INTRODUCTION: Ultrasonography (US) is the current standard of care for imaging surveillance in patients at risk of hepatocellular carcinoma (HCC). Magnetic resonance imaging (MRI) has been explored as an alternative, given the higher sensitivity of MRI, although this comes at a higher cost. We performed a cost-effective analysis comparing US and dual-sequence non-contrast-enhanced MRI (NCEMRI) for HCC surveillance in the local setting. METHODS: Cost-effectiveness analysis of no surveillance, US surveillance and NCEMRI surveillance was performed using Markov modelling and microsimulation. At-risk patient cohort was simulated and followed up for 40 years to estimate the patients' disease status, direct medical costs and effectiveness. Quality-adjusted life years (QALYs) and incremental cost-effectiveness ratio were calculated. RESULTS: Exactly 482,000 patients with an average age of 40 years were simulated and followed up for 40 years. The average total costs and QALYs for the three scenarios - no surveillance, US surveillance and NCEMRI surveillance - were SGD 1,193/7.460 QALYs, SGD 8,099/11.195 QALYs and SGD 9,720/11.366 QALYs, respectively. CONCLUSION Despite NCEMRI having a superior diagnostic accuracy, it is a less cost-effective strategy than US for HCC surveillance in the general at-risk population. Future local cost-effectiveness analyses should include stratifying surveillance methods with a variety of imaging techniques (US, NCEMRI, contrast-enhanced MRI) based on patients' risk profiles.
Humans ; Adult ; Carcinoma, Hepatocellular/diagnostic imaging* ; Liver Neoplasms/diagnostic imaging* ; Cost-Effectiveness Analysis ; Cost-Benefit Analysis ; Quality-Adjusted Life Years ; Magnetic Resonance Imaging/methods*

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

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

Neurodegenerative diseases (NDs) have become a significant threat to an aging human society. Numerous studies have been conducted in the past decades to clarify their pathologic mechanisms and search for reliable biomarkers. Magnetic resonance imaging (MRI) is a powerful tool for investigating structural and functional brain alterations in NDs. With the advantages of being non-invasive and non-radioactive, it has been frequently used in both animal research and large-scale clinical investigations. MRI may serve as a bridge connecting micro- and macro-level analysis and promoting bench-to-bed translational research. Nevertheless, due to the abundance and complexity of MRI techniques, exploiting their potential is not always straightforward. This review aims to briefly introduce research progress in clinical imaging studies and discuss possible strategies for applying MRI in translational ND research.
Animals ; Humans ; Neurodegenerative Diseases/pathology* ; Translational Research, Biomedical ; Magnetic Resonance Imaging/methods* ; Brain/pathology* ; Head/pathology*

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*

BACKGROUND: Amygdala plays an important role in the neurobiological basis of panic disorder (PD), and the amygdala contains different subregions, which may play different roles in PD. The aim of the present study was to examine whether there are common or distinct patterns of functional connectivity of the amygdala subregions in PD using resting-state functional magnetic resonance imaging and to explore the relationship between the abnormal spontaneous functional connectivity patterns of the regions of interest (ROIs) and the clinical symptoms of PD patients. METHODS: Fifty-three drug-naïve, non-comorbid PD patients and 70 healthy controls (HCs) were recruited. Seed-based resting-state functional connectivity (rsFC) analyses were conducted using the bilateral amygdalae and its subregions as the ROI seed. Two samples t test was performed for the seed-based Fisher's z -transformed correlation maps. The relationship between the abnormal spontaneous functional connectivity patterns of the ROIs and the clinical symptoms of PD patients was investigated by Pearson correlation analysis. RESULTS: PD patients showed increased rsFC of the bilateral amygdalae and almost all the amygdala subregions with the precuneus/posterior cingulate gyrus compared with the HC group (left amygdala [lAMY]: t  = 4.84, P  <0.001; right amygdala [rAMY]: t  = 4.55, P  <0.001; left centromedial amygdala [lCMA]: t  = 3.87, P  <0.001; right centromedial amygdala [rCMA]: t  = 3.82, P  = 0.002; left laterobasal amygdala [lBLA]: t  = 4.33, P  <0.001; right laterobasal amygdala [rBLA]: t  = 4.97, P  <0.001; left superficial amygdala [lSFA]: t  = 3.26, P  = 0.006). The rsFC of the lBLA with the left angular gyrus/inferior parietal lobule remarkably increased in the PD group ( t  = 3.70, P  = 0.003). And most of the altered rsFCs were located in the default mode network (DMN). A significant positive correlation was observed between the severity of anxiety and the rsFC between the lSFA and the left precuneus in PD patients ( r  = 0.285, P  = 0.039). CONCLUSIONS Our research suggested that the increased rsFC of amygdala subregions with DMN plays an important role in the pathogenesis of PD. Future studies may further explore whether the rsFC of amygdala subregions, especially with the regions in DMN, can be used as a biological marker of PD.
Humans ; Panic Disorder ; Magnetic Resonance Imaging/methods* ; Amygdala ; Gyrus Cinguli ; Comorbidity

The registration of preoperative magnetic resonance (MR) images and intraoperative ultrasound (US) images is very important in the planning of brain tumor surgery and during surgery. Considering that the two-modality images have different intensity range and resolution, and the US images are degraded by lots of speckle noises, a self-similarity context (SSC) descriptor based on local neighborhood information was adopted to define the similarity measure. The ultrasound images were considered as the reference, the corners were extracted as the key points using three-dimensional differential operators, and the dense displacement sampling discrete optimization algorithm was adopted for registration. The whole registration process was divided into two stages including the affine registration and the elastic registration. In the affine registration stage, the image was decomposed using multi-resolution scheme, and in the elastic registration stage, the displacement vectors of key points were regularized using the minimum convolution and mean field reasoning strategies. The registration experiment was performed on the preoperative MR images and intraoperative US images of 22 patients. The overall error after affine registration was (1.57 ± 0.30) mm, and the average computation time of each pair of images was only 1.36 s; while the overall error after elastic registration was further reduced to (1.40 ± 0.28) mm, and the average registration time was 1.53 s. The experimental results show that the proposed method has prominent registration accuracy and high computational efficiency.
Humans ; Imaging, Three-Dimensional/methods* ; Magnetic Resonance Imaging/methods* ; Ultrasonography/methods* ; Algorithms ; Surgery, Computer-Assisted/methods*

Aiming at the problems of missing important features, inconspicuous details and unclear textures in the fusion of multimodal medical images, this paper proposes a method of computed tomography (CT) image and magnetic resonance imaging (MRI) image fusion using generative adversarial network (GAN) and convolutional neural network (CNN) under image enhancement. The generator aimed at high-frequency feature images and used double discriminators to target the fusion images after inverse transform; Then high-frequency feature images were fused by trained GAN model, and low-frequency feature images were fused by CNN pre-training model based on transfer learning. Experimental results showed that, compared with the current advanced fusion algorithm, the proposed method had more abundant texture details and clearer contour edge information in subjective representation. In the evaluation of objective indicators, Q ^AB/F, information entropy (IE), spatial frequency (SF), structural similarity (SSIM), mutual information (MI) and visual information fidelity for fusion (VIFF) were 2.0%, 6.3%, 7.0%, 5.5%, 9.0% and 3.3% higher than the best test results, respectively. The fused image can be effectively applied to medical diagnosis to further improve the diagnostic efficiency.
Image Processing, Computer-Assisted/methods* ; Neural Networks, Computer ; Tomography, X-Ray Computed ; Magnetic Resonance Imaging/methods* ; Algorithms

Alzheimer's disease (AD) is a progressive and irreversible neurodegenerative disease. Neuroimaging based on magnetic resonance imaging (MRI) is one of the most intuitive and reliable methods to perform AD screening and diagnosis. Clinical head MRI detection generates multimodal image data, and to solve the problem of multimodal MRI processing and information fusion, this paper proposes a structural and functional MRI feature extraction and fusion method based on generalized convolutional neural networks (gCNN). The method includes a three-dimensional residual U-shaped network based on hybrid attention mechanism (3D HA-ResUNet) for feature representation and classification for structural MRI, and a U-shaped graph convolutional neural network (U-GCN) for node feature representation and classification of brain functional networks for functional MRI. Based on the fusion of the two types of image features, the optimal feature subset is selected based on discrete binary particle swarm optimization, and the prediction results are output by a machine learning classifier. The validation results of multimodal dataset from the AD Neuroimaging Initiative (ADNI) open-source database show that the proposed models have superior performance in their respective data domains. The gCNN framework combines the advantages of these two models and further improves the performance of the methods using single-modal MRI, improving the classification accuracy and sensitivity by 5.56% and 11.11%, respectively. In conclusion, the gCNN-based multimodal MRI classification method proposed in this paper can provide a technical basis for the auxiliary diagnosis of Alzheimer's disease.
Humans ; Alzheimer Disease/diagnostic imaging* ; Neurodegenerative Diseases ; Magnetic Resonance Imaging/methods* ; Neural Networks, Computer ; Neuroimaging/methods* ; Cognitive Dysfunction/diagnosis*