Search Results (1,034)

Objectives: This study evaluates the protocol-level image quality and quantitative diffusion metrics of a clinically implemented deep-learning–reconstructed readout-segmented echo-planar imaging protocol with water-excitation spectral fat saturation (DL-rs-EPI with WEXfs) compared with conventional rs-EPI using spectral attenuated inversion recovery (SPAIR) at 3 T. Methods: Overall, 80 patients underwent breast magnetic resonance imaging (MRI) with both conventional rs-EPI with SPAIR and DL-rs-EPI with WEXfs protocols (b-values: 0, 800, and 1200 s/mm²). ROI-based relative image-quality metrics, including signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and lesion contrast, were assessed at b = 800 and b = 1200 s/mm²; apparent diffusion coefficient (ADC) values were calculated using multi-b-value data. Fat suppression, background diffusion signal, lesion conspicuity, and artifact severity were qualitatively evaluated. A temperature-controlled diffusion phantom (CaliberMRI) was scanned; ADC values were compared with reference values at 24 °C. Results: DL-rs-EPI with WEXfs demonstrated higher ROI-based relative SNR estimates (b800: 5.79 vs. 5.28; b1200: 5.41 vs. 4.94; p < 0.001) and CNR estimates (b800: 3.35 vs. 3.12, p = 0.024; b1200: 3.67 vs. 3.37, p = 0.001), with unchanged lesion contrast. Tumor ADC values were comparable between protocols, whereas normal fibroglandular tissue ADC values were slightly higher, and ADC contrast increased with DL-rs-EPI with WEXfs. Phantom ADC values from both protocols closely matched reference values at 24 °C, without significant differences. DL-rs-EPI with WEXfs demonstrated more homogeneous fat suppression and reduced background diffusion signal, with comparable lesion conspicuity and artifact severity. Conclusions: The combined DL-rs-EPI with WEXfs protocol demonstrated improved qualitative and relative quantitative image quality while preserving tumor ADC measurements. As a protocol-level evaluation, these composite improvements support its clinical feasibility for high-quality breast DWI without implying the isolated effect of DL reconstruction alone. Full article

(1) Background: This study aimed to evaluate hypothalamic–hypophyseal (HH) axis involvement in Primary Sjögren’s syndrome (pSS) using MRI and assess its relationship with hypothalamic–pituitary–adrenal (HPA) axis dysfunction. (2) Methods: A total of 22 postmenopausal women with pSS and 17 healthy controls were enrolled. Midline sagittal T1-weighted MRI was used to measure pituitary gland height (PGH). Dynamic contrast-enhanced imaging assessed hypothalamic–hypophyseal (HH) microcirculation, while diffusion tensor imaging (DTI) evaluated hypothalamic microstructure. Biochemical variables, including cortisol and complement factors, were measured. Linear regression analysis was performed to identify predictors of PGH. (3) Results: Patients had a mean disease duration of 11.5 ± 6.7 years. PGH was significantly different in patients than in controls (3.6 ± 1.1 mm vs. 4.4 ± 0.6 mm, p = 0.004). Cortisol levels were also reduced (8.9 ± 4.6 µg/dL vs. 12.6 ± 4.7 µg/dL, p = 0.040), while ACTH levels were not significantly different. Dynamic imaging demonstrated delayed enhancement of the anterior pituitary lobe. DTI revealed no hypothalamic microstructural abnormalities. PGH was positively associated with C3 (p = 0.029). (4) Conclusions: pSS is associated with pituitary structural and functional alterations consistent with HPA axis hypofunction, likely reflecting immune-mediated pituitary involvement with preserved hypothalamic integrity. Full article

Introduction: Currently, chronic kidney disease (CKD) is detected based on glomerular filtration rate (GFR), proteinuria levels or kidney biopsy. However, the development of MRI techniques and AI algorithms gives hope to the assessment of CKD activity and kidney function with profound MRI image analysis. Methods: MRI images from healthy volunteers with no history of CKD were compared with those from CKD patients who had undergone both kidney MRI and kidney biopsy; the latter group was also divided into two subgroups based on CKD histopathological activity. Patients from both groups were scanned using either a 1.5 T or 3 T MRI scanner following sequential allocation (nine healthy controls and 28 CKD patients and 11 healthy volunteers and 43 CKD patients respectively for each scanner). Results: The final algorithm based on T1-weighted, T2-weighted and DWI images was able to distinguish patients with sensitivity ranging 77.78–87.50%, specificity 86.67–94.12% and precision 77.78–87.50%. Features of T1-weighted images and of T2-weighted images were found to correlate strongly with GFR with coefficients ranging from −0.5922 to −0.7090 and from 0.6126 to 0.6380, respectively. Conclusions: MRI image texture analysis may be suitable for assessing CKD activity, irrespective of the type of MRI scanner used. Furthermore, MRI image texture features correlate with eGFR values. Full article

Pathological complete response (pCR) after neoadjuvant chemotherapy (NACT) provides an endpoint for treatment evaluation in breast cancer. Multi-sequence breast MRI can support pCR prediction, but routine examinations may lack usable T1-weighted or T2-weighted sequences. Many models merge radiomic and deep features by concatenation, leaving the interaction between handcrafted descriptors and learned representations weakly specified. We developed a radiomics-guided framework for pCR prediction from multi-sequence breast MRI. The model uses a multi-branch 2.5D encoder for sequence-specific features, radiomics-guided channel recalibration, and masked token fusion to aggregate available sequence tokens. We evaluated the framework on 157 patients from the I-SPY1 Trial cohort with patient-level five-fold cross-validation, fixed sequence-combination analysis, and slice-window sensitivity analysis. The full model achieved 78.4% accuracy and 0.809 AUC, compared with 75.8% accuracy and 0.788 AUC for the strongest channel-concatenation baseline. In this cohort, radiomics-guided multi-sequence learning was feasible, with external validation required before clinical interpretation. Full article

Background/Objectives: Apparent diffusion coefficient (ADC) calculated from diffusion-weighted MRI (DWI) can predict tumor response to neoadjuvant chemotherapy for breast cancer. However, obtaining consistently adequate image quality in breast DWI can be challenging, and the effect of image quality on ADC’s predictive performance is unclear. The objective of this study was to evaluate inter-reader variability in image quality assessment and the effect of DWI image quality on the predictive performance of ADC. Methods: This multi-institutional study included 428 patients. Two readers assessed three DWI image quality factors—fat suppression, artifacts, and signal-to-noise ratio (SNR). Inter-reader agreement was estimated using Fleiss’ Kappa. The percent change in tumor ADC from pretreatment (T0) to early treatment (T1) was used to predict pathologic complete response (pCR), assessed at surgery. Results: Out of 428 patients, 134 were excluded (missing pCR [n = 17]; missing/incorrect DWI [n = 23]; inability to define region-of-interest [ROI, n = 94]) and 294 were included in the analysis. Kappa coefficients were estimated as: 0.47 (95% confidence interval [CI]: 0.42, 0.52) for fat suppression, 0.54 (0.50, 0.59) for artifact, and 0.38 (0.32, 0.44) for SNR. The AUC of ADC calculated from DWI with adequate (high or medium at both time points) image quality was 0.61 (95% CI: 0.52, 0.702), while it was 0.68 (95% CI: 0.53, 0.83) from DWI with inadequate image quality at either T0 or T1. The p-value for the difference in AUCs was 0.45. Conclusions: The inter-reader agreement was moderate to fair across all three quality categories. When a manually delineated tumor ROI was possible, no statistically significant difference in ADC predictive performance was observed between the quality-adequate and quality-inadequate cohorts; still, both were predictive of pCR. Furthermore, no statistically significant differences were observed in inter-reader agreement or ADC predictive performance between 1.5T and 3T scanners. These findings are clinically relevant to the use of ADC as an imaging biomarker in real-world conditions. Full article

Background: Radiology underpins diagnosis and treatment across pediatrics, yet most artificial intelligence (AI) tools are developed for adults and validated on adult datasets only. Of more than 200 AI systems cleared by the United States (U.S.) Food and Drug Administration (FDA), only about 3% include pediatric validation. Because children differ from adults in anatomy, physiology, pathology, epidemiology, and imaging protocols, adult-trained models often perform sub-optimally in pediatric settings. Methods: A narrative review of peer-reviewed literature from 2000 to 2025 was conducted using PubMed, MEDLINE, Google Scholar, and Scopus. Studies involving AI applications in pediatric X-ray, ultrasound, computed tomography (CT), magnetic resonance imaging (MRI), echocardiography, and point-of-care ultrasound with quantitative performance metrics were included. Findings were synthesized by imaging modality, clinical task, and differences between high-income countries (HICs) and low- and middle-income countries (LMICs). Results: AI demonstrated strong performance across multiple pediatric imaging tasks. In X-ray interpretation, AI detected fractures with area under the curve (AUC) values up to 0.96 (sensitivity, 90.8%; specificity, 88.7%). Pneumonia classification achieved 76.5% accuracy, and foreign body aspiration detection showed 95.3% specificity in HICs. In ultrasound, AI improved junior sonographers’ detection of intussusception (AUC 0.857 to 0.966) and reduced scan time by more than 50%. AI-assisted bone age estimation achieved a mean error of 0.39 years. In echocardiography, AI-derived ejection fraction showed excellent agreement with experts’ interclass correlation coefficient (ICC 0.983), and AI support improved atrioventricular septal defect detection (84.4% to 86.5%). In MRI, the use of AI enhanced lesion detection and supported quantitative analysis. Deep-learning models trained on routine T1- and T2-weighted sequences predicted liver stiffness across multi-site datasets, while advanced neuroimaging pipelines improved the identification of subtle epileptogenic lesions that are often missed on conventional pediatric MRI. However, adult-trained models showed limited generalizability to children. Still, excluding children under the age of two years improved the reading accuracy of pediatric chest X-rays (CXRs) by adult-trained models from 88% to 97%. AI faces challenges beyond the development of age-specific models. Substantial heterogeneity, limited pediatric-specific datasets, and unresolved medicolegal responsibility further restrict adoption worldwide. Challenges are amplified in LMICs, where unstable electricity, limited radiology resources, weak digital infrastructure, and scarce pediatric providers limit implementation. Additionally, many large language models underperform and lack inclusive algorithms suitable for pediatric radiology in many LMICs. Conclusions: AI can enhance diagnostic accuracy, efficiency, and access to pediatric imaging, particularly in resource-limited settings, through task-shifting and decision support. However, it cannot replace pediatric radiologists as of today. Safe adoption requires pediatric-specific model development, standardized validation metrics, diverse datasets that include LMIC populations, stronger digital infrastructure, robust radiologist training in AI capabilities, and the establishment of clear guidelines and medicolegal policies. Full article

Brain tumor segmentation from 3D MRI presents significant challenges due to small lesion sizes, ambiguous boundaries, arbitrary spatial distributions, and heterogeneous morphological properties. To tackle these issues, this paper presents a fully automatic 3D brain tumor segmentation network that integrates morphological and anatomical information under a multi-task learning framework for whole tumor, tumor core, and enhanced tumor segmentation. We propose a multimodal feature fusion module to adaptively weight features from four MRI modalities (T1, T1ce, T2, FLAIR), enabling discriminative information integration and helping reduce modality intensity discrepancy and data imbalance. Furthermore, a ConvReXt downsampling module is introduced to preserve fine-grained semantic details by reducing information loss caused by conventional pooling. A dense parallel global attention module is also developed to capture both local details and long-range dependencies, addressing the limited receptive field of standard convolutions. Extensive experiments on the BraTS2020 dataset show that the proposed model obtains average Dice coefficients of 92.54%, 89.21%, and 86.54% for whole tumors, tumor cores, and enhanced tumors. The proposed model achieves competitive performance compared with state-of-the-art methods including nnFormer, validating that it can effectively fuse multimodal and multi-scale features and improve brain tumor segmentation accuracy. Full article

Objectives: This study evaluated the utility of adaptive complex signal average (ACSA) diffusion-weighted imaging (DWI) specifically in breath-hold (BH) liver imaging, with a focus on signal intensity (SI) improvement, intrahepatic signal homogeneity, and apparent diffusion coefficient (ADC) behavior, and compared these findings with conventional non-ACSA DWI and free-breathing (FB) ACSA DWI. Methods: This retrospective study included 62 patients (mean age, 67.8 ± 13.6 years; 27 women) who underwent liver MRI with both FB and BH DWI on a 3-T system. Non-ACSA images were generated using conventional magnitude reconstruction, and ACSA images were reconstructed from identical raw data. SI, signal-to-noise ratio (SNR) and ADC were measured in the left lateral segment and right hepatic lobe. The signal intensity difference ratio (SIDR) between ACSA and non-ACSA, signal intensity ratio (SIR) and ADC ratio between right lobe and lateral segment were calculated. Results: In both FB and BH imaging, SI and SNR in both liver regions were significantly higher on ACSA DWI than on non-ACSA DWI (p < 0.01). ADC values were significantly lower with ACSA. SIDR was significantly higher in the left lateral segment (p < 0.01), indicating greater SI improvement in motion-prone regions. SIR and ADC ratios between lobes were significantly smaller with ACSA in both respiratory conditions (p < 0.01). FB-ACSA showed smaller SIR than BH-ACSA, while ADC ratios did not differ. Conclusions: ACSA DWI significantly improves SI, intrahepatic uniformity, and ADC reliability even under BH liver imaging. BH ACSA DWI may represent a potentially useful application complementary to FB ACSA DWI, supporting its consideration as a post-processing strategy for improving qualitative and quantitative liver DWI in future investigations. Full article

Aphasia is a complex neurological syndrome that includes a multitude of signs and symptoms that describe a patient’s inability to use language (understanding and producing spoken and/or written language) after it has already been acquired, which is caused by cerebral lesions situated in the dominant (left) cerebral hemisphere in right-handed people. Aphasia has a prevalence of 25–30% in acute ischemic stroke (especially in arterial infarcts). In patients who suffered cerebral venous and dural sinuses thrombosis (CVST), aphasia has been noticed in almost 20% of cases, its presence being considered a negative predictive factor. We report the case of a 22-year-old right-handed woman with obesity and active smoking (10 cigarettes/day), undergoing treatment with oral contraceptives who presented to the Emergency Department with an intense headache, resistant to usual analgesic treatment, accompanied by language disorders onset within 24 h. The neurological examination was normal, except for language assessment, which revealed the severe impairment of the repetition domain (she was unable to repeat simple words), and difficulty in naming objects with some hesitations and mild comprehension difficulties (especially in complex orders). She underwent neuroimaging examinations at admission. Native Head Computed Tomography revealed spontaneous hyperdensity (parenchymatous hematoma) in the left temporal lobe. Cranial magnetic resonance imaging (MRI) confirmed venous infarction in the left temporal area and a hypointense signal on MRI T2*SW (susceptibility-weighted) in the region of the left lateral sinus and left jugular vein bulb, which confirmed the thrombosis at this level. Associated cortical vein thrombosis was diagnosed on indirect radiological grounds, since hemorrhagic transformation obscured the direct visualization of the adjacent cortical veins. MR venography was not performed at that time, but instead at the 1-month follow-up, MR venography confirmed the chronic, partial thrombosis of the left lateral sinus and left jugular vein bulb. Laboratory data demonstrated an elevated D-dimer and the presence of homozygosity for MTHFR C677T and PAI-1 4G/4G. Anticoagulation in the form of low-molecular-weight heparin was immediately started, followed by chronic treatment with oral anticoagulant (apixaban) and folic acid. The headaches resolved within three days, and her neurological examination was almost normal: the repetition continued being altered for complex phrases. We did not observe any left lateral sinus thrombosis recurrence, or other extra-cerebral embolic events (deep vein thrombosis or pulmonary embolism) during the follow-up year. The immediate anticoagulation since the admission resulted in a favorable outcome. Taking into consideration our interest in monitoring patients with aphasia secondary to CVST, we also analyzed data from the literature regarding the incidence of conduction aphasia and other aphasic syndromes in this CVST. Due to the limited number of articles identified in the last 21 years (2005–2026) in the literature, we concluded that conduction aphasia is an extremely rare clinical presentation in this kind of pathology and further studies should be conducted in order to identify significant statistical data. Full article

Background/Objectives: Brain tumor analysis using magnetic resonance imaging (MRI) remains a challenging task due to tumor heterogeneity, complex anatomical structures, and reliance on expert interpretation. Although deep learning approaches have shown promising results in medical image analysis, many existing studies focus on either tumor classification or segmentation independently, limiting their applicability in comprehensive automated brain tumor analysis workflows. This study proposes an integrated dual-task deep learning framework for automated brain tumor classification and segmentation using MRI scans. The framework aims to provide complementary diagnostic support by combining tumor-type prediction and tumor boundary delineation within an integrated workflow. Methods: The proposed framework utilizes EfficientNet-based convolutional neural networks for multi-class brain tumor classification and U-Net++ architectures with EfficientNet encoders for tumor segmentation. Experiments were conducted using the BRISC2025 dataset, consisting primarily of 6000 T1-weighted 2D MRI slices collected from axial, coronal, and sagittal planes. Standard preprocessing, augmentation, transfer learning, and selective fine-tuning strategies were applied. Multiple architectures were systematically evaluated using evaluation metrics. Results: EfficientNet-B1 achieved a classification accuracy of 99.70% with near-perfect precision, recall, and F1-scores across glioma, meningioma, pituitary tumor, and no-tumor classes. For segmentation, U-Net++ with an EfficientNet-B1 encoder achieved a Dice score of 0.9055, an IoU score of 0.8442, and an HD95 value of 12.21 pixels on the held-out test set. The proposed framework demonstrated robust performance in detecting small and low-contrast tumor regions while maintaining strong generalization performance across diverse MRI samples. Conclusions: The proposed integrated framework demonstrated strong performance in both brain tumor classification and segmentation tasks, effectively detecting small and low-contrast tumor regions while maintaining good generalization across diverse MRI samples. These findings suggest that the framework may serve as a reliable decision-support tool for automated brain tumor analysis in clinical practice. Full article

Background/Objectives: Contrast-enhanced (CE) breast MRI is highly sensitive for evaluating breast cancer extent and response to neoadjuvant therapy (NAT) but requires intravenous administration of gadolinium-based contrast agents (GBCA), increasing cost, time, patient discomfort, and health concerns. This study explored the feasibility of reducing GBCA use in treatment monitoring using a deep learning (DL) model to synthesize CE-MRI from non-contrast MRI. Methods: This IRB-approved retrospective pilot study evaluated women with breast cancer enrolled in an ongoing trial using serial MRI to monitor NAT prior to surgery. A pre-trained DL model was used to synthesize CE-MRI from T1-, T2-, and diffusion-weighted MRI. Changes in tumor volume at early (post-1-cycle NAT) and mid-treatment were measured on synthetic and acquired CE-MRI. Performance for predicting residual cancer burden (RCB) class 0/1 was evaluated using AUC and compared with DeLong’s test. Results: 27 women were included in the study (median age, 47 years [range = 28–75]); 14 (52%) achieved RCB class 0 and six (22%) achieved class 1. Synthetic CE-MRI-derived tumor volumes showed strong correlation with those from acquired CE-MRI at pre-treatment ($\rho$ = 0.92, p < 0.001) and early treatment ($\rho$ = 0.83, p < 0.001), but lower agreement at mid-treatment ($\rho$ = 0.57, p = 0.002). Change in tumor volume on synthetic CE-MRI was numerically similar to acquired CE-MRI for predicting RCB class 0/1 vs. 2/3 at both early (AUC = 0.84 vs. 0.86, p = 0.83) and mid-treatment (AUC = 0.73 vs. 0.75, p = 0.80). Conclusions: Synthetic CE-MRI demonstrates preliminary feasibility as a non-contrast surrogate for predicting favorable outcomes (RCB class 0/1) in this pilot study, but inconsistencies in tumor volume measurement vs. acquired CE-MRI warrant further model refinement and validation. Full article

Background: While disease-modifying therapies reduce inflammatory activity in multiple sclerosis (MS), long-term disability progression remains insufficiently controlled. Increasing evidence points to modifiable environmental and lifestyle factors—such as smoking, sun exposure, comorbidities, and obesity—as contributors to neurodegeneration and progression independent of relapse activity. Objective: To evaluate the associations between smoking, comorbid conditions, sun exposure, and obesity on clinical and radiological progression in patients with relapsing–remitting MS (RRMS) over a 48-month observational period. Methods: We performed a retrospective secondary analysis of a previously described longitudinal cohort of 132 patients with RRMS who were monitored over four years with serial assessments of EDSS, magnetic resonance imaging (MRI) inflammatory activity as gadolinium-enhancing lesions (GELs), new or enlarged T2-weighted lesions, serum 25(OH)D levels, and linear brain atrophy metrics. Sun exposure, smoking status, obesity, and comorbidity burden were recorded at each time point. Results: Low sun exposure was associated with higher EDSS trajectories and lower serum 25(OH)D levels (p < 0.01). Smoking was associated with a higher probability of GELs (p < 0.05), while comorbidities were associated with relapse occurrence and GELs. Obesity was associated with vitamin D insufficiency but not clearly with clinical relapse activity, GELs, or EDSS trajectories. MRI-based indices confirmed increasing brain atrophy during follow-up, particularly in patients with multiple risk factors. Conclusions: Our findings suggest that selected modifiable lifestyle and clinical factors are associated with distinct clinical and radiological outcomes in RRMS. Integrating sun-safe outdoor activity, smoking cessation, comorbidity management, and weight control into MS care may support comprehensive risk management alongside pharmacological therapy. Full article

Background/Objectives: Intramedullary tumors are uncommon spinal cord lesions that account for a small proportion of central nervous system neoplasms but are associated with a high risk of neurological morbidity. Accurate preoperative characterization is essential because therapeutic strategies, surgical planning, and functional prognosis depend strongly on tumor biology and growth behavior within the confined spinal cord environment. This study aims to characterize the radiological phenotype of intramedullary tumors and to identify imaging patterns that may assist in lesion characterization and diagnostic stratification. Methods: A retrospective analysis of preoperative MRI findings in patients with histopathologically confirmed intramedullary tumors was performed. Preoperative MRI examinations were systematically analyzed to describe imaging features according to tumor histology using conventional sequences (T1-weighted, T2-weighted, and contrast-enhanced imaging). Results: Distinct radiological phenotypes were observed across a wide spectrum of lesions. Glial tumors, including subependymoma, ependymoma, pilocytic astrocytoma, diffuse midline glioma H3K27M, glioblastoma, high-grade astrocytoma with piloid features, ganglioglioma, and diffuse leptomeningeal glioneural tumors, demonstrated variable combinations of cord expansion, margin definition, enhancement patterns, and tract involvement, reflecting differences between expansile and infiltrative growth. Secondary tumors such as metastases frequently exhibited aggressive imaging features, including extensive edema and intense or heterogeneous enhancement. Vascular lesions, including hemangioblastoma and cavernoma, showed characteristic vascular signatures, such as nodular enhancement with flow voids or susceptibility-related signal changes. Developmental lesions, such as epidermoid cysts, neurenteric cysts, and lipoma, displayed distinctive signal characteristics, especially on diffusion and T1, that aided differentiation from neoplastic processes. Conclusions: In conclusion, the structured radiological interpretation functions proposed herein are not only useful for diagnostic purposes, but could also be useful for risk stratification and therapeutic guidance. Full article

Objectives: Contrast-enhanced magnetic resonance imaging (CE-MRI) plays an important role in the diagnosis, treatment monitoring, and follow-up of brain tumors. However, the use of gadolinium-based contrast agents (GBCAs) is limited in patients with contraindications, such as severe renal impairment or situations requiring repeated examinations. This study aimed to develop a diffusion model-based Difference-Aware Guided Control Network (DAGCN) for synthesizing high-quality contrast-enhanced T1-weighted MRI (T1-CE) from non-contrast T1-weighted images in combination with an auxiliary sequence. Methods: Using the BraTS 2021 dataset, we proposed a two-stage generative framework that first localizes lesion-related enhancement cues and then guides image synthesis. In the first stage, a Difference-Aware Fusion and Prediction (DAFP) module was designed to extract complementary information from non-contrast T1-weighted images and an auxiliary sequence (T2-weighted or FLAIR) through dual-branch feature extraction and cross-modal channel attention fusion, followed by prediction of a lesion-related discrepancy map. In the second stage, the predicted discrepancy map was concatenated with the original T1-weighted images and introduced into a ControlNet-guided diffusion model to constrain the reverse denoising process and generate the target T1-CE image. Model performance was evaluated by visual comparison, quantitative metrics including peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), visual information fidelity (VIF), and normalized cross-correlation (NCC), as well as blinded radiologist scoring of image quality (IQ), clinical replaceability (IC), contrast enhancement (CE), and lesion conformity (CF). Results: DAGCN generated synthetic T1-CE images with preserved global anatomical structure and faithful local lesion enhancement without the need for contrast agent administration. Compared with baseline methods, DAGCN achieved the highest PSNR and NCC under both T1 + T2 and T1 + FLAIR settings, while showing competitive SSIM and VIF performance. Visual comparison and radiologist-based subjective evaluation further indicated improved lesion-focused enhancement fidelity and reduced false-positive enhancement. Among the two auxiliary sequence settings, the T1 + FLAIR configuration provided more specific lesion localization and cleaner background suppression than the T1 + T2 configuration, particularly by reducing interference from cerebrospinal fluid signals. Conclusions: The proposed DAGCN framework enables the synthesis of clinically informative contrast-enhanced-like MRI from non-contrast multi-sequence inputs and may provide a promising alternative for patients in whom gadolinium administration is contraindicated or should be avoided. In particular, the FLAIR-guided setting showed advantages in lesion specificity, background cleanliness, and overall diagnostic quality. Full article

Purpose: The aim of this study was to compare the contributions of putative glymphatic-related imaging indices—diffusion-weighted image analysis along the perivascular space (DWI-ALPS) index and choroid plexus volume (CPV)—and total cortical gray matter volume (TCGMV) to cognitive function in cerebral amyloid angiopathy (CAA). Methods: Forty-four CAA patients and 22 controls underwent 3.0T MRI. Cognitive function was assessed by the Mini-Mental State Examination (MMSE). The mean DWI-ALPS index, CPV/intracranial volume (ICV), and TCGMV/ICV were compared between groups; hierarchical multivariable regression and mediation analyses evaluated MMSE correlates. Results: Compared with controls, CAA showed a lower mean DWI-ALPS index and TCGMV/ICV (both adjusted p < 0.05), whereas CPV/ICV did not differ significantly after adjustment. In hierarchical multivariable regression analysis, mean DWI-ALPS index was associated with MMSE before adjustment for TCGMV/ICV (p = 0.022), but this association was attenuated after TCGMV/ICV was added to the model (p = 0.665). CPV/ICV was not associated with MMSE in either model, whereas TCGMV/ICV was independently associated with MMSE (p = 0.013). Exploratory mediation analysis suggested an indirect association between mean DWI-ALPS and MMSE via TCGMV/ICV (indirect: p = 0.023; direct: p = 0.720). Conclusions: Cortical atrophy appeared to be the strongest imaging correlate of cognitive impairment in CAA, while the association between DWI-ALPS and MMSE in multivariable models was attenuated after accounting for cortical gray matter volume. The ALPS index may provide indirect information on glymphatic-related pathways, but its biological specificity in CAA requires cautious interpretation because ALPS measurements may be influenced by underlying microstructural alterations in white matter. Full article