Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine最新文献

Purpose: This study aimed to evaluate the diagnostic utility of locus coeruleus (LC) signal intensity on high-resolution T1-weighted imaging with magnetization transfer (T1WI with MT) for distinguishing Parkinson's disease (PD) from a broad range of atypical parkinsonism (AP) subtypes, including early-stage cases.

Methods: We retrospectively analyzed T1WI with MT data from 214 participants, including patients with PD (n = 125), corticobasal syndrome (CBS, n = 12), multiple system atrophy (MSA, n = 16), progressive supranuclear palsy (PSP, n = 19), essential tremor (n = 17), vascular parkinsonism (n = 4), drug-induced parkinsonism (DIP, n = 7), and healthy subjects (HS, n = 14). Circular ROIs were placed on the LC and substantia nigra pars compacta to calculate contrast ratios (CRs). Conventional MRI findings of AP, focusing on characteristic regional atrophy patterns, were also evaluated. Diagnostic performance was assessed using receiver operating characteristic (ROC) analysis. A subanalysis was performed for early-stage cases (within 2 years of onset). Three independent neuroradiologists evaluated T1WI with MT, and interobserver agreement was assessed using intraclass correlation coefficients (ICC).

Results: The LC-CR was significantly lower in PD than in HS (P < 0.01) and all AP subtypes except DIP (P = 0.37). ROC analysis revealed that LC-CR had the highest diagnostic accuracy for differentiating PD from AP (area under the curve [AUC] = 0.83, sensitivity = 67%, specificity = 90%). In early-stage cases, LC-CR maintained high specificity (98%) with an AUC of 0.80. The diagnostic utility of LC-CR was comparable or superior to conventional MRI findings in distinguishing PD from CBS, MSA, and PSP. Interobserver agreement for LC-CR measurements was good, with an ICC of 0.87 (95% confidence interval: 0.85-0.89).

Conclusion: LC-CR measured on T1WI with MT serves as a reliable imaging biomarker for differentiating PD from various forms of AP, even in early disease stages.

This study aimed to clarify the accuracy and variability of heart rate variability effects on T1 relaxation time values acquired using different sampling schemes of the modified Look-Locker inversion recovery (MOLLI) method. Five MOLLI sampling schemes-5(3)3, 5s(3s)3s, 4(1)3(1)2, 4s(1s)3s(1s)2s, and 3(3)3(3)5-were compared using a simulated electrocardiogram signal in a phantom experiment. The 5(3)3 and 5s(3s)3s schemes demonstrated high accuracy across the range of native and contrast-enhanced T1 values, with correlation coefficients exceeding 0.95 compared with the reference T1. Regarding heart rate variability, the 5(3)3 scheme showed minimal variation in the measured value across different heart rates, regardless of the T1 value, with an error rate below 6% relative to the reference T1. Therefore, the 5(3)3 scheme can achieve highly accurate measurements over a wide T1 range and is robust against heart rate variability.

Bladder cancer carries one of the highest lifetime costs among malignancies, and accurate distinction between non-muscle-invasive and muscle-invasive disease is essential for appropriate treatment selection. Multiparametric MRI (mpMRI) and the Vesical Imaging-Reporting and Data System (VI-RADS) have emerged as key tools for standardizing local staging of bladder cancer; however, their clinical uptake in Japan remains limited. This non-systematic narrative review summarizes the fundamentals and current evidence of VI-RADS, outlines Japan-specific barriers to its implementation, and proposes practical solutions and future perspectives. It describes patient preparation and VI-RADS-compliant mpMRI protocols, sequence-specific criteria for estimating muscle invasion, and the diagnostic performance and reproducibility reported in recent meta-analyses. It also evaluates VI-RADS within the context of major international and Japanese guidelines, highlighting the current gap between imaging-based risk stratification and transurethral resection of bladder tumor (TURBT)-centered decision-making. Particular focus is placed on challenges arising from Japan's healthcare structure, heterogeneous MRI quality, and shortage of subspecialized radiologists, as well as common diagnostic pitfalls related to technical, reader, and tumor factors. Recent diagnostic advances-including deep learning-based image reconstruction, improved diffusion and dynamic contrast techniques, and qualitative or quantitative adjunct biomarkers, such as peritumoral enhancement, tumor contact length, diffusion kurtosis metrics, radiomics, and artificial intelligence-based prediction models-are reviewed as promising avenues to enhance diagnostic confidence and inter-reader agreement. Finally, the review discusses MRI-first and MRI-guided clinical pathways under investigation, in which VI-RADS-based risk stratification informs the selective use of TURBT, and facilitates more timely, tailored, definitive therapy. In the future, sustained educational efforts, protocol standardization, quality monitoring, and outcome-based prospective trials will be crucial for establishing bladder MRI and VI-RADS as integral components of personalized bladder cancer care in Japan.

Purpose: The primary objective of this study was to determine the relationship between Wingate test, athletic performance, and MRI parameters in athletes. Additionally, we examined whether there were significant differences in these parameters between athletes and non-athletes during dorsiflexion exercises.

Methods: Twenty-two male athletes and 9 non-athletes performed dorsiflexion exercises with a 4-kg load. MRI scans, including T2* mapping and diffusion tensor imaging, were conducted pre-exercise, immediately after exercise, and 30 minutes post-exercise. Quantitative parameters, including T2* values, fractional anisotropy, mean diffusivity, and eigenvalues (λ2, λ3), were analyzed. Wingate test results and athletics scoring based on the 2022 World Athletics Scoring Tables were used to evaluate anaerobic power and sprint performance.

Results: MRI parameters, particularly T2* changes and λ3, showed significant correlations with Wingate test results and athletic performance. Pre-exercise λ3, reflecting muscle fiber orientation and thickness, emerged as a key predictor of athletic performance alongside T2* changes and Wingate power. The integration of MRI-derived metrics with Wingate test results improved the prediction of athletic scores compared to Wingate power alone. Although differences between athletes and non-athletes in T2* and λ2 were observed, these findings serve as supplementary evidence supporting the role of MRI in identifying muscle characteristics critical for athletic performance.

Conclusion: MRI-derived parameters combined with performance tests can provide valuable insights into muscle recovery, structure, and athletic performance, with potential for predicting athlete scores and optimizing training strategies.

Purpose: To develop and evaluate short-TR acquisition time-of-flight (STRA-TOF) MR angiography (MRA), which combines an optimized STRA with deep learning-based reconstruction to achieve scan-time reduction while maintaining image quality in the visualization of intracranial arteries.

Methods: Ten healthy volunteers and 3 patients with moyamoya disease were examined using 3D TOF MRA with the clinical moyamoya protocol and 2 STRA-TOF protocols employing 4-slab (STRA4) and 9-slab (STRA9) configurations. STRA-TOF employed a TR of approximately 10 ms with variable-density Poisson-disc sampling and unrolled deep learning reconstruction. Bloch equation simulations validated the theoretical basis for STRA. Quantitative assessment included SNR and contrast-to-noise ratio measurements. Two radiologists independently evaluated image quality using a 3-point scale across 9 vascular territories and overall image quality, with blinded assessment. Statistical analysis was performed using the Friedman test with post hoc Wilcoxon signed-rank tests.

Results: STRA-TOF achieved approximately a 50% reduction in scan time compared with conventional protocols. Both STRA sequences demonstrated significantly higher SNR and contrast-to-noise ratio than conventional TOF (P < 0.001). Overall image quality scores were higher for STRA4 and STRA9 compared with conventional TOF in both readers. Across the 9 vascular territories, both readers consistently rated STRA sequences equal to or superior to conventional TOF, particularly for distal branches. In the small patient cohort with moyamoya disease (n = 3), STRA-TOF demonstrated feasibility for visualizing complex arterial pathology, including stenotic vessels, collateral circulation, and postsurgical vascular changes.

Conclusion: STRA-TOF achieved approximately a 50% reduction in scan time while maintaining or improving image quality compared with conventional 3D TOF MRA. This technique addresses the fundamental scan-time limitations of the conventional method, with potential for significant clinical benefits in terms of patient comfort, workflow efficiency, and improved access to intracranial artery evaluation.

Purpose: Interpretability and robustness are both critical for developing trustworthy artificial intelligence, especially in high-stakes domains such as medical diagnosis. However, few studies have explored how to enhance robustness within interpretable model frameworks. This work aims to improve the robustness of interpretable multimodal medical imaging diagnostic models, particularly under missing modality conditions.

Methods: We propose the Modality-Robust and Explainable Network (MoRE-Net), a robust and interpretable model for brain tumor grading. Built on a variant of the interpretable prototypical part network, MoRE-Net uses independent per-modality encoders to extract modality-specific features. To address the absence of inter-modality interactions, we introduce 2 key designs: (1) Mamba-based per-modality encoders for efficient global-context modeling; and (2) an online multimodal teacher that guides the per-modality encoders via an alignment loss during early training, which is gradually annealed and removed. We evaluate MoRE-Net on 369 subjects with multimodal MRI from the BraTS2020 dataset, using balanced accuracy (BAC) for grading performance and activation precision (AP) for interpretability. We further validate the model on the real-world ReMIND dataset.

Results: MoRE-Net achieves an average BAC of 73.5% and AP of 61.2% across all missing modality scenarios on BraTS2020 dataset, surpassing baseline methods by about 15% and 21%, respectively. Results on ReMIND dataset and ablation studies confirm its effectiveness of each proposed strategy and the overall robustness.

Conclusion: We introduce MoRE-Net, a novel interpretable and modality-robust model for brain tumor grading. Experimental results demonstrate its strong performance in both diagnostic accuracy and interpretability under missing modality conditions, indicating its potential for clinical deployment.

We have developed a practical method to determine the optimal number of signal averages (NSAs) for acquiring a spectrum in the human brain and determined the optimal number of NSA at 7-Tesla. We performed 50 sequential data acquisitions with an NSA of 2 in healthy volunteers and then generated spectra with NSAs ranging from 2 to 100. After identifying the metabolites with Cramér-Rao lower bounds (CRLB) ≤ 15% in the NSA 100 spectrum, we examined the CRLB values, creatine+phosphocreatine (Cr + PCr) ratios and agreement of Cr + PCr ratios. Eight metabolites and 5 metabolite pairs spectrum showed CRLB values ≤ 15% when the NSA was 40 or higher. Additionally, the Cr + PCr ratios at NSA 40 closely matched those at NSA 100. By analyzing the CRLB values of metabolites in varying NSAs spectra generated from sequential data, we determined the optimal NSA needed to accurately measure the spectrum within a reasonable acquisition time.

Purpose: This study evaluated the performance of artificial intelligence (AI)-based brain aneurysm detection software in clinical settings, aiming to assess its utility as a supportive tool for radiologists. Metrics included sensitivity, positive predictive value (PPV), F1 score, and false positives (FPs) per case.

Methods: A retrospective analysis of 442 cases (March 2023-August 2024) compared AI detections against a reference standard derived from the radiologists' assessments and image re-review. Findings were categorized into true positives (TPs), FPs, and false negatives (FNs). Subgroup analyses covered aneurysm size, magnetic field strength of the MRI, patient age, and aneurysm location.

Results: The study included 442 cases (226 males, 216 females; median age 72). Out of 94 total aneurysms, the AI detected 73 TP and missed 21 FN. It also identified 520 FP. Overall, sensitivity was 77.7%, PPV was 12.3%, and the F1 score was 0.212. The FPs averaged 1.18 per case. Sensitivity varied by aneurysm size: 85.1% for ≤ 3 mm, 69.2% for 3-5 mm, and 50.0% for > 5 mm. Significant variability in FPs per case was observed across different magnetic field strengths. Performance also varied by patient age and aneurysm location.

Conclusion: The AI software demonstrated moderate sensitivity, especially for smaller aneurysms. Variations in performance across different magnetic field strengths and aneurysm size suggest a need for more robust AI algorithms. Detailed analysis of aneurysm locations provides insights into areas where AI performance could be enhanced. Integrating the AI software as a supportive tool, combined with radiologist expertise, is hypothesized to enhance detection accuracy, though further studies are needed to quantify this combined effect.

Purpose: Glioblastoma (GBM) is an aggressive brain tumor with poor prognosis. O6-methylguanine-DNA-methyltransferase (MGMT) promoter methylation is a critical biomarker for guiding chemotherapy decisions, yet current testing requires invasive tissue sampling. This study aimed to systematically evaluate the diagnostic accuracy of artificial intelligence (AI) models using MRI for non-invasive prediction of MGMT promoter methylation status in GBM.

Methods: We conducted a systematic search of PubMed, ScienceDirect, Scopus, Google Scholar, Cochrane, Web of Science and EMBASE, identifying 480 records. After duplicate removal and screening, 14 studies met inclusion criteria. Data extracted included AI model architecture, MRI sequences, segmentation methods, and diagnostic metrics. A bivariate random-effects model was used to pool sensitivity and specificity. Meta-regression analyses assessed the effect of AI model type on diagnostic performance. Study quality was evaluated using the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool.

Results: The bivariate random-effects model yielded a pooled sensitivity of 0.536 (95% confidence interval [95% CI]: 0.509-0.563) and a pooled specificity of 0.514 (95% CI: 0.454-0.574), indicating moderate between-study heterogeneity, with an area under the curve of 0.56. The best-performing models included MGMT-net and transformer-based architectures, particularly when using multimodal MRI inputs. Studies employing automated segmentation and single-sequence input (e.g., T2-weighted only) generally demonstrated lower performance. QUADAS-2 assessment indicated a low risk of bias in most domains, with concerns regarding index test thresholds and external validation in some studies.

Conclusion: AI-based MRI models show moderate-to-high potential for non-invasive MGMT methylation prediction in GBM. However, heterogeneity in study design, imaging protocols, and validation approaches highlights the need for standardized methodologies and robust external validation before clinical adoption.

Purpose: This preliminary study aimed to investigate the agreement in endolymphatic hydrops (EH) grading between high-resolution non-contrast and contrast-enhanced HYDROPS (HYbriD of Reversed image Of Positive endolymph signal and native image of positive perilymph Signal) techniques in the same subjects and to clarify the effect of patient age on the concordance between the 2 methods.

Methods: A retrospective study was performed on 12 patients (24 ears; age range: 24-76 years) suspected of EH, all of whom underwent both non-contrast and 4-hour post-contrast 3T MRI. EH in the cochlea and vestibule was graded by the Nakashima scale. Agreement analyses were assessed using the weighted Cohen's kappa (κ). Statistical significance, including the difference between Younger (24-54 years) and Older (57-76 years) patient subgroups, was determined via bootstrap analysis.

Results: The overall agreement between non-contrast and contrast-enhanced methods was fair for both the cochlea (κ = 0.343) and vestibule (κ = 0.398). Subgroup analysis revealed a significant age-related difference in vestibular agreement (P = 0.005), showing substantial agreement in the Older group (κ = 0.795) but only slight agreement in the Younger group (κ = 0.113). No significant age-related difference was found for cochlear agreement. Quantitatively, the non-contrast method demonstrated a significantly lower contrast-to-noise ratio compared to the contrast-enhanced method.

Conclusion: The concordance between non-contrast and contrast-enhanced MRI for EH assessment is site- and age-dependent. The substantially higher agreement found in older patients suggests that age-related physiological changes facilitate non-contrast visualization. However, caution is advised when interpreting non-contrast findings in younger patients due to potential risks of vestibular overestimation and cochlear underestimation. Given the preliminary nature and small sample size of this study, further investigations with larger cohorts are necessary to validate these findings and the appropriateness of age-based categorization.