Evaluation of deep learning-based reconstruction late gadolinium enhancement images for identifying patients with clinically unrecognized myocardial infarction.

BACKGROUND: The presence of infarction in patients with unrecognized myocardial infarction (UMI) is a critical feature in predicting adverse cardiac events. This study aimed to compare the detection rate of UMI using conventional and deep learning reconstruction (DLR)-based late gadolinium enhancement (LGEO and LGEDL, respectively) and evaluate optimal quantification parameters to enhance diagnosis and management of suspected patients with UMI. METHODS: This prospective study included 98 patients (68 men; mean age: 55.8 ± 8.1 years) with suspected UMI treated at our hospital from April 2022 to August 2023. LGEO and LGEDL images were obtained using conventional and commercially available inline DLR algorithms. The myocardial signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and percentage of enhanced area (Parea) employing the signal threshold versus reference mean (STRM) approach, which correlates the signal intensity (SI) within areas of interest with the average SI of normal regions, were analyzed. Analysis was performed using the standard deviation (SD) threshold approach (2SD-5SD) and full width at half maximum (FWHM) method. The diagnostic efficacies based on LGEDL and LGEO images were calculated. RESULTS: The SNRDL and CNRDL were two times better than the SNRO and CNRO, respectively (P < 0.05). Parea-DL was elevated compared to Parea-O using the threshold methods (P < 0.05); however, no intergroup difference was found based on the FWHM method (P > 0.05). The Parea-DL and Parea-O also differed except between the 2SD and 3SD and the 4SD/5SD and FWHM methods (P < 0.05). The receiver operating characteristic curve analysis revealed that each SD method exhibited good diagnostic efficacy for detecting UMI, with the Parea-DL having the best diagnostic efficacy based on the 5SD method (P < 0.05). Overall, the LGEDL images had better image quality. Strong diagnostic efficacy for UMI identification was achieved when the STRM was ≥ 4SD and ≥ 3SD for the LGEDL and LGEO, respectively. CONCLUSIONS: STRM selection for LGEDL magnetic resonance images helps improve clinical decision-making in patients with UMI. This study underscored the importance of STRM selection for analyzing LGEDL images to enhance diagnostic accuracy and clinical decision-making for patients with UMI, further providing better cardiovascular care.

Nomogram for Predicting Microvascular Invasion in Hepatocellular Carcinoma Using Gadoxetic Acid-Enhanced MRI and Intravoxel Incoherent Motion Imaging.

RATIONALE AND OBJECTIVES: Microvascular invasion (MVI) is an important risk factor in hepatocellular carcinoma (HCC), but it can only be determined through histopathological results. The aim of this study was to develop and validate a nomogram for preoperative prediction MVI in HCC using gadoxetic acid-enhanced magnetic resonance imaging (MRI) and intravoxel incoherent motion imaging (IVIM). MATERIALS AND METHODS: From July 2017 to September 2022, 148 patients with surgically resected HCC who underwent preoperative gadoxetic acid-enhanced MRI and IVIM were included in this retrospective study. Clinical indicators, imaging features, and diffusion parameters were compared between the MVI-positive and MVI-negative groups using the chi-square test, Mann-Whitney U test, and independent sample t test. Receiver operating characteristic (ROC) curve was used to evaluate the diagnostic performance in predicting MVI. Univariate and multivariate analyses were conducted to identify the significant clinical-radiological variables associated with MVI. Subsequently, a predictive nomogram that integrates clinical-radiological risk factors and diffusion parameters was developed and validated. RESULTS: Serum alpha-fetoprotein level, tumor size, nonsmooth tumor margin, peritumoral hypo-intensity on hepatobiliary phase (HBP), apparent diffusion coefficient value and D value were statistically significant different between MVI-positive group and MVI-negative group. The results of multivariate analysis identified tumor size (odds ratio [OR], 0.786; 95% confidence interval [CI], 0.675-0.915; P < .01), nonsmooth tumor margin (OR, 2.299; 95% CI, 1.005-5.257; P < .05), peritumoral hypo-intensity on HBP (OR, 2.786; 95% CI, 1.141-6.802; P < .05) and D (OR, 0.293; 95% CI,0.089-0.964; P < .05) was the independent risk factor for the status of MVI. In ROC analysis, the combination of peritumoral hypo-intensity on HBP and D demonstrated the highest area under the curve value (0.902) in prediction MVI status, with sensitivity 92.8% and specificity 87.7%. The nomogram exhibited excellent predictive performance with C-index of 0.936 (95% CI 0.895-0.976) in the patient cohort, and had well-fitted calibration curve. CONCLUSION: The nomogram incorporating clinical-radiological risk factors and diffusion parameters achieved satisfactory preoperative prediction of the individualized risk of MVI in patients with HCC.

Combination of three-dimensional arterial spin labeling and stretched-exponential model in grading of gliomas.

To evaluate the diagnostic value of combining 3D arterial spin labeling (ASL) and stretched-exponential diffusion model in grading of gliomas.A total of 72 patients with histo-pathology proved gliomas (34 low-grade, 38 high-grade) were included in this study. 3D ASL and multi-b diffusion weighted imaging (DWI) images were retrospectively analyzed. The ASL and DWI parameters-tumor blood flow (TBF), distributed diffusion coefficient (DDC), and diffusion heterogeneity α were compared between high-grade and low-grade groups and P < .05 was regarded as statistically significant. TBF was also normalized to the corresponding values in contralateral mirror regions of interest (ROI) (M-TBF), normal grey matter (G-TBF), and white matter (W-TBF) and were compared between high and low-grade tumors.TBF values were significantly higher in high-grade gliomas (P < .001). In stretched-exponential model, the α value of low-grade gliomas showed significant higher than high-grade gliomas group (P < .001), but there was no difference of DDC (P > .05). When TBF values were normalized to contralateral mirror ROI, normal grey matter and white matter, G-TBF showed the highest sensitivity and specificity for differentiation high-grade and low-grade gliomas. The area under area under curve (AUC) of G-TBF and α for glioma grading were 0.926 and 0.892, respectively. The area under AUC of the G-TBF combination with α was 0.960 and corresponding sensitivity and specificity were 94.1% and 98.7%.The combination of 3D ASL and stretched-exponential model parameters can be used to differentiate high-grade and low-grade gliomas. Combination G-TBF and α value can obtain best diagnostic performance.

Quantitative T2 mapping evaluation for articular cartilage lesions in a rabbit model of anterior cruciate ligament transection osteoarthritis.

BACKGROUND: Quantitative T2 mapping has been a widely used method for the evaluation of pathological cartilage properties, and the histological assessment system of osteoarthritis in the rabbit has been published recently. The aim of the study was to investigate the effectiveness of quantitative T2 mapping evaluation for articular cartilage lesions of a rabbit model of anterior cruciate ligament transection (ACLT) osteoarthritis. METHODS: Twenty New Zealand White (NZW) rabbits were divided into ACLT surgical group and sham operated group equally. The anterior cruciate ligaments of the rabbits in ACLT group were transected, while the joints were closed intactly in sham operated group. Magnetic resonance (MR) examinations were performed on 3.0T MR unit at week 0, week 6, and week 12. T2 values were computed on GE ADW4.3 workstation. All rabbits were killed at week 13, and left knees were stained with Haematoxylin and Eosin. Semiquantitative histological grading was obtained according to the osteoarthritis cartilage histopathology assessment system. Computerized image analysis was performed to quantitate the immunostained collagen type II. RESULTS: The average MR T2 value of whole left knee cartilage in ACLT surgical group ((29.05±12.01) ms) was significantly higher than that in sham operated group ((24.52±7.97) ms) (P=0.024) at week 6. The average T2 value increased to (32.18±12.79) ms in ACLT group at week 12, but remained near the baseline level ((27.66±8.08) ms) in the sham operated group (P=0.03). The cartilage lesion level of left knee in ACLT group was significantly increased at week 6 (P=0.005) and week 12 (P<0.001). T2 values had positive correlation with histological grading scores, but inverse correlation with optical densities (OD) of type II collagen. CONCLUSION: This study demonstrated the reliability and practicability of quantitative T2 mapping for the cartilage injury of rabbit ACLT osteoarthritis model.

Targeted molecular imaging of antigen OC183B2 in ovarian cancers using MR molecular probes.

RATIONALE AND OBJECTIVES: This study was designed to develop a novel magnetic resonance (MR) probe for the antigen OC183B2 in ovarian cancer cells and investigate its imaging features in vitro and in vivo. MATERIALS AND METHODS: Molecular probes were achieved through ultrasmall superparamagnetic iron oxide nanoparticles (USPIOs) conjugated to ovarian cancer monoclonal antibodies 183B2 (OCMab183B2) using a chemical method. In the control group, USPIOs were coupled with murine immunoglobulin G (mIgG) and conjugated the same way. Native polyacrylamide gel electrophoresis was used to evaluate the conjugation reaction. The cytotoxicity of the probe was measured using the methyl thiazolyl tetrazolium assay, and its cell-labeling efficiency was evaluated by Prussian blue staining. In vitro cell MR imaging was performed to evaluate the targeting of the probe to the cells. After that, the OCMab183B2 USPIOs and mIgG USPIOs were injected intravenously into nude mice implanted with ovarian cancer xenograft tumors, respectively. T2-weighted imaging and T2 mapping were then performed on a 3.0-T MR imaging system equipped with an animal birdcage coil at different times. Finally, the nude mice were sacrificed for histologic examination to confirm the imaging results. RESULTS: Native polyacrylamide gel electrophoresis displayed an optimal conjugation of USPIOs to OCMab183B2 and mIgG. Various blue-staining particles were found in the cells labeled with the molecular probe at different iron concentrations, and the density of particles was positively related to the iron concentration. Its labeling rate was 96.06%, which was higher than that of USPIOs (62.5%) at the same iron concentration (20 µg/mL). The methyl thiazolyl tetrazolium assay showed that there was no difference in cellular bioactivity between OCMab183B2 USPIO-labeled and nonlabeled cells (P > .05). In vitro cell MR imaging showed that there was an obvious decrease in signal intensity for the probe-labeled cells compared to mIgG USPIO-labeled cells. For in vivo MR imaging, distinct changes of signal intensities and T2 values of ovarian cancers were detected after the injection of OCMab183B2 USPIOs compared to mIgG USPIOs. The histologic analysis showed that iron depositions were visualized in the experimental group but not in the control group. CONCLUSION: OCMab183B2 USPIO conjugates have the potential to be useful as OC183B2-targeted MR imaging agents for the early detection of ovarian cancers.