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

Purpose: This study aims to assess thalamocortical tract fiber injury using diffusion-tensor imaging (DTI) and to characterize metabolic alterations in the dorsal thalamus with proton magnetic resonance spectroscopy (MRS) in patients with cervical spondylotic myelopathy (CSM).

Methods: A prospective study involved 98 CSM patients and 66 age-matched controls without neurological disease, recruited from May 2021 to December 2021. Neurological function was evaluated using the modified Japanese Orthopaedic Association (mJOA) score. DTI and MRS were analyzed by 2 experienced radiologists, with statistical analysis performed via SPSS (v.26), including t-tests, chi-square tests, and Pearson's correlation.

Results: DTI revealed significantly lower fractional anisotropy (FA) and higher radial and axial diffusivity in the bilateral postcentral gyrus of CSM patients compared to controls (P < 0.001). MRS showed decreased ratios of N-acetylaspartate (NAA)/creatinine (Cr), choline (Cho)/Cr, and myo-inositol (mI)/Cr in the dorsal thalamus of CSM patients (P < 0.001). Correlation analysis indicated a moderate association between mJOA scores and NAA/Cr and mI/Cr ratios, and a weaker correlation with FA and Cho/Cr.

Conclusion: CSM patients exhibit significant thalamocortical disruptions and metabolic changes in the dorsal thalamus, correlating with clinical severity. These findings suggest that DTI and MRS could provide valuable insights into the extent of neural damage in CSM.

Purpose: To compare diagnostic reliability between an intravoxel incoherent motion (IVIM) imaging and an arterial spin labeling (ASL) in assessment of renal blood flow in rodents.

Methods: We first evaluated 3 different fitting methods on 5 datasets of diffusion-weighted imaging (DWI) with 10 b-values (0-1000 s/mm²) for bi-exponential analysis in IVIM imaging to calculate pseudo-diffusion parameters. Coefficient of variation (CV) for each parameter and correlation among the parameters was assessed to test the robustness of the 3 fitting methods. Subsequently, DWI and ASL methods were performed before and 14 days after onset of acute kidney injury (AKI) in a rat model. Temporal change before and after AKI onset in the pseudo-diffusion parameters in 3 fitting methods was compared with that in the renal blood flow (RBF) derived in the ASL method.

Results: The CVs in all IVIM parameters were the lowest in the fitting method that estimated pseudo-diffusion parameters after a fixed true-diffusion was determined where the pseudo- and true-diffusion coefficients had no correlation. The RBF substantially reduced (~50%, P < 0.001) due to the AKI onset; however, no pseudo-diffusion parameters in any of 3 fitting methods could not detect the change. Further, any pseudo-diffusion parameters showed no correlation with the RBF.

Conclusion: Pseudo-diffusion parameters in the IVIM concept were not reliable to estimate RBF in the study. Since the kidney has a unique profile in the "tissue flow", our data indicate that study design and interpretation of results needs to be carefully considered when IVIM imaging is used for evaluation of blood flow in tissue, especially in the kidney.

Purpose: A cyst-like structure near superior sagittal sinus (Arachnoid Cuff Exit Site cysts: ACES cysts) has been reported in MRI. The purpose of this study was to investigate the association between presence of ACES cysts and cognitive function, as assessed using mini-mental state examination (MMSE) scores.

Methods: We retrospectively analyzed patients who underwent head MRI for dementia screening. Differences in patient ages and MMSE scores between patients with and without ACES cysts were examined using the Mann-Whitney U test. Correlations between patient ages and MMSE scores were examined for patients with and without ACES cysts using Spearman's rank correlation coefficient. Multivariate logistic regression analysis was performed to examine the influence of presence or absence of ACES cysts on MMSE score.

Results: A total of 112 patients (male: 28, female: 84) were included for the analysis. The patient ages ranged from 66 to 94 years (median: 83 years). MMSE scores ranged from 6 to 30 (median: 24). ACES cysts were detected in 57 patients (50.9%). There was no significant difference in patient ages between the patients with and without ACES cysts (P = 0.058). The patients with ACES cysts showed significantly lower MMSE scores compared to the patients without ACES cysts (P < 0.001). In the patients with ACES cysts, there was no significant correlation between patient ages and MMSE scores (ρ = -0.178, P = 0.185), whereas a significant negative correlation was observed in the patients without ACES cysts (ρ = -0.347, P = 0.001). The presence of ACES cysts was determined as an independent predictor for the lower MMSE score (odds ratio = 15.2, 95% confidence interval = 5.59-41.4, P < 0.001).

Conclusion: The presence of the ACES cysts showed significant association with lower MMSE score. ACES cysts might be involved in the pathological processes affecting cognitive function.

Purpose: To re-evaluate images recovered from JCOG0911, a randomized phase 2 trial for newly diagnosed glioblastoma (nGBM) conducted by the Japan Clinical Oncology Group (JCOG) Brain Tumor Study Group.

Methods: The correlation between tumor volumes and survival was evaluated, followed by progression-free survival (PFS) analysis by independent central review based on Response Assessment in Neuro-Oncology (RANO) criteria using MRI recovered from 118 nGBM patients enrolled in the JCOG0911 trial. A radiomic analysis was also performed to identify radiomic features predictive of nGBM prognosis.

Results: The distribution of the Gd-enhancing and T2-weighted image/fluid attenuated inversion recovery-high intensity lesions mainly occupied white matter. JCOG0911 consisted of more subjects with right-sided lesions. The median extent of resection of the Gd-enhancing lesions was 99%. The overall survival showed a nonsignificant negative trend with postoperative Gd-enhancing lesion volume (P = 0.22), with the hazard ratio increasing in parallel with its volume. The median PFS after registration was 302 and 308 days for local Response Evaluation Criteria in Solid Tumors (RECIST)-based and central RANO-based diagnoses. However, an apparent discrepancy was observed between the two in the early phase, presumably due to the misdiagnosis of pseudoprogression by local RECIST-based diagnosis. Radiomic analysis identified 28 radiomic features predictive of nGBM prognosis, 5 of which were those previously identified in a separate cohort. The constructed radiomics-based prognostic model stratified the cohort into high- and low-risk groups (P = 0.028).

Conclusion: Novel analytical methods that could be incorporated into future clinical trials were successfully tested. RANO and RECIST may not differ in progression calls if pseudoprogression is appropriately handled.

Purpose: The purposes of this study were 1) to improve vessel visibility of our MR sequence by modifying k-space filling and 2) to verify the usefulness of applying artificial intelligence (AI) for volume isotropic simultaneous interleaved bright- and black-blood examination (VISIBLE) with compressed sensitivity encoding (CS) in autodetecting brain metastases.

Methods: We modified 3 sequences of VISIBLE (Centric, Reversed Centric, and Startup Echo 30). The Centric sequence is a prototype. The Reversed Centric filled the k-space in a reversed centric manner to improve vessel visibility. The Startup Echo 30 implemented dummy echoes to further improve vessel visibility. Vessel visibility was evaluated in one slice at the level of the centrum semiovale. The sensitivity, specificity, the area under the curve (AUC), and false positives of detecting brain metastases using AI were evaluated among 3 sequences. Statistical comparisons were performed using a one-way analysis of variance, followed by Friedman and Dunn's multiple comparison tests.

Results: The number of visualized vessels was significantly lower in the Centric (39.3 ± 9.7, P < 0.05) and Reversed Centric (44.2 ± 9.8, P < 0.05) methods than in the magnetization-prepared rapid gradient echo (49.3 ± 9.1) but comparable in the Startup Echo 30 method (44.9 ± 8.8, P > 0.05). No significant differences existed in sensitivity, specificity, and AUC among the 3 methods. False positives achieved using the Reversed Centric method were significantly fewer (54 false positives) than those achieved using the Centric (85 false positives) and Startup Echo 30 (68 false positives) methods (P = 0.0092).

Conclusion: Vessel visibility was improved by modifying the k-space filling, which may reduce false positives. The AI model for VISIBLE with CS achieved good performance in autodetection of brain metastases. The AI model for VISIBLE with CS can help radiologists diagnose brain metastases in clinical practice.

Purpose: This study investigated the ability of three-dimentional motion-sensitized driven-equilibrium prepared T₁-weighted fast spin echo (3D MSDE-FSE) imaging to identify distal dural rings (DDRs) and paraclinoid aneurysms (ParaC-ANs) and differentiate between intradural and extradural ParaC-ANs and compared it with that of established MR cisternography-based techniques.

Methods: 3D MSDE-FSE images were acquired along with fast imaging employing steady state acquisition (FIESTA), and time-of-flight magnetic resonance angiography (TOF-MRA) on a 3T MRI system in 53 patients with unruptured and untreated ParaC-ANs. Two radiologists applied a 3-point scale to rate the clarity with which the DDR (53 left and 53 right) and ParaC-ANs (total of 55) were depicted in the 3D MSDE-FSE and FIESTA images. The clarity scores, which were determined by averaging the scores of the 2 assessors, on the 3D MSDE-FSE and FIESTA images were compared using the Wilcoxon signed-rank test. Furthermore, the same radiologists classified the ParaC-ANs as intradural, extradural, or transitional on 3D MSDE-FSE images. A third radiologist independently classified the ParaC-ANs as intradural, extradural, or transitional based on the FIESTA and MRA fusion images. The kappa coefficient was used to compare this classification with that based on 3D MSDE-FSE images.

Results: The Wilcoxon signed-rank test revealed no significant difference between 3D MSDE-FSE images and FIESTA images in the scores for the clarity of depiction of the DDRs (P = 0.119). However, the scores for the clarity of the depiction of the ParaC-ANs were significantly greater for the 3D MSDE-FSE images than for the FIESTA images (P < 0.001). The kappa coefficient for comparison of classification based on 3D MSDE-FSE images and FIESTA and MRA fusion images was 0.82.

Conclusion: 3D MSDE-FSE imaging has the potential to differentiate between intradural and extradural ParaC-ANs by directly recognizing the DDR.

The integration of deep learning (DL) in breast MRI has revolutionized the field of medical imaging, notably enhancing diagnostic accuracy and efficiency. This review discusses the substantial influence of DL technologies across various facets of breast MRI, including image reconstruction, classification, object detection, segmentation, and prediction of clinical outcomes such as response to neoadjuvant chemotherapy and recurrence of breast cancer. Utilizing sophisticated models such as convolutional neural networks, recurrent neural networks, and generative adversarial networks, DL has improved image quality and precision, enabling more accurate differentiation between benign and malignant lesions and providing deeper insights into disease behavior and treatment responses. DL's predictive capabilities for patient-specific outcomes also suggest potential for more personalized treatment strategies. The advancements in DL are pioneering a new era in breast cancer diagnostics, promising more personalized and effective healthcare solutions. Nonetheless, the integration of this technology into clinical practice faces challenges, necessitating further research, validation, and development of legal and ethical frameworks to fully leverage its potential.

This review explores the significant progress and applications of artificial intelligence (AI) in obstetrics and gynecological MRI, charting its development from foundational algorithmic techniques to deep learning strategies and advanced radiomics. This review features research published over the last few years that has used AI with MRI to identify specific conditions such as uterine leiomyosarcoma, endometrial cancer, cervical cancer, ovarian tumors, and placenta accreta. In addition, it covers studies on the application of AI for segmentation and quality improvement in obstetrics and gynecology MRI. The review also outlines the existing challenges and envisions future directions for AI research in this domain. The growing accessibility of extensive datasets across various institutions and the application of multiparametric MRI are significantly enhancing the accuracy and adaptability of AI. This progress has the potential to enable more accurate and efficient diagnosis, offering opportunities for personalized medicine in the field of obstetrics and gynecology.

Purpose: To evaluate the clinical value of early renal changes in type 2 diabetes mellitus (T2DM) using multiparameter MRI.

Methods: The study included 41 diabetics (normoalbuminuria: n = 23; microalbuminuria: n = 18) and 30 healthy controls. All subjects underwent intravoxel incoherent motion diffusion-weighted imaging (IVIM), blood oxygen level dependent (BOLD) and arterial spin labeling (ASL) examinations. One-way analysis of variance was used to compare MRI parameters among the three groups. Pearson correlation analysis was used to evaluate the relationship between MRI parameters and estimated glomerular filtration rate (eGFR) and albumin-creatinine ratio (ACR). Receiver operating characteristic analysis was performed to assess the diagnostic performance.

Results: There were statistical differences in cortical D, D*, f, renal blood flow (RBF) and medulla D, D*, f, R2* among the three groups (P < 0.05). The cortical or medullary D, cortical f, and RBF were significantly positively correlated with eGFR (all P < 0.01). The cortical or medullary D, D*, f, cortical RBF were negatively correlated with ACR (all P < 0.05).To evaluate early kidney changes and degree of diabetes, cortical combined D and RBF (AUC [area under the curve]  = 0.796 and 0.947, respectively) was better than single D or RBF (all P > 0.05); medullary combined D and R2* (AUC = 0.899 and 0.923, respectively) was better than single D or R2* (all P > 0.05), except single D (P = 0.005) in differentiating normoalbuminuria group from control group.

Conclusion: The early changes of renal diffusion and perfusion, oxygenation level, and blood flow in T2DM could be evaluated noninvasively and quantitatively using IVIM, BOLD and ASL. Renal medullary combined IVIM-derived D and BOLD-derived R2* and cortical combined IVIM-derived D and ASL-derived RBF were better for evaluating early renal changes in T2DM.