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*

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

OBJECTIVE: To investigate the effectiveness of "hybrid" suture with en masse combined with double-layer repair under arthroscopy in repair of delaminated rotator cuff tear by comparison with en masse suture. METHODS: Fifty-six patients with delaminated rotator cuff tears met selection criteria between June 2020 and January 2022 were included in the study. Patients were divided into two groups ( n=28) using a random number method. The patients in trial group underwent arthroscopic "hybrid" suture with the combination en masse and double-layer suture. The patients in control group underwent en masse suture under arthroscopy. There was no significant difference between the two groups ( P>0.05) in terms of gender, age, rotator cuff tear side, tear size, cause of injury, disease duration, and preoperative American Association of Shoulder and Elbow Surgeons (ASES) score, the University of California at Los Angeles (UCLA) shoulder scoring, visual analogue scale (VAS) score, and shoulder range of motion (forward flexion and lateral external rotation). The operation time, the difference of ASES score, UCLA score, VAS score, and shoulder range of motion (forward flexion and lateral external rotation) between pre- and post-operation were recorded and compared between the two groups ( P>0.05). The rotator cuff healing was examined by MRI and evaluated based on the classification criteria of rotator cuff healing proposed by Sugaya et al. RESULTS: Three cases (1 case in the trial group and 2 cases in the control group) were excluded from the study due to loss of follow-up. Twenty-seven cases in the trial group and 26 cases in the control group were included in the final study analysis. All operations of the two groups were completed successfully. There was no significant difference in the operation time between groups ( P>0.05). The follow-up time was 10-12 months (mean, 10.9 months) in the trial group and 10-13 months (mean, 11.4 months) in the control group. All incisions healed by first intention. No surgery-related complications occurred. The UCLA score, ASES score, VAS score, and shoulder range of motion (forward flexion and lateral external rotation) of both groups at 9 months after operation were significantly superior to those before operation ( P<0.05). The difference of UCLA score, ASES score, and VAS score between before and after operation in the trial group were significantly better than those in the control group ( P<0.05). There was no significant differences between the two groups in the difference of shoulder range of motion (forward flexion and lateral lateral rotation) ( P>0.05). At 9 months after operation, according to the classification criteria of rotator cuff healing proposed by Sugaya et al, MRI showed that the rotator cuff healing of the trial group was significantly better than that of the control group ( P<0.05). CONCLUSION Compared with en masse suture, arthroscopic "hybrid" suture for the repair of delaminated rotator cuff tear has advantages in relieving pain and improving shoulder joint function, and the rotator cuff healing is better.
Humans ; Rotator Cuff Injuries/surgery* ; Prospective Studies ; Shoulder Joint/surgery* ; Treatment Outcome ; Arthroscopy/methods* ; Sutures ; Range of Motion, Articular ; Magnetic Resonance Imaging

OBJECTIVE: To investigate the feasibility of MRI three-dimensional (3D) reconstruction model in quantifying glenoid bone defect by comparing with CT 3D reconstruction model measurement. METHODS: Forty patients with shoulder anterior dislocation who met the selection criteria between December 2021 and December 2022 were admitted as study participants. There were 34 males and 6 females with an average age of 24.8 years (range, 19-32 years). The injury caused by sports injury in 29 cases and collision injury in 6 cases, and 5 cases had no obvious inducement. The time from injury to admission ranged from 4 to 72 months (mean, 28.5 months). CT and MRI were performed on the patients' shoulder joints, and a semi-automatic segmentation of the images was done with 3D slicer software to construct a glenoid model. The length of the glenoid bone defect was measured on the models by 2 physicians. The intra-group correlation coefficient ( ICC) was used to evaluate the consistency between the 2 physicians, and Bland-Altman plots were constructed to evaluate the consistency between the 2 methods. RESULTS: The length of the glenoid bone defects measured on MRI 3D reconstruction model was (3.83±1.36) mm/4.00 (0.58, 6.13) mm for physician 1 and (3.91±1.20) mm/3.86 (1.39, 5.96) mm for physician 2. The length of the glenoid bone defects measured on CT 3D reconstruction model was (3.81±1.38) mm/3.80 (0.60, 6.02) mm for physician 1 and (3.99±1.19) mm/4.00 (1.68, 6.38) mm for physician 2. ICC and Bland-Altman plot analysis showed good consistency. The ICC between the 2 physicians based on MRI and CT 3D reconstruction model measurements were 0.73 [95% CI (0.54, 0.85)] and 0.80 [95% CI (0.65, 0.89)], respectively. The 95% CI of the difference between the two measurements of physicians 1 and 2 were (-0.46, 0.49) and (-0.68, 0.53), respectively. CONCLUSION The measurement of glenoid bone defect based on MRI 3D reconstruction model is consistent with that based on CT 3D reconstruction model. MRI can be used instead of CT to measure glenoid bone defects in clinic, and the soft tissue of shoulder joint can be observed comprehensively while reducing radiation.
Male ; Female ; Humans ; Young Adult ; Adult ; Imaging, Three-Dimensional/methods* ; Tomography, X-Ray Computed/methods* ; Joint Instability ; Shoulder Joint/diagnostic imaging* ; Shoulder Dislocation ; Magnetic Resonance Imaging/methods*