrated the prognostic and predictive value of the ODXRS. However, the test is costly and requires tumor tissue samples. Methods: This paper systemically reviewed the current literature on the use of radiomic analysis of breast MRI to predict Oncotype DX. The literature search was performed from 2016 to 2024 using PubMed. We compared different image types, methods of analysis, sample size, numbers of high/intermediate and low scores, MRI image types, performance indices, among others. We also discussed lessons learned and suggested future research directions. Results: Recent studies have investigated the potential of radiomics applied to breast MRI to non-invasively predict the Oncotype DX RS. Quantitative imaging features extracted from dynamic contrast-enhanced MRI, diffusion-weighted imaging, and T2-weighted sequences have shown promise for distinguishing between low and high RS groups. Multiparametric MRI-based models integrating multiple sequences have achieved the highest performance. Conclusions: While further validation is needed, MRI radiomics may offer a non-invasive, cost-effective alternative for assessing recurrence risk.