Magnetic resonance imaging最新文献

Free-water elimination (FWE) modeling in diffusion magnetic resonance imaging (dMRI) is crucial for accurate estimation of diffusion properties by mitigating the partial volume effects caused by free water, particularly at the interface between white matter and cerebrospinal fluid. The presence of free water partial volume effects leads to biases in estimating diffusion properties. Additionally, the existing mathematical FWE model is a two-compartment model, which can be well posed for multi-shell data. However, single-shell acquisitions are more common in clinical cohorts due to time constraints. To overcome these problems, we proposed a deep-learning framework that focuses on mapping and correcting free-water partial volume contamination in DWI. It utilizes data-driven techniques to infer plausible free-water volumes across different diffusion MRI acquisition schemes, including single-shell acquisitions. In this work, we study the Human Connectome Project Young Adults (HCP-ya), the HCP Aging dataset (HCP-a) as well as Brain Tumor Connectomics Data (BTC). The evaluation demonstrates that it produces more plausible results compared to previous single-shell free water estimation approaches. The proposed method is generalizable through model fine-tuning and b-value re-mapping when dealing with new data. The results have demonstrated improved consistency of properties estimation between scan/rescan data and accuracy in identifying neural pathways, as well as enhanced clarity in the visualization of white matter tracts.

Magnetic resonance imaging (MRI) techniques have recently been developed for obtaining high T₁ contrast images using inversion recovery (IR) images at two inversion times (TIs) rather than a single TI. They use simple mathematical operations - multiplication, addition, subtraction, division - to create images not attainable by conventional IR. The present study describes a novel two-point IR technique formed by the subtraction of log images. Results show it has a near-linear response to T₁ between the nullpoints that peaks sharply at the nullpoints. This produces a bright isoT₁ contour at interfaces between tissues where partial volume mixing generates specific T₁s. This can provide anatomical information in areas where the signal is not well-differentiated on conventional images.

Microstructural parameters are essential in tumor research, aiding in the understanding tumor pathogenesis, grading, and therapeutic efficacy. The imaging microstructural parameters using limited spectrally edited diffusion (IMPULSED) model is the most widely used MR cell size imaging technique, demonstrating success in measuring microstructural parameters of solid tumors in vivo. However, its clinical application is limited by the longer scan times required for both pulsed gradient spin-echo (PGSE) and multiple oscillating gradient spin-echo (OGSE) acquisitions across a range of b-values, which can be burdensome for patients and disrupt clinical workflows. In this work, we propose and evaluate an accelerating method that integrates parallel acquisition technique (PAT) and simultaneous multi-slice (SMS) with local principal component analysis (LPCA) denoising to reduce scan times while maintaining image quality in MR cell size imaging. PGSE and OGSE (25 Hz, 50 Hz) images were acquired using P2S2 (PAT2-SMS2), P2S3 (PAT2-SMS3), and P3S3 (PAT3-SMS3) configurations, incorporating LPCA denoising, and compared to standard P2 (PAT2-SMS1) in healthy volunteers and brain tumor patients at 3 T. Additionally, clinical feasibility was further assessed through qualitative and quantitative evaluations. Qualitative assessment, conducted by two radiologists using a 5-point Likert scale, and quantitative analysis, including noise estimation, apparent diffusion coefficient (ADC) calculation, and estimation of microstructural parameters-cell diameter (Dmean), intracellular volume fraction (Vin), and extracellular diffusivity (Dex), were performed. Overall, the integration of PAT and SMS techniques reduces acquisition time by approximately 60 % compared to standard P2 acceleration, while maintaining comparable image quality and structural fidelity with LPCA denoising.

Purpose: Compressed Sensing (CS) is an emerging technique to accelerate MRI acquisitions. The aim of this study was to assess the reliability and accuracy of cartilage thickness measurements in the knee using a CS-enabled isotropic 3D Fast Spin-Echo (FSE) sequence on a 3-T MRI scanner.

Methods: Twenty-eight tibial condyle sections were collected from 14 adult patients who underwent total knee arthroplasty. An isotropic 3D PDw FSE with variable flip-angle (CUBE) sequence with CS was used to acquire MR images of the tibial condyle sections. Minimum cartilage thickness measurements were independently performed by two experienced readers (R1 and R2) on MR images and compared to corresponding anatomical sections measurements. Intraclass correlation coefficients (ICCs) and Bland-Altman analyses, were used to assess agreement between MR and anatomical measurements.

Results: A total of 84 paired cartilage areas were analyzed [cartilage thickness measurements ranged from 0 to 3.40 mm at anatomical evaluation (mean, 1.08 mm ± 0.83)]. The agreements between MR and anatomical measurements were excellent (mean differences, 0.06 ± 0.31 mm for R1 and 0.03 ± 0.43 mm for R2) with respective ICC values of 0.93 and 0.88. Bland-Altman analyses revealed small differences between MR and anatomical measurements, with 95 % Limit of Agreements values falling within clinically acceptable ranges (-0.54 to 0.66 mm for R1, -0.87 to 0.80 mm for R2).

Conclusion: The 3D PDw FSE sequence with Compressed Sensing acceleration technique demonstrated reliable and accurate assessment of cartilage thickness of the tibial condyles within a timeframe suitable for routine clinical practice.

3D BFFE and TRANCE can provide a visualization of pulmonary vessels without injection of contrast agent. T-SLIP can observe a large area of vessels using an arterial spin labeling technique. 3D BFFE and TRANCE with two T-SLIP placement strategies were compared on pulmonary artery imaging. Ten male and thirteen female healthy volunteers were recruited in the present study. Each subject underwent non-contrast-enhanced pulmonary MRA. Four protocols were involved in the present study as follows: 3D BFFE with T-SLIP covering vena cava (VC) and right atrium and ventricle (RV), respectively; 3D TRANCE with T-SLIP covering VC and RV, respectively. The SNR were measured at eight ROIs, while CNR were measured at five ROIs. The number of visualized branch and image quality were analyzed by two radiologists. The differences of metrics among different imaging strategies were compared. SNR and CNR were compared between two T-SLIP placement strategies using a paired t-test. The branches number and image quality were compared between two T-SLIP placement strategies using a rank-sum test. The agreement of subjective assessment was conducted using a Kappa test. There were significant differences at all ROIs between TRANCE and BFEE with T-SLIP on RV (all P values <0.01). TRANCE with T-SLIP on RV exhibited a higher CNR at all five ROIs compared with BFFE with T-SLIP on RV. TRANCE with T-SLIP on RV performed better on the number of observed branches and image quality compared with BFFE with T-SLIP on RV. 3D TRANCE with T-SLIP on RV can provide a higher SNR and image quality among four MR protocols, and thus may be a potential non-contrast enhanced technique in the visualization of pulmonary artery.

Objective: To explore the application value of MRI-based imaging histology and deep learning model in the identification and classification of breast phyllodes tumors.

Methods: Seventy-seven patients diagnosed as breast phyllodes tumors and fibroadenomas by pathological examination were retrospectively analyzed, and traditional radiomics features, subregion radiomics features, and deep learning features were extracted from MRI images, respectively. The features were screened and modeled using variance selection method, statistical test, random forest importance ranking method, Spearman correlation analysis, least absolute shrinkage and selection operator (LASSO). The efficacy of each model was assessed using the subject operating characteristic (ROC) curve, The DeLong test was used to assess the differences in the AUC values of the different models, and the clinical benefit of each model was assessed using the decision curve (DCA), and the predictive accuracy of the model was assessed using the calibration curve (CCA).

Results: Among the constructed models for classification of breast phyllodes tumors, the fusion model (AUC: 0.97) had the best diagnostic efficacy and highest clinical benefit. The traditional radiomics model (AUC: 0.81) had better diagnostic efficacy compared with subregion radiomics model (AUC: 0.70). De-Long test, there is a statistical difference between the fusion model traditional radiomics model, and subregion radiomics model in the training group. Among the models constructed to distinguish phyllodes tumors from fibroadenomas in the breast, the TDT_CIDL model (AUC: 0.974) had the best predictive efficacy and the highest clinical benefit. De-Long test, the TDT_CI combination model was statistically different from the remaining five models in the training group.

Conclusion: Traditional radiomics models, subregion radiomics models and deep learning models based on MRI sequences can help to differentiate benign from junctional phyllodes tumors, phyllodes tumors from fibroadenomas, and provide personalized treatment for patients.

Objective: This study aimed to investigate the feasibility of diffusion-derived vessel density (DDVD) in characterizing tumor microvasculature in endometrial carcinoma (EC), and to explore the correlations with Ki-67 proliferation status and histological type based on DDVD values.

Methods: There were in total 81 EC patients. There were 64 cases of non-aggressive histological type, and 17 cases of aggressive histological type. Ki-67 labeling index was low (<50 %) in 35 cases and high (≥50 %) in 46 cases. DDVD_(b0b20) is calculated according to: DDVD_(b0b20) = S_b0/ROI_area0 - S_b20/ROI_area20, where S_b0 and S_b20 refer to the tissue signal when b is 0 or 20 s/mm². Intraclass correlation coefficient (ICC); two-tailed independent samples t-test and Mann-Whitney U test, and Receiver operating characteristic area under the curve (AUC) were applied for statistical analysis.

Results: Endometrial carcinoma showed lower DDVD_(b0b20) values (34.9 ± 21.2, au/pixel) compared with myometrium (65.3 ± 37.4, P < 0.001). Tumors with Ki-67 high-proliferation or aggressive histological type had higher DDVD values than those with Ki-67 low-proliferation (44.17 (median) vs. 16.08, P < 0.001]] or non-aggressive histological type (47.92 vs. 30.77, P = 0.002). DDVD_(b0b20) ROC curve analysis shows AUC of 0.842 for distinguishing between Ki-67 low- and high-expression, and AUC of 0.771 for distinguishing between non-aggressive and aggressive histological types. DDVD_(b0b20) > 32.9 and DDVD_(b0b20) > 50.1 provided a specificity of 85 % for identifying Ki67 high expression (sensitivity 78.3 %) and histological aggressive type (sensitivity 47.1 %), respectively.

Conclusion: DDVD can act as an imaging marker reflecting Ki-67 proliferation and histological aggressiveness of EC, thus helping pretreatment risk assessment in EC.

Purpose: This study aimed to evaluate the diagnostic efficacy of time-dependent diffusion magnetic resonance imaging (td-dMRI) and dynamic contrast-enhanced MRI (DCE-MRI)-based kinetic heterogeneity in differentiating suspicious breast lesions (categorised as Breast Imaging Reporting and Data System 4 or 5).

Methods: This prospective study included 51 females with suspicious breast lesions who underwent preoperative breast MRI, including DCE-MRI and td-dMRI. Six kinetic parameters, namely peak, persistent, plateau, washout component, predominant curve type, and heterogeneity, were extracted from the DCE series using MATLAB and SPM software. The td-dMRI data were analysed using the JOINT model to obtain five microstructural parameters and apparent diffusion coefficient at 50 ms (ADC_50ms). Chi-square or Fisher's exact test and the Mann-Whitney U test were used to compare these parameters between benign and malignant breast lesions. Univariate and multivariate logistic regression analyses with forward stepwise covariate selection were performed to identify significant clinical and radiologic variables. Differential diagnostic performance was evaluated using receiver operating characteristic curves and logistic regression analyses.

Results: For td-dMRI-derived parameters, the values of f_in and cellularity were significantly higher in malignant breast lesions compared to benign lesions (P = 0.001 and P<0.001, respectively), while ADC_50ms was significantly lower in malignant lesions (P = 0.001). In the kinetic heterogeneity analysis, the washout component was higher in malignant lesions compared to benign lesions (P = 0.003). When combining significant td-dMRI and kinetic heterogeneity parameters, the area under the curve (AUC) value was 0.875, with an accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of 82.69 %, 86.11 %, 75.00 %, 88.57 %, and 70.59 %, respectively. Notably, margin and kinetic pattern emerged as independent predictors of malignant breast lesions (P = 0.019 and 0.006, respectively). Furthermore, incorporating these two clinical-radiologic characteristics further enhanced diagnostic accuracy, yielding an AUC of 0.969, with accuracy, sensitivity, specificity, PPV, and NPV improving to 90.38 %, 86.11 %, 100 %, 100 %, and 76.19 %, respectively.

Conclusions: Kinetic heterogeneity- and td-dMRI-derived parameters are potentially non-invasive biomarkers for distinguishing suspicious breast lesions.

While typical qualitative T1-weighted magnetic resonance images reflect scanner and protocol differences, quantitative T1 mapping aims to measure T1 independent of these effects. Changes in T1 in the brain reflect structural changes in brain tissue. Magnetization-prepared two rapid acquisition gradient echo (MP2RAGE) is an acquisition protocol that allows for efficient T1 mapping with a much lower scan time per slab compared to multi-TI inversion recovery (IR) protocols. We collect and register B1-corrected MP2RAGE acquisitions with an additional inversion time (MP3RAGE) alongside multi-TI selective inversion recovery acquisitions for four subjects. We use a maximum a posteriori (MAP) T1 estimation method for both MP2RAGE and compare to typical point estimate MP2RAGE T1 mapping, finding no bias from MAP MP2RAGE but a sensitivity to B₁⁺ inhomogeneities with MAP MP3RAGE. We demonstrate a tissue-dependent bias between MAP MP2RAGE T1 estimates and the multi-TI inversion recovery T1 values. To correct this bias, we train a patch-based ResNet-18 to calibrate the MAP MP2RAGE T1 estimates to the multi-TI IR T1 values. Across four folds, our network reduces the RMSE significantly (white matter: from 0.30 ± 0.01 s to 0.11 ± 0.02 s, subcortical gray matter: from 0.26 ± 0.02 s to 0.10 ± 0.02 s, cortical gray matter: from 0.36 ± 0.02 s to 0.17 ± 0.03 s). Using limited paired training data from both sequences, we can reduce the error between quantitative imaging methods and calibrate to one of the protocols with a neural network.

Objective: To develop a novel combined nomogram based on 3D multi-echo Dixon (qDixon), magnetization transfer imaging (MTI) and clinical risk factors for the diagnosis of osteoporosis.

Materials and methods: A total of 287 subjects who underwent MR examination with qDixon and MTI sequences participated in this study. These participants were randomly assigned to a training cohort and a validation cohort at a ratio of 7:3. We extracted and analyzed the bone marrow fat fraction (FF) and magnetization transfer ratio (MTR) of L1 ∼ 3 vertebrae, along with clinical data. Univariate and multivariate logistic regression analyses was used to assess independent predictors of OP in the training cohort. We established a diagnostic nomogram and evaluated its performance in terms of discrimination, calibration, and clinical value using the receiver operating characteristic curve (ROC) and calibration curve. Decision curve analysis (DCA) was performed to determine the clinical validity of the nomogram by measuring the net benefits at different threshold probabilities.

Results: Gender, age, FF, and MTR (all P﹤0.05) emerged as independent indicators for diagnosing osteoporosis. The AUCs for the FF, MTR, FF + MTR, and nomogram models were 0.842, 0.903, 0.923, and 0.941, respectively, in the training cohort and 0.779, 0.872, 0.901, and 0.929, respectively, in the validation cohort. The nomogram model exhibited good calibration and discrimination. DCA revealed that the nomogram model yielded a higher net benefit than the FF and MTR models.

Conclusion: The nomogram model, integrating qDixon, MTI, and clinical parameters, could serve as a reliable tool for diagnosing the individual risk for the osteoporosis in the elderly.