Magnetic resonance imaging最新文献

Objective: To investigate whether radiomic features obtained from the intratumoral and peritumoral regions of pretreatment magnetic resonance imaging (MRI) can predict progression in patients with triple-negative breast cancer (TNBC) undergoing neoadjuvant chemotherapy (NAC) in comparison with the previously determined clinical score.

Methods: This single-center retrospective study evaluated 224 women with TNBC who underwent NAC between 2010 and 2019. Women were randomly allocated to the training set (n = 169) for model development and the test set (n = 55) for model validation. The clinical score consisted of the histologic type, Ki-67 index, and degree of edema on T2-weighted imaging. Intratumoral and peritumoral radiomic features were extracted from T2-weighted images and the first- and last-phase images of dynamic contrast-enhanced MRI. The radiomics model was built using only radiomic features, whereas the combined model incorporated the clinical score along with radiomic features. The area under the receiver operating characteristic curve (AUC) was used to assess performance.

Results: Progression occurred in 18 and five patients in the training and test sets, respectively. The radiomics model selected three radiomic features (two peritumoral and one intratumoral), while the combined model selected the clinical score and five radiomic features (four peritumoral and one intratumoral). Among the total radiomic features, Inverse Difference Normalized of the peritumoral region of the T2-weighted images, reflective of peritumoral heterogeneity, demonstrated the highest level of association with tumor progression. In the test set, the AUC values of the radiomics-only model, the combined model, and the clinical score were 0.592, 0.764, and 0.720, respectively. Compared to the clinical score, the radiomics-only model (0.720 vs. 0.592, p = 0.468) and the combined model (0.720 vs. 0.764, p = 0.553) did not show superior performance.

Conclusion: The radiomics features were not superior in predicting the progression of TNBC compared to the clinical score, although the peritumoral heterogeneity on T2-weighted images showed a potential.

Objective: To design a metasurface-inspired conformal elliptical-cylinder resonator (MICER) for wrist magnetic resonance imaging at 1.5 T and evaluate its potential for clinical applications.

Methods: An electromagnetic simulation was used to characterize the effect of MICER on radio frequency fields. A phantom and 14 wrists from 7 healthy volunteers were examined using a 1.5 T MRI system. The examination included T1-weighted spin echo, fat-saturation proton density-weighted fast spin echo, and three-dimensional T1-weighted gradient echo sequences. All scans were repeated using two methods: MICER combined with the spinal coil, which is a surface coil built-in examination table, and the 12-channel wrist array coil, to receive signals. Image signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated, and the differences between the two methods were compared using a paired Student's t-test.

Results: In the phantom study, the image obtained with MICER had a higher SNR compared to the image obtained with the 12-channel wrist coil. Almost all wrist tissues showed a higher SNR on the images obtained with MICER than on the images obtained with the 12-channel wrist coil (P < 0.05). And the CNR between wrist tissues on images obtained with MICER was higher than that obtained with the 12-channel wrist coil (P < 0.05).

Conclusions: The quality of the MRI using MICER is superior to that of the commercially available 12-channel wrist coil, indicating its potential value for clinical practice.

Object: To develop and validate a nomogram for predicting recurrence in individuals suffering single hepatocellular carcinoma (HCC) after curative hepatectomy.

Material and methods: A retrospective analysis was conducted on 189 patients with single HCC undergoing curative resection in our center were randomized into training and validation cohorts. P53 status was determined using immunohistochemistry. Clinical data, such as age, and gender were collected. MRI findings, such as tumor size, intratumoral arteries, the presence of peritumoral enhancement and intratumoral necrosis were also recorded. Nomograms were established based on the predictors selected in the training cohort, and receiver operating characteristic (ROC) curve analyses were used to compare the predictive ability among single predictors and nomogram model. The Kaplan-Meier method was used to assess the impact of each predictor and nomogram model on HCC recurrence. The results were validated in the validation cohort.

Results: Multivariate Cox regression analysis showed that P53 (P < 0.001), tumor size (P = 0.009), and intratumoral artery (P = 0.026) were the independent risk factors for HCC recurrence. The nomogram model demonstrated favorable C-index of 0.740 (95 %CI:0.653-0.826) and 0.767 (95 %CI: 0.633-0.900) in the training and validation cohorts, and the areas under the curve was 0.740 and 0.752, which was better than the performance of P53 and MR factors alone. Calibration curves indicated a good agreement between observed actual outcomes and predicted values. Kaplan-Meier curves indicated that nomogram model was powerful in discrimination and clinical usefulness.

Conclusions: The integrated nomogram combining P53 status and MRI findings can be a valuable prognostic tool for predicting postoperative recurrence of single HCC.

Objective: The aim of this study was to evaluate a novel respiratory frequency-modulated continuous-wave radar-trigger (FT) technique for multiple -b-value diffusion-weighted imaging (DWI) of liver and compare it with conventional free breathing (FB) DWI technique.

Material and methods: 39 patients with focal liver lesions underwent both frequency-modulated continuous-wave radar-trigger (FT) and conventional free-breathing (FB) multi-b-value diffusion-weighted imaging (DWI,b = 0,50,400,800 s/mm²⁾. Two abdominal radiologists independently assessed the quality of liver DWI images obtained using both techniques, measured and compared liver signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) at different b-values, as well as apparent diffusion coefficient (ADC) values calculated from all b-values.

Results: In terms of image quality, the FT technique is superior to the conventional FB technique, with overall image quality scores (Reader 1, 3.56 ± 0.50 and Reader 2, 3.59 ± 0.55)vs (Reader 1, 2.90 ± 0.75 and Reader 2, 2.97 ± 0.71), respectively. The liver SNR (at b-values of 50,400,and 800) obtained by FT was (138.5 ± 43.48, 96.67 ± 31.95, 71.54 ± 22.03), respectively, which was significantly higher than that obtained by conventional FB (110.90 ± 39.28, 80.86 ± 29.13, 60.43 ± 18.61, P < 0.05). The lesion CNR with FT was significantly higher than that with conventional FB (258.99 ± 151.38 vs 174.60 ± 99.90; 164.56 ± 87.25 vs 111.12 ± 42.43; 118.83 ± 68.76 vs 76.01 ± 35.48, P < 0.001). There was no significant difference in ADC values of liver and lesions between the two techniques: ADCliver-L and ADCliver-R: (FT 1479.3 ± 270.0 vs FB 1529.3 ± 275.5 and FT 1219.6 ± 127.4 vs FB 1248.7 ± 168.2, P > 0.05); ADC lesion:FT(969.0 ± 261.3) vs FB (1017.5 ± 240.4, P > 0.05).

Conclusion: For multi-b-value liver diffusion-weighted imaging, FT technique has higher image quality and better lesion visibility than conventional FB technique and there is no significant difference in ADC values of liver and lesions between the two techniques.

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.