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*

We aimed to study radiomics approach based on biparametric magnetic resonance imaging (MRI) for determining significant residual cancer after androgen deprivation therapy (ADT). Ninety-two post-ADT prostate cancer patients underwent MRI before prostatectomy (62 with significant residual disease and 30 with complete response or minimum residual disease [CR/MRD]). Totally, 100 significant residual, 52 CR/MRD lesions, and 70 benign tissues were selected according to pathology. First, 381 radiomics features were extracted from T2-weighted imaging, diffusion-weighted imaging, and apparent diffusion coefficient (ADC) maps. Optimal features were selected using a support vector machine with a recursive feature elimination algorithm (SVM-RFE). Then, ADC values of significant residual, CR/MRD lesions, and benign tissues were compared by one-way analysis of variance. Logistic regression was used to construct models with SVM features to differentiate between each pair of tissues. Third, the efficiencies of ADC value and radiomics models for differentiating the three tissues were assessed by area under receiver operating characteristic curve (AUC). The ADC value (mean ± standard deviation [s.d.]) of significant residual lesions ([1.10 ± 0.02] × 10^-3 mm² s^-1) was significantly lower than that of CR/MRD ([1.17 ± 0.02] × 10^-3 mm² s^-1), which was significantly lower than that of benign tissues ([1.30 ± 0.02] × 10^-3 mm² s^-1; both P < 0.05). The SVM feature models were comparable to ADC value in distinguishing CR/MRD from benign tissue (AUC: 0.766 vs 0.792) and distinguishing residual from benign tissue (AUC: 0.825 vs 0.835) (both P > 0.05), but superior to ADC value in differentiating significant residual from CR/MRD (AUC: 0.748 vs 0.558; P = 0.041). Radiomics approach with biparametric MRI could promote the detection of significant residual prostate cancer after ADT.
Male ; Humans ; Prostatic Neoplasms/drug therapy* ; Androgen Antagonists/therapeutic use* ; Androgens ; Neoplasm, Residual ; Retrospective Studies ; Magnetic Resonance Imaging/methods* ; Diffusion Magnetic Resonance Imaging/methods*

OBJECTIVES: Currently, it is difficult to assess the expression status of hormone receptor (HR) in breast malignant tumors with human epidermal growth factor receptor 2 (HER-2)-positive in the early preoperative stage, and it is difficult to predict whether it is non-invasively. This study aims to explore the value of MRI on the different HR expression status (HR⁺/HR^-) in HER-2 positive breast cancer. METHODS: Thirty patients with HR+ HER-2-positive breast cancer (HR+ group) and 23 patients with HR-HER-2-positive breast cancer (HR- group) from the First Hospital of Hunan University of Traditional Chinese Medicine between January 7, 2015 and November 26, 2021 were selected as subjects, and all the patients were examined by MRI and all were confirmed by surgery or pathological biopsy puncture. The immunohistochemical staining results were used as the gold standard to analyze the basic clinical conditions, peri-lesion conditions and MRI sign characteristics in the 2 groups. RESULTS: There were all significant differences in terms of mass margins, internal reinforcement features, and apparent diffusion coefficient (ADC) values between the HR+ group and the HR- group (all P<0.05). The logistic multivariate regression model showed that: when the lesion presented as a mass-type breast cancer on MRI, the internal enhancement features of the lesion were an independent predictor for differentiation in the 2 types of breast cancer [odds ratio (OR)=5.95, 95% CI: 1.223 to 28.951, P<0.05], and the mass margin (OR=0.386, 95% CI: 0.137 to 1.082, P>0.05) and ADC value (OR=0.234, 95% CI: 0.001 to 105.293, P>0.05) were not the independent predictors in distinguishing the 2 types of breast cancer. CONCLUSIONS Multiparametric MRI has good diagnostic value for HR expression status in HER-2-positive breast cancer. Combined logistic regression analysis to construct a predictive model may be helpful to the identical diagnosis.
Humans ; Female ; Breast Neoplasms/surgery* ; Magnetic Resonance Imaging/methods* ; Diffusion Magnetic Resonance Imaging/methods* ; Breast ; Magnetic Resonance Spectroscopy ; Retrospective Studies

OBJECTIVES: Magnetic resonance diffusion-weighted imaging (DWI) has important clinical value in diagnosis and curative effect evaluation on endometrial carcinoma. How to improve the detection rate of endometrial small lesions by DWI is the research focus of MRI technology. This study aims to analyze the image quality of small field MRI ZOOMit-DWI sequence and conventional single-shot echo-planar imaging (SS-EPI) DWI sequence in the scanning of endometrial carcinoma, and to explore the clinical value of ZOOMit-DWI sequence. METHODS: A total of 37 patients with endometrial carcinoma diagnosed by operation and pathology in the Second Xiangya Hospital of Central South University from July 2019 to May 2021 were collected. All patients were scanned with MRI ZOOMit-DWI sequence and SS-EPI DWI sequence before operation. Two radiologists subjectively evaluated the anatomical details, artifacts, geometric deformation and focus definition of the 2 groups of DWI images. At the same time, the signal intensity were measured and the signal-to-noise ratio (SNR), contrast to noise ratio (CNR), and apparent diffusion coefficient (ADC) of the 2 DWI sequences were calculated for objective evaluation. The differences of subjective score, objective score and ADC value of the 2 DWI sequences were analyzed. RESULTS: The SNR of the ZOOMit-DWI group was significantly higher than that of the SS-EPI DWI group (301.96±141.85 vs 94.66±41.26), and the CNR of the ZOOMit-DWI group was significantly higher than that of the SS-EPI DWI group (185.05±105.45 vs 57.91±31.54, P<0.05). There was no significant difference in noise standard deviation between the ZOOMit-DWI group and the SS-EPI DWI group (P>0.05). The subjective score of anatomical detail and focus definition in the ZOOMit-DWI group was significantly higher than that of the SS-EPI DWI group (both P<0.05). The subjective score of artifacts and geometric deformation of ZOOMit-DWI group was significantly lower than that of the SS-EPI DWI group (both P<0.05). ADC had no significant difference between the ZOOMit-DWI group and the SS-EPI DWI group (P>0.05). CONCLUSIONS The image quality of ZOOMit-DWI is significantly higher than that of conventional SS-EPI DWI. In the MRI DWI examination of endometrial carcinoma, ZOOMit-DWI can effectively reduce the geometric deformation and artifacts of the image, which is more conducive to clinical diagnosis and treatment.
Female ; Humans ; Signal-To-Noise Ratio ; Endometrial Neoplasms/diagnostic imaging* ; Diffusion Magnetic Resonance Imaging/methods* ; Endometrium ; Echo-Planar Imaging/methods* ; Reproducibility of Results

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