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*

OBJECTIVE: To explore the brain effect mechanism and the correlation between brain functional imaging and cognitive function in treatment of depressive disorder (DD) with transcutaneous auricular vagus nerve stimulation (taVNS) based on the resting-state functional magenetic reasonance imaging (rs-fMRI). METHODS: Thirty-two DD patients were included in a depression group and 32 subjects of healthy condition were enrolled in a normal group. In the depression group, the taVNS was applied to bilateral Xin (CO₁₅) and Shen (CO₁₀), at disperse-dense wave, 4 Hz/20 Hz in frequency and current intensity ≤20 mA depending on patient's tolerance, 30 min each time, twice daily. The duration of treatment consisted of 8 weeks. The patients of two groups were undertaken rs-fMRI scanning. The scores of Hamilton depression scale (HAMD), Hamilton anxiety scale (HAMA) and Wisconsin card sorting test (WCST) were observed in the normal group at baseline and the depression group before and after treatment separately. The differential brain regions were observed before and after treatment in the two groups and the value of degree centrality (DC) of fMRI was obtained. Their correlation was analyzed in terms of HAMD, HAMA and WCST scores. RESULTS: The scores of HAMD and HAMA in the depression group were all higher than those in the normal group (P<0.05). After treatment, the scores of HAMD and HAMA were lower than those before treatment in the depression group; the scores of total responses, response errors and perseverative errors of WCST were all lower than those before treatment (P<0.05). The brain regions with significant differences included the left inferior temporal gyrus, the left cerebellar peduncles region 1, the left insula, the right putamen, the bilateral supplementary motor area and the right middle frontal gyrus. After treatment, the value of DC in left supplementary motor area was negatively correlated to HAMD and HAMA scores respectively (r=-0.324, P=0.012; r=-0.310, P=0.015); the value of DC in left cerebellar peduncles region 1 was negatively correlated to the total responses of WCST (r=-0.322, P=0.013), and the left insula was positively correlated to the total responses of WCST (r=0.271, P=0.036). CONCLUSION The taVNS can modulate the intensity of the functional activities of some brain regions so as to relieve depressive symptoms and improve cognitive function.
Humans ; Depression/therapy* ; Magnetic Resonance Imaging/methods* ; Vagus Nerve Stimulation/methods* ; Brain/diagnostic imaging* ; Transcutaneous Electric Nerve Stimulation/methods* ; Vagus Nerve

Magnetic resonance-guided focused ultrasound (MRgFUS) is a novel and minimally invasive technology. Since the US Food and Drug Administration approved unilateral ventral intermediate nucleus-MRgFUS for medication-refractory essential tremor in 2016, studies on new indications, such as Parkinson's disease (PD), psychiatric diseases, and brain tumors, have been on the rise, and MRgFUS has become a promising method to treat such neurological diseases. Currently, as the second most common degenerative disease, PD is a research hotspot in the field of MRgFUS. The actions of MRgFUS on the brain range from thermoablation, blood-brain barrier (BBB) opening, to neuromodulation. Intensity is a key determinant of ultrasound actions. Generally, high intensity can be used to precisely thermoablate brain targets, whereas low intensity can be used as molecular therapies to modulate neuronal activity and open the BBB in conjunction with injected microbubbles. Here, we aimed to summarize advances in the application of MRgFUS for the treatment of PD, with a focus on thermal ablation, BBB opening, and neuromodulation, in the hope of informing clinicians of current applications.
Humans ; Parkinson Disease/therapy* ; Brain ; Blood-Brain Barrier ; Essential Tremor/surgery* ; Brain Neoplasms ; Magnetic Resonance Imaging/methods* ; Magnetic Resonance Spectroscopy

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*

Magnetic resonance (MR) imaging is an important tool for prostate cancer diagnosis, and accurate segmentation of MR prostate regions by computer-aided diagnostic techniques is important for the diagnosis of prostate cancer. In this paper, we propose an improved end-to-end three-dimensional image segmentation network using a deep learning approach to the traditional V-Net network (V-Net) network in order to provide more accurate image segmentation results. Firstly, we fused the soft attention mechanism into the traditional V-Net's jump connection, and combined short jump connection and small convolutional kernel to further improve the network segmentation accuracy. Then the prostate region was segmented using the Prostate MR Image Segmentation 2012 (PROMISE 12) challenge dataset, and the model was evaluated using the dice similarity coefficient (DSC) and Hausdorff distance (HD). The DSC and HD values of the segmented model could reach 0.903 and 3.912 mm, respectively. The experimental results show that the algorithm in this paper can provide more accurate three-dimensional segmentation results, which can accurately and efficiently segment prostate MR images and provide a reliable basis for clinical diagnosis and treatment.
Male ; Humans ; Prostate/diagnostic imaging* ; Image Processing, Computer-Assisted/methods* ; Magnetic Resonance Imaging/methods* ; Imaging, Three-Dimensional/methods* ; Prostatic Neoplasms/diagnostic imaging*

Magnetic ferrite nanoparticles (MFNPs) have great application potential in biomedical fields such as magnetic resonance imaging, targeted drugs, magnetothermal therapy and gene delivery. MFNPs can migrate under the action of a magnetic field and target specific cells or tissues. However, to apply MFNPs to organisms, further modifications on the surface of MFNPs are required. In this paper, the common modification methods of MFNPs are reviewed, their applications in medical fields such as bioimaging, medical detection, and biotherapy are summarized, and the future application directions of MFNPs are further prospected.
Ferric Compounds ; Magnetic Resonance Imaging/methods* ; Magnetics ; Magnetite Nanoparticles/therapeutic use* ; Nanoparticles