http://med-sci.cn/cgzcx/en/contents_list.asp?issue=202410 zh-cn An RSS feed for Chinese Journal of Magnetic Resonance Imaging http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2024.10.001 Sun,20 Oct 2024 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2024.10.002 ObjectiveTo explore the application value of deep learning reconstruction (DLR) technology in enhancing the image quality and reducing the scan time of fast-spin echo proton density weighted imaging (FSE-PD) in MRI of the temporomandibular joint (TMJ). Materials and MethodsRecruit 40 healthy volunteers and undergo MRI scans of the TMJ. Each healthy volunteer underwent conventional FSE-PD MRI scans and accelerated FSE-PD scans using DLR, the original accelerated FSE-PD images without DLR were simultaneously preserved. Two radiologists qualitatively and quantitatively evaluated the image quality of the three FSE-PD image sets, individually. Qualitative assessments utilized a Likert scale (5-point) for subjective scoring of anatomical structure clarity and overall image quality. Quantitative assessments utilized signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) for objective evaluation of image quality. One-way ANOVA and Kruskal-Wallis test were used to compare the differences in subjective scores and objective indicators among the three groups. The intra-class correlation coefficient (ICC) was used to evaluate the consistency of the subjective scores of the two radiologists. ResultsCompared to the conventional FSE-PD group, the DLR-accelerated FSE-PD group demonstrated a 67% reduction in scan time. The two radiologists exhibited good consistency in subjective scores for anatomical structure clarity and overall image quality (ICC of 0.80 and 0.78, respectively). There were significant differences in anatomical clarity and overall image quality scores among the conventional FSE-PD group, accelerated FSE-PD group, and DLR-accelerated FSE-PD group (P<0.05). The differences in SNR and CNR among the three FSE-PD groups were statistically significant (P<0.05). Qualitative and quantitative evaluation results for the DLR-accelerated FSE-PD group were both significantly superior to the conventional FSE-PD group. ConclusionsDLR technology could shorten the scanning time of conventional FSE-PD MRI of the TMJ, enhance image quality, and help patients complete the examination faster. ]]> Sun,20 Oct 2024 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2024.10.003 ObjectiveTo investigate the diagnostic value of deep learning reconstruction (DLR)-based late gadolinium enhancement (LGE_DL) in improving the recognition rate of unrecognized myocardial infarction (UMI). Materials and MethodsThis prospective study included 98 patients with suspected UMI who underwent cardiac magnetic resonance imaging (CMR) at our hospital from April 2022 to August 2023. Original LGE of conventional reconstruction (LGE_O) and LGE_DL 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 (P_area) were analysed using the standard deviation (SD) threshold approach (2SD-5SD) and full width at half maximum (FWHM) method. The diagnostic efficacies based on LGE_DL and LGE_O images were calculated. ResultsThe SNR_DL and CNR_DL were two times better than the SNR_O and CNR_O, respectively (P<0.001). P_area-DL was higher than P_area-O, especially in the 2SD method (P<0.001). However, there was no intergroup difference based on the FWHM method (P>0.05). Overall myocardial SNR, CNR, and P_area measurements with different threshold methods had good intra- and interobserver agreement [intra-class correlation coefficient (ICC)>0.600, all P<0.001]. The receiver operating characteristic curve analysis revealed that each SD method exhibited good diagnostic efficacy for detecting UMI, with the P_area-DL having the best diagnostic efficacy based on the 5SD method (P<0.001). Overall, the LGE_DL images had better image quality. Strong diagnostic efficacy for UMI identification was achieved when the signal threshold versus reference mean (STRM) was ≥4SD and ≥3SD for the LGE_DL and LGE_O, respectively. ConclusionsSTRM selection for LGE_DL magnetic resonance images helps improve clinical decision-making in patients with UMI. ]]> Sun,20 Oct 2024 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2024.10.004 ObjectiveTo evaluate the impact of deep learning reconstruction (DLR) on the image quality of pulmonary diffusion-weighted imaging (DWI) and to explore the value of DLR in the identification of benign and malignant pulmonary lesions. Materials and MethodsIn this prospective study, 61 patients with pulmonary nodules or masses (including 49 malignant and 12 benign cases) were recruited. Each patient underwent T2WI and DWI imaging using a 3.0 T MRI scanner, with DWI images reconstructed using both conventional reconstruction (ConR) and deep learning reconstruction (DLR). Two radiologists with 4 and 10 years of experience independently evaluated the overall image quality, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and apparent diffusion coefficient (ADC) of the lesion. Interobserver agreement on subjective scores was assessed using Kappa values, while intra-class correlation coefficient (ICC) were used to evaluate interobserver agreement on SNR, CNR, and ADC values. Wilcoxon rank sum tests were used to compare the differences between DLR DWI and ConR DWI in terms of subjective scores, SNR, CNR, and ADC. Mann-Whitney tests were performed to compare differences between benign and malignant lesions. The diagnostic performance of ADC for identifying benign and malignant lesions was evaluated using receiver operating characteristics (ROC) curves, with the area under the curve (AUC) compared between ConR and DLR using the DeLong test. ResultsBoth DLR and ConR images showed good interobserver agreement in subjective scoring (Kappa>0.60). In objective assessments, SNR and ADC demonstrated excellent interobserver consistency (ICC>0.75), while CNR showed only fair interobserver agreement (ICC>0.40). Compared to ConR DWI, DLR DWI had higher overall image quality scores (P=0.003), lesion SNR (P<0.001), and higher ADC values (P=0.017). Additionally, the CNR of DLR DWI was higher than that of ConR DWI, but the difference was not significant (P=0.258). For both ConR and DLR DWI, the ADC of malignant lesions was significantly lower than that of benign lesions (P<0.05). ROC curve analysis indicated that DLR DWI (AUC=0.891) had higher diagnostic performance in distinguishing between benign and malignant lesions compared to ConR DWI (AUC=0.808), with a significant difference observed by DeLong test (P=0.044). ConclusionsDLR DWI significantly improves overall image quality and enhances the SNR of images, offering superior diagnostic performance for distinguishing benign from malignant lesions compared to ConR DWI. ]]> Sun,20 Oct 2024 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2024.10.005 ObjectiveTo explore the value of image deep learning model based on dynamic contrast-enhanced magnetic resonance imaging in differential diagnosis of benign and malignant breast tumors. Materials and MethodsA total of 303 breast tumor patients diagnosed pathologically in the Second Affiliated Hospital of Xiamen Medical College from September 2018 to December 2022 were retrospectively collected, including 144 benign and 159 malignant. Stratified random sampling patients were divided into 212 training set and 91 test set according to the ratio of 7:3. Six DCE-MRI Deep learning models were constructed: 50-layer deep residual network (ResNet-50), Inception-V3, Googlenet, Densely connected convolutional networks (DenseNet)-121, visual geometry group (VGG)-19 and mobile neural network (MobileNet)-V3 were used to visualize the model simultaneously with gradient-weighted class activation mapping. Finally, the diagnostic results of the deep learning model, junior and senior radiologists were compared by the first and second rounds of reading. According to the receiver operating characteristic (ROC) curve, accuracy, sensitivity, specificity, negative predictive value and positive predictive value analyze the diagnostic efficiency of different deep learning models and two rounds of reading, calculate the area under the curve of each deep learning model, compare the ROC curves among the models with DeLong test, and compare the diagnostic efficiency of two rounds of reading with paired chi-square test. ResultsThe AUC of the six deep learning models ResNet-50, Inception-V3, Googlenet, DenseNet-121, VGG-19 and MobileNet-V3 was 0.874 [95% confidence interval (CI): 0.828-0.920], 0.771 (95% CI: 0.707-0.834), 0.993 (95% CI: 0.986-0.999), 0.926 (95% CI: 0.888-0.958), 0.947 (95% CI: 0.918-0.975) and 0.945 (95% CI: 0.918-0.973). The test sets of ResNet-50, Inception-V3, Googlenet, DenseNet-121, VGG-19 and MobileNet-V3 had an AUC of 0.841 (95% CI: 0.755-0.927), 0.746 (95% CI: 0.641-0.851), 0.822 (95% CI: 0.736-0.909), 0.752 (95% CI: 0.650-0.855), 0.827 (95% CI: 0.737-0.918) and 0.779 (95% CI: 0.685-0.874). ResNet-50 model Grad-CAM visualization images showed that malignant breast tumors were activated in the center and benign tumors were activated in the periphery. In the first round of reading, the accuracy, specificity and sensitivity of ResNet-50 deep learning model were 80.2%, 86.7% and 73.9%, junior doctors were 73.6%, 73.3% and 73.9%, and senior doctors were 80.2%, 80.0% and 80.4%, respectively. In the second round of reading, with the assistance of ResNet-50 model, the accuracy, specificity and sensitivity of junior doctors increased by 15.4%, 17.8% and 13.1% (P<0.05), while the accuracy, specificity and sensitivity of senior doctors increased by 12.1%, 13.3% and 10.9% (P=0.001, 0.031, 0.063). ConclusionsResNet-50 model has the best performance in differential diagnosis of benign and malignant breast tumors, and visual images may become the basis of imaging diagnosis. With the help of this model, radiologists significantly improve the accuracy of differential diagnosis of benign and malignant breast tumors, which provides an objective basis for clinical decision-making. ]]> Sun,20 Oct 2024 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2024.10.006 ObjectiveTo evaluate the value of deep learning reconstruction (DL Recon) technique in improving the image quality of rectal MRI turbo spin-echo (TSE) sequences. Materials and MethodsSixty new cases of rectal cancer diagnosed by pathology in the Chinese Academy of Medical Sciences from September 2023 to January 2024 were studied retrospectively. Each patient was subjected to a conventional TSE sequence and DL-TSE sequence, and the scanning time was recorded. Two imaging doctors had subjective evaluation for the two groups (conventional TSE, DL-TSE). The "five-point method" was used to score lesion contour clarity, the image artifacts, the clarity of the lesion and the reliability of the diagnosis, and the statistical description of the results was performed using the quartile interval M (Q25, Q75). The signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) between the DL-TSE and the conventional TSE images were computed by two imaging technicians. Paired sample t test was used for statistical analysis of the data conforming to normal distribution, and paired sample non-parametric test (Wilcoxon symbolic significance test) was used for statistical analysis of the data not conforming to normal distribution, and the results were statistically described by the quartile interval M (Q25, Q75). ResultsSixty cases of rectal carcinoma aged 35-69 (53±10) years old were enrolled. The subjective evaluation results of conventional TSE sequences and DL-TSE sequences: The focal contour clarity, image artifacts, focal structure clarity and subjective score of diagnostic confidence of DL-TSE sequence were better than those of traditional TSE sequence, and the differences were statistically significant (P<0.001). Objective evaluation results of traditional TSE sequence and DL-TSE sequence images: The SNR of DL-TSE and conventional TSE sequences were 24.26 (15.95, 42.79) and 11.84 (7.63, 18.88). The CNR of DL-TSE and conventional TSE sequences were 10.75 (7.19, 15.63) and 5.47 (3.72, 8.86), the difference was statistically significant (Z=-14.271, P<0.001). The SNR and the CNR of the DL-TSE were obviously higher than those of conventional TSE sequences. ConclusionsDL-TSE sequence uses the original K-space data DL Recon reconstruction algorithm to improve the SNR and CNR of sequence images of rectal cancer patients, and can shorten the scanning time by 36.6%, while ensuring the image quality and lesion detectability. ]]> Sun,20 Oct 2024 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2024.10.007 ObjectiveTo investigate the value of deep learning reconstruction (DLR) orbital diffusion weighted imaging (DWI) images in the assessment of active and inactive stages of thyroid-associated ophthalmopathy (TAO). Materials and MethodsThis prospectively study included 73 clinically diagnosed TAO patients (46 active TAO, 27 inactive TAO) and 26 healthy controls from April to September 2023. All participants underwent orbital MRI scans using a 3.0 T MRI scanner and a 21ch head-and-neck combined coil. DWI sequences with field of view optimized and constrained undistorted single-shot imaging and multiplexed sensitivity encoding (FOCUS MUSE) were reconstructed by conventional reconstruction (ConR) and DLR. Two diagnostic radiologists independently subjectively evaluated the image quality of the two sequences using a four-point Likert scale. The image quality was objectively evaluated by measuring the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of extraocular muscle (EOM). The DWI apparent diffusion coefficient (ADC) of EOM was used to distinguishing active from inactive TAO. The Wilcoxon was applied to test the difference of SNR, CNR, and ADC between ConR and DLR DWI, separately. Using the Clinical Activity Score (CAS) as the gold standard. The Kruskal-Wallis test was used to compare the difference of ADC between healthy controls, active and inactive TAO patients. Receiver operating characteristics (ROC) curves were used to compare the diagnostic performance of EOM ADC for differentiating active from inactive TAO patients between ConR and DLR DWI. The correlation between the EOM ADC and CAS of TAO patients was analyzed using Spearman^,s rank correlation coefficient. ResultsDLR DWI had significantly higher subjective scores than ConR DWI for Sharpness of boundaries and overall image quality. The intra- and inter-reader agreement for both sequences was good (Kappa>0.650). Significantly higher SNR and CNR in EOM DLR DWI compared to ConR (all P<0.001). No significant difference of EOM ADC was observed between ConR and DLR DWI (P>0.05). In both sequences, the EOM ADC obtained was significantly higher in the active TAO than in both inactive TAO and healthy controls, respectively (all P<0.001). There was no significant difference of EOM ADC between inactive TAO and healthy controls (P>0.05). The EOM ADC extracted from both ConR DWI (r=0.637, P<0.001) and DLR DWI (r=0.662, P<0.001) was significantly positively correlated with the CAS. Compared with ConR DWI, DLR DWI presented better performance for discriminating active from inactive TAO patients (area under the curve: 0.959 vs. 0.939, P=0.020). ConclusionsDLR improved the image quality of orbital DWI without increasing scan time. Compared to ConR, ADC values obtained based on DLR DWI were improved in identifying the activity of TAO and correlation with CAS. ]]> Sun,20 Oct 2024 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2024.10.008 ObjectiveTo investigate the impact of deep learning reconstruction (DLR) technology on the quality of breast MRI images and scan time. Materials and MethodsA total of 60 patients with a pathological diagnosis of breast cancer at first diagnosis were prospectively enrolled in this study. Conventional fast recovery fast spin echo T2-Weighted imaging, DLR fast fast recovery fast spin echo (FRFSE)-T2WI and conventional short tau inversion recovery-diffusion weighted imaging (STIR-DWI), DLR fast STIR-DWI scanning were performed, respectively. The overall image quality score and artifacts score of two T2WI and DWI (conventional FRFSE-T2WI, DLR fast FRSE-T2WI, and STIR-DWI, DLR fast STIR-DWI) were evaluated subjectively (5-point scale) by two radiologists. One senior radiologist measured the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR). Shapiro-Wilk test was used to evaluate the normal distribution of quantitative values and subjective scores. Wilcoxon signed rank test was used to evaluate the statistical difference of data that did not conform to the normal distribution. The study compared the differences in subjective scores and objective metrics between conventional and DLR-accelerated FRFSE-T2WI scans, as well as conventional STIR-DWI and DLR-accelerated STIR-DWI images. The consistency of researchers^, ratings of breast lesion images was quantified using Weighted-Kappa to ensure the reliability of the evaluations. ResultsA total of 60 patients [25-68 (49.8±8.2) years old] with breast tumors were enrolled in this study. The FRFSE-T2WI scan time was reduced by 47.8% compared to conventional FRFSE-T2WI, and the STIR-DWI scan time was reduced by 47.6% compared to conventional STIR-DWI. The subjective evaluations by two senior physicians reveal that both FRFSE-T2WI and DLR-accelerated FRFSE-T2WI, as well as standard STIR-DWI and DLR-accelerated STIR-DWI, demonstrate significantly superior overall image quality, reduced artifact levels, and enhanced clarity in breast lesion visualization compared to conventional FRFSE-T2-weighted imaging and STIR-DWI. These differences are statistically significant (P<0.05). The SNR for conventional FRFSE-T2WI and DLR-accelerated FRFSE-T2WI were 102.37 (63.24, 141.85) and 132.37 (77.25, 218.62), respectively, with a statistically significant difference (P<0.001). The CNR for lesions were 2.87 (6.35, 57.01) and 3.10 (8.94, 22.34), also showing a statistically significant difference (P<0.001). For conventional STIR-DWI with a b-value of 1000 s/mm² and DLR-accelerated STIR-DWI, the SNRs were 197.34 (157.01, 202.52) and 387.32 (265.06, 464.30), with a statistically significant difference (P<0.001). The CNRs were 1.86 (0.96, 3.23) and 2.22 (1.46, 5.89), also demonstrating a statistically significant difference (P<0.001). ConclusionsDL Recon can significantly improve the image quality of rapidly acquired breast MRI sequences while shortening scan time across different modalities. This advancement is beneficial for promoting the clinical application of fast breast MRI sequences. ]]> Sun,20 Oct 2024 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2024.10.009 ObjectiveTo investigate the application value of high-resolution single-shot fast spin-echo (SSFSE) acquisition with deep learning reconstruction (DLR) in follicle counting compared to transvaginal ultrasonography (TVUS), conventional reconstruction (CR) SSFSE and periodically rotated overlapping parallel lines with enhanced reconstruction (PROPELLER) images. Materials and MethodsParticipants with clinically confirmed or suspected polycystic ovary syndrome (PCOS) were prospectively recruited and underwent ovarian MRI. Those with no history of sexual activity also underwent ovarian TVUS. High-resolution PROPELLER and SSFSE T2-weighted sequences were obtained on three matched planes. The SSFSE sequences implemented both DLR and CR, generating SSFSE-DLR and SSFSE-CR images respectively. Qualitative indicators including blurring artifacts, subjective noise, and conspicuity of follicles were compared using Wilcoxon signed-rank tests. Follicle counting was performed by two observers, with repeatability assessed using intraclass correlation coefficient (ICC) and Bland-Altman method. Absolute values of intra-observer and inter-observer differences were compared using a paired t-test. Follicle count between SSFSE-DL and TVUS was also compared using a paired t-test. ResultsTwenty-four participants underwent MRI, with 18 of them also undergoing TVUS. Observer 1 assigned higher subjective scores to SSFSE-DLR in comparison to SSFSE-CR and PROPELLER (P<0.05), despite the similar subjective noise observed between SSFSE-DLR and PROPELLER (P>0.05). Observer 2 also rated SSFSE-DLR higher than SSFSE-CR and PROPELLER (P<0.05). Furthermore, SSFSE-DLR demonstrated the best repeatability for follicle counting, achieving the highest ICC, narrowest 95% limits of agreement, and the lowest absolute values of intra-observer and inter-observer differences (P<0.05). Moreover, SSFSE-DL detected more follicles than TVUS (P<0.001). ConclusionsSSFSE-DLR images significantly improved the display of ovarian morphology and the repeatability of follicle counting, thereby fortifying the reliability of future polycystic ovary morphology determinations. ]]> Sun,20 Oct 2024 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2024.10.010 ObjectiveExploring the role of functional magnetic resonance imaging neurofeedback (rtfMRI-NF) to modulate bilateral nucleus ambiguus to improve obesity. Materials and MethodsA total of 24 obese patients from December 2022 to December 2023 were recruited as study subjects. A 3-week rtfMRI-NF training intervention was conducted on the obese subjects, the Barratt Impulsiveness Scale Version 11 (BIS-11), three-factor eating questionnaire (TFEQ) with its three subscales: Uncontrolled Eating (UE), Cognitive Restriction (CR), and Emotional Eating (EE), the Food Grade Rating Scale, and resting-state functional magnetic resonance imaging datawere collected before and after the intervention. Paired-samples t-tests were used to compare changes in clinical scales before and after the subjects^, intervention. Paired-sample t-tests were used to analyse the functional connectivity (FC) values of the obese subjects before and after the 3-week rtfMRI-NF intervention using SPM12 software. Pearson^,s correlation analysis was performed between the FC values of the statistically significant differences in brain regions and the scores of the clinical scales. ResultsBIS-11, TFEQ-UE, TFEQ-EE, and food grade scores decreased and TFEQ-CR scores increased after the intervention in obese patients (P<0.05). FC values of the left nucleus ambiguus with the left middle temporal gyrus decreased and those with the left middle frontal gyrus increased after the intervention in obese subjects (P<0.05, GRF-corrected). FC values of the right nucleus ambiguus with the right cerebellum and the right inferior frontal gyrus increased and FC values with the left precuneus decreased (P<0.05, GRF corrected). FC values in the right nucleus ambiguus-right cerebellar area 8 were negatively correlated with TFEQ-UE scores after intervention (r=-0.549, P=0.008), and FC values in the right nucleus ambiguus-left precuneus were positively correlated with BIS-11 scores (r=0.658, P<0.001). ConclusionsThe rtfMRI-NF intervention may ameliorate poor eating habits in obese patients by altering functional connectivity of the nucleus ambiguus with distributed brain regions of cognitive control, attentional bias, and emotional memory. ]]> Sun,20 Oct 2024 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2024.10.011 ObjectiveTo explore the impact of early diabetic kidney disease on cerebral function using regional homogeneity (ReHo) and seed-based functional connectivity (FC). Materials and MethodsA total of 88 type 2 diabetes patients were prospectively recruited and divided into an early diabetic kidney disease group (n=39) and a diabetes without kidney disease group (n=49) based on the urinary albumin-to-creatinine ratio (UACR). The clinical symptoms for all participants were also collected and their cognitive scales were assessed using the Montreal Cognitive Assessment (MoCA) and the Mini-mental State Examination (MMSE). Moreover, resting state functional magnetic resonance imaging data were collected, and the cerebral functional differences between the two groups were analyzed using ReHo and seed-based FC. The partial correlation analysis was performed to identify the correlation of UACR, cognitive scores, and the brain functional imaging indices. ResultsIn terms of cognitive performance, the scores of MoCA (t=-5.58, P<0.001) and MMSE (t=-2.68, P=0.016) in the early diabetic kidney disease group decreased significantly compared to diabetic patients without kidney disease. Regarding neuroimaging findings, significant differences in ReHo values were found in the right middle occipital gyrus (P<0.05, FWE correction). Using this region as a seed point for whole-brain FC analysis, it was found that there was an enhanced FC with the left thalamus (P<0.05, FWE correction). Partial correlation analysis results showed that in patients with early diabetic kidney disease, MoCA scores were positively correlated with ReHo values in the right middle occipital gyrus (r=0.349, P=0.043) and negatively correlated with FC values in the left thalamus (r=-0.464, P=0.006). Similarly, MMSE scores were positively correlated with ReHo values in the right middle occipital gyrus (r=0.367, P=0.033) and negatively correlated with FC values in the left thalamus (r=-0.455, P=0.007). Additionally, UACR was negatively correlated with MoCA scores (r=-0.449, P=0.008) and MMSE scores (r=-0.372, P=0.030). In contrast, there were no significant correlations among UACR, brain functional imaging indices, and cognitive scale scores in diabetes without kidney disease group. ConclusionsThis study reveals that patients with early diabetic kidney disease may experience neuro-functional disorders in the visually related brain regions, and the imbalance in the functional integration of these brain regions may exacerbate cognitive impairment. ]]> Sun,20 Oct 2024 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2024.10.012 ObjectiveTo assess the mechanism of brain functional impairment, the intra- and interhemispheric functional connectivity density (FCD) analysis was applied to investigate abnormal alterations in functional connectivity within brain regions in adolescent males with internet gaming disorder. Materials and MethodsGlobal FCD calculation on resting-state functional magnetic resonance images of 55 individuals with internet gaming disorder and demographically matched 50 healthy controls. The global FCD was then divided into ipsilateral and contralateral components. The global, intrahemisphere, and interhemispheric FCD between internet gaming disorder group and healthy control group were analyzed using a two-sample t-test. Finally, Pearson correlation analyses were performed between alternations of global, intrahemisphere, and interhemispheric FCD value in individuals with internet gaming disorder and clinical behavior. ResultsThe global FCD differences between the internet gaming disorder and the healthy control were mainly located in the left precuneus, the left posterior cingulate gyrus and the left middle temporal gyrus. Compared to healthy controls, individuals with internet gaming disorder showed decreased interhemispheric FCD in the left precuneus and left posterior cingulate gyrus (t=-5.317, -3.556). In addition, individuals with internet gaming disorder also demonstrated decreased intrahemispheric FCD in the left middle temporal gyrus, the left precuneus, and the left posterior cingulate gyrus (t =-5.044, -5.359, -4.183) (voxel level P<0.005, cluster level P<0.05, Gaussian random fields corrected). ConclusionsFunctional intrahemispheric and interhemispheric connectivity abnormalities within default mode network regions are observed in individuals with internet gaming disorder. This study will provide new insights into the pathophysiologic mechanisms and clinical diagnosis and treatment of individuals with internet gaming disorder. ]]> Sun,20 Oct 2024 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2024.10.013 ObjectiveTo analyze the brain function of patients with neuropsychiatric systemic lupus erythematosus (NPSLE) using independent component analysis (ICA), investigating the appearance of cognitive impairment in NPSLE from the perspective of imaging. Materials and MethodsTixty-seven patients with NPSLE admitted to the Rheumatology Department of the Affiliated Hospital of Inner Mongolia Medical University from September 2021 to December 2023 were included, along with 33 healthy controls (HC) recruited during the same period. Resting-state and structural data were collected, and the data were preprocessed using the Dpabi based on the MATLAB. The GIFT was used to analyze the data of both groups of subjects, extracting 30 independent components. After strict matching according to the Yeo 7 network template and confirmation by experienced doctors, 8 networks were constructed by selecting independent components, and the work between different brain networks was calculated. The connectivity between different brain networks was calculated, and the functional connectivity (FC) values between different brain networks were correlated with clinical data and cognitive assessments. ResultsCompared with HC, the FC between the left frontoparietal network (LFPN) and the sensorimotor network (SMN), the default network (DMN) was reduced in the NPSLE group (P<0.05), and the FC between DMN and SMN, executive control network (ECN) was also reduced (P<0.05). The FC between the LFPN and the visual network (VN) was increased (P<0.05), and the FC between the SMN and the auditory network (AN), salience network (SN), and VN was increased (P<0.05). The correlation results show that the FC between the DMN_SMN was negatively correlated with anti-ds DNA (P=0.028, r=‍-‍0.270), the FC between the SMN_SN was negatively correlated with AVL_first 3 times (P=0.006, r=‍-‍0.275), and the FC value between the DMN_ECN was positively correlated with hematocrit (P=0.043, r=0.250) and negatively correlated with AVL_first 3 times (P=0.025, r=‍-‍0.224). ConclusionsIn NPSLE, there are varying degrees of changes in functional connectivity between different brain networks, which are related to the presence of lupus autoantibodies, hematological indicators, and neuropsychological assessments, suggesting that changes in brain networks in lupus patients may be related to cognitive impairment. ]]> Sun,20 Oct 2024 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2024.10.014 ObjectiveTo investigate the whole-brain functional connectivity (FC) of the left hippocampus in children with autism spectrum disorder (ASD). Materials and MethodsResting-state functional magnetic resonance imaging (rs-fMRI) data were obtained from the Autism Brain Imaging Data Exchange (ABIDE) database, including 110 children with ASD and 182 typically developing (TD) controls. Seed-based resting-state FC analysis was performed using the left hippocampus as the seed region to assess whole-brain FC patterns. Two-sample t-tests were used to analyze FC differences between the ASD and TD groups, with a significance threshold of P_FDR<0.05. Pearson correlation analysis was conducted to examine the relationship between abnormal FC values in the ASD group and scores on the Autism Diagnostic Observation Schedule (ADOS), with P<0.05 considered significant. ResultsCompared with the TD group, the FC between the left hippocampus and multiple brain regions, including right middle frontal gyrus, inferior frontal gyrus (orbital part), superior temporal gyrus, and middle temporal gyrus were enhanced in the ASD group. Correlation analysis showed that the FC values of the left hippocampus with the right middle frontal gyrus, bilateral inferior frontal gyrus (orbital part), right superior frontal gyrus (medial part), left caudate nucleus, left superior temporal gyrus, and left middle temporal gyrus were negatively correlated with ADOS_G_TOTAL scores (r values are -0.313, -0.395, -0.321, -0.303, -0.380, -0.366, -0.355, respectively, P<0.05). The FC values of the left hippocampus with the left inferior frontal gyrus (orbital part) and right superior parietal gyrus were negatively correlated with the ADOS_G_COMM score (r values are -‍0.339 and -‍0.316, both P<0.05). ConclusionsChildren with ASD exhibit significant abnormalities in whole-brain FC of the left hippocampus, which are significantly correlated with clinical manifestations. These findings not only highlight the critical role of left hippocampal FC in the pathogenesis of ASD but also provide a theoretical basis for future intervention strategies, promoting the development of more targeted treatment options. Moreover, this research offers new perspectives for understanding the neurobiological mechanisms underlying ASD. ]]> Sun,20 Oct 2024 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2024.10.015 ObjectiveTo explore the efficacy of multidelay arterial spin labeling imaging (ASL) MRI in predicting long-term favorable neurological function after mechanical thrombectomy in patients with acute ischemic stroke (AIS). Materials and MethodsPatients who received mechanical thrombectomy in the AIS Greenway Department of Xuanwu Hospital, Capital Medical University from June 2021 to November 2023 were retrospectively analyzed. All patients underwent noncontrast CT, CT perfusion (CTP), and CT angiography (CTA) before mechanical thrombectomy. Diffusion weighted imaging (DWI) and multidelay ASL MRI [post labeling delay (PLD)=1.00, 1.22, 1.48, 1.78, 2.15, 2.62, 3.32 s] were performed within 24 hours after surgery. Patients were divided into good perfusion group and poor perfusion group according to the volume of hypoperfusion in the affected side and unaffected side. Clinical data of the two groups were compared. The modified Rankin Scale (mRS) was used to evaluate the 90 d post-surgery neurological function prognosis of patients. 0~2 was defined as favorable function. The value of multidelay ASL MRI in predicting favorable neurological function at 90 d after surgery was analyzed by binary logistic regression model and receiver operating characteristic (ROC) curve. ResultsA total of 32 patients with AIS after mechanical thrombectomy were included, with 14 (43.8%) in good perfusion group and 18 (56.2%) in poor perfusion group. Compared to poor perfusion group, NIHSS_{7 d} (4.10±3.76 vs. 7.80±4.51, P=0.02) was significantly lower in good perfusion group, ∆NIHSS (8.10±4.99 vs. 4.20±3.81, P=0.016) and the incidence of favorable neurological function at 90 d (92.9% vs. 50.0%, P=0.019) were significantly higher in good perfusion group. Of the 32 patients, 22 achieved favorable neurological function at 90 d (68.8%). After adjusting age, NIHSS at admission, pre-surgery ischemic penumbra, the binary logistic regression showed that 24-hour post-surgery good perfusion in multidelay ASL MRI was an independent predictor of favorable neurological function at 90 d (OR=14.246; 95% CI: 1.090-186.273, P=0.043). The area under ROC curve was 0.828 (95% CI: 0.666-0.990), the sensitivity was 75.0% and the specificity was 87.0%. ConclusionsThere were significant differences in short-term and long-term neurological function prognosis between the good perfusion group and the poor perfusion group. 24-hour post-surgery good perfusion in multidelay ASL MRI could be used as an independent predictor of the favorable neurological function at 90 d. ]]> Sun,20 Oct 2024 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2024.10.016 ObjectiveTo explore the metabolic status and bilateral thalamic glutamate-glutamine complex (Glx) in patients with vestibular migraine (vestibular migraine, VM). Materials and MethodsTwenty VM patients and 20 healthy controls were selected to detect thalamic Glx in VM group and healthy controls by magnetic resonance spectroscopy (MRS) imaging technology. Relevant data between two groups to analyze the metabolic differences of Glx and their risk factors. ResultsThe healthy controls (n=20) had 2 513.60 ± 998.20 and the left thalamic Glx were 2 386.50 ± 862.03. The right thalamic Glx metabolic values in the VM group (n=20) were 3 712.00 ± 980.80 and 3 350.40 ± 944.20 for the left thalamic Glx. The t-test analysis showed statistical differences in Glx values in the left and left thalamus in the VM group compared with healthy controls (P<0.05). Spearman correlation analysis showed duration of disease, diabetes, hypertension, sleep disturbance, headache and no statistically significant correlation with VM thalamic Glx; Pearson correlation analysis showed that vertigo disorder scale (DHI), SAS anxiety scale and SDS depression scale showed no statistically significant correlation with VM thalamic Glx, and age was statistically associated with left thalamic Glx (r=0.570, P<0.001). ConclusionsVM patients had higher bilateral thalamic Glx values than healthy controls. Age was the relevant contributing factor for the elevated Glx in the left thalamus. ]]> Sun,20 Oct 2024 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2024.10.017 ObjectiveTo investigate the predicting value of local and global brain radiomics of MR perfusion weighted imaging (PWI) in the outcome of acute stroke after endovascular. Materials and MethodsA total of 180 acute stroke patients with PWI images in our hospital were retrospectively enrolled. The ITK-SNAP software was used to segment the regions of interest of abnormal perfusion areas in Tmax. The SPM software was used to automatically segment the global brain in Tmax. The AK software was used to extract the local and global brain radiomics and reduce the dimensionality. The support vector machine classifier was used to construct the models for predicting the outcome of acute stroke, and further searching for the optimal prediction model. ResultsAfter least absolute shrinkage and selection operator (LASSO) dimensionality reduction, there are 6 local features, 5 global brain features, and 10 combined local and global brain features that are highly related to outcome. Receiver operating characteristic (ROC) analysis showed that area under the curve (AUC) of the outcome prediction model based on both local and global brain features was 0.900, with the sensitivity and specificity were 82.3% and 89.1% respectively, which is significantly better than the local features model (AUC=0.706; Z=-3.248; P=0.001) and the global brain features (AUC=0.711; Z=-3.393; P<0.001). ConclusionsCombining local and global PWI features can more accurately predict the outcome of acute stroke patients and provide personalized guidance for early clinical intervention. ]]> Sun,20 Oct 2024 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2024.10.018 ObjectiveTo explore the application value of using a column chart based on preoperative contrast enhancement T1WI (CE-T1WI) omics combined with pathological parameters to predict postoperative recurrence in patients with gliomas. Materials and MethodsA retrospective analysis was conducted on 115 patients diagnosed with glioma after surgery at the General Hospital of Ningxia Medical University from April 2020 to April 2023. They were randomly divided into a training set (n=81) and a validation set (n=34) at a ratio of 7∶3. Draw the volume of interest (VOI) on preoperative enhanced T1WI (CE-T1WI) and extract imaging omics features. U test and least absolute shrinkage and selection operator (LASSO) algorithm were used to screen imaging omics features. The final selected features were included in imaging omics labels and an imaging omics model was established. Calculate the radiomics score (Radscore) based on the corresponding coefficients of the selected omics features. Screening pathological predictive factors that are correlated with recurrence through logistic regression and establishing a pathological parameter model. The combination of the two forms a joint model, and a column chart is drawn to visualize the joint model. Evaluate the predictive performance of each model using the area under curve (AUC) of the subject^,s working characteristic curve. Using the DeLong test to compare the differences in AUC values between different models, and observe the clinical value of each model using decision curve analysis (DCA). ResultsBased on preoperative CE-T1WI image delineation, a total of 200 imaging omics features were extracted from VOI, and 6 omics features related to recurrence were selected. Logistic regression analysis was used to include isocitrate dehydrogenase 1 (IDH-1) genotype (OR=2.070, P=0.041) and Ki-67 expression level (OR=1.065, P<0.001) as pathological parameters associated with glioma recurrence. Compared to individual pathological parameter models and radiomics models, the combined model showed the best predictive performance (AUC: 0.875 vs. 0.835, 0.769 in the training group, Z=-1.585, -2.458, P=0.013, 0.014). DCA analysis showed that when the probability of risk threshold was greater than 0.32, the clinical benefit level of using the combined model was higher than the other two models. ConclusionsThe combined model based on preoperative CE-T1WI imaging omics and pathological parameters has good clinical application value in predicting glioma recurrence, providing important predictive information for treatment decision-making and prognosis of glioma patients. ]]> Sun,20 Oct 2024 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2024.10.019 ObjectiveTo assess the value of prostate imaging-reporting and data system version 2.1 (PI-RADS v2.1) and multi-parametric magnetic resonance imaging (mp-MRI) derived biomarkers in detecting clinically significant prostate cancer (csPCa) in transition zone. Materials and MethodsA retrospective analysis was conducted on clinical and imaging data from patients with transition zone prostate disease who underwent mp-MRI and pathological biopsy at our hospital from January 2020 to February 2024. MRI images were evaluated by a chief physician with 8 years of experience in prostate imaging, using PI-RADS v2.1 to assess the images and outline lesion contours. This provided MRI characteristics including 3D diameter, relative lesion volume (calculated by dividing the lesion volume by the prostate volume), sphericity, flatness, and surface volume ratio. Logistic analysis was used to determine the relationship between PI-RADS scores, multiparametric MRI-derived biomarkers, and the detection of csPCa in the transition zone. ResultsThe study included 403 patients. The detection rates of csPCa for PI-RADS categories 1 (n=25), 2 (n=119), 3 (n=130), 4 (n=43), and 5 (n=86) were 0.00%, 0.00%, 3.85%, 32.56%, and 70.93%, respectively. The differences in csPCa detection rates among PI-RADS categories 3, 4, and 5 were statistically significant (P<0.001). Predictive factors for csPCa in the transition zone included serum prostate-specific antigen (PSA) [OR=1.05 (95% CI: 1.00-1.10); P=0.047], PI-RADS score [OR=8.92 (95% CI: 2.94-27.13); P<0.001], maximum two-dimensional diameter [OR=0.84 (95% CI: 0.71-0.98); P=0.046], and grid volume [OR=1.00 (95% CI: 1.00-1.00); P=0.041]. ConclusionsSerum PSA, PI-RADS score, lesion diameter, and grid volume are independent predictors of clinically significant prostate cancer in the transition zone. ]]> Sun,20 Oct 2024 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2024.10.020 ObjectiveThe study utilized the iteraterative decomposition of water and fat with echo asymmetry and least-squares estimation quantitation (IDEAL-IQ) method to quantitatively evaluate the relationship between the severity of supraspinatus tendon injuries and the degree of fat infiltration of the rotator cuff muscle group and subject characteristics. Materials and MethodsA retrospective collection of 33 patients with partial supraspinatus tendon tears and 89 patients with complete tears confirmed by shoulder arthroscopy in our hospital from August 2022 to June 2024 was conducted. Conventional MRI and IDEAL-IQ sequence scans were performed. Two radiologists independently evaluated the MRI images of all subjects. Based on the supraspinatus tendon injury performance on conventional MRI images, the supraspinatus tendon in the fully tear group was divided into Patte 1 (Grade Ⅱ), Patte 2 (Grade Ⅲ), and Patte 3 (Grade Ⅳ) according to the Patte classification. The partial tear group was defined as Grade I. At the same time, the Goutallier score and Thomazeau atrophy grading were performed on the oblique sagittal plane. Fat fraction (FF) of supraspinatus muscle, infraspinatus muscle, subscapularis muscle and teres minor muscle were measured on the fat fraction image generated by IDEAL-IQ sequence using GE ADW 4.7 workstation post-processing software. Intra-observer and intra-observer consistency were evaluated by intra-class correlation coefficient (ICC) and Kappa consistency test. Kruskal-Wallis H test and One-Way ANOVA test were used to analyze the differences of FF values among different groups, and Bonferroni test was utilized for pairwise comparison between groups. Pearson correlation was used to analyze the correlation between rotator cuff muscle FF value and age and duration of symptoms (the correlation coefficient is r). Spearman correlation was used to analyze the correlation between supraspinatus tendon injury grade and rotator cuff muscle FF value, Goutallier grade and Thomazeau atrophy grade (the correlation coefficient is r_s). Results(1) The FF values of the supraspinatus muscle, infraspinatus muscle, and subscapular muscle were significantly higher in Grade Ⅳ than those of the same muscles in Grade Ⅲ, Grade Ⅱ and Grade Ⅰ, with statistical significance (P<0.001, <0.001, 0.005, respectively). There was no significant difference in the FF value of the teres minor muscle among different grades (P=0.073). Results of intra-group comparision suggested that there was no significant difference in the FF values of the supraspinatus muscle, infraspinatus muscle, subscapularis muscle, and teres minor between Grade Ⅰ and Grade Ⅱ (P=0.026, 0.102). There was a significant difference in the FF value between Grade Ⅲ and Grade Ⅳ (P<0.001). (2) The FF values of supraspinatus, infraspinatus, subscapularis and teres minor were moderately correlated with age (with r values of 0.381, 0.339, 0.349, respectively, all P＜0.001), while FF values of subscapular muscle were weakly correlated with age (r=0.216, P=0.017). The FF values of supraspinatus, infraspinatus, subscapularis were moderately correlated with the duration of symptoms(with r values of 0.442, 0.412, 0.314, respectively, all P＜0.001), while FF values of teres minor were weakly correlated with symptom duration (r=0.277, P=0.002). The degree of injury of the supraspinatus tendon was significantly correlated with the FF value of the supraspinatus muscle (r_s=0.740, P<0.001), was strongly correlated with the FF value of the infraspinatus muscle (r_s=0.596, P<0.001), and was weakly correlated with the FF values of the subscapularis muscle and the teres minor muscle (with r_s value of 0.257, 0.212, P=0.004, 0.019). There was a positive correlation between the degree of supraspinatus injury grade and Goutallier grade and Thomazeau grade (with r_s value of 0.757, 0.737, all P<0.001). The FF value of supraspinatus muscle was significantly different in Goutallier grade and Thomazeau grade (all P<0.001). Conclusions3.0 T MR IDEAL-IQ sequence could objectively assessed the degree of rotator cuff injury, and the quantified FF was positively correlated with supraspinatus injury grade, and associated with age and duration of symptoms. ]]> Sun,20 Oct 2024 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2024.10.021 ObjectiveAnalysis of alterations in the proton density fat fraction (PDFF) of the infrapatellar fat pad using magnetic resonance-based mDIXON-Quant technique in patients with knee osteoarthritis (KOA), and its correlation with KOA severity. Materials and MethodsProspectively recruited 44 patients with KOA, performing conventional MRI and mDIXON-Quant sequence scans on a total of 70 knees, to measure the PDFF of the infrapatellar fat pad in KOA patients. The severity of the subjects^, KOA was evaluated using the whole-organ magnetic resonance imaging score (WORMS). The correlation between the PDFF of the infrapatellar fat pad and the independent scores of the 11 features of the WORMS, and the total scores of each knee were analysed. ResultsThe PDFF of the infrapatellar fat pad was found to be negatively correlated with a number of variables, including the total knee WORMS scores, articular cartilage integrity, marginal osteophytes, subarticular bone attrition, subarticular bone marrow abnormality, subarticular cysts, medial and lateral meniscal integrity, loose bodies, periarticular cysts/bursitis, anterior and posterior cruciate ligament integrity, and synovitis/effusion WORMS (r=-0.94, -0.85, -0.83, -0.80, -0.72, -0.52, -0.54, -0.39, -0.27, -0.27, -0.24, P<0.05). There was no significant correlation with the medial and lateral collateral ligament integrity (r=0.27, P=0.826). The inter-observer agreement was found to be excellent, with an ICC value of 0.793 (P<0.001), and a 95% confidence interval of (0.667-0.875). ConclusionsMagnetic resonance mDIXON-Quant technology enables quantitative evaluation of changes in the PDFF of the infrapatellar fat pad in patients with KOA. The PDFF of the infrapatellar fat pad decreases with the progression of KOA severity, suggesting its potential as an objective indicator to reflect the severity of KOA. ]]> Sun,20 Oct 2024 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2024.10.022 ObjectiveTo investigate the changes in cross-sectional area (CSA) and proton density fat fraction (PDFF) of calf muscles in patients with chronic ankle instability (CAI) using magnetic resonance imaging (MRI) quantitative analysis, and to explore the related influencing factors. Materials and MethodsA prospective study was conducted in 50 patients with CAI and 32 healthy volunteers. Clinical data were collected, and MRI scans of the calf muscles were performed. The CSA of each calf muscle was delineated on axial T1-weighted fast spoild gradient echo sequences, while the PDFF of corresponding muscles was obtained through least squares estimation and iterative decomposition of water and fat with echo asymmetry. Differences in calf muscle CSA and PDFF between CAI patients and healthy controls were analyzed, and their correlations with the number of sprains, duration of interruption in activities, time since last sprain, Foot and Ankle Ability Measure-Activities of Daily Living (FAAM-ADL) score, and FAAM-SPORTS score were examined. ResultsCompared to the healthy control group, CAI patients had significantly reduced CSA in the medial and lateral heads of the gastrocnemius, soleus, tibialis anterior, tibialis posterior, and peroneus longus muscles on the affected side, with a concurrent increase in PDFF (P<0.05, all). The CSA of the extensor digitorum longus and flexor hallucis longus muscles was also reduced, but the differences were not statistically significant (P=0.307, 0.320, respectively); however, their PDFF was significantly increased (P=0.047, 0.029, respectively). Correlation analysis showed that reduced CSA was strongly positively correlated with the number of sprains (r=0.785, P<0.001) and moderately positively correlated with the duration of interruption in activities (r=0.642, P<0.001), while it was strongly negatively correlated with FAAM-ADL (r=-0.754, P<0.001) and FAAM-SPORTS (r=-0.766, P<0.001). Increased PDFF was strongly positively correlated with the number of sprains (r=0.757, P<0.001) and moderately positively correlated with the duration of interruption in activities (r=0.600, P<0.001), while it was strongly negatively correlated with FAAM-SPORTS (r=-0.740, P<0.001) and moderately negatively correlated with FAAM-ADL (r=-0.681, P<0.001). ConclusionsMRI can quantitatively assess changes in calf muscle CSA and PDFF in patients with CAI, and these changes are related to the number of sprains, FAAM-ADL, FAAM-SPORTS, and duration of interruption in activities. ]]> Sun,20 Oct 2024 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2024.10.023 ObjectiveTo investigate the correlation between diffusion weighted imaging (DWI), intravoxel incoherent motion (IVIM), and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) parameters and the expression of Ki-67 in soft tissue tumors. Materials and MethodsA retrospective study included 56 patients with pathologically confirmed soft tissue tumors, divided into a high expression group (n=22) with a Ki-67 index of >20% and a low expression group (n=34) with a Ki-67 index of ≤20% according to the Ki-67 index. The study compared DWI, IVIM, and DCE-MRI parameters between the groups, including apparent diffusion coefficient (ADC), pure diffusion coefficient (D), pseudo-diffusion coefficient (D^*), perfusion fraction (f), rate constant (K_ep), volume transfer constant (K^trans), and extracellular extravascular space volume fraction (V_e), and analyzed their correlation with Ki-67 index. It also applied the Pearson correlation coefficient to analyze the significant correlation of parameters with the Ki-67 index that showed significant differences between the groups. ResultsThe ADC and D values were significantly higher in the low Ki-67 expression group than in the high expression group, while K^trans and K_ep values were significantly lower (P<0.05 for all). ADC and D values showed a negative correlation with Ki-67 index (r=-0.637, -0.625, P<0.001), whereas K^trans showed a positive correlation (r=0.263, P=0.050). ADC had the highest area under the curve (AUC) in distinguishing Ki-67 expression status in soft tissue tumors, at 0.920 (0.845-0.994). ConclusionsADC, D, K^trans, and K_ep can effectively predict Ki-67 expression in soft tissue tumors, with ADC and D being the best parameters for predicting the Ki-67 expression status, providing assistance in clinical diagnosis, treatment, and prognosis assessment. ]]> Sun,20 Oct 2024 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2024.10.024 ObjectiveUtilizing deep learning methodologies, we study the features of arteries and plaques within high-resolution magnetic resonance imaging (HR-MRI) of the carotid artery. This enables the generation of virtual contrast-enhanced T1WI (vce-T1WI) from plain T1WI. Furthermore, the detection level of the lipid-rich necrotic core within the carotid artery plaque in these generated vce-T1WI images is evaluated. Materials and MethodsIncorporating 303 cases of patients with carotid artery stenosis, a total of 486 bilateral carotid arteries were scanned using T1WI images and actual contrast-enhanced T1WI (CE-T1WI) images. These were divided into training, validation, and testing sets at a ratio of 4∶1∶1, and the generative network was trained using five-fold cross-validation. The test set comprised 81 carotid artery images. Two distinct deep learning strategies (pix2pix, Cycle GAN) were employed to generate vce-T1WI images from plain T1WI scans. The quality of the vce-T1WI images generated by the two models was evaluated using the peak signal-to-noise ratio (PSNR), structural similarity (SSIM), and subjective visual quality scores. The diagnostic efficacy of the generated vce-T1WI images for the lipid-rich necrotic core within the plaque was assessed, with the physician^,s judgment based on the actual CE-T1WI images serving as the gold standard. ResultsThe vce-T1WI images generated by pix2pix and Cycle GAN achieved PSNR scores of 20.206 and 19.717 respectively in the same test set, with SSIM scores of 0.591 and 0.635 respectively. The proportion of images scoring more than 2 points in the subjective visual quality assessment was 95.1% and 97.5% respectively. The detection accuracy for the lipid-rich necrotic core within the plaque was 82.7% and 74.1% respectively. ConclusionsDeep learning methodologies can effectively generate high-quality vce-T1WI images from plain T1WI scans of the carotid artery HR-MRI. Furthermore, the virtual contrast-enhanced images generated by Cycle GAN exhibit a high detection accuracy for the lipid-rich necrotic core within the plaque. Deep learning techniques can broaden the clinical application scope of HR-MRI and reduce the risk of adverse reactions to contrast agents. ]]> Sun,20 Oct 2024 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2024.10.025 ObjectiveTo explore the application value of Intelligent Quick Magnetic Resonance (IQMR) in the supraspinatus tendon injury. Materials and Methods40 patients with supraspinatus tendon injuries underwent coronal fast T2WI fat saturation (T2WI-FS) and conventional T2WI-FS sequences scanning of the shoulder, the images of fast T2WI-FS sequence were transferred to IQMR post-processing system to generate T2WI-FS_IQMR images automatically. Three groups of MRI images were independently scored by two radiologists for the clarity of lesion detail, the clarity of anatomical structure, overall image artifacts and overall image quality. The degree of supraspinatus tendon injury of three groups of MRI images were independently graded by two radiologists according to Zlatkin classification. The signal-to-noise ratio (SNR) of the supraspinatus, humeral head, and deltoid muscles, contrast noise ratio (CNR1) of the supraspinatus muscle to the humeral head, and contrast noise ratio (CNR2) of deltoid muscle to the humeral head were measured and compared among the three groups of MRI images. ResultsThe scanning time of T2WI-FS_IQMR sequence was 41% shorter than that of conventional T2WI-FS sequence. Qualitative analysis: The image quality scores of T2WI-FS_IQMR were higher than those of fast T2WI-FS and conventional T2WI-FS in terms of the clarity of lesion detail, the clarity of anatomical structure, overall image artifacts and overall image quality (P<0.001). There was no significant difference among the three groups in the diagnosis of supraspinatus tendon injury (P>0.05). Quantitative analysis: SNR of the supraspinatus, humeral head and deltoid, CNR1 and CNR2 of T2WI-FS_IQMR were higher than those of fast T2WI-FS and conventional T2WI-FS (P<0.001). ConclusionsIn the supraspinatus tendon injury MRI scanning, IQMR technology can significantly reduce the scanning time and improve image quality, which is worthy of clinical application. ]]> Sun,20 Oct 2024 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2024.10.026 Sun,20 Oct 2024 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2024.10.027 ^, health. The pathogenesis of non-NPSLE is still unclear. Functional magnetic resonance techniques can provide non-invasive and valuable research results, which can help to study the neurobiological mechanisms and biological markers of non-NPSLE from multiple levels and perspectives. In recent years, a variety of functional magnetic resonance techniques have been applied to non-NPSLE to explore its brain functional changes. In summary, this paper reviews the findings and potential shortcomings of functional magnetic resonance techniques in the application of non-NPSLE, aiming to provide a direction for future research on non-NPSLE. ]]> Sun,20 Oct 2024 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2024.10.028 ^, daily life and physical and mental health. There is a bidirectional relationship between ID and depression, and they often present as multimorbidity, but the mechanism is unclear. The amygdala is a key center of the emotional circuit, and 5-hydroxytryptamine (5-HT) participates in emotion regulation by regulating amygdala activity. 5-HT neural circuit to amygdala is involved in ID and depression. In this review, we illustrated the changes in the structure and function of the amygdala and 5-HT neural circuit to amygdala in patients with ID and depression through functional magnetic resonance imaging (fMRI), with the aim of providing an objective neuroimaging basis for exploring the possible mechanisms of the 5-HT neural circuit to amygdala in the multimorbidity of ID and depression. ]]> Sun,20 Oct 2024 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2024.10.029 Sun,20 Oct 2024 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2024.10.030 Sun,20 Oct 2024 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2024.10.031 Sun,20 Oct 2024 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2024.10.032 Sun,20 Oct 2024 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2024.10.033 Sun,20 Oct 2024 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2024.10.034 Sun,20 Oct 2024 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2024.10.035 Sun,20 Oct 2024 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2024.10.036 Sun,20 Oct 2024 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2024.10.037 Sun,20 Oct 2024 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2024.10.038 Sun,20 Oct 2024 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2024.10.039 Sun,20 Oct 2024 00:00:00 GMT