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

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.

Purpose: This study aimed to compare the differences in the imaging findings for dedifferentiated liposarcoma (DDLS) and myxoid liposarcoma (MLS).

Methods: The study included 30 patients with histopathologically confirmed DDLS and 13 patients with MLS. All DDLSs and MLSs were diagnosed immunohistochemically using MDM2 and DDIT3 staining, respectively. Conventional MRI, CT, and ¹⁸F-fluorodeoxyglucose-positron emission tomography/CT findings were retrospectively evaluated and compared between the 2 pathologies.

Results: The median age of patients with DDLS was higher than that of patients with MLS (74 vs. 46 years, P < 0.01). In 10 DDLSs and 7 MLSs with fatty areas, the well-differentiated liposarcoma-like fatty areas were more common in DDLS than in MLS (70% vs. 14%), whereas septal/linear fatty areas were less common in DDLS than in MLS (30% vs. 86%, P < 0.05). The T2-hyperintense area of non-fatty area was less common in DDLS than in MLS (50% vs. 92%, P < 0.05), and the tumor-to-muscle signal intensity ratio of non-fatty areas on T2-weighted images was lower in DDLS than in MLS (3.18 vs. 5.92, P < 0.01). Apparent diffusion coefficient value was lower in DDLS than in MLS (1.29 vs. 2.10 × 10^-3mm²/sec, P < 0.01). Unenhanced CT attenuation of non-fatty area was greater in DDLS than in MLS (33 vs. 19 Hounsfield unit, P < 0.01).

Conclusion: MRI features are valuable in differentiating MLS from DDLS. Younger age, septal/linear fatty areas, and high signal intensity of non-fatty areas on T2-weighted images were useful for diagnosing MLS.

Purpose: Pulmonary valve regurgitation after repaired Tetralogy of Fallot (TOF) or double-outlet right ventricle (DORV) causes hypertrophy and papillary muscle enlargement. Cardiac magnetic resonance imaging (CMR) can evaluate the right ventricular (RV) dilatation, but the effect of trabecular and papillary muscle (TPM) exclusion on RV volume for TOF or DORV reoperation decision is unclear.

Methods: Twenty-three patients with repaired TOF or DORV, and 19 healthy controls aged ≥15, underwent CMR from 2012 to 2022. TPM volume is measured by artificial intelligence. Reoperation was considered when RV end-diastolic volume index (RVEDVI) >150 mL/m² or RV end-systolic volume index (RVESVI) >80 mL/m².

Results: RV volumes were higher in the disease group than controls (P α 0.001). RV mass and TPM volumes were higher in the disease group (P α 0.001). The reduction rate of RV volumes due to the exclusion of TPM volume was 6.3% (2.1-10.5), 11.7% (6.9-13.8), and 13.9% (9.5-19.4) in the control, volume load, and volume α pressure load groups, respectively. TPM/RV volumes were higher in the volume α pressure load group (control: 0.07 g/mL, volume: 0.14 g/mL, volume α pressure: 0.17 g/mL), and correlated with QRS duration (R α 0.77). In 3 patients in the volume α pressure, RV volume included TPM was indicated for reoperation, but when RV volume was reduced by TPM removal, reoperation was no indicated.

Conclusion: RV volume measurements, including TPM in volume α pressure load, may help determine appropriate volume recommendations for reoperation.

Purpose: To study the potential advantages of phosphorus magnetic resonance spectroscopy (³¹P-MRS) in differentiating advanced from mild fibrosis in non-alcoholic fatty liver disease (NAFLD) and early diagnosis at high field strength MR (9.4 Tesla).

Methods: Fibrosis of normal and carbon tetrachloride (CCl₄)-treated male rats was staged into: none (F0), perisinusoidal or periportal (F1), perisinusoidal and portal/periportal (F2), bridging fibrosis (F3) and cirrhosis (F4) by Sirius Red staining. The degree of steatosis and inflammatory activity were also graded based on Hematoxylin and Eosin staining. Rats were divided into different groups by different stages of fibrosis (F0, F1-2, F3-4) and laboratory blood tests were performed to verify the degree of liver injury. ³¹P-MRS was performed at 9.4T MR to obtain signal peaks of different phosphorus metabolites and the changes of the ratios between the peaks were observed.

Results: At 9.4 T, phosphoethanolamine (PE), phosphocholine (PC) and glycerophosphorylethanolamine (GPE), glycerophosphorylcholine (GPC) could be separated respectively from the peaks of phosphomonoesters (PME) and phosphodiesters (PDE), meanwhile nicotinamide adenine dinucleotide phosphate (NADPH) and uridine diphosphate glucose (UDPG) showed up as well. The marker of cell membrane metabolism, in F1-2, PME/PDE (P < 0.001), PC/GPE (P < 0.01), PC/GPC (P < 0.05) and PC/(PME + PDE) (P < 0.05) decreased while GPE/(PME + PDE) (P < 0.05) and GPC/(PME + PDE) (P < 0.05) increased significantly. In F3-4, there was a recovery trend of most ratios, especially for PC/(PME + PDE) (P < 0.05). As for the main ratio related to energy metabolism, β-ATP/P_total (P < 0.05) decreased in the early stage of the disease (F1-2) and this decline was maintained in advanced stage (F3-4). NADPH/P_total (P < 0.01) and β-ATP/Pi (inorganic phosphate) (P < 0.05) ratio was lower in F3-4 comparing with F0.

Conclusion: ³¹P-MRS can generally stage the liver fibrosis by comparing the ratios of the phosphorus metabolites resonance peaks at 9.4 T and more importantly it can be used for early diagnosis.

Recent advances in musculoskeletal (MSK) radiology have markedly improved diagnostic accuracy through innovations in MRI, CT, and artificial intelligence (AI). This review summarizes 7 key domains shaping current MSK imaging: (1) CT-like contrast MRI techniques for bone visualization, (2) quantitative MRI approaches, (3) AI applications in image reconstruction and diagnostic support, (4) MR spectroscopy (MRS) for metabolic assessment, (5) whole-body MRI for systemic disease evaluation, (6) positron emission tomography (PET) for metabolic and inflammatory imaging, and (7) advanced CT techniques such as weight-bearing CT. Zero echo time and ultrashort echo time MRI sequences enable the visualization and quantitative assessment of short-T2 tissues such as cortical bone, tendons, and fibrocartilage. Deep learning-based image reconstruction improves SNR and shortens scan time, enhancing image quality and diagnostic confidence. In parallel, AI-driven diagnostic support systems, including convolutional neural networks for lesion detection and natural language processing for report generation, are transforming workflow efficiency and consistency. MRS offers metabolic insights into muscle disorders such as sarcopenia, and whole-body-MRI provides comprehensive, radiation-free evaluation of tumor burden and inflammatory joint or enthesis involvement, making it valuable in oncology and rheumatic diseases. PET complements MRI by identifying metabolically active or inflammatory lesions. CT-based innovations further contribute to evaluating joint biomechanics with high spatial resolution. Together, these technological developments are enabling earlier disease detection, more precise diagnosis, and improved treatment monitoring, representing a paradigm shift in MSK imaging and clinical practice.

Purpose: Acupuncture is believed to significantly modify neural circuits in the brain. However, the effects of moxibustion stimulation remain unclear. Therefore, we used functional MRI to investigate brain activation sites induced by moxibustion stimulation using an electric moxibustion device that mimics Japanese Tonetsu-kyu half-grain-sized direct moxibustion.

Methods: Twenty-two healthy adult participants underwent 6 rounds of 7-s moxibustion stimulations on the right acupuncture point ST36 using electric moxibustion during functional MRI measurement. The maximum output temperature of electric moxibustion was 67.5°C. However, the contact surface temperature was adjusted to 58.6 ± 0.1°C using cooking wrap to avoid small burns caused by heating. The ON time was divided into 3 periods: ON1, 2s from the start of moxibustion stimulation (<45°C); ON2, 5s from 2s after the start of output to the end of stimulation (>45°C); and ON3, 3s after the completion of stimulation. Each block was designed with all options other than ON set to OFF.

Results: Common and different activations were observed in all ON times. During stimulation, common activation was observed in the insula, S1, and supramarginal gyrus. Activation in the central operculum, frontal operculum and supplementary motor area was observed only in the ON1 condition, while activation in the frontal pole, cerebellum, and right S2 was observed only in the ON2 condition. Using electric moxibustion that mimics a grain-sized direct moxa cone, common and different activations were confirmed from the start of output to 45°C and above 45°C, and the activation was sustained after the completion of stimulation.

Conclusion: This study showed that moxibustion could affect almost the same areas of pain-related regions. Based on the findings of this study, further research on moxibustion-induced brain activation may help elucidate the mechanism of its therapeutic effects.

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.

This study evaluated the utility of deep learning reconstruction (DLR) in vessel wall imaging (VWI) for visualizing the entire cerebral arterial system, including cortical arteries. Seventeen patients underwent post-contrast 3D T1WI-CUBE VWI with 0.5 mm isotropic resolution. Images with and without DLR were compared using qualitative and quantitative assessments. Qualitative image quality was rated on a 4-point scale across 29 arterial segments, including the internal carotid, vertebral, basilar, and the 1st to 4th segments of the major cerebral arteries. Quantitative evaluation of the vertebral artery wall assessed SNR and contrast-to-noise ratio (CNR). DLR significantly improved overall image quality compared to the without-DLR group, with cortical arteries rated as optimal in all cases with DLR (all P < 0.001). SNR and CNR were also significantly higher with DLR (P = 0.004). These results suggest that DLR enables high-resolution VWI of intracranial cortical arteries within a clinically acceptable scan time.

Purpose: To evaluate the effect of model-based deep-learning reconstruction (DLR) compared with that of compressed sensing-sensitivity encoding (CS) on cine cardiac magnetic resonance (CMR).

Methods: Cine CMR images of 10 healthy volunteers were obtained with reduction factors of 2, 4, 6, and 8 and reconstructed using CS and DLR. The visual image quality scores assessed sharpness, image noise, and artifacts. Left-ventricular (LV) end-diastolic volume (EDV), end-systolic volume (ESV), stroke volume (SV), and ejection fraction (EF) were manually measured. LV global circumferential strain (GCS) was automatically measured using the software. The precision of EDV, ESV, SV, EF, and GCS measurements was compared between CS and DLR using Bland-Altman analysis with full-sampling data as the gold standard.

Results: Compared with CS, DLR significantly improved image quality with reduction factors of 6 and 8. The precision of EDV and ESV with a reduction factor of 8, and GCS with reduction factors of 6 and 8 measurements improved with DLR compared with CS, whereas those of SV and EF measurements were not different between DLR and CS.

Conclusion: The effect of DLR on cine CMR's image quality and precision in evaluating quantitative volume and strain was equal or superior to that of CS. DLR may replace CS for cine CMR.

Purpose: Diffusion models (DMs) excel in pixel-level and spatial tasks and are proven feature extractors for 2D image discriminative tasks when pretrained. However, their capabilities in 3D MRI discriminative tasks remain largely untapped. This study seeks to assess the effectiveness of DMs in this underexplored area.

Methods: We use 59830 T1-weighted MR images (T1WIs) from the extensive, yet unlabeled, UK Biobank dataset. Additionally, we apply 369 T1WIs from the BraTS2020 dataset specifically for brain tumor classification, and 421 T1WIs from the ADNI1 dataset for the diagnosis of Alzheimer's disease. Firstly, a high-performing denoising diffusion probabilistic model (DDPM) with a U-Net backbone is pretrained on the UK Biobank, then fine-tuned on the BraTS2020 and ADNI1 datasets. Afterward, we assess its feature representation capabilities for discriminative tasks using linear probes. Finally, we accordingly introduce a novel fusion module, named CATS, that enhances the U-Net representations, thereby improving performance on discriminative tasks.

Results: Our DDPM produces synthetic images of high quality that match the distribution of the raw datasets. Subsequent analysis reveals that DDPM features extracted from middle blocks and smaller timesteps are of high quality. Leveraging these features, the CATS module, with just 1.7M additional parameters, achieved average classification scores of 0.7704 and 0.9217 on the BraTS2020 and ADNI1 datasets, demonstrating competitive performance with that of the representations extracted from the transferred DDPM model, as well as the 33.23M parameters ResNet18 trained from scratch.

Conclusion: We have found that pretraining a DM on a large-scale dataset and then fine-tuning it on limited data from discriminative datasets is a viable approach for MRI data. With these well-performing DMs, we show that they excel not just in generation tasks but also as feature extractors when combined with our proposed CATS module.