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

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.

Multiparametric quantitative MRI (MP-qMRI) methods, which measure multiple relaxation times simultaneously, are becoming into practical tools, but the relationship between different MP-qMRI methods has not been clarified. In particular, since 2D multidynamic multiple-echo (2D-MDME) was provided by multiple major MRI vendors, many patient studies have been reported, but its relationship with 3D-QALAS (3D-QuAntification using an interleaved Look-Locker Acquisition Sequence with T₂ preparation pulse), which is expected to be its 3D version successor, is unknown. In this study, we implemented 2D-MDME and 3D-QALAS on an originally designed 1.5T and a clinical 3T MRI systems, and measured relaxation times of phantoms, focusing on the effect of magnetization transfer (MT) effect on T₁ measurements. As a result, in 2D-MDME, T₁ shortening effects of around 20%-35% was observed due to the MT effect, and in 3D-QALAS, no effect of the MT effect on the T₁ measurements was observed. Therefore, we concluded that the MT effect needs to be considered when comparing MP-qMRI methods in clinical studies.

Purpose: Glymphatic system impairment has been suggested in previous animal and human studies regarding traumatic brain injury (TBI). Diffuse axonal injury (DAI) is an important pathological feature of TBI and is frequently diagnosed in patients with moderate to severe TBI. This study evaluated the glymphatic system function in patients with DAI using diffusion tensor imaging analysis along the perivascular space (DTI-ALPS), a non-invasive technique.

Methods: A total of 162 patients with TBI, including 84 with DAI and 78 without DAI, underwent MRI with DTI within 6 months of the date of injury. The ALPS index was calculated to assess the glymphatic system activity and compared between patients with and without DAI. Analysis of covariance (ANCOVA) was used to compare the ALPS index between patients with DAI grades 1, 2, and 3. Correlation analysis was performed between the ALPS index, DAI grade, and Glasgow Coma Scale (GCS) score.

Results: Patients with DAI (1.29 ± 0.17) had a significantly lower ALPS index than those without DAI (1.42 ± 0.19, P < 0.001). The ALPS index differed significantly between patients with different DAI grades (ANCOVA, P < 0.001). The ALPS index and DAI grades were negatively correlated (r =-0.47, P < 0.001). The ALPS index and GCS scores showed a weak positive correlation (r = 0.174, P = 0.027).

Conclusions: Patients with DAI have a lower ALPS index, indicating impaired glymphatic system activity, which is more severe in patients with a higher DAI grade. These findings broaden the understanding of the pathophysiology of DAI and help predict patients' prognoses and recovery trends.

A novel imaging technique, CT-like MRI, specifically Fast Field Echo Resembling a CT Using Restricted Echo-spacing (FRACTURE), has been developed to depict bones. The purpose of this study is to evaluate the feasibility of FRACTURE MRI in cranial and facial bone lesions. Our results suggest that FRACTURE MRI can be used in addition to conventional MRI to evaluate a variety of pathologies of the cranial and facial bones.

We have proposed a T2-based free water suppression diffusion MRI (T2wsup-dMRI) technique to address parameter quantification issues due to cerebrospinal fluid (CSF) partial volume effects (PVEs), using a closed form (CF) algorithm. This study optimizes data patterns in (TE, b-value) space and analyzes algorithms for enhanced accuracy and precision. We simulated noise-added numerical, phantom, and brain MRI data to evaluate relative error and coefficient of variation in quantitative parameters using various data patterns and analysis algorithms (CF and least squares [LSQ] fitting). With 4 minimum data points applied to healthy brain tissue with T2 < 100 ms, the CF algorithm with water volume separation was optimal. For more than 4 points, a smaller b-value with shorter TE combined with 2d single- and bi-exponential LSQ fitting provided the best results. The T2wsup-dMRI technique reduces CSF-PVE artifacts in tissue-specific parameter quantification, enhancing approaches for patient needs, data acquisition, and computing costs.

Stroke is a major cause of disability and death in adults worldwide. In clinical setting, time efficient MRI protocols including diffusion weighted images, fluid attenuated inversion recovery, fast T2*-weighted images and MR angiography aim to establish ischemic stroke diagnosis, visualize vessel occlusion and determine the extent of ischemia damage distinguishing between the salvageable ischemic penumbra and the infarct core. Thus, MRI plays a pivotal role in diagnosis, treatment decision-making, and prognostic assessment, although prediction based on these elements remains limited and can be improved. We reviewed the added-values of alternative MRI methods such as sodium(²³Na) MRI and ¹H MR spectroscopic imaging that open new temporal and pathophysiological windows on ionic dys-homeostasis and metabolism alteration in the context of ischemic stroke and reperfusion. Insights on the timeline of the cellular events accessible using these alternative methods and perspectives to improve clinical outcome prediction of ischemic stroke patients are discussed.

Purpose: These guidelines aim to support MRI diagnosis in patients receiving anti-amyloid β (Aβ) antibody treatment without restricting treatment eligibility.

Materials and methods: These guidelines were collaboratively established by Japan Radiological Society, The Japanese Society of Neuroradiology, and Japanese Society for Magnetic Resonance in Medicine by reviewing existing literature and the results of clinical trials.

Results: Facility standards should comply with the "Optimal Use Promotion Guidelines" of Japan, and physicians should possess comprehensive knowledge of amyloid-related imaging abnormalities (ARIA) and expertise in brain MRI interpretation. The acquisition of knowledge regarding amyloid-related imaging abnormalities, brain MRI, anti-Aβ antibody introduction, and post-treatment diagnosis are also recommended.

Conclusion: These guidelines facilitate the accurate diagnosis and effective management of ARIA; ensure the safe administration of anti-Aβ drugs; and provide a framework for MRI facilities, includes staffing requirements and the use of MRI management systems.

Purpose: To assess the effects of industry-developed deep learning reconstruction with super resolution (DLR-SR) on single-shot turbo spin-echo (SshTSE) images with thickness of 2 mm with DLR (SshTSE^2mm) relative to those of images with a thickness of 5 mm with DLR (SSshTSE^5mm) in the patients with pancreatic cystic lesions.

Methods: Thirty consecutive patients who underwent abdominal MRI examinations because of pancreatic cystic lesions under observation between June 2024 and July 2024 were enrolled. We qualitatively and quantitatively evaluated the image qualities of SshTSE^2mm and SshTSE^5mm with and without DLR-SR.

Results: The SNRs of the pancreas, spleen, paraspinal muscle, peripancreatic fat, and pancreatic cystic lesions of SshTSE^2mm with and without DLR-SR did not decrease in compared to that of SshTSE^5mm with and without DLR-SR. There were no significant differences in contrast-to-noise ratios (CNRs) of the pancreas-to-cystic lesions and fat between 4 types of images. SshTSE^2mm with DLR-SR had the highest image quality related to pancreas edge sharpness, perceived coarseness pancreatic duct clarity, noise, artifacts, overall image quality, and diagnostic confidence of cystic lesions, followed by SshTSE^2mm without DLR-SR and SshTSE^5mm with and without DLR-SR (P  <  0.0001).

Conclusions: SshTSE^2mm with DLR-SR images had better quality than the other images and did not have decreased SNRs and CNRs. The thin-slice SshTSE with DLR-SR may be feasible and clinically useful for the evaluation of patients with pancreatic cystic lesions.