Search Results (962)

Background/Objectives: The heterogenous nature of renal cell carcinomas (RCCs) is increasingly recognized. The purpose of this proof-of-concept pilot study was to evaluate correlations between multiparametric MRI (mpMRI)-derived and histopathological parameters in RCCs from spatially matched regions on both MRI and pathological examination to support targeted biopsy planning. Methods: In this prospective single-center pilot study, patients with solid renal tumors ≥2 cm undergoing nephrectomy were prospectively enrolled. Each patient underwent preoperative 3.0T-mpMRI including T2-weighted and pre-/post-contrast T1-weighted sequences, chemical-shift imaging, IVIM-DWI, and T1/T2*/R2 mapping. Tumor regions were defined jointly by a pathologist and radiologist, and identical regions of interest were assessed for each tumor region across all sequences to gain quantitative mpMRI-derived parameters. Histopathology provided quantitative regional fractions of viable tumor, fibrosis, hemorrhage, and cystic/necrotic components. Spearman’s rank correlations and univariable linear regression assessed associations between mpMRI and histopathological parameters on a regional level. Results: Across 49 tumor regions in eight patients (65.3% clear cell, 34.7% papillary RCCs), the mean viable tumor fraction was 80.9% (SD 17.6). The viable tumor fraction showed inverse correlations with nephrographic and delayed phase signal intensity changes (rho = −0.59/rho = −0.51), T1 values (rho = −0.56), true diffusion coefficient D (rho = −0.47), and ADC (rho = −0.45), and a positive correlation with R² times (rho = 0.55). Delayed and nephrographic phase signal intensity changes (R² = 0.41/R² = 0.39) were the strongest single exploratory imaging correlates of viable tumor fraction. Conclusions: These findings support the feasibility of quantitative mpMRI parameters to capture regional intratumoral heterogeneity in RCCs, thereby highlighting regions with high viable tumor burden, which may help to refine the imaging-based assessment of RCCs in the future. Full article

To address the challenges of severe optical attenuation and dynamic feature extraction for moving target depth retrieval in complex underwater remote sensing environments, this paper proposes a dynamic target depth estimation method based on multi-source data fusion. Taking optical RGB imagery and neuromorphic vision (NeuroIV) data as joint inputs, the proposed method constructs a three-channel feature extraction and fusion network. By leveraging a hypergraph structure, it establishes association weights between dynamic (temporal) and static (spatial) nodes to capture spatiotemporal correlations. To efficiently process the high-dimensional multi-modal data, the traditional dot-product attention is replaced with element-wise multiplication, significantly reducing computational complexity. Furthermore, a lightweight deformable attention pyramid (DAP) and diffusion model is introduced to refine depth image edges, effectively suppressing discontinuities and abruptness in the estimation results. Compared to single-modality optical imagery, the fused multi-modal data yields a superior signal-to-noise ratio and foreground contrast, achieving an improvement of over 20% in the MAE index. These results validate the effectiveness and superiority of the proposed multi-modal fusion strategy for dynamic target observation and depth retrieval in aquatic environments. Full article

Translating Synthetic aperture radar (SAR) images into optical images is intrinsically ill-posed because microwave backscatter and optical reflectance describe different physical properties of the observed scene. Although frequency-domain modeling has been introduced into diffusion-based translation, existing methods mainly rely on independent weighting of individual Fourier coefficients and provide limited modeling of interactions among neighboring frequencies and feature channels. To address this limitation, we propose dualCDM, a conditional diffusion model that jointly exploits spatial- and frequency-domain representations. In the diffusion backbone, a spatial-frequency hybrid residual block (SFHRB) combines a spatial convolution branch with complex-valued convolution in the Fourier domain. The complex convolution aggregates neighboring Fourier coefficients across all input feature channels, enabling local cross-frequency and cross-channel modeling, while its response is modulated by the diffusion timestep. In the SAR conditional encoder, an adaptive frequency-domain feature recalibration block (AFFRB) predicts input-dependent real-valued gains from magnitude and trigonometric phase representations of intermediate GRD features. These gains adaptively recalibrate the complex frequency responses without introducing an additional phase shift, while the residual connection preserves the original conditional information. A dual-domain objective further constrains both the predicted diffusion noise and the one-step optical reconstruction in the spatial and frequency domains. We also construct the S1S2 dataset using 16-bit Sentinel-2 reflectance data, retaining the original 0–10,000 value range and including the near-infrared band. Experiments on SEN1-2 and S1S2 show that dualCDM improves radiometric accuracy, spectral consistency, and structural preservation over six representative methods. Paired statistical tests further confirm significant improvements over the strongest competing method across all six evaluation metrics on both datasets. Full article

Objective: The objective of this study was to investigate the relationship between intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) and dynamic contrast-enhanced MRI (DCE-MRI) parameters in soft tissue tumors (STTs). Methods: This retrospective study included patients with histopathologically confirmed STTs who underwent both DCE-MRI and IVIM-DWI between March 2022 and February 2024. Patients with prior therapy and lipomatous tumors were excluded. DCE-MRI parameters (K_trans, K_ep, V_e, iAUC) were obtained from pharmacokinetic maps using manually placed regions of interest (ROIs) in the most perfused tumor areas, avoiding necrotic and cystic regions. Corresponding ROIs were applied to IVIM-DWI maps. IVIM parameters (D, D*, f) were calculated using 11 b-values. Results: Twenty-nine patients (mean age, 56 ± 18 years; 14 malignant, 15 benign) were included. Interobserver agreement was excellent for DCE-MRI parameters, whereas IVIM-DWI parameters showed moderate-to-good agreement, with D showing the lowest reproducibility. In malignant tumors, f demonstrated strong positive correlations with K_trans (r = 0.81, p < 0.001) and iAUC (r = 0.79, p < 0.001), both of which remained significant after correction for multiple comparisons. fD* was higher in malignant than in benign lesions in the unadjusted group comparison; however, diagnostic performance was not evaluated in the present study. No significant differences were observed for DCE-MRI parameters between benign and malignant tumors. Conclusions: IVIM-DWI parameters demonstrated associations with DCE-MRI metrics in malignant STTs and may provide complementary information regarding tumor perfusion. However, the findings should be interpreted cautiously because ROI analysis was limited to a single representative slice. Further validation using larger cohorts and volumetric tumor assessment is required. Full article

Accurate, automated delineation of adult diffuse gliomas from multi-parametric magnetic resonance imaging (mpMRI) is central to quantitative neuro-oncology. Volumetric 3D networks dominate the BraTS leaderboard but require expensive GPUs, long training cycles, and provide diminishing returns relative to their compute budget. Slice-wise 2D models, by contrast, discard inter-slice context that is informative for thin tumor rims and small enhancing foci. We introduce GDNet, a 2.5D multimodal MRI segmentation framework for adult glioma evaluated on the BraTS 2024 cohort. GDNet consumes a stack of three adjacent axial slices from the four standard BraTS modalities (T1, T1ce, T2, FLAIR) as a 12-channel input to a compact U-shaped encoder–decoder with Group Normalization and predicts whole tumor (WT), tumor core (TC), and enhancing tumor (ET) masks for the central slice. The training pipeline pairs the 2.5D backbone with: (i) Exponential Moving Average (EMA) of model weights with decay 0.999, (ii) mixed tumor-aware slice sampling (p_tumor = 0.50), (iii) a compound Cross-Entropy + Soft-Dice loss, and (iv) AdamW with warm-up plus cosine annealing under Automatic Mixed Precision. We performed a systematic, step-by-step ablation covering a 2D baseline, EMA + mixed sampling, tumor-centered crop fine-tuning, a GDNet-inspired architectural integration, a region-aware loss, 3-slice and 5-slice 2.5D inputs, and connected-component post-processing, and we report multi-seed results to quantify reproducibility. On the held-out BraTS 2024 test partition, the final 3-slice 2.5D GDNet achieved positive-only Dice scores of 0.791 ± 0.000 (WT), 0.736 ± 0.003 (TC), 0.654 ± 0.004 (ET), and a mean foreground positive-only Dice of 0.820 ± 0.000 across seeds; the all-slice mean foreground Dice exceeded 0.927 ± 0.000. Validation positive-only scores were 0.805 ± 0.002 (WT), 0.757 ± 0.004 (TC), 0.683 ± 0.009 (ET). The inter-seed standard deviation was small for every region (≤0.01 Dice points), indicating low inter-seed variance across the two seeds evaluated; with only two seeds, we regard this as preliminary evidence of training stability rather than a strong reproducibility claim. The ablation isolated EMA + mixed tumor sampling and the 2.5D context window as the dominant sources of improvement; notably, a GDNet-style architectural integration with a region-aware loss did not outperform the simpler 2.5D U-Net on positive-only WT/TC/ET, and light post-processing improved only all-slice Dice. A failure-mode audit found that the residual catastrophic predictions are concentrated on a small minority of diffuse, infiltrative tumors with mass effect. Conclusions: Carefully engineered training strategies, tumor-aware sampling, EMA stabilization, and a modest 2.5D context window recover a substantial fraction of the accuracy of much heavier 3D networks at a fraction of the compute, are reproducible across seeds, and outperform a heavier GDNet-inspired architectural variant on the same data. GDNet is therefore a practical and, pending external validation, potentially clinically deployable framework for multimodal glioma segmentation on workstation-class GPU hardware. Full article

Background/Objectives: Multiple sclerosis (MS) can cause neurodegeneration leading to accelerated brain atrophy. Brain-predicted age (BA) is an emerging neuroimaging biomarker for neurodegeneration but remains underexplored in MS. This study examines the pathophysiological substrates associated with the brain age gap in MS compared with healthy controls (HCs) through a combination of volumetric, spectroscopic, and diffusion imaging. Methods: This retrospective cross-sectional study included 33 HCs and 124 MS patients. Participants underwent 3T MRI including 3D-T1, MR spectroscopy, magnetization transfer, and diffusion imaging. BA and volumes were estimated from T1-weighted scans using brainageR. Metabolic integrity (total N-acetylaspartate to total creatine ratio, tNAA/tCr) and microstructural damage (magnetization transfer ratio [MTR], fractional anisotropy [FA]) were evaluated independently in normal-appearing tissues. Multivariate linear regression assessed MS diagnosis as an independent predictor of BA metrics, controlling for age, sex, and race. Results: MS patients showed significantly higher predicted brain age (53.3 vs. 31.8 years) and a markedly larger age gap (10.2 vs. −0.1 years) compared to HCs. Beyond macroscopic volume loss, accelerated aging paralleled profound subclinical degradation, including lower neuronal integrity (tNAA/tCr: 2.0 vs. 2.4) and widespread microstructural damage, evidenced by reduced MTR and FA across both normal-appearing gray and white matter. Linear regression confirmed MS diagnosis as an independent predictor of both BA and Age Gap (15.09 and 13.50 years) after adjusting for confounders. Conclusions: MS patients exhibit accelerated biological brain aging, characterized by a significant age gap and concurrent tissue volume loss. The brain age gap in MS extends beyond macroscopic atrophy, capturing underlying subclinical metabolic failure and widespread microstructural degradation in normal-appearing tissues. This positions BA as a robust, multi-dimensional proxy for neuroaxonal pathology. Full article

Objectives: The histologic grade is an important prognostic factor in hepatocellular carcinoma (HCC). A Gd-EOB-DTPA-enhanced MRI may provide noninvasive imaging markers related to tumour differentiation. This study aimed to evaluate the association of Gd-EOB-DTPA-enhanced MRI features, together with the albumin–bilirubin (ALBI) score and alpha-fetoprotein (AFP), with the HCC histologic grade and to assess the performance of combined predictive models. Methods: In this prospective cross-sectional study, 75 patients (mean age, 56.4 years; 66 men) with 88 histopathologically confirmed HCC lesions were enrolled. Patients were classified into well-differentiated (grades I–II, n = 24) and poorly differentiated (grades III–IV, n = 51) groups according to the Edmondson–Steiner system. The MRIs were performed on a 1.5-T scanner and included T1-weighted in-phase/opposed-phase imaging; T2-weighted imaging; diffusion-weighted imaging; and dynamic Gd-EOB-DTPA-enhanced sequences, including arterial, portal venous, transitional, and 20 min hepatobiliary phases. Two radiologists, blinded to the pathology, assessed predefined imaging features, and the lesion-to-liver ratio (LLR) was measured. Group comparisons were performed using Student’s t-test, a Mann–Whitney U test, and a chi-square or Fisher’s exact test, followed by a multivariable logistic regression and ROC analysis with bootstrap resampling. Results: Compared with well-differentiated HCC, poorly differentiated HCC showed a higher frequency of peritumoral hepatobiliary phase (HBP) hypointensity (62.7% vs. 4.2%, p < 0.001) and peritumoral arterial hyperintensity (39.2% vs. 0%, p < 0.001). In the multivariable analysis, peritumoral HBP hypointensity remained independently associated with poorly differentiated HCC (OR = 30.89, p = 0.002). The two-parameter MRI model, including peritumoral HBP hypointensity and HBP tumour signal, yielded an AUC of 0.84. The combined MRI + ALBI + AFP model yielded an AUC of 0.87 and an accuracy of 78.7%, representing only a small exploratory improvement over the two-parameter MRI model (AUC = 0.84) in this cohort. Conclusions: Gd-EOB-DTPA-enhanced MRI features, particularly peritumoral HBP hypointensity, were associated with a high histologic grade in HCC. In this surgically treated, predominantly HBV-related cohort with mostly preserved liver function, these findings provide a preliminary basis for preoperative histologic risk stratification; however, they remain exploratory and require external validation in larger, more diverse cohorts before broader clinical application. Full article

Background/Objectives: This study aimed to evaluate the diagnostic performance of post-neoadjuvant chemotherapy (NAC) contrast-enhanced MRI in breast cancer treatment response assessment and to determine whether apparent diffusion coefficient (ADC) parameters contribute to predicting and confirming pathologic complete response (pCR). Methods: This retrospective cohort study enrolled patients with histopathologically confirmed invasive breast cancer who underwent breast MRI before and after NAC, followed by surgical resection. Post-NAC MRI response was classified into four categories and subsequently dichotomised into complete response versus residual disease. On diffusion-weighted imaging (DWI), pre-treatment minimum and maximum ADC values, post-treatment ADC, and the percentage change in ADC (ΔADC) were calculated. Diagnostic performance was assessed by sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), receiver operating characteristic (ROC) curve analysis, and Cohen’s kappa coefficient. Results: Of 188 patients, 19.7% achieved pCR. Post-NAC MRI complete response predicted pCR with 100% sensitivity and 90.1% specificity, with a Cohen’s kappa of 0.781 and 15 false-positive cases. Post-treatment ADC achieved the highest predictive performance, with an area under the ROC curve (AUC) of 0.967 (optimism-corrected 0.958); at the derived threshold, sensitivity was 100.0% and specificity 96.7%. ΔADC was likewise a statistically significant predictor of pCR, and post-treatment ADC remained independent after adjustment. Pre-treatment ADC parameters carried no meaningful predictive value in the full-cohort analysis. Conclusions: Post-treatment ADC and ΔADC help identify true complete responders among cases that contrast-enhanced MRI alone misclassifies as positive. Incorporating quantitative diffusion parameters into standard post-NAC MRI assessment may support pCR confirmation and warrants prospective external validation. Full article

Multiparametric magnetic resonance imaging (mpMRI) is the standard imaging modality for the detection and evaluation of prostate cancer (PCa); however, diagnostic challenges remain due to overlapping imaging features of malignant and benign tissue. The healthy prostate is approximately reflectionally symmetric, whereas malignant transformation introduces structural asymmetry. A novel algorithm that exploits this asymmetry to detect PCa on T2-weighted (T2W), diffusion weighted (DWI), and apparent diffusion coefficient (ADC) images is presented. This study represents a proof-of-concept evaluation of a symmetry-based, training-free approach. Asymmetry features are extracted across all three sequences using band-pass filtering, adaptive thresholding, and intensity-based criteria, and fused into a unified detection map. The method was evaluated in 66 men with histopathological confirmation (33 biopsy-confirmed PCa cases and 33 biopsy-negative controls). The algorithm correctly detected cancer in 29 of 33 cases (sensitivity 87.9%) and correctly classified 15 of 33 non-cancer cases, yielding a specificity of 45.5% and an overall accuracy of 66.7%. Detection performance was higher for lesions ≥ 10 mm (sensitivity 91.7%) than for lesions < 10 mm (sensitivity 77.8%). The PcaAsym framework demonstrated complete intra-reader reproducibility and substantial inter-reader agreement. These results demonstrate the feasibility of symmetry-based analysis as an interpretable and deterministic approach for PCa detection. Validation in larger, consecutive cohorts is warranted to assess performance in routine clinical settings. Full article

Text-to-image generation aims to produce images that match the semantic content of a text prompt. In style transfer tasks, the model must further integrate reference styles while preserving prompt semantics. However, balancing semantic consistency and style fidelity remains challenging. Existing methods commonly rely on fixed feature weights and lack adaptive control, which often leads to style over-injection and content distortion. To address these issues, we propose a novel framework that performs dynamic regulation at both the feature and temporal levels. At the feature level, we propose an Entropy-Aware Adaptive Fusion (EAAF) module. It incorporates a bidirectional distribution transformation mechanism to enhance the statistical correlation between content and style features. The module further uses information entropy as a dynamic control signal to adaptively adjust the strength of style injection, thereby achieving a balance between semantic consistency and style fidelity. At the temporal level, we design a Progressive Feature Reweighting (PFR) strategy. By applying stage-wise weighting to content and style features at different diffusion steps, this strategy effectively improves structural stability and color consistency. In addition, our framework is modular and can be integrated into existing diffusion-based style transfer models without additional fine-tuning or retraining. Experimental results demonstrate that applying our approach to current state-of-the-art models, such as StyleStudio and CSGO, significantly enhances their performance, particularly in maintaining strong prompt alignment while achieving high-fidelity style transfer. Full article

Background: Intraductal carcinoma (IDC) and invasive cribriform (Cr) histologic patterns are important adverse morphologies in prostate cancer (PCa) and may influence pretreatment risk stratification. This study evaluated the feasibility of time-dependent diffusion magnetic resonance imaging (t_d-dMRI)-based microstructural mapping for preoperative characterization of these aggressive morphologies. Methods: This retrospective study included 95 men with pathologically confirmed PCa on radical prostatectomy specimens from March 2023 to March 2025. T_d-dMRI was performed using pulsed and oscillating gradient diffusion sequences. Microstructural parameters, including extracellular diffusivity (D_ex), cell diameter (d), intracellular volume fraction (f_in), cellularity, and diffusivities at 0, 17, and 33 Hz (ADC_0Hz, ADC_17Hz, and ADC_33Hz), were estimated using a two-compartment model. Conventional apparent diffusion coefficient (ADC_DWI) values were obtained from standard diffusion-weighted imaging. Parameters were compared between tumors with and without Cr/IDC patterns, and diagnostic performance was assessed using receiver operating characteristic analysis. Pairwise comparisons of AUCs were performed using the DeLong test. Results: Among 95 participants, 62 (65.3%) had Cr/IDC patterns. Compared with Cr/IDC-negative tumors, Cr/IDC-positive tumors showed higher f_in and cellularity (both p < 0.001) and lower ADC_DWI, ADC_0Hz, ADC_17Hz, and ADC_33Hz values (all p < 0.05). D_ex and d did not differ significantly between groups. Among t_d-dMRI-derived parameters, f_in showed the highest diagnostic performance (AUC = 0.757; 95% CI, 0.654–0.860). Conclusions: T_d-dMRI-based microstructural mapping demonstrates promise for characterizing the Cr/IDC morphologies in PCa. Full article

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

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: Acute focal neurological deficits require rapid differentiation between ischemic stroke and stroke mimics to avoid treatment delays and inappropriate therapy. Seizures, including ictal deficits, status epilepticus, and post-ictal/Todd’s phenomena, are among the most challenging mimics. This review summarizes the role of multimodal neuroimaging in distinguishing acute ischemic stroke from seizure-related deficits. Methods: We performed a focused narrative review of neuroimaging findings in acute stroke mimics, emphasizing non-contrast computed tomography (CT), CT angiography, CT perfusion, magnetic resonance imaging (MRI), including diffusion weighted imaging (DWI), apparent diffusion coefficient (ADC), fluid attenuated inversion recovery (FLAIR), and arterial spin labeling (ASL) sequences. Imaging patterns, diagnostic pitfalls, and practical clues for hyperacute stroke pathways were synthesized. Results: Acute ischemic stroke is typically suggested by vascular-territorial abnormalities, including arterial occlusion or stenosis, territorial hypoperfusion, and congruent DWI/ADC restriction. Seizure-related deficits more often show non-territorial cortical perfusion changes, ictal or status-related hyperperfusion, reversible MRI abnormalities, and absence of arterial occlusion. However, post-ictal hypoperfusion, peri-ictal diffusion restriction, and reperfusion-related hyperperfusion may overlap with ischemic patterns. Conclusions: A multimodal approach integrating vascular imaging, perfusion distribution, DWI/ADC, ASL, clinical timing, and EEG findings can improve diagnostic accuracy in the stroke–seizure differential without delaying treatment in true acute ischemic stroke. Full article