BACKGROUND: Gait impairment is closely related to quality of life in patients with Parkinson's disease (PD). This study aimed to explore alterations in brain microstructure in PD patients and healthy controls (HCs) and to identify the correlation of gait impairment in the ON and OFF states of patients with PD, respectively. METHODS: We enrolled 24 PD patients and 29 HCs from the Movement Disorders Program at Beijing Friendship Hospital Capital Medical University between 2019 and 2020. We acquired magnetic resonance imaging (MRI) scans and processed the diffusion kurtosis imaging (DKI) images. Preprocessing of diffusion-weighted data was performed with Mrtrix3 software, using a directional distribution function to track participants' main white matter fiber bundles. Demographic and clinical characteristics were recorded. Quantitative gait and clinical scales were used to assess the status of medication ON and OFF in PD patients. RESULTS: The axial kurtosis (AK), mean kurtosis (MK), and radial kurtosis (RK) of five specific white matter fiber tracts, the bilateral corticospinal tract, left superior longitudinal fasciculus, left anterior thalamic radiation, forceps minor, and forceps major were significantly higher in PD patients compared to HCs. Additionally, the MK values were negatively correlated with Timed Up and Go Test (TUG) scores in both the ON and OFF in PD patients. Within the PD group, higher AK, MK, and RK values, whether the patients were ON or OFF, were associated with better gait performance (i.e., higher velocity and stride length). CONCLUSIONS PD exhibits characteristic regional patterns of white matter microstructural degradation. Correlations between objective gait parameters and DKI values suggest that dopamine-responsive gait function depends on preserved white matter microstructure. DKI-based Tract-Based Spatial Statistics (TBSS) analysis may serve as a tool for evaluating PD-related motor impairments (e.g., gait impairment) and could yield potential neuroimaging biomarkers.
Humans ; Parkinson Disease/diagnostic imaging* ; White Matter/physiopathology* ; Male ; Female ; Middle Aged ; Aged ; Gait/physiology* ; Diffusion Magnetic Resonance Imaging/methods* ; Diffusion Tensor Imaging/methods*

Among numerous medical imaging modalities, diffusion weighted imaging (DWI) is extremely sensitive to acute ischemic stroke lesions, especially small infarcts. However, magnetic resonance imaging is time-consuming and expensive, and it is also prone to interference from metal implants. Therefore, the aim of this study is to design a medical image synthesis method based on generative adversarial network, Stroke-p2pHD, for synthesizing DWI images from computed tomography (CT). Stroke-p2pHD consisted of a generator that effectively fused local image features and global context information (Global_to_Local) and a multi-scale discriminator (M ²Dis). Specifically, in the Global_to_Local generator, a fully convolutional Transformer (FCT) and a local attention module (LAM) were integrated to achieve the synthesis of detailed information such as textures and lesions in DWI images. In the M ²Dis discriminator, a multi-scale convolutional network was adopted to perform the discrimination function of the input images. Meanwhile, an optimization balance with the Global_to_Local generator was ensured and the consistency of features in each layer of the M ²Dis discriminator was constrained. In this study, the public Acute Ischemic Stroke Dataset (AISD) and the acute cerebral infarction dataset from Yantaishan Hospital were used to verify the performance of the Stroke-p2pHD model in synthesizing DWI based on CT. Compared with other methods, the Stroke-p2pHD model showed excellent quantitative results (mean-square error = 0.008, peak signal-to-noise ratio = 23.766, structural similarity = 0.743). At the same time, relevant experimental analyses such as computational efficiency verify that the Stroke-p2pHD model has great potential for clinical applications.
Humans ; Tomography, X-Ray Computed/methods* ; Diffusion Magnetic Resonance Imaging/methods* ; Cerebral Infarction/diagnostic imaging* ; Stroke/diagnostic imaging* ; Neural Networks, Computer ; Image Processing, Computer-Assisted/methods* ; Algorithms

Objective:To explore the application value of high-resolution temporal bone CT and DW-MRI fusion technology in achieving precise diagnosis and anatomical localization of middle ear cholesteatoma during endoscopic surgery. Methods:Eighteen patients initially diagnosed with middle ear cholesteatoma in the Department of Otolaryngology Head and Neck Surgery, Shaanxi Provincial People's Hospital, from January to June 2024 were enrolled.Preoperative high-resolution temporal bone CT and DW-MRI were performed, and rtStation software was used for image fusion to construct CT-MRI fused images. The involvement of cholesteatoma in six anatomical subregions of the temporal bone was evaluated. Using surgical pathology as the gold standard, and combining surgical videos and anatomical records, the sensitivity, specificity, and accuracy of pure CT, pure DW-MRI, and CT-MRI fused images in evaluating middle ear cholesteatoma lesions were compared. Results:A total of 18 patients were included, and 17 cases were pathologically confirmed as middle ear cholesteatoma postoperatively. The sensitivity of the preoperative of preoperative CT was 100%, but the specificity was only 44.44%, with an overall accuracy of 72.22%; the sensitivity and specificity of DW-MRI evaluation were 81.46% and 85.19%, the accuracy was 83.33%, respectively. In contrast, the sensitivity and specificity of CT-MRI fusion image to the spatial localization of cholesteatoma were higher than that of DW-MRI alone（92.59% vs 81.46%; 98.15% vs 85.19%）, and the diagnostic accuracy was also significantly improved（95.37% vs 83.33%）. The Kappa values for the agreement between HRCT, DW-MRI, and CT-MRI segmentation localization and pathological results were 0.444, 0.667, and 0.907 respectively. The chi-square paired t-test confirmed statistically significant diagnostic differences between groups（P<0.001）. Results demonstrated that CT-MRI significantly outperformed HRCT and DW-MRI in diagnostic efficacy for segmental localization of primary posterior congenital middle ear cholesteatoma. Conclusion:High-resolution temporal bone CT combined with DW-MRI fusion technology demonstrates higher sensitivity, specificity, and accuracy in the diagnosis and spatial localization of middle ear cholesteatoma than single imaging modalities. It can provide more precise evaluation of lesion scope for endoscopic surgery, showing important clinical application value.
Humans ; Cholesteatoma, Middle Ear/diagnostic imaging* ; Tomography, X-Ray Computed ; Temporal Bone/diagnostic imaging* ; Diffusion Magnetic Resonance Imaging ; Female ; Male ; Adult ; Sensitivity and Specificity ; Middle Aged ; Endoscopy

OBJECTIVES: To qualitatively assess the diagnostic performance of dynamic contrast enhancement (DCE), diffusion-weighted imaging (DWI), and T2-weighted imaging (T2WI), alone or in combination, in the evaluation of breast cancer. METHODS: We retrospectively reviewed the records of 394 consecutive patients with pathologically confirmed breast lesions who had undergone 3-T magnetic resonance imaging (MRI). The morphological characteristics of breast lesions were evaluated using DCE, DWI, and T2WI based on BI-RADS lexicon descriptors by trained radiologists. Patients were categorized into mass and non-mass groups based on MRI characteristics of the lesions, and the differences between benign and malignant lesions in each group were compared. Clinical prediction models for breast cancer diagnosis were constructed using logistic regression analysis. Diagnostic efficacies were compared using the area under the receiver operating characteristic curve (AUC) and DeLong test. RESULTS: For mass-like lesions, all the morphological parameters significantly differentiated benign and malignant lesions on consensus DCE, DWI, and T2WI (P<0.05). The combined method (DCE+DWI+T2WI) had a higher AUC (0.865) than any of the individual modality (DCE: 0.786; DWI: 0.793; T2WI: 0.809) (P<0.05). For non-mass-like lesions, DWI signal intensity was a significant predictor of malignancy (P=0.036), but the model using DWI alone had a low AUC (0.669). CONCLUSIONS Morphological assessment using the combination of DCE, DWI, and T2WI provides better diagnostic value in differentiating benign and malignant breast mass-like lesions than assessment with only one of the modalities.
Humans ; Female ; Breast Neoplasms/pathology* ; Retrospective Studies ; Middle Aged ; Adult ; Diffusion Magnetic Resonance Imaging/methods* ; Aged ; Magnetic Resonance Imaging/methods* ; Young Adult ; Aged, 80 and over

The rich club, as a community of highly interconnected nodes, serves as the topological center of the network. However, the similarities and differences in how the rich club supports functional integration and segregation in the brain across different species remain unknown. In this study, we first detected and validated the rich club in the structural networks of mouse, monkey, and human brains using neuronal tracing or diffusion magnetic resonance imaging data. Further, we assessed the role of rich clubs in functional integration, segregation, and balance using quantitative metrics. Our results indicate that the presence of a rich club facilitates whole-brain functional integration in all three species, with the functional networks of higher species exhibiting greater integration. These findings are expected to help to understand the relationship between brain structure and function from the perspective of brain evolution.
Animals ; Humans ; Brain/diagnostic imaging* ; Mice ; Male ; Nerve Net/diagnostic imaging* ; Macaca ; Female ; Neural Pathways/diagnostic imaging* ; Magnetic Resonance Imaging ; Biological Evolution ; Adult ; Diffusion Magnetic Resonance Imaging ; Brain Mapping ; Species Specificity ; Mice, Inbred C57BL

Humans ; Lupus Vasculitis, Central Nervous System/pathology* ; Magnetic Resonance Imaging/methods* ; Diffusion Tensor Imaging/methods* ; Patients ; Lupus Erythematosus, Systemic/pathology* ; Brain/pathology*

Objective: To investigate the value of T2 map and synthetic T2WI generated by T2 mapping in evaluating the histological type, pathological classification and depth of myometrial invasion of endometrial carcinoma (EC). Methods: Seventy-three patients with pathologically proven EC diagnosed at the First Affiliated Hospital of Zhengzhou University from December 2019 to December 2021 and 42 healthy volunteers were enrolled in the study. All subjects underwent conventional MRI, diffusion weighted imaging (DWI) and T2 mapping sequence for the pelvic cavity to test the T2 values and the apparent diffusion coefficient (ADC) of the focus nidus of the patients and the normal endometrium of the volunteers. The T2 and ADC values of EC vs normal endometrium, and those of different histological types and pathological grades were compared. The receiver operating characteristic (ROC) curves were constructed to evaluate the diagnostic performance of T2 and ADC values in determining the pathological type and classification of EC. In addition, two radiologists used synthetic T2WI combined with T2 map and conventional T2WI combined with DWI, respectively, to evaluate the depth of myometrial invasion, and compared the imaging results with the results of pathological diagnosis to evaluate the diagnostic efficacy of the two methods in determining the depth of myometrial invasion. Results: The T2 and ADC values of endometrial carcinoma were 85.0 (80.8, 92.5) ms and 0.71 (0.64, 0.77) ×10(-3) mm(2)/s, respectively, which were significantly lower than those of normal endometrium [147.4 (123.4, 176.7) ms and 1.46 (1.26, 1.76)×10(-3) mm(2)/s, respectively; both P<0.05]. The T2 values of endometrioid carcinoma (EA) [84.1 (79.5, 88.7) ms] were significantly lower than those of non-EA [98.8 (92.1, 102.8) ms; P<0.05]. There was no significant difference in ADC values between EA and non-EA (P=0.075). The T2 values of G1, G2 and G3 groups in EA were 89.1 (84.4, 94.4) ms, 83.6 (80.9, 86.2) ms, and 76.5 (71.4, 80.3) ms, respectively. There were significant differences in the T2 values between G1 vs G2, G1 vs G3, and G2 vs G3 groups, respectively (all P<0.017). Significant difference was also found in the ADC values between the G1 and G3 groups (P<0.017). The area under the ROC curve (AUC) of T2 values in distinguishing EA from non-EA was 0.867. The AUC of T2 values, ADC values and their combination in predicting high-grade EA was 0.888, 0.730 and 0.895, respectively. The accuracy of synthetic T2WI+ T2 map and conventional T2WI+ DWI in the diagnosis of deep myometrial invasion was 78.1% and 79.5%, respectively, with no significant difference (P>0.05). Conclusions: T2 mapping has great potential in preoperative evaluation of EC. The quantitative T2 value can be used in the diagnosis, pathological classification and grading of EC. The combination of synthetic T2WI and T2 map may be helpful to determine the depth of myometrial invasion.
Female ; Humans ; Neoplasm Invasiveness/pathology* ; Endometrial Neoplasms/diagnostic imaging* ; Diffusion Magnetic Resonance Imaging/methods* ; Magnetic Resonance Imaging/methods* ; ROC Curve ; Retrospective Studies

Objective: To investigate the value of T2 map and synthetic T2WI generated by T2 mapping in evaluating the histological type, pathological classification and depth of myometrial invasion of endometrial carcinoma (EC). Methods: Seventy-three patients with pathologically proven EC diagnosed at the First Affiliated Hospital of Zhengzhou University from December 2019 to December 2021 and 42 healthy volunteers were enrolled in the study. All subjects underwent conventional MRI, diffusion weighted imaging (DWI) and T2 mapping sequence for the pelvic cavity to test the T2 values and the apparent diffusion coefficient (ADC) of the focus nidus of the patients and the normal endometrium of the volunteers. The T2 and ADC values of EC vs normal endometrium, and those of different histological types and pathological grades were compared. The receiver operating characteristic (ROC) curves were constructed to evaluate the diagnostic performance of T2 and ADC values in determining the pathological type and classification of EC. In addition, two radiologists used synthetic T2WI combined with T2 map and conventional T2WI combined with DWI, respectively, to evaluate the depth of myometrial invasion, and compared the imaging results with the results of pathological diagnosis to evaluate the diagnostic efficacy of the two methods in determining the depth of myometrial invasion. Results: The T2 and ADC values of endometrial carcinoma were 85.0 (80.8, 92.5) ms and 0.71 (0.64, 0.77) ×10(-3) mm(2)/s, respectively, which were significantly lower than those of normal endometrium [147.4 (123.4, 176.7) ms and 1.46 (1.26, 1.76)×10(-3) mm(2)/s, respectively; both P<0.05]. The T2 values of endometrioid carcinoma (EA) [84.1 (79.5, 88.7) ms] were significantly lower than those of non-EA [98.8 (92.1, 102.8) ms; P<0.05]. There was no significant difference in ADC values between EA and non-EA (P=0.075). The T2 values of G1, G2 and G3 groups in EA were 89.1 (84.4, 94.4) ms, 83.6 (80.9, 86.2) ms, and 76.5 (71.4, 80.3) ms, respectively. There were significant differences in the T2 values between G1 vs G2, G1 vs G3, and G2 vs G3 groups, respectively (all P<0.017). Significant difference was also found in the ADC values between the G1 and G3 groups (P<0.017). The area under the ROC curve (AUC) of T2 values in distinguishing EA from non-EA was 0.867. The AUC of T2 values, ADC values and their combination in predicting high-grade EA was 0.888, 0.730 and 0.895, respectively. The accuracy of synthetic T2WI+ T2 map and conventional T2WI+ DWI in the diagnosis of deep myometrial invasion was 78.1% and 79.5%, respectively, with no significant difference (P>0.05). Conclusions: T2 mapping has great potential in preoperative evaluation of EC. The quantitative T2 value can be used in the diagnosis, pathological classification and grading of EC. The combination of synthetic T2WI and T2 map may be helpful to determine the depth of myometrial invasion.
Female ; Humans ; Neoplasm Invasiveness/pathology* ; Endometrial Neoplasms/diagnostic imaging* ; Diffusion Magnetic Resonance Imaging/methods* ; Magnetic Resonance Imaging/methods* ; ROC Curve ; Retrospective Studies

OBJECTIVE: To propose a diffusion tensor field estimation network based on 3D U-Net and diffusion tensor imaging (DTI) model constraint (3D DTI-Unet) to accurately estimate DTI quantification parameters from a small number of diffusion-weighted (DW) images with a low signal-to-noise ratio. METHODS: The input of 3D DTI-Unet was noisy diffusion magnetic resonance imaging (dMRI) data containing one non-DW image and 6 DW images with different diffusion coding directions. The noise-reduced non-DW image and accurate diffusion tensor field were predicted through 3D U-Net. The dMRI data were reconstructed using the DTI model and compared with the true value of dMRI data to optimize the network and ensure the consistency of the dMRI data with the physical model of the diffusion tensor field. We compared 3D DTI-Unet with two DW image denoising algorithms (MP-PCA and GL-HOSVD) to verify the effect of the proposed method. RESULTS: The proposed method was better than MP-PCA and GL-HOSVD in terms of quantitative results and visual evaluation of DW images, diffusion tensor field and DTI quantification parameters. CONCLUSION The proposed method can obtain accurate DTI quantification parameters from one non-DW image and 6 DW images to reduce image acquisition time and improve the reliability of quantitative diagnosis.
Diffusion Tensor Imaging ; Reproducibility of Results ; Diffusion Magnetic Resonance Imaging ; Algorithms ; Signal-To-Noise Ratio

Acute carbon monoxide poisoning and its delayed encephalopathy have obvious damage to the central nervous system. There are different neuroimaging changes in different stages of the disease, and they are relatively specific. This article reviews the clinical research progress on the imaging changes of carbon monoxide poisoning and delayed encephalopathy, including computed tomography (CT) , conventional magnetic resonance imaging (MRI) , diffusion weighted imaging (DWI) , diffusion tensor imaging (DTI) , diffusion kurtosis imaging (DKI) , magnetic resonance spectroscopy (MRS) and other imaging changes reflecting the function and metabolic state of the brain tissue.
Humans ; Brain Diseases/etiology* ; Carbon Monoxide Poisoning/diagnostic imaging* ; Diffusion Magnetic Resonance Imaging ; Diffusion Tensor Imaging ; Magnetic Resonance Imaging ; Tomography, X-Ray Computed ; Magnetic Resonance Spectroscopy