Purpose: Synthetic MRI can generate multiple contrasts from a single acquisition, yet synthetic fluid-attenuated inversion recovery (FLAIR) generally shows lower quality than conventional FLAIR. We aimed to improve 3D synthetic FLAIR image quality using deep learning, without losing the scan-time advantage of synthetic MRI.

Methods: We studied 55 adults with inflammatory demyelinating diseases who underwent 3T MRI. For each participant, five 3D quantification using an interleaved Look–Locker acquisition sequence with T2 preparation (3D-QALAS) source images and a conventional 3D-FLAIR image were acquired, and a synthetic FLAIR image was generated from the 3D-QALAS data. We trained a deep learning model in which a 3D U-shaped convolutional network (U-Net)–based attention network predicted voxel-wise weights for generating FLAIR images from the five 3D-QALAS source images, using conventional 3D-FLAIR images as the reference. The model was trained with a combined loss function of mean squared error, content loss, and style loss. Agreement with the reference was assessed using image similarity and error metrics, lesion overlap using the Dice similarity coefficient, and overall image quality and focal lesion visibility using blinded radiologist ratings. Synthetic FLAIR and the deep learning–generated FLAIR images were compared using 2-sided Wilcoxon signed-rank tests; P < 0.05 was considered statistically significant.

Results: The deep learning–generated FLAIR images showed significantly higher agreement with the reference image than synthetic FLAIR images (all P < 0.001). Lesion overlap was higher with the deep learning–generated FLAIR images (median [interquartile range]: 0.642 [0.528–0.711] versus 0.487 [0.344–0.641]). Qualitatively, the deep learning–generated FLAIR images improved overall image quality and focal lesion visibility, but reader scores remained lower than those for the reference conventional 3D-FLAIR images (all P < 0.001).

Conclusion: A deep learning–based approach applied to five 3D-QALAS source images improved the image quality of 3D synthetic FLAIR. These improvements may increase the clinical utility of 3D synthetic MRI for neuroradiologic assessment.

Purpose: To investigate relationships among relaxation, susceptibility, and diffusion parameters in healthy white matter (WM) and to characterize WM MRI feature organization using an integrative multimodal quantitative MRI framework.

Methods: Twenty-two healthy volunteers underwent 3T MRI. Quantitative parameter mapping provided R₁, R₂*, R₁·R₂*, and quantitative susceptibility mapping, while diffusion kurtosis imaging provided fractional anisotropy (FA), mean kurtosis, axial kurtosis (AK), and radial kurtosis. All maps were spatially normalized to Montreal Neurological Institute (MNI) space, and mean values were extracted from WM tracts defined by the Johns Hopkins University (JHU) White Matter Atlas. Pearson correlation analysis with false discovery rate correction, principal component analysis (PCA), hierarchical clustering, and bootstrap stability analysis were performed.

Results: The first 2 principal components explained 77.7% of the total variance. The first principal component (PC1) was mainly associated with relaxation-related parameters and diffusion kurtosis metrics, whereas the second principal component (PC2) was characterized by opposing contributions from AK and FA. Quantitative susceptibility mapping showed weak correlations with other parameters (r = −0.37 to −0.08), suggesting relatively independent susceptibility-related information. The PCA structure was preserved after excluding R₁·R₂*, and bootstrap analysis supported loading stability, with mean loading correlations of 0.94 for PC1 and 0.90 for PC2. WM tracts formed 4 major cluster-like groups and showed tract-specific multimodal fingerprints.

Conclusion: Multimodal quantitative MRI provides a concise tract-level MRI feature representation of WM by integrating relaxation, susceptibility, and diffusion information. This exploratory framework may support future studies investigating subtle WM alterations, although validation in pathological cohorts is required.

Purpose: To evaluate the clinical feasibility and quantitative performance of adaptive complex signal average (ACSA) diffusion-weighted imaging (DWI) in abdominal MRI and to compare ACSA DWI with conventional non-ACSA DWI in terms of signal intensity (SI), SNR, apparent diffusion coefficient (ADC), signal intensity difference ratio (SIDR), and lesion contrast across different numbers of excitations (NEX).

Methods: This retrospective study included 82 patients who underwent free-breathing abdominal DWI on a 3T system between January and July 2025. ACSA DWI was reconstructed from the same raw data as non-ACSA DWI using a combination of adaptive averaging and complex-domain averaging. ROIs were placed in the liver, pancreas, left adrenal gland, and spinal muscles to measure SI, SNR, and ADC. SIDR and inter-lobar right–left (R–L) ratio in SI and ADC were calculated at NEX 2, 3, and 4. For 37 patients with non-cystic hepatic nodules, lesion SI, ADC, contrast ratio, and contrast-to-noise ratio (CNR) were assessed.

Results: ACSA DWI significantly increased SI and SNR in motion-prone organs, including the lateral hepatic segment, pancreas, and left adrenal gland, at all NEX levels (P < 0.05). ADC values in abdominal parenchymal organs and hepatic nodules were lower with ACSA DWI, reflecting the noise-floor suppression and adaptive average effects. R–L SI and ADC ratios were significantly different and were closer to 1 in ACSA DWI, indicating improved hepatic signal homogeneity. Lesion SI, contrast ratio, and CNR were significantly higher with ACSA DWI, enhancing nodule conspicuity.

Conclusion: ACSA DWI improves signal uniformity, SNR, and lesion contrast while maintaining reasonable ADC quantification. Without prolonging scan time or modifying acquisition parameters, ACSA provides a robust, clinically feasible post-processing technique for enhancing both image quality and quantitative reliability in abdominal DWI.

Purpose: Most existing multi-contrast MRI super-resolution (MCMSR) methods rely on spatial-domain fusion and global attention, overlooking explicit high-frequency (HF) priors while incurring high computational costs. This work addresses these limitations through a general reference-guided MCMSR framework designed for low computational cost, applicable to any contrast pairing rather than a fixed clinical protocol.

Methods: We introduce a wavelet-guided HF prior modeling block for directional-based decomposition and bounded nonlinear enhancement, enabling precise extraction and controlled amplification of anatomical details from both reference and target contrasts. We further introduce a triple cross-contrast fusion module, based on a windowed cross-contrast attention module, to efficiently transfer high-frequency information between contrasts and reduce computational complexity compared to global attention schemes. Additionally, to reduce feature differences across contrasts, a consistent feature fusion module with selective spatial adaptive modulation is incorporated.

Results: Extensive experiments on IXI and M4Raw datasets demonstrate that our proposed windowed cross-contrast attention network (WCCAN) framework consistently outperforms state-of-the-art single and multi-contrast MRI SR methods in terms of quantitative accuracy and visual fidelity. In addition, the WCCAN model achieves lower computational complexity and faster inference time compared to the other MCMSR methods.

Conclusion: The proposed WCCAN framework provides an efficient and accurate solution for MCMSR, demonstrating superior reconstruction quality with reduced computational cost compared to existing methods.

Purpose: There is a significant association between carotid artery (CA) stenosis and atherosclerosis, with those patients often presenting with carotid tortuosity. However, the relationship between atherosclerotic risk factors and carotid tortuosity remains controversial. Previous assessments of tortuosity have predominantly utilized qualitative 2D methods. This retrospective study aimed to quantitatively evaluate the tortuosity of CAs in 3D geometry and to investigate the relationship with atherosclerotic risk factors.

Methods: A total of 168 CAs in 96 patients diagnosed with at least either-side CA stenosis on magnetic resonance angiography (MRA) between 2011 and 2019 were included in this single-center study, following the exclusion of post-treatment CAs and those with poor imaging quality. The 3D geometries of CAs were segmented using MRA and the centerlines were generated. Vascular tortuosity rate (VTR) was calculated as the curved distance divided by the linear distance between the specified 2 points. The relationship between VTRs and 8 atherosclerotic risk factors—age, body mass index, systolic blood pressure, hemoglobin A1c, high-density lipoprotein, total cholesterol (TC), non-fasting serum triglyceride (TG), and Brinkman index—was analyzed.

Results: Higher VTRs were associated with ≥65 years of age, body mass index ≥25 [kg/m²], serum TC ≥220 [mg/dL], and non-fasting serum TG ≥175 [mg/dL] in common CAs, and body mass index ≥25 [kg/m²], and systolic blood pressure ≥140 [mmHg] in internal CAs.

Conclusion: Quantitative assessment of carotid tortuosity revealed significant associations with several atherosclerotic risk factors, including obesity, hypertension, advanced age, and dyslipidemia, suggesting that VTRs may reflect the presence of these risk factors.

This study aimed to demonstrate the technical feasibility of a dedicated prostate phantom for standardized qualitative evaluation of diffusion-weighted MRI sequences, focusing on signal-to-noise characteristics and image artifacts. A custom-designed phantom simulating prostate tissue, tumor lesions, and rectal gas was used to evaluate 6 diffusion-weighted imaging techniques at 1.5 and 3.0 tesla. Quantitative assessments included signal-to-noise measurements and susceptibility artifacts, while spatial distortion was evaluated by comparison with T2-weighted imaging. Qualitative image quality was assessed using corresponding clinical images as references. Clinical images were evaluated by a radiologist and a urologist, and phantom images were evaluated by 2 radiological technologists. The phantom demonstrated high inter-reader agreement for qualitative image assessment. Clinical image evaluation was performed to confirm whether artifact trends observed in the phantom were similarly observed in clinical images and showed comparable tendencies across sequences. These findings indicate that the proposed prostate phantom provides a practical and reproducible platform for qualitative evaluation.