Magnetic resonance imaging最新文献

Background: Myocardial fibrosis of the left ventricle (LV) has been associated with atrial fibrillation and other arrhythmias in individuals with hypertrophic cardiomyopathy (HCM). However, few studies have quantitatively examined the segmental relationship between diffuse LV fibrosis and atrial arrhythmias in HCM using T1 mapping and extracellular volume fraction (ECV). The aim of this study is to explore this relationship through T1 mapping, offering imaging insights into the pathophysiology of HCM with atrial arrhythmia.

Methods: A total of 38 patients with HCM were classified into two groups-those with atrial arrhythmia and those without-based on electrocardiographic and Holter monitor recordings. A covariance analysis was conducted to compare T1 mapping parameters between the two groups, adjusting for wall thickness (WT) as a covariate. Analysis was performed collectively for all 16 myocardial segments, as well as for each segment individually.

Results: Native T1 values were elevated in the entire LV myocardium and in segments S1-3 in patients with HCM with atrial arrhythmias compared to those without (P < 0.001; P < 0.05, 1316.0 ms ± 15.9 vs 1263.1 ms ± 13.6, 1350.5 ms ± 14.2 vs 1311.9 ms ± 11.7, 1305.7 ms ± 2.5 vs 1271.5 ms ± 10.6, respectively). Notably, the basal anterior segment (S1) and basal inferotseptal segment (S3) exhibited prolonged ECV and elevated native T1 values in patients with HCM and atrial arrhythmia (P < 0.05). Multivariable binary logistic regression analysis identified myocardial native T1 values in the basal anteroseptal segment (S2) as a predictor of atrial arrhythmia presence in HCM, with values exceeding 1350 ms correlating with an increased likelihood of arrhythmia development. No significant difference in WT was observed between the groups in hypertrophic myocardial regions (P > 0.05), while non-hypertrophic myocardium in individuals with HCM and atrial arrhythmias exhibited reduced wall thickness (7.7 mm ± 3.0 vs 9 mm ± 3.0, P < 0.001) compared to those without arrhythmias.

Conclusion: Fibrosis in the basal septal and anterior regions of the left ventricle plays a crucial role in myocardial tissue remodeling, contributing to the development of atrial arrhythmia in HCM. Elevated native T1 values in the basal anteroseptal segment may may serve as a significant marker for the concurrent occurrence of atrial arrhythmias in individuals with HCM.

Purpose: To develop a multi-parametric MRI model for the prediction of molecular subtypes of breast cancer using five types of breast cancer preoperative MRI images.

Methods: In this study, we retrospectively analyzed clinical data and five types of MRI images (FS-T1WI, T2WI, Contrast-enhanced T1-weighted imaging (T1-C), DWI, and ADC) from 325 patients with pathologically confirmed breast cancer. Using the five types of MRI images as inputs to the ResNeXt50 model respectively, five base models were constructed, and then the outputs of the five base models were fused using an ensemble learning approach to develop a multi-parametric MRI model. Breast cancer was classified into four molecular subtypes based on immunohistochemical results: luminal A, luminal B, human epidermal growth factor receptor 2-positive (HER2-positive), and triple-negative (TN). The whole dataset was randomly divided into a training set (n = 260; 76 luminal A, 80 luminal B, 50 HER2-positive, 54 TN) and a testing set (n = 65; 20 luminal A, 20 luminal B, 12 HER2-positive, 13 TN). Accuracy, sensitivity, specificity, receiver operating characteristic curve (ROC) and area under the curve (AUC) were calculated to assess the predictive performance of the models.

Results: In the testing set, for the assessment of the four molecular subtypes of breast cancer, the multi-parametric MRI model yielded an AUC of 0.859-0.912; the AUCs based on the FS-T1WI, T2WI, T1-C, DWI, and ADC models achieved respectively 0.632-0. 814, 0.641-0.788, 0.621-0.709, 0.620-0.701and 0.611-0.785.

Conclusion: The multi-parametric MRI model we developed outperformed the base models in predicting breast cancer molecular subtypes. Our study also showed the potential of FS-T1WI base model in predicting breast cancer molecular subtypes.

Purpose: The purpose of this study was to develop DiffDL, a generative diffusion probabilistic model designed to produce high-quality diffusion tensor imaging (DTI) and diffusion kurtosis imaging (DKI) metrics from a reduced set of diffusion-weighted images (DWIs). This model addresses the challenge of prolonged data acquisition times in diffusion MRI while preserving metric accuracy.

Methods: DiffDL was trained using data from the Human Connectome Project, including 300 training/validation subjects and 50 testing subjects. High-quality DTI and DKI metrics were generated using many DWIs and combined with subsets of DWIs to form training pairs. A UNet architecture was used for denoising, trained over 500 epochs with a linear noise schedule. Performance was evaluated against conventional DTI/DKI modeling and a reference UNet model using normalized mean absolute error (NMAE), peak signal-to-noise ratio (PSNR), and Pearson correlation coefficient (PCC).

Results: DiffDL showed significant improvements in the quality and accuracy of fractional anisotropy (FA) and mean diffusivity (MD) maps compared to conventional methods and the baseline UNet model. For DKI metrics, DiffDL outperformed conventional DKI modeling and the UNet model across various acceleration scenarios. Quantitative analysis demonstrated superior NMAE, PSNR, and PCC values for DiffDL, capturing the full dynamic range of DTI and DKI metrics. The generative nature of DiffDL allowed for multiple predictions, enabling uncertainty quantification and enhancing performance.

Conclusion: The DiffDL framework demonstrated the potential to significantly reduce data acquisition times in diffusion MRI while maintaining high metric quality. Future research should focus on optimizing computational demands and validating the model with clinical cohorts and standard MRI scanners.

Diffusion tensor imaging (DTI) and its parameters such as fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), radial diffusivity (RD) are increasingly being used to assess peripheral nerve integrity alongside nerve conduction studies. This pilot study aims to compare DTI values of lumbar spinal nerve roots before (T0) and after (T1) treatment with an interspinous process device (IPD). Seven patients (5 females, 2 males; mean age: 68) suffering from neurogenic claudication and lumbar spinal canal and foraminal stenosis were evaluated. Visual Analog Scale (VAS) for perceived pain, Oswestry Disability Index (ODI), and DTI parameters were assessed between T0 and T1. No significant difference in FA was found in treated roots, while MD (p = 0.0015), RD (p = 0.0032), and AD (p = 0.0221) were significantly altered. At untreated levels, all DTI parameters showed highly significant differences (p < 0.0001) between T0 and T1. In treated roots, FA values significantly increased in the intraforaminal segment(p = 0.0229), while MD(p = 0.0124), AD(p = 0.0128), and RD (p = 0.0143) values decreased in the pre-foraminal segment. In untreated roots, FA significantly increased in pre(p = 0.0039)and intraforaminal(p = 0.0003) segments, and MD, AD, and RD decreased in all segments (p < 0.0001). VAS (p < 0.0001) also decreased between T0 and T1. This pilot study aims to clarify the biomechanical impact of interspinous spacers through microstructural analysis of both treated and adjacent untreated nerve roots. To our knowledge, no studies have examined the short- to medium-term changes in DTI values of lumbar nerve roots before and after IPD placement, or compared changes between treated and untreated roots.

Magnetic Resonance Imaging (MRI) stands out as a notable non-invasive method for medical imaging assessments, widely employed in early medical diagnoses due to its exceptional resolution in portraying soft tissue structures. However, the MRI method faces challenges with its inherently slow acquisition process, stemming from the sequential sampling in k-space and limitations in traversal speed due to physiological and hardware constraints. Compressed Sensing in MRI (CS-MRI) accelerates image acquisition by utilizing greatly under-sampled k-space information. Despite its advantages, conventional CS-MRI encounters issues such as sluggish iterations and artefacts at higher acceleration factors. Recent advancements integrate deep learning models into CS-MRI, inspired by successes in various computer vision domains. It has drawn significant attention from the MRI community because of its great potential for image reconstruction from undersampled k-space data in fast MRI. This paper proposes a lightweight Adaptive Spatial-Channel Attention EfficientNet B3-based Generative Adversarial Network (ASCA-EffNet GAN) for fast, high-quality MR image reconstruction from greatly under-sampled k-space information in CS-MRI. The proposed GAN employs a U-net generator with ASCA-based EfficientNet B3 for encoder blocks and a ResNet decoder. The discriminator is a binary classifier with ASCA-based EfficientNet B3, a fully connected layer and a sigmoid layer. The EfficientNet B3 utilizes a compound scaling strategy that achieves a balance amongst model depth, width, and resolution, resulting in optimal performance with a reduced number of parameters. Furthermore, the adaptive attention mechanisms in the proposed ASCA-EffNet GAN effectively capture spatial and channel-wise features, contributing to detailed anatomical structure reconstruction. Experimental evaluations on the dataset demonstrate ASCA-EffNet GAN's superior performance across various metrics, surpassing conventional reconstruction methods. Hence, ASCA-EffNet GAN showcases remarkable reconstruction capabilities even under high under-sampling rates, making it suitable for clinical applications.

Purposes: This study aimed to assess the effectiveness of Super-Resolution Deep Learning Reconstruction (SR-DLR) -a deep learning-based technique that enhances image resolution and quality during MRI reconstruction- in improving the image quality of thin-slice 3D T2-weighted imaging (T2WI) and Prostate Imaging-Reporting and Data System (PI-RADS) assessment in prostate Magnetic Resonance Imaging (MRI).

Methods: This retrospective study included 33 patients who underwent prostate MRI with SR-DLR between November 2022 and April 2023. Thin-slice 3D-T2WI of the prostate was obtained and reconstructed with and without SR-DLR (matrix: 720 × 720 and 240 × 240, respectively). We calculated the contrast and contrast-to-noise ratio (CNR) between the internal and external glands of the prostate, as well as the slope of pelvic bone and adipose tissue. Two radiologists evaluated qualitative image quality and assessed PI-RADS scores of each reconstruction.

Results: The final analysis included 28 male patients (age range: 47-88 years; mean age: 70.8 years). The CNR with SR-DLR was significantly higher than without SR-DLR (1.93 [IQR: 0.79, 3.83] vs. 1.88 [IQR: 0.63, 3.82], p = 0.002). No significant difference in contrast was observed between images with and without SR-DLR (p = 0.864). The slope with SR-DLR was significantly higher than without SR-DLR (0.21 [IQR: 0.15, 0.25] vs. 0.15 [IQR: 0.12, 0.19], p < 0.01). Qualitative scores for contrast, sharpness, artifacts, and overall image quality were significantly higher with SR-DLR than without SR-DLR (p < 0.05 for all). The kappa values for 2D-T2WI and 3D-T2WI increased from 0.694 and 0.640 to 0.870 and 0.827 with SR-DLR for both readers.

Conclusions: SR-DLR has the potential to improve image quality and the ability to assess PI-RADS scores in thin-slice 3D-T2WI of the prostate without extending MRI acquisition time.

Summary: Super-Resolution Deep Learning Reconstruction (SR-DLR) significantly improved image quality of thin-slice 3D T2-weighted imaging (T2WI) without extending the acquisition time. Additionally, the PI-RADS scores from 3D-T2WI with SR-DLR demonstrated higher agreement with those from 2D-T2WI.

Purpose: This study aimed to compare the suppression of arterial signal intensity between tracking and fixed saturation pulses in lower extremity magnetic resonance venography (MRV).

Methods: Forty patients with varicose veins who underwent 2D true fast imaging with steady-state free precession using tracking and fixed saturation pulses on MRV were included. A fixed saturation pulse was applied from April 2020 to May 2021, and a tracking saturation pulse was applied from June 2021 to July 2022. The arterial, venous, and muscle signal intensities obtained at the femoral and popliteal levels were used to calculate the contrast ratios between veins and arteries (CR_VA) and veins and muscles (CR_VM). Two experienced radiologists graded the images based on vein-artery contrast, suppression of arterial signal intensity, and visualization of lower leg perforators using a 9-point scale.

Results: Tracking saturation pulse images yielded significantly superior CR_VA and CR_VM compared with fixed saturation pulse images at both the femoral and popliteal levels. For the same saturation pulse type, the CR_VA was higher at the femoral level than at the popliteal level, while the CR_VM was comparable between the two levels. MRV with a tracking saturation pulse showed significantly superior vein-artery contrast, arterial signal suppression, and lower leg perforator visualization. Most scores for vein-artery contrast and arterial signal suppression with the tracking saturation pulse were positive (3.5-5), whereas few scores with the fixed saturation pulse were positive.

Conclusion: Tracking saturation pulse was more effective in suppressing arterial signal intensity in lower extremity MRV.

Purpose: Multi-contrast magnetic resonance imaging is a significant and essential medical imaging technique. However, multi-contrast imaging has longer acquisition time and is easy to cause motion artifacts. In particular, the acquisition time for a T2-weighted image is prolonged due to its longer repetition time (TR). On the contrary, T1-weighted image has a shorter TR. Therefore, utilizing complementary information across T1 and T2-weighted image is a way to decrease the overall imaging time. Previous T1-assisted T2 reconstruction methods have mostly focused on image domain using whole-based image fusion approaches. The image domain reconstruction method has the defects of high computational complexity and limited flexibility. To address this issue, we propose a novel multi-contrast imaging method called partition-based k-space synthesis (PKS) which can achieve better reconstruction quality of T2-weighted image by feature fusion.

Methods: Concretely, we first decompose fully-sampled T1 k-space data and under-sampled T2 k-space data into two sub-data, separately. Then two new objects are constructed by combining the two sub-T1/T2 data. After that, the two new objects as the whole data to realize the reconstruction of T2-weighted image.

Results: Experimental results showed that the developed PKS scheme can achieve comparable or better results than using traditional k-space parallel imaging (SAKE) that processes each contrast independently. At the same time, our method showed good adaptability and robustness under different contrast-assisted and T1-T2 ratios. Efficient target modal image reconstruction under various conditions were realized and had excellent performance in restoring image quality and preserving details.

Conclusions: This work proposed a PKS multi-contrast method to assist in target mode image reconstruction. We have conducted extensive experiments on different multi-contrast, diverse ratios of T1 to T2 and different sampling masks to demonstrate the generalization and robustness of our proposed model.

Objectives: To evaluate the performance of diffusion-weighted imaging (DWI) with an optimal b-value and field-of-view in identifying wall inflammation in abdominal aortic aneurysm (AAA) by comparing it to delayed enhancement T1-weighted imaging (DEI).

Methods: Twenty-five males with AAA were prospectively enrolled and underwent fat-suppressed T1-weighted dark-blood imaging (T1WI), full field-of-view (f-FOV) and reduced field-of-view (r-FOV) DWI (b values = 0, 100, 400 and 800 s/mm²), and DEI. Corresponding images on f-FOV, r-FOV DWI and DEI at the same level were evaluated qualitatively and quantitatively using the paired t-test and Wilcoxon signed-rank test. The agreement in detecting wall inflammation between DWI and DEI sequences was analyzed using weighted kappa statistics.

Results: For both r-FOV and f-FOV DWI, the scores of delineation of aneurysm wall and lesion conspicuity, signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were highest on DWI₁₀₀ (Ps < 0.05). The scores of delineation of aneurysm wall, geometric distortion, lesion conspicuity, and SNR, CNR were significantly higher on r-FOV DWI than those on f-FOV DWI (Ps < 0.05). r-FOV DWI₁₀₀ showed comparable performance to DEI in detecting wall inflammation (κ = 0.715), with superior blood suppression and higher SNR and CNR (Ps < 0.05).

Conclusions: DWI with r-FOV and low b-value could be a promising alternative to DEI in identifying wall inflammation in AAA.

Objectives: This study aimed to investigate the correlation between imaging phenotypes of endometrial cancer (EC) and clinical, pathologic, and molecular characteristics, as well as disease-free survival (DFS).

Methods: The clinical, pathologic, and molecular characteristics, along with MRI radiomics features, of 356 patients with EC were collected retrospectively. The patients were divided into 2 groups based on radiomics features using unsupervised machine learning. The obtained characteristics and DFS of patients were compared between the various imaging phenotypes.

Results: The lesions with deep myometrial invasion (DMI), lymphovascular space invasion (LVSI), cervical stromal invasion (CSI), lymph node metastasis, aggressive histologic type, advanced postoperative International Federation of Gynecology and Obstetrics (FIGO) stage, overexpression of p53, and absent expression of estrogen receptor or progesterone receptor were associated with poor DFS. Two clusters were identified and defined as imaging phenotype 1 and 2, respectively. Compared with phenotype 2, phenotype 1 exhibited a higher correlation with DMI (33.7 % vs 13.0 %), LVSI (23.8 % vs 9.2 %), CSI (16.3 % vs 3.8 %), aggressive histologic type (36.0 % vs 17.4 %), and advanced FIGO stage (IB or higher, 43.6 % vs 22.3 %) (p < 0.001). The incidence of p53 overexpression was higher in phenotype 1 than in phenotype 2 (20.2 % vs 8.5 %, p = 0.022). Survival analysis exhibited a higher risk of poor DFS in phenotype 1 than in phenotype 2 (log-rank p = 0.002).

Conclusion: EC imaging phenotypes identified through MRI radiomics features were associated with pathologic, molecular characteristics, and DFS, suggesting potential for preoperative risk stratification.