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

The optimal TR for prostate diffusion-weighted imaging (DWI) remains unclear. Given prostate cancers' shorter T1/T2 relaxation times versus benign tissues, TR adjustment may improve contrast. We evaluated 56 clinically significant cancers in 33 patients, comparing synthetic DWI (b-value = 2000 s/mm²) at TR500/1000/1500/2000 ms against conventional TR6000 ms. Assessments included contrast ratio, apparent SNR, lesion conspicuity score, DWI scores based on Prostate Imaging Reporting and Data System (PI-RADS) v2.1, and background suppression. TR6000 showed significantly lower contrast ratio but higher apparent SNR compared with shorter TRs. TR1000-2000 showed higher lesion conspicuity score than TR500, while TR1500-6000 had higher DWI scores than TR500. TR500-1500 provided better background suppression than TR2000/6000. For optimal balance of contrast and noise, TR1000 or TR1500 is recommended for prostate DWI, potentially enhancing prostate cancer detection in clinical practice.

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: 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 a fast and precise method for searching rectangular regions in brain tumor images.

Methods: The authors propose a new method for searching rectangular tumor regions in brain MR images. The proposed method consisted of a segmentation network and a fast search method with a user-controllable search metric. As the segmentation network, the U-Net whose encoder was replaced by the EfficientNet was used. In the fast search method, summed-area tables were used for accelerating sums of voxels in rectangular regions. Use of the summed-area tables enabled exhaustive search of the 3D offset (3D full search). The search metric was designed for giving priority to nearly isotropic rectangles over undesirable thin rectangles, and assigning better values for higher tumor fractions even if they exceeded target tumor fractions. The proposed computation and metric were compared with those used in a conventional method using the Brain Tumor Image Segmentation dataset.

Results: When the 3D full search was used, the proposed computation (8 seconds) was 100-500 times faster than the conventional computation (11-40 minutes). When the user-controllable parts of the search metrics were changed variously, the tumor fractions of the proposed metric were higher than those of the conventional metric. In addition, the conventional metric preferred undesirable thin rectangles whereas the proposed metric preferred nearly isotropic rectangles.

Conclusion: The proposed method is promising for implementing fast and precise search of rectangular tumor regions, which is useful for brain tumor diagnosis using MRI systems. The proposed computation reduced processing times of the 3D full search, and the proposed metric improved the quality of the assigned rectangular tumor regions.

Purpose: Aortopathy, characterized by aortic dilatation caused by cystic medial necrosis, typically develops in adulthood but has been observed at a young age in patients with tetralogy of Fallot. We hypothesized that some patients with tetralogy of Fallot or double-outlet right ventricle experience early-onset aortic disturbed flow. This study aimed to identify and analyze the causes of disturbed flow using 4D flow MRI.

Methods: This study included 24 patients who underwent 4D flow MRI at our institution between January 2022 and September 2024. MRI and cardiac catheterization were performed during follow-up. 4D flow MRI was used to detect disturbed flow and investigate its underlying causes.

Results: The mean age of patients with tetralogy of Fallot or double-outlet right ventricle was 12 years (range, 1 to 37 years). Of the 24 participants, 11 (46%) exhibited disturbed flow. Patients with disturbed flow had significantly higher Valsalva Z-scores (4.7 ± 3.1 vs. 2.4 ± 1.4, P = 0.013) and a significantly narrower left ventricular outflow tract-ascending aorta angle (113.5 ± 11.6 vs. 127.1 ±  6.7°, P = 0.002). Wall shear stress and energy loss were not significantly different between the 2 groups.

Conclusion: Aortic disturbed flow may occur in patients with tetralogy of Fallot or double-outlet right ventricle, regardless of age, suggesting an association with the left ventricular outflow tract-ascending aorta angle. The occurrence of disturbed flow at a young age should be noted as it may contribute to the future progression of aortopathy.

Purpose: To evaluate the feasibility of high-spatial-resolution hepatobiliary phase (HBP) imaging using optimized integrated combination with the compressed sensing and parallel imaging technique (Compressed SENSE).

Methods: Sixty consecutive participants underwent liver MRI and breath-hold HBP imaging using enhanced T1 high-resolution isotropic volume excitation (eTHRIVE; SENSE factor, 1.7; slice thickness/gap, 4/-2 mm; and acquisition time, 20s), eTHRIVE with Compressed SENSE (CS-eTHRIVE_4mm; C SENSE factor, 3.45; slice thickness/gap, 4/-2 mm; and acquisition time, 10s), and thin-slice eTHRIVE with Compressed SENSE (CS-eTHRIVE_2mm; C SENSE factor, 3.45; slice thickness/gap, 2/0 mm; and acquisition time, 20s). The signal intensity ratio (SIR) and signal-to-noise ratio (SNR) of the liver on each HBP image were calculated. The image quality and conspicuity of hypointense nodules on HBP images were qualitatively assessed. Then, the sensitivity for detecting hypointense nodules was calculated. The quantitative and qualitative parameters of three HBP images were compared.

Results: The SIR of the three HBP images did not differ (P = 0.36). The SNR of CS-eTHRIVE_2mm was lower than that of eTHRIVE and CS-eTHRIVE_4mm (P < 0.001). CS-eTHRIVE_2mm had a better image quality than eTHRIVE and CS-eTHRIVE_4mm (P < 0.001). CS-eTHRIVE_2mm (97.5%) had a significantly better sensitivity for detecting hypointense nodules on HBP image than eTHRIVE (86.4%) and CS-eTHRIVE_4mm (89.0%) (P = 0.001‒0.006).

Conclusion: CS-eTHRIVE_2mm had an excellent image quality and lesion detectability due to its high-spatial-resolution.

This study presents a novel data augmentation approach to improve deep learning (DL)-based segmentation for 3D phase-contrast magnetic resonance angiography (PC-MRA) images affected by pulsation artifacts. Augmentation was achieved by simulating pulsation artifacts through the addition of periodic errors in k-space magnitude. The approach was evaluated on PC-MRA datasets from 16 volunteers, comparing DL segmentation with and without pulsation artifact augmentation to a level-set algorithm. Results demonstrate that DL methods significantly outperform the level-set approach and that pulsation artifact augmentation further improves segmentation accuracy, especially for images with lower velocity encoding. Quantitative analysis using Dice-Sørensen coefficient, Intersection over Union, and Average Symmetric Surface Distance metrics confirms the effectiveness of the proposed method. This technique shows promise for enhancing vascular segmentation in various anatomical regions affected by pulsation artifacts, potentially improving clinical applications of PC-MRA.

Purpose: Among patients with hepatitis B virus-associated liver cirrhosis (HBV-LC), there may be differences in the hepatic parenchyma between those with and without hepatocellular carcinoma (HCC). Proton MR spectroscopy (¹H-MRS) is a well-established tool for noninvasive metabolomics, but has been challenging in the liver allowing only a few metabolites to be detected other than lipids. This study aims to explore the potential of ¹H-MRS of the liver in conjunction with deep learning to differentiate between HBV-LC patients with and without HCC.

Methods: Between August 2018 and March 2021, ¹H-MRS data were collected from 37 HBV-LC patients who underwent MRI for HCC surveillance, without HCC (HBV-LC group, n = 20) and with HCC (HBV-LC-HCC group, n = 17). Based on a priori knowledge from the first 10 patients from each group, big spectral datasets were simulated to develop 2 kinds of convolutional neural networks (CNNs): CNNs quantifying 15 metabolites and 5 lipid resonances (qCNNs) and CNNs classifying patients into HBV-LC and HBV-LC-HCC (cCNNs). The performance of the cCNNs was assessed using the remaining patients in the 2 groups (10 HBV-LC and 7 HBV-LC-HCC patients).

Results: Using a simulated dataset, the quantitative errors with the qCNNs were significantly lower than those with a conventional nonlinear-least-squares-fitting method for all metabolites and lipids (P ≤0.004). The cCNNs exhibited sensitivity, specificity, and accuracy of 100% (7/7), 90% (9/10), and 94% (16/17), respectively, for identifying the HBV-LC-HCC group.

Conclusion: Deep-learning-aided ¹H-MRS with data augmentation by spectral simulation may have potential in differentiating between HBV-LC patients with and without HCC.

Purpose: To evaluate the efficacy of radiomic analysis applied to pretreatment gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA)-enhanced MRI (Gd-EOB-DTPA-MRI) for predicting the response to transcatheter arterial chemoembolization (TACE) for hepatocellular carcinoma.

Methods: Data and images from 40 consecutive patients (28 men, 12 women) who underwent pretreatment Gd-EOB-DTPA-MRI and a total of 52 TACE procedures for 75 non-treated hepatocellular carcinomas were retrospectively analyzed. Two radiologists manually outlined lesions on pretreatment arterial- and hepatobiliary-phase hepatic images to extract radiomic features. The radiomics data from one observer were randomly divided into a training dataset and a validation dataset in the ratio of 7:3. Radiomic features extracted using least absolute shrinkage and selection operator (LASSO) binomial regression applied to the training dataset and that showed intraclass correlation coefficients (ICC) >0.7 were used to construct a radiomic model. The predictive performance of the model was evaluated using receiver operating characteristics curves. Lesions classified as showing a complete or partial response according to the modified RECIST criteria were allocated to a response group.

Results: There was no significant difference in Child-Pugh score, tumor marker values, or TACE procedure between response and non-response groups. Six radiomic features were selected using the LASSO binomial regression and 5 of them showing an ICC >0.7 were used to establish the radiomic model. The area under the curve of the radiomic model was 0.89 for the training dataset, 0.83 for the validation dataset, and 0.83 for the other observer's data. The sensitivity and specificity for the prediction of tumor response to TACE were 78% and 92% for the training dataset; 71% and 50% for the validation dataset; and 75% and 79% for the other observer's data.

Conclusion: The pretreatment Gd-EOB-DTPA-MRI-based radiomic model is useful for predicting the response to TACE of hepatocellular carcinoma.

Purpose: To evaluate short T₂ components potentially reflecting calcification or other susceptibility-affected tissue components in atherosclerotic plaques, using multicomponent analysis with ultrashort TE (UTE) MRI.

Methods: A phantom experiment was conducted using a 4-echo UTE sequence, mimicking the sample as a small amount of calcification found intra-voxel. The phantom included 6 samples containing varying concentrations of hydroxyapatite (calcification) and mayonnaise (lipid-water emulsion). Data acquired from the UTE sequence were compared with those obtained using a conventional multi-echo gradient-echo (mGRE) method.

Results: Bi-exponential analysis of UTE data successfully separated short- and long-T₂* components, with ranges of 0.44-4.81 ms and 4.29-24.37 ms, respectively. Short T₂* values derived from UTE showed minor changes with increasing hydroxyapatite concentration. Using bi-exponential analysis of mGRE data, short and long T₂* values ranged from 0.17-0.77 ms and 6.16-39.20 ms, respectively. For mono-exponential fitting of mGRE data, T₂* values ranged from 4.84-38.32 ms. In all datasets, 1/T₂* increased with hydroxyapatite concentration. The signal fraction of short T₂* components in the UTE dataset decreased as hydroxyapatite concentration increased. A clinical scan of 1 patient with an atherosclerotic plaque yielded mean short and long T₂* values of 0.12 ± 0.35 ms and 33.22 ± 17.25 ms, respectively.

Conclusion: T₂* analysis using UTE data enabled the separation of mixed calcification and mayonnaise (lipid-water emulsion) within a sample into 2 components and detected short T₂* components that may reflect calcification-related susceptibility effects, without directly indicating calcification. Multicomponent T₂* analysis with UTE-MRI is a promising technique for evaluating calcification and other short T₂* components in atherosclerotic plaques.