PURPOSE:Multiple sclerosis (MS) is a demyelinating disease of the central nervous system influenced by both genetic and environmental factors. Although a polygenic risk score for MS (MS-PRS) is associated with disease susceptibility, its relationship with white matter (WM) microstructure and cognition in neurologically healthy individuals remains unclear. This study investigated associations of MS-PRS with diffusion MRI metrics and cognitive function in healthy individuals. METHODS:This cross-sectional study included 35952 neurologically healthy UK Biobank participants with available MS-PRS, diffusion MRI, and cognitive data. Associations between MS-PRS and 11 cognitive measures were tested using generalized linear models adjusted for age, age squared, sex, genetic principal components, body mass index, and intracranial volume, with false discovery rate (FDR) correction. Participants were then stratified by MS-PRS quartiles. For cognitive measures showing significant associations in the highest MS-PRS group, follow-up analyses evaluated associations between MS-PRS and diffusion MRI metrics in WM regions previously associated with reaction time (RT). Mediation analysis was then performed. RESULTS:In the full sample, MS-PRS was not significantly associated with any cognitive measure after multiple-comparison correction. In the highest MS-PRS group, the higher MS-PRS was significantly associated with longer RT (β = 0.048, FDR-corrected P = 0.047). In the highest MS-PRS group, the higher MS-PRS was associated with higher mean diffusivity (MD) in the splenium of the corpus callosum (CC) (β = 0.021, FDR-corrected P = 0.024). MD in the splenium of the CC showed a small partial mediation effect on the association between MS-PRS and RT in the highest MS-PRS group (mediation effect 3.0%). CONCLUSION:In neurologically healthy individuals, a higher genetic burden for MS was associated with slower information processing speed in the highest-risk subgroup and with altered WM microstructure in the splenium of the CC. These findings suggest that subtle WM differences may partly underlie the association between MS genetic susceptibility and cognitive performance.
MRI has become indispensable in the diagnosis and management of urologic and nephrological diseases. Technological advances over the past 2 decades-including multiparametric imaging, quantitative MRI, standardized reporting systems, deep learning-based reconstruction, and artificial intelligence (AI)-have expanded the role of MRI beyond anatomical assessment to tissue characterization, treatment planning, image-guided intervention, and therapeutic monitoring. This special issue of Magnetic Resonance in Medical Sciences comprises 7 review articles and 2 original investigations that summarize recent advances in MRI of the kidney, prostate, bladder, and upper urinary tract. Together, these contributions highlight recent advances in MRI and illustrate its evolving role as an integrated imaging platform that combines quantitative imaging, imaging biomarkers, AI, and image-guided therapy. This Editorial highlights the 7 review articles included in this special issue and provides an overview of recent advances in MRI for urologic and nephrological diseases.
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.
MRI has become an increasingly important modality for evaluating the upper urinary tract. Although CT urography remains the standard imaging modality for assessing hematuria, MRI offers distinct advantages, including superior soft-tissue contrast, functional imaging capability, and a radiation-free evaluation, making it particularly valuable in certain clinical scenarios. This review provides a comprehensive overview of MRI of the upper urinary tract, with a focus on upper tract urothelial carcinoma (UTUC). Optimized MRI techniques, including T2-weighted imaging, MR urography, diffusion-weighted imaging (DWI), and contrast-enhanced T1-weighted imaging, are discussed with respect to their complementary roles in tumor detection, characterization, and local staging. MRI findings of UTUC are summarized, emphasizing the role of DWI in improving tumor detection and assessing renal parenchymal invasion. Beyond UTUC, the review addresses MRI features of non-UTUC neoplastic lesions, infectious and inflammatory diseases, and congenital anomalies of the upper urinary tract, underscoring the value of MRI in differential diagnosis. We discuss the potential role of MRI-derived imaging biomarkers, focusing on apparent diffusion coefficient measurements as noninvasive indicators of tumor aggressiveness and invasiveness. We also introduce emerging radiomics approaches as promising, albeit investigational, tools. As technical innovations continue and clinical evidence grows, the role of MRI in the personalized evaluation and management of upper urinary tract diseases is expected to expand.
Prostate MRI has become a cornerstone of contemporary prostate cancer diagnosis, enabling improved detection of clinically significant disease while reducing unnecessary biopsies and overtreatment. However, prostate MRI remains technically demanding, time-consuming, and subject to inter-reader variability, particularly as healthcare systems move toward abbreviated protocols such as non-contrast MRI (biparametric MRI). In this context, artificial intelligence (AI) has emerged as a promising tool to enhance image quality, diagnostic consistency, and workflow efficiency across the prostate MRI pathway. This non-systematic narrative review provides a comprehensive overview of the technical foundations, clinical applications, and workflow implications of AI integration into prostate MRI. It summarizes key concepts in machine learning and deep learning relevant to prostate imaging and reviews current evidence supporting AI-based solutions for image quality assessment and reconstruction, automated prostate segmentation, lesion detection, and risk stratification. Particular attention is given to human-AI collaboration models, the role of AI in supporting equivocal lesions, and the integration of imaging with clinical variables for personalized risk estimation. In addition, it discusses the impact of AI on reporting efficiency, training, and standardization, as well as the current landscape of commercially available AI tools. Despite encouraging results from large multicenter studies, important challenges remain, including heterogeneity in study design, limited prospective validation, generalizability across institutions, and ethical and regulatory considerations. Overall, AI should be regarded as a complementary decision-support technology rather than a replacement for radiologists. Thoughtful implementation, robust validation, and appropriate user training are essential to ensure that AI meaningfully enhances the quality, efficiency, and reliability of prostate MRI-based care.
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.
Purpose: To evaluate the performance of a Transformer-based U-Net, TransDisCo, for distortion correction in clinical diffusion-weighted imaging (DWI), including high b-value DWI, diffusion tensor imaging (DTI), and diffusion kurtosis imaging (DKI) and, furthermore, to compare its efficacy with an established U-Net-based method (Synb0) on a clinical dataset including lesion cases. Methods: This study utilized a clinical dataset acquired at the University of Tokyo Hospital, comprising DWI data from patients (including cases with brain tumors and cerebrovascular disorders) suitable for DTI and DKI analysis. We compared the distortion-correction performance of our proposed TransDisCo with that of Synb0, a U-Net-based model. Both methods were trained on T1-weighted images and uncorrected DWI images, with FMRIB Software Library-corrected DWI images used as ground truth. To assess the contribution of the self-attention mechanism in TransDisCo, an ablation study was conducted using a convolutional neural network (CNN)-based variant, in which encoder Transformer blocks were replaced with convolutional blocks. We evaluated corrected DWI images and derived DTI/DKI maps using peak SNR, structural similarity, mutual information, and Dice coefficients. Results: All 3 models corrected distortions in the clinical DWI datasets, including high b-value images and derived DTI/DKI maps. Furthermore, TransDisCo demonstrated significantly superior performance compared to Synb0 across quantitative metrics, particularly in preserving anatomical details and reducing artifacts, even in the presence of pathological changes. Additionally, in the ablation study, TransDisCo outperformed the CNN-based variant trained under identical conditions, indicating that the self-attention mechanism, which enables content-adaptive long-range feature interactions, of the Transformer-based encoder contributed to the observed performance gains. Conclusion: TransDisCo alleviates limitations of existing CNN-based models and represents a promising approach to DWI distortion correction that surpasses conventional U-Net-based methods on clinical data with diverse pathologies. It has the potential to improve the precision and reliability of diffusion MRI across a wide range of imaging conditions and clinical applications.
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.
PURPOSE:This study aimed to evaluate CT- and MRI-based radiomic features of ovarian mature and immature teratomas (MTs and ITs) and to investigate the differences between low- and high-grade ITs. METHODS:A total of 41 patients aged ≤ 30 years with histopathologically confirmed ovarian MT and 23 patients with ovarian IT who underwent preoperative MRI were included. Among them, 51 (31 MTs and 20 ITs) underwent preoperative CT. We investigated the differences in visual findings by radiologists and radiomics analysis not only between MTs and ITs but also between low- (grade 1) and high-grade (grades 2-3) ITs. RESULTS:Multiple CT and MRI findings, along with radiomic features, showed significant differences between MTs and ITs (P < 0.05). Although the maximum diameter of nodular fat was greater in low-grade ITs than in high-grade ITs (P < 0.05), no other visual CT or MRI findings showed significant differences between low- and high-grade ITs. In contrast, several MRI-based radiomic features derived from fat-suppressed T1-weighted images, along with selected CT-based radiomic features, showed significant differences between low- and high-grade ITs (P < 0.05). CONCLUSION:Visual assessments by radiologists and radiomics analysis effectively distinguished MTs from ITs. However, differentiating between low- and high-grade ITs using visual assessment alone was difficult. Radiomics analysis-particularly MRI-based features from fat-suppressed T1-weighted images-showed potential as a noninvasive approach for grading ITs, although further validation is required.
Purpose: To investigate the effects of free-water (FW) correction on mean diffusivity (MD) values in glioblastomas using diffusion tensor imaging (DTI) with different b-value settings. We also examined regional differences between contrast-enhanced (CE) tumor and peritumoral fluid-attenuated inversion recovery (FLAIR) hyperintense regions. Methods: DTI data from 787 participants with glioblastoma were obtained from 2 open-source datasets: the University of California, San Francisco Preoperative Diffuse Glioma MRI (UCSF-PDGM; b-value, 2000) and the Multi-parametric Magnetic Resonance Imaging Scans for De Novo Glioblastoma Patients from the University of Pennsylvania Health System (UPENN-GBM; b-value, 1000). Conventional and FWcorrected MD maps were generated, and median MD values were calculated from CE and FLAIR hyperintense regions. The impact of FW correction was assessed via the Wilcoxon matched-pairs signed rank test, Bland-Altman plot, and Spearman's correlation analysis. Multivariate Cox regression was used to evaluate prognostic significance, adjusting for age and CE volume. Results: Both median MD values were significantly lower in the CE regions than in the FLAIR hyperintense regions. In the UCSF-PDGM dataset, FW correction caused a greater reduction of MD in the CE regions, whereas the greater reduction occurred in the FLAIR hyperintense regions in the UPENN-GBM dataset. Bland-Altman plot also indicated lesions with lower MD values showing greater FW correction in the UCSF-PDGM dataset, whereas those with relatively high MD values showed greater FW correction in the UPENN-GBM dataset. The correlation between both MD values was high (Rs > 0.98). Neither conventional MD nor FW-corrected MD values in the CE regions were significantly associated with overall survival. (Ps > 0.30). Conclusion: A paradoxical relationship of MD values with and without FW correction was identified depending on regional characteristics and b-value settings, underscoring the importance of acquisition parameters when interpreting FW correction.
Advances in imaging diagnostics, particularly MRI, have enabled the visualization of clinically significant prostate cancer. Furthermore, advances in image-fusion technology have made it possible to accurately target these visualized lesions and obtain tissue samples through precise needle placement. These developments now enable systematic intraprostatic localization of clinically significant prostate cancer. Traditionally, standard treatment options for localized prostate cancer have involved whole-gland therapies, such as radical prostatectomy and radiation therapy. Although these approaches provide excellent oncological control and favorable long-term outcomes, they are associated with treatment-related adverse events, including urinary incontinence and sexual dysfunction, which can substantially impair quality of life. Focal therapy has emerged as a treatment strategy designed to address these limitations by selectively targeting only the clinically significant cancer within the prostate, thereby aiming to achieve a balance between oncological control and functional preservation. This approach has become technically feasible due to the MRI visibility of clinically significant cancer and precise image-guidance techniques, as well as the development of various ablation technologies. In this review, we provide an overview of MRI/ultrasound fusion-guided biopsy and discuss the current status and clinical implications of focal therapy for localized prostate cancer enabled by this imaging-guided approach.
PURPOSE:Assessing brain stiffness alterations associated with normal aging, sex-related differences, and regional variations is important from clinical perspective. The study aimed to investigate the changes in brain stiffness according to age and sex using virtual magnetic resonance elastography (vMRE), a noninvasive and novel technique based on diffusion-weighted imaging (DWI). METHODS:We retrospectively reviewed 96 adults (31-80 years, 51 men and 45 women) who underwent MRI as part of routine medical examinations. The shifted apparent diffusion coefficient was calculated from DWI (b = 200 and 1500 sec/mm2) and converted to the DWI-based virtual shear modulus (μdiff). Brain stiffness was measured in 12 brain regions: the cerebrum, cerebral gray/white matter, basal ganglia, thalamus, frontal lobe, parietal lobe, temporal lobe, occipital lobe, cerebellum, hippocampus, and amygdala. Linear regression and multiple-comparison tests were performed to evaluate the effects of age and regional differences. RESULTS:Brain stiffness exhibited regional differences, with higher shear modulus values observed in deep structures (thalamus and basal ganglia) compared to that in lobar regions (P < 0.01). Among lobar areas, the occipital lobe had the highest stiffness, whereas the temporal lobe had the lowest (P < 0.01). Linear regression revealed a significant negative correlation between age and brain stiffness (-0.06%/year, R2 = 0.09, P = 0.04). The most pronounced decline in elasticity (-0.28%/year, R2 = 0.66, P < 0.01) was observed in cerebral gray matter. Hippocampal stiffness significantly decreased after the fifth decade of life. Sex-related differences were found only in the parietal lobe (P = 0.02) and basal ganglia (P = 0.03). CONCLUSION:Our findings suggest that vMRE can effectively measure the elastic properties of the brain. Brain stiffness decreases with age, but these changes vary across different regions.
Subclinical inflammation (SI) is clinically relevant in psoriasis (PsO) and psoriatic arthritis (PsA), but quantitative imaging biomarkers are lacking. We evaluated the feasibility of ultrashort echo time (UTE) bicomponent MRI for quantifying Achilles enthesis SI. Conventional MRI showed no group differences, whereas short T2* (T2*s) values were significantly elevated in PsO/PsA compared with healthy volunteers. These findings support the feasibility of UTE bicomponent analysis, as a promising tool for evaluating SI and warrant further investigation.
PURPOSE:This study evaluated salivary gland function using MR cine sialography (MRCS) based on the time-spatial labeling inversion pulse (Time-SLIP) technique combined with a deep learning reconstruction-based denoising method (dDLR). Bilateral salivary flow from the submandibular glands (SMGs) and parotid glands (PGs) was visualized, and its correlation with whole-saliva volume was examined. METHODS:Optimal MRCS parameters were identified, and imaging was performed in 11 healthy volunteers. Correlations between salivary flow on MRCS and unstimulated whole-saliva volume (UWS) and stimulated whole-saliva volume (SWS), as well as side-to-side differences, were analyzed. UWS was obtained using the spitting method, and SWS was obtained using the gum-chewing test. Salivary-flow distance was defined as the length from the labeling position to the point where the labeled saliva signal became indistinguishable from background signal. Spearman's rank correlation coefficients (ρ) were calculated to evaluate the association between whole-saliva volume and MRCS, and Wilcoxon's signed-rank test was used to assess side-to-side differences. RESULTS:Correlation coefficients between MRCS and whole-saliva volume were as follows: under unstimulated conditions, SMG ρ = 0.825, PG ρ = -0.05, SMG + PG ρ = 0.802; under stimulated conditions, SMG ρ = 0.606, PG ρ = 0.72, and SMG + PG ρ = 0.820. Strong correlations (ρ > 0.8) were observed for SMG and SMG + PG under unstimulated conditions and for SMG + PG under stimulation. No significant overall side-to-side differences were found except for PG under stimulation (P = 0.016). However, several individuals showed notable side-to-side differences, underscoring the value of bilateral imaging. CONCLUSION:Bilateral SMG and PG salivary flows obtained using MRCS with dDLR showed strong correlations with UWS and SWS. These findings suggest that MRCS is a promising noninvasive method for assessing salivary volume and supports both separate and combined evaluation of SMG and PG function.
PURPOSE:To evaluate whether reverse encoding distortion correction (RDC) improves image quality and maintains apparent diffusion coefficient (ADC) measurements in diffusion-weighted imaging (DWI) on female pelvic MRI using a 1.5-T in in vitro and in vivo studies. METHODS:This retrospective, institutional review board-approved study included both in vitro and in vivo analyses of 31 women (mean age, 41 ± 15 years; range, 24-80 years) who underwent pelvic MRI between January and March 2022. T2-weighted image (T2WI) and DWI with and without RDC were acquired, and ADC maps were generated. Quantitative metrics included SNR, contrast-to-noise ratio (CNR), ADC values, and deformation ratio (DR), defined as the proportional area discrepancy between DWI and T2WI for the uterine corpus, cervix, ovary, and lesions. Qualitative assessments-overall image quality (OIQ), deformation severity (DS), and diagnostic confidence level (DCL)-were independently scored by 2 blinded radiologists using 5-point scales. RESULTS:In vitro SNR and ADC values showed no significant differences between DWI with and without RDC, with strong correlations to reference measurements (ρ = 0.99, P < 0.001). In vivo, SNR, CNR, and ADC values of the myometrium, cervix, and ovary did not differ significantly between 2 methods (P > 0.05). DRs were significantly lower in DWI with RDC than in DWI without RDC (P ≤ 0.0003). ADC values showed strong correlations between DWI with and without RDC on uterine corpus, cervix, ovary and lesions (ρ = 0.82-0.90, P < 0.001). Qualitative scores improved with RDC: higher OIQ (P = 0.0004), lower DS (P = 0.0004), and higher DCL (P = 0.03). Interobserver agreement ranged from substantial to almost perfect (κ = 0.78-0.97). CONCLUSION:RDC improves image quality and reduces image distortion in DWI on female pelvic MRI at a 1.5-T, while maintaining ADC measurements in in vitro and in vivo settings.
Purpose: To compare accelerated T2-weighted turbo spin-echo imaging with deep learning reconstruction (DLR-TSE) with conventional T2-weighted TSE (conv-TSE) and accelerated TSE without DLR (non-DLR-TSE), and to evaluate image quality and diagnostic performance of Prostate Imaging Reporting and Data System version 2.1 (PI-RADS v2.1)-based T2 scoring in prostate MRI. Methods: This single-center retrospective study included 60 patients who underwent prostate MRI with all 3 T2-weighted image sets acquired in the same examination. Qualitative image quality was independently assessed by 2 radiologists using 6 parameters on a 5-point Likert scale. Quantitative metrics included apparent SNR (aSNR), apparent contrast-to-noise ratio (aCNR), and contrast ratio (CR). Diagnostic performance of PI-RADS T2 scores for transition zone lesions was evaluated using receiver operating characteristic (ROC) analysis, sensitivity, specificity, and accuracy. Noninferiority of DLR-TSE relative to conv-TSE was tested with predefined margins. Inter-reader agreement was assessed using weighted kappa statistics. Results: DLR-TSE demonstrated noninferiority to conv-TSE for all qualitative parameters and quantitative metrics for both readers. Both DLR-TSE and conv-TSE showed significantly higher image quality scores and quantitative values than non-DLR-TSE. For PI-RADS T2 scoring, DLR-TSE achieved diagnostic performance comparable to conv-TSE. For reader 1, the area under the ROC curve (AUC) was identical for DLR-TSE and conv-TSE (0.83; 95%CI 0.69-0.95), and significantly higher than for non-DLR-TSE (0.70; 95%CI 0.56-0.83). Specificity and overall accuracy were markedly reduced with non-DLR-TSE for both readers, whereas sensitivity did not differ significantly among methods. Inter-reader agreement was substantial to almost perfect for DLR-TSE and conv-TSE, and lower for non-DLR-TSE. DLR-TSE reduced acquisition time by approximately 60% compared with conv-TSE. Conclusion: Accelerated T2-weighted imaging with DLR allows substantial reduction in scan time while maintaining image quality, diagnostic performance, and inter-reader agreement comparable to those of conventional T2-weighted imaging. DLR-TSE may serve as a practical option for improving examination efficiency in clinical prostate MRI.
PURPOSE:To compare the image quality and acquisition time of thoracic great vessels using a novel, 3D isotropic non-contrast-enhanced MR angiography that combines respiratory compensation, electrocardiogram-triggering, compressed sensing, and Dixon water-fat separation at 3T (CS-MRA) to a conventional MRA employing parallel imaging and spectral attenuated inversion recovery (cMRA). METHODS:This study included 20 healthy volunteers and 19 patients with thoracic vascular diseases. All scans were performed on a 3T MRI scanner using a T2-prepared 3D fast low angle shot (FLASH) sequence. Visual image quality of thoracic great vessels was assessed using a 4-point scale. Quantitative parameters including SNR, contrast-to-noise ratio (CNR), contrast ratio (CR), and coefficient of variation (CV) were evaluated. Aortic diameters were measured, and reproducibility was assessed using Bland-Altman analysis and the intraclass correlation coefficient (ICC). Image acquisition times were also compared. RESULTS:Visual assessment demonstrated significantly higher scores with CS-MRA compared to cMRA across all evaluated thoracic vessels in both volunteers and patients (median score: 3 vs. 2, P < 0.05). Quantitative analysis showed that CS-MRA yielded significantly higher SNR, CNR, and CR (P < 0.05 for all), along with better signal homogeneity (lower CV). CS-MRA showed excellent intra- and inter-observer agreement for aortic diameter measurements, with ICCs ≥ 0.91 in volunteers and ≥ 0.95 in patients. CS-MRA also achieved significantly shorter acquisition times (volunteers: 5.3 ± 1.4 min vs. 6.0 ± 1.7 min, P < 0.01; patients: 5.1 [4.6-6.3] min vs. 6.9 [5.0-9.6] min, P < 0.01). CONCLUSION:CS-MRA provides superior image quality, comparable measurement reproducibility, and significantly reduced acquisition times compared to cMRA. These findings support the clinical utility of CS-MRA as an efficient and reliable non-contrast technique for thoracic vascular evaluation.
PURPOSE:The present study directly compared the quantitative capabilities of regional perfusion and pulmonary functional change assessments among electrocardiography (ECG)- and photoplethysmography (PPG)-monitored phase-resolved functional lung (PREFUL) MRI and dynamic contrast-enhanced (CE) perfusion MRI in thoracic oncologic patients. METHODS:Seventeen thoracic oncologic patients prospectively underwent ECG- and PPG-monitored PREFUL MRI, dynamic CE-perfusion MRI, and pulmonary function tests. ECG- and PPG-monitored perfusion-weighted PREFUL MRI (PW-MRI) and quantitative perfusion maps from dynamic CE-perfusion MRI were generated. Regional perfusions were determined using ROI measurements. For each patient, the overall perfusion from each method was determined as the average ROI measurement value. To determine the relationship between regional perfusion among all methods, Pearson's correlations were performed. Tukey's honest significant difference test was performed to compare regional perfusion among ventral, middle, and dorsal slice positions on ECG- and PPG-monitored PW-MRI and quantitative perfusion maps. To assess the pulmonary functional loss evaluation capability of each MRI method, each overall perfusion was correlated with %VC and %FEV1 using Pearson's correlation. RESULTS:The correlation of regional perfusion between ECG- and PPG-monitored PW-MRI was significant and good (r = 0.79, P < 0.0001). However, the correlations between ECG- or PPG-monitored PW-MRI and quantitative perfusion maps were assessed as significant and fair (ECG: r = 0.4, P < 0.0001; PPG: r = 0.36, P < 0.0001). ECG- and PPG-monitored PW-MRI demonstrated significantly higher perfusion than the quantitative perfusion map (P < 0.0001). Furthermore, ECG-and PPG-monitored PW-MRI and quantitative perfusion maps had significant and moderate correlations (%VC: 0.60 ≤ r ≤ 0.63, P < 0.05; %FEV1: 0.52 ≤ r ≤ 0.69, P < 0.05). CONCLUSION:ECG- and PPG-monitored PREFUL MRI had similar potential to dynamic CE-perfusion MRI for quantitatively assessing regional perfusion and pulmonary functional changes in thoracic oncologic patients. Furthermore, PPG-monitored PREFUL MRI showed little difference in regional perfusion evaluation compared with ECG-monitored PREFUL MRI and the potential to play a complementary role in this setting.
We report a case of Wallenberg syndrome associated with vertebral artery occlusion and suspected atherosclerotic changes. The patient was evaluated using accelerated 3D fluid-attenuated inversion recovery MRI incorporating T2 preparation and magnetization transfer contrast pulses. This technique enabled a shortened acquisition time while preserving high image contrast, allowing clear visualization of the lateral medullary infarction. This approach provides a non-contrast, high-resolution method for assessing brainstem infarcts and may contribute to improved diagnostic accuracy in the acute setting.
