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

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

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 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 investigate the feasibility and value of histogram analysis based on two-dimensional gray-scale ultrasonography in the differential diagnosis of medullary thyroid carcinoma (MTC) and thyroid adenoma (TA). Methods: The preoperative ultrasound images of 86 newly diagnosed MTC patients and 100 TA patients treated in the Cancer Hospital of Chinese Academy of Medical Sciences from January 2015 to October 2021 were collected. Histograms were performed based on the regions of interest (ROIs) delineated manually by two radiologists, thereafter, mean, variance, skewness, kurtosis, percentiles (1st, 10th, 50th, 90th, 99th) were generated. The histogram parameters between the MTC group and the TA group were compared, and the independent predictors were screened by multivariate logistic regression analysis. Receiver operating characteristic (ROC) analysis was used to compare the individual diagnostic efficacy and joint diagnostic efficacy of independent predictors. Results: Multivariate regression analysis showed that mean, skewness, kurtosis and 50th percentile were independent factors. The skewness and kurtosis in the MTC group were significantly higher than those in the TA group, and the mean and 50th percentile were significantly lower than those in the TA group. The area under the individual ROC curve of mean, skewness, kurtosis and 50th percentile is 0.654-0.778. The area under the combined ROC curve is 0.826. Conclusion: Histogram analysis based on two-dimensional gray-scale ultrasonography is a promising tool to distinguish MTC from TA, in which the joint diagnosis value of mean, skewness, kurtosis and 50th percentile is the highest.
Humans ; ROC Curve ; Diagnosis, Differential ; Retrospective Studies ; Thyroid Neoplasms/diagnostic imaging* ; Ultrasonography ; Diffusion Magnetic Resonance Imaging/methods*