Objective:To explore the morphological characteristics of the corpus callosum (CC) in patients with unilateral medial temporal lobe epilepsy (mTLE) with hippocampal sclerosis (HS), and their correlation with hippocampal volume and clinical indicators.Methods:This was a cross-sectional study. Clinical (age of onset, disease duration, seizure frequency, seizure duration, etc.) and imaging data of 44 patients mTLE with unilateral HS confirmed by postoperative pathology and 42 healthy controls (HCs) recruited at West China Hospital of Sichuan University from June 2017 to May 2023 were analyzed retrospectively. Among the 44 patients, 19 had left-sided HS and 25 had right-sided HS. All subjects underwent high-resolution 3D T 1WI. Hippocampal volumes were obtained using FreeSurfer. ART was used to measure the morphological parameters of the CC for each participant, including total CC area, volume, perimeter, length, thickness, circularity, and the area of seven CC subregions defined by Witelson: rostrum, genu, body, anterior midbody, posterior midbody, isthmus and splenium. Differences in these metrics between two or three groups were compared using independent samples t-test or one-way ANOVA. Pearson or Spearman correlation analysis was used to observe the correlation between morphological features of the CC and hippocampal volume and other clinical indicators in patients with mTLE with unilateral HS. Results:Compared with HCs, patients with mTLE with unilateral HS had significantly reduced total CC area, CC circularity, as well as the area and thickness of the genu, anterior midbody, posterior midbody, isthmus, splenium, and the area of the rostrum ( P<0.05). Significant differences were observed in the total area, circularity, and subregional areas (genu, rostrum, anterior midbody, posterior midbody, splenium), as well as thickness (genu, anterior midbody, posterior midbody, isthmus) of the CC among mTLE with left-sided HS, mTLE with right-sided HS, and HCs ( P<0.05). When compared to HCs, the total area of the CC, circularity and the areas of the genu, rostrum, anterior midbody, posterior midbody, and splenium, and the thicknesses of the genu, anterior midbody, posterior midbody, and isthmus of the CC were significantly reduced in patients with mTLE with right-sided HS ( P<0.05), and the thicknesses of the midbody and isthmus of the CC were significantly reduced in patients with mTLE with left-sided HS compared to HCs ( P<0.05), and the two-by-two comparison of the rest of the indicators did not show statistically significant differences ( P>0.05). Correlation analysis showed that some morphological abnormalities in the CC in mTLE with unilateral HS patients were significantly correlated with age of onset, disease duration, seizure frequency, seizure duration, and hippocampal volume. Conclusions:mTLE with unilateral HS patients can exhibit morphological abnormalities in the CC, particularly in those with right-sided lesions. These abnormalities are significantly associated with seizure-related factors and hippocampal atrophy.

Objective:To investigate the changes in brain functional network and structural-functional network coupling in children with drug-resistant epilepsy (DRE), and to analyze their correlation with cognitive function, disease duration, and age of onset.Methods:This study was a cross-sectional study. Clinical and imaging data of 19 children with DRE who received consultation and treatment at the Affiliated Hospital of Zunyi Medical University from August 2021 to August 2023 (DRE group) were prospectively included. Another 27 age-and sex-matched healthy children were collected as the healthy control group. All subjects had 3D-T 1WI, T 2 fluid-attenuated inversion recovery, diffusion tensor imaging (DTI), resting-state functional magnetic resonance imaging (rs-fMRI) scans and Wechsler Intelligence Scale assessments. Independent sample t-test and Mann-Whitney U test were used to analyze the global and local topological attributes, as well as the structural-functional coupling (SFC) values at the whole brain and modular levels in two groups. Correlations between abnormal resting state brain functional network indicators and the Wechsler Intelligence Scale score [verbal comprehension index (VCI), perceptual reasoning index (PRI), working memory index (WMI), processing speed index (PSI), full scale intelligence quotient (FSIQ)], disease duration and age of onset was evaluated using a Spearman or Pearson correlation analysis. Results:Compared to the healthy control group, DRE group exhibited decreased VCI, PRI, WMI, PSI, FSIQ and the differences were all statistically significant (all P<0.05). Both brain functional networks had small world attributes. There was a statistically significant difference in the area under the curve of sparsity of degree centrality (DC) in the left pallidum between the DRE group and healthy control group (2.998±0.942, 4.992±1.945, t=-4.07, FDR corrected P<0.05). Compared with the control group, the DRE group had decreased SFC within the limbic network (LN) ( P<0.05), increased SFC within the sensorimotor (SMN) ( P<0.05), decreased SFC between the default mode network-LN ( P<0.05), and increased SFC between the SMN-attentional network (AN) ( P<0.05). There was no statistically significant difference in SFC at the whole brain level between the two groups. Correlation analysis indicated that DC in left pallidum in DRE group negatively correlated with the PSI ( r=-0.537, P=0.018), and SFC between the SMN and AN demonstrated a negative correlation with age of onset ( r=-0.537, P=0.018). Conclusion:The altered DC in left pallidum may be related to cognitive impairment in children with DRE, providing biomarker information for the study of neural mechanisms in children with DRE.

Objective:To evaluate the topological alterations of resting-state brain networks in patients with idiopathic generalized epilepsy with generalized tonic-clonic seizure (IGE-GTCS) using minimum spanning tree (MST) based on graph theoretic analysis, and to further analyze the relationships between topological features, duration, and antiepileptic drug response.Methods:This study was a cross-sectional study. Retrospectively, 75 IGE-GTCS patients and 37 healthy controls (HC) who underwent brain MR imaging at the Affiliated of Nanjing University Medical School Drum Tower Hospital from January 2013 to December 2020 were enrolled. IGE-GTCS patients were grouped into well-controlled subgroup (WC; n=55) and drug-resistant subgroup (DR; n=20) according to their response to antiepileptic drugs. Firstly, the time series correlations between 116 regions of the whole brain of each subject were calculated to construct functional connectivity matrices. For each functional connectivity matrix, the Kruskal algorithm was used to MST, and the topological metrics of each MST were calculated, including leaf fraction, tree hierarchy, and diameter. The comparison of MST topological metrics between the two groups was performed using two-sample t-test. Pearson correlation analysis was used to calculate the correlation between disease duration and MST metrics in the WC subgroup and the DR subgroup. Results:Compared with the HC group, the MST leaf fraction ( t=2.27, P=0.025) increased in the IGE-GTCS patient group, and the diameter decreased ( t=-2.24, P=0.027), there was no statistically significant difference in tree hierarchy between IGE-GTCS patient group and HC group ( t=0.98, P=0.328). The MST leaf fraction ( t=-2.39, P=0.019) and tree hierarchy ( t=-2.24, P=0.027) in the WC subgroup was decreased compared with the DR subgroup, while there was no statistically significant difference in diameter between WC subgroup and DR subgroup ( P=0.093). The correlation analysis showed the MST diameter in WC subgroup was significantly correlated with disease duration ( r=0.452, P<0.001), while the MST diameter in DR subgroup was not significantly correlated with disease duration ( r=-0.062, P=0.847). Conclusions:Patients with IGE-GTCS exhibit specific alterations in the global topology of brain network, characterized by increased centralization and efficiency. The effective antiepileptic drug treatment is associated with a recovery of brain network abnormalities.

Objective:To explore the capability of deep learning image reconstruction (DLIR) compared to adaptive statistical iterative reconstruction (ASiR-V) in improving the image quality and myocardial edge sharpness of dynamic stress myocardial CT perfusion imaging (CTP).Methods:Thirty subjects who underwent dynamic stress myocardial CTP at Guangdong Provincial People′s Hospital from September 2023 to February 2024 were recruited. Image data of all enrolled patients were reconstructed using ASiR-V 50%, ASiR-V 80%, medium-intensity DLIR(DLIR-M), and high-intensity DLIR(DLIR-H), respectively. Regions of interest were selected in the left ventricular cavity, interventricular septum, and left ventricular lateral wall for measurement of CT values and standard deviations (SD), and calculation of signal to noise ratio (SNR) and contrast to noise ratio (CNR). Matlab was utilized to obtain the differences (d) and slopes (s) of CT value changes at four left ventricular myocardial edges for objective edge sharpness evaluation. Two radiologists subjectively scored the images for noise, natural appearance, and edge sharpness. In case of disagreement between the two radiologists, a third senior radiologist′s score was decisive. Left ventricular myocardial blood flow (MBF) of ASiR-V and DLIR images with lower SD, higher SNR and CNR were calculated, respectively. When the normal distribution was satisfied, the independent sample t test was used for comparison between two groups, and the random block design ANOVA was used for comparison between multiple groups. And analysis was conducted using Friedman test for non-normally distributed data, and Bonferroni correction for pairwise comparisons. Results:There were statistically significant differences in SD, SNR, and CNR among the four images in the interventricular septum and left ventricular lateral wall (all P<0.05), with ASiR-V 80% and DLIR-H demonstrating the lowest SD, highest SNR and CNR, and the subjective image noise score. Statistically significant differences were observed in d and s for the four left ventricular myocardial edges (all P<0.05), with DLIR-M and DLIR-H exhibiting the best objective edge sharpness [5 (5, 5)], and ASiR-V 80% the worst [3.5 (3, 4)]. In the subjective scores for natural appearance, DLIR-M and DLIR-H received the highest scores [5 (5, 5)], while ASiR-V 80% received the lowest scores [3 (3, 4)], with statistically significant differences (all P<0.05). There was no statistically significant difference in MBF values calculated from ASiR-V 80% and DLIR-H images (all P>0.05). Conclusions:The SD value, SNR and CNR of dynamic stress myocardial CTP images reconstructed by DLIR-H are equivalent to ASiR-V 80%, and using DLIR-H can improve the edge sharpness of left ventricular myocardium without affecting the calculation of MBF.

Objective:To explore the value of deep learning models based on ultrafast dynamic contrast-enhanced MRI (UF-DCE MRI) in predicting malignant breast lesions.Methods:The study was a cross-sectional study. Clinical and imaging data of 347 patients with breast lesions who received treatment at Tianjin Medical University Cancer Institute and Hospital from March 2023 to January 2024 were analyzed retrospectively. A total of 347 lesions were observed in the 347 patients, including 75 benign and 272 malignant lesions. The random number method was used to divide into the training set with 243 cases and the validation set with 104 cases in a ratio of 7∶3. All patients underwent breast UF-DCE MRI and conventional dynamic-enhanced MRI (DCE-MRI). A 27-channel model (27-phase enhancement images of input UF-DCE MRI), a 3-channel model (3-phase enhancement images of input DCE-MRI), and a 1-channel model (1st-phase enhancement images of DCE-MRI) were built based on the pre-trained ResNet18 deep learning model on ImageNet. The efficacy of each model in predicting breast malignant lesions was analyzed using receiver operating characteristic curves and area under the curve (AUC). The differences of AUC were compared using DeLong test.Results:In the training and validation sets, the 27-channel model had the highest AUC for diagnosing malignant breast lesions, which were 0.848 (95% CI 0.818-0.877) and 0.784 (95% CI 0.752-0.817), respectively. DeLong test showed no statistically significant difference in the AUC values of the three models in the validation set for the diagnosis of malignant lesions of the breast in a two-by-two comparison ( P>0.05). UF-DCE MRI scans were 27 phases totaling 81 s with a temporal resolution of 3 s/phase; DCE-MRI scans were 3 phases totaling 270 s with a temporal resolution of 90 s/phase. Conclusions:The model combining UF-DCE MRI with deep learning demonstrates comparable efficacy to DCE-MRI deep learning model in diagnosing breast malignant lesions. However the UF-DCE MRI has the advantages of high temporal resolution and short scanning time, which makes this model valuable for precise diagnosis and treatment of breast cancer.

Objective:To explore the value of amide proton transfer weighted imaging (APTWI) combined with human epidermal growth factor receptor 2 (HER2) expression in predicting pathological complete response (pCR) after neoadjuvant chemotherapy (NAC) in breast cancer.Methods:The study was a cross-sectional study. Clinicopathological [estrogen receptor (ER), progesterone receptor (PR), HER2, Ki-67 status, and molecular subtypes] and imaging data were retrospectively analyzed in 100 female patients who had invasive ductal carcinoma of the breast confirmed pathologically by preoperative puncture in the Henan Cancer Hospital from May 2023 to May 2024. All patients underwent MRI, including enhanced MRI, APTWI, and diffusion-weighted imaging (DWI) before NAC. The reference enhanced MRI images were segmented into lesions using the threshold extraction method, and the three-dimensional region of interest within the tumor was automatically outlined by the software and replicated in the amide proton transfer map generated by APTWI and the apparent diffuse coefficient (ADC) map generated by DWI. The magnetization transfer ratio asymmetry (MTRasym) value and the ADC value were measured, respectively. Tumor response to NAC was assessed using the Miller-Payne grading system, where Grade 5 indicated pCR and Grades 1-4 were classified as non-pCR. Independent sample t-tests and χ2 tests were used to compare clinical pathological and imaging parameters between pCR and non-pCR patients. Statistically significant variables were included in multivariate logistic regression to identify independent predictors of pCR. The diagnostic performance of individual and combined indicators for pCR was evaluated using receiver operating characteristic curves and the area under the curve (AUC). DeLong′s test was used to compare AUCs. Results:There were 39 pCR and 61 non-pCR patients. Significant differences were observed between the pCR and non-pCR patients in molecular subtypes, ER, PR, HER2, and Ki-67 statuses ( P<0.05). Pre-treatment MTRasym values were significantly higher in the pCR patients compared to the non-pCR patients ( P=0.005), whereas ADC values showed no statistical difference ( P=0.372). Multivariate logistic regression analysis showed HER2 positivity ( OR=5.87, 95% CI 1.99-17.30, P=0.001) and MTRasym values>2.61% (OR=4.39, 95% CI 1.37-14.08, P=0.013) was independent predictors of pCR after NAC. HER2 positivity combined with MTRasym value>2.61% predicted pCR after NAC in breast cancer with AUC of 0.819, which was superior to HER2 positivity and MTRasym value alone in predicting efficacy ( Z=3.91, P<0.001; Z=2.63, P=0.009). Conclusions:The MTRasym value of pre-treatment APTWI is valuable in predicting pCR after NAC in breast cancer. APTWI combined with HER2 expression status can further enhance the predictive efficacy.

Objective:To explore the feasibility of high-level deep learning image reconstruction (DLIR-H) algorithm in dynamic myocardial perfusion (CTP) and coronary CT angiography (CCTA) extraction.Methods:From October 2021 to October 2022, 41 patients with confirmed or suspected coronary heart disease who underwent traditional CCTA and dynamic CTP examinations on GE Apex CT were prospectively collected. Traditional CCTA used 100 kVp tube voltage scan and DLIR-H to reconstruct the original image, while dynamic CTP used 80 kVp tube voltage scan to reconstruct the original image with ASiR-V 100% and DLIR-H, respectively. Comparing subjective and objective scores and myocardial blood flow (MBF) values?of rest and stress CTP between ASiR-V100% and DLIR-H. Subjective and objective scores, as well as stenosis degree and coronary CT blood flow reserve fraction (CT-FFR) value were analyzed on a vessel basis, and the image quality and diagnostic performance of traditional CCTA and single-phase CCTA (SP-CCTA) extracted under DLIR-H CTP were compared. Statistical analysis were performed using paired t test, Wilcoxon signed-rank test and χ2 test. Results:In the subjective image quality analysis of resting and stress CTP, DLIR-H was improved compared with ASiR-V100%, and the difference was statistically significant (all P<0.05). There was no significant difference in MBF values obtained by the two reconstruction methods in the assessment of quantitative myocardial perfusion ( P>0.05). Compared with traditional CCTA, the vascular CT value of SP-CCTA increased by 15.12%, the noise value increased by 32.27%, and the subjective score was also slightly lower (4.23±0.05). However, there were no statistically significant differences in total plaque volume, maximum stenosis degree, and number of CT-FFR positive vessels between SP-CCTA and traditional CCTA (all P>0.05). Conclusion:The deep learning reconstruction algorithm can not only improve the quality of the original image to a certain extent on dynamic CTP, but also extract high-quality single-phase CCTA to meet clinical diagnosis and realize a “one-stop” dynamic myocardial perfusion examination, which will help simplify the examination process, reduce contrast agent and radiation doses in the future.

Objective:To explore the feasibility of applying deep learning image reconstruction (DLIR) in low-radiation dose liver dual-energy CT to further improve image quality, diagnostic confidence of lesion, and accuracy of iodine concentration (IC) measurement.Methods:This prospective cohort study enrolled 60 patients scheduled for enhanced liver CT at the First Affiliated Hospital of Anhui Medical University from June 2023 to January 2024. The participants were randomly assigned into the standard dose group and low radiation dose group with 30 cases in each using randomized block method. The standard radiation dose group underwent standard-radiation dose 120 kVp scans during the venous phase, while the low radiation dose group underwent low radiation dose scans with a rapid kVp-switching spectral scanning mode at 80 kVp and 140 kVp. The effective radiation dose (ED) was calculated for both groups. The standard radiation dose group was reconstructed using adaptive statistical iterative reconstruction-V (ASIR-V) algorithm 40% (AR40 120 kVp). The low radiation dose group using high-intensity DLIR (DLIR-H) to reconstructed 40 keV and 50 keV virtual monoenergetic images (VMI) (DH-VMI 40 keV, DH-VMI 50 keV). The image quality of the above three groups was objectively evaluated through the measurement of image noise and calculation of contrast-to-noise ratio (CNR) and signal-to-noise ratio (SNR) for the liver and portal vein; and the image quality was subjectively scored for image noise, contrast, lesion conspicuity, and diagnostic confidence. In the low radiation dose group, DLIR-H and ASIR-V40% reconstructed iodine maps were used to measure the liver and portal vein of IC values, standard deviations (SD), and coefficients of variation (CV). One-way analysis of variance or Kruskal-Wallis H test was used to compare the differences of subjective and objective image quality among the three groups, and paired t-test was used to compare the differences in measurement indexes between DLIR-H and ASIR-V40% reconstructed iodine maps. Results:The ED in the low radiation dose group [(2.2±0.5) mSv] was reduced by 56.8% compared to the conventional radiation dose group [(5.4±1.4) mSv]. Objective evaluations demonstrated that DH-VMI 40 keV had higher image noise, CNR, and SNR for liver and portal veins compared to AR40 120 kVp ( P<0.001). DH-VMI 50 keV had lower image noise ( P=0.200), with higher CNR and SNR for the liver and portal vein compared to AR40 120 kVp( P<0.001). In subjective evaluation, there was no statistically significant difference in image noise scores between DH-VMI 40 keV and AR40 120 kVp ( P>0.05), while the image noise score for DH-VMI 50 keV was lower than that of AR40 120 kVp ( P<0.05). Both DH-VMI 40 keV and DH-VMI 50 keV had higher scores for contrast, lesion conspicuity, and diagnostic confidence compared to those of AR40 120 kVp ( P<0.05). In the low radiation dose group, there was no statistically significant difference in IC values for the liver and portal vein between the ASIR-V40% and DLIR-H algorithm reconstructed iodine maps ( P>0.05). The SD and CV of liver and portal vein in the DLIR-H reconstructed iodine maps were lower than those in the ASIR-V40% reconstructed iodine maps ( P<0.001). Conclusions:DLIR can effectively reduce the image noise of low-energy (40, 50 keV) VMI, enhance lesion conspicuity and diagnostic confidence, and improve measurement accuracy without affecting IC values.

Objective:To investigate the differences in complications after transcatheter aortic valve replacement (TAVR) between patients with pure aortic regurgitation (PAR) and aortic valve stenosis (AS), and to explore the risk factors for the occurrence of major complications.Methods:Totally 124 patients who underwent TAVR at the First Affiliated Hospital of Wannan Medical College between October 2021 and January 2024 were retrospectively included. Patients were divided into the PAR group (64 cases) and AS group (60 cases) based on preoperative echocardiography findings. Preoperative clinical data, echocardiography, CT imaging data,and postoperative complications were collected. The differences between the two groups in postoperative complications and preoperative clinical data, imaging data were compared. Multivariate logistic regression analysis was used to assess the risk factors for major complications in the two groups, receiver operating characteristic (ROC) curve analysis was performed to evaluate the predictive efficacy of major risk factors for common postoperative complications in both groups.Results:The major complication in the PAR group was conduction disturbance (CD, 33 cases); the major complication in the AS group was perivalvular leakage (PVL, 41 cases). Comparison of preoperative data showed that in the PAR group, there were statistically significant differences in the shortest diameter of the membranous septum (MS) and the ratio of the shortest to longest MS diameter between patients with and without postoperative CD (both P<0.05). In the AS group, there were statistically significant differences in the long diameter of the aortic annulus, annular area, annular perimeter, and calcification score of the anchoring zone between patients with and without postoperative PVL (all P<0.05). Multivariate logistic regression analysis revealed that the shortest diameter of the MS was an independent risk factor for postoperative CD in PAR patients ( OR=0.628,95% CI 0.437-0.902, P=0.012), and the area under the ROC curve was 0.751 (95% CI 0.630-0.872, P=0.001). Meanwhile, the calcification score of the anchoring zone was an independent risk factor for postoperative PVL in AS patients ( OR=1.454,95% CI 1.055-2.003, P=0.022), and the area under the ROC curve was 0.740 (95% CI 0.601-0.879, P=0.003). Conclusions:After TAVR, complications differ between PAR and AS patients. CD is the major complication in PAR patients, with the shortest diameter of the MS being a risk factor. Conversely, PVL is the major complication in AS patients, with the calcification score of the anchoring zone identified as a risk factor.

Objective:To explore the hemodynamic characteristics of the middle cerebral artery (MCA) in atherosclerotic stenosis using four-dimensional flow (4D Flow) MRI, and combining high-resolution magnetic resonance vessel wall imaging (HR VW-MRI) to analyze the relationship between hemodynamics and the degree of stenosis, as well as the morphological characteristics of plaques.Methods:The study was a cross-sectional study. A total of 24 patients with middle cerebral artery(MCA) M1 atherosclerotic stenosis and 10 age and sex matched healthy controls (HC group) were prospectively recruited from September 2018 to March 2021 at Beijing Tiantan Hospital, Capital Medical University. All subjects underwent MRI examination. The hemodynamic of MCA were collected by 4D Flow MRI, and the hemodynamic parameters of proximal and distal MCA stenosis were calculated by blood flow post-processing software, including average blood flow rate (FR avg), average blood flow velocity (V avg), peak blood flow velocity (V pk), time average wall shear stress (TAWSS), minimum wall shear stress (WSS min) and oscillatory shear index (OSI). The differences in hemodynamic parameters among the proximal and distal ends of MCA stenosis and the HC group were compared using one-way ANOVA or Kruskal-Wallis H test. The stenosis rate and characteristics of MCA plaque were analyzed by HR VW-MRI, including remodeling index (RI), normalized wall index (NWI) and plaque length. Pearson or Spearman correlation analysis was used to explore the correlation between stenosis rate and hemodynamic parameters. Taking the stenosis rate as the control variable, partial correlation analysis was used to explore the correlation between plaque morphological characteristics and hemodynamic parameters. Results:There were statistically significant differences in FR avg, V avg, V pk, TAWSS, OSI, WSS min among the proximal and distal stenosis of MCA and HC groups ( P<0.05). The proximal end of the MCA stenosis had significantly higher FR avg, V avg, TAWSS and WSS min than those of the distal end of the stenosis ( P<0.01). The FR avg, V avg, V pk, TAWSS, and WSS min in the distal end of MCA stenosis were lower than those in the HC group, while the OSI was higher than that in the HC group ( P<0.01). The correlation analysis results showed that the MCA proximal V pk ( r=-0.425, P=0.027) and distal V pk ( r=-0.538, P=0.004) were negatively correlated with the diameter stenosis rate. When the stenosis rate was taken as the control factor, in the MCA proximal stenosis, V avg ( r=0.553, P=0.003), TAWSS ( r=0.543, P=0.004) and WSS min ( r=0.547, P=0.004) were positively correlated with RI, proximal OSI was negatively correlated with RI ( r=-0.492, P=0.011), and was positively correlated with the plaque length ( r=0.437, P=0.026). At the distal end of the stenosis, V pk was negatively correlated with NWI ( r=-0.556, P=0.003), OSI was negatively correlated with RI ( r=-0.511, P=0.008), NWI ( r=-0.390, P=0.049). TAWSS was positively correlated with RI ( r=0.393, P=0.047). Conclusions:The 4D Flow MRI demonstrates characteristic hemodynamic changes in the proximal and distal ends of the stenotic MCA. The local hemodynamic characteristics of the stenotic MCA are correlated with plaque morphological parameters, including lumen stenosis, plaque load, and RI. It suggests an interaction between the occurrence and development of MCA plaque and local hemodynamic changes.