Scalp acupuncture plus low-frequency rTMS promotes repair of brain white matter tracts in stroke patients: A DTI study.

To study the clinical effects of scalp acupuncture plus low frequency rTMS in hemiplegic stroke patients. A total of 28 hemiplegic stroke patients were recruited and randomly assigned to the experimental group (scalp acupuncture low frequency rTMS routine rehabilitation treatment) or the control group (scalp acupuncture routine rehabilitation treatment). All patients received a diffusion tensor imaging examination on the day of admission and on the fourteenth day. Compared with pre-treatment, the upper limb motor function score and ability of daily life score increased significantly in the two groups, and motor function improvement was much greater in the experimental group. Fractional anisotropy values significantly increased in white matter tracts, such as the corticospinal tract, forceps minor, superior longitudinal fasciculus and uncinate fasciculus in the two groups. Compared with pretreatment, the fractional anisotropy values increased and mean diffusion values decreased synchronously in the forceps minor, left inferior fronto-occipital fasciculus, left inferior longitudinal fasciculus, left superior longitudinal fasciculus and left uncinate fasciculus in the experimental group. Before and after treatment, there were no significant differences in the changes of fractional anisotropy values between the two groups, but the changes of the mean diffusion values in the experimental group were much greater than those in the control group in the left superior longitudinal fasciculus and the left uncinate fasciculus (p<0.05). Moreover, the increased fractional anisotropy values in the forceps minor in the experimental group were significantly positively correlated with the increased Fugl-Meyer assessment score. Our study concluded that based on routine rehabilitation treatment, scalp acupuncture plus low frequency rTMS can promote white matter tracts repair better than scalp acupuncture alone; the motor function improvement of the hemiplegic upper limb may be closely related to the rehabilitation of the forceps minor; the combination of scalp acupuncture and low frequency rTMS is expected to provide a more optimal rehabilitation protocol for stroke hemiplegic patients.

Dependency criterion based brain pathological age estimation of Alzheimer's disease patients with MR scans.

OBJECTIVES: Traditional brain age estimation methods are based on the idea that uses the real age as the training label. However, these methods ignore that there is a deviation between the real age and the brain age due to the accelerated brain aging. METHODS: This paper considers this deviation and obtains it by maximizing the correlation between the estimated brain age and the class label rather than by minimizing the difference between the estimated brain age and the real age. Firstly, set the search range of the deviation as the deviation candidates according to the prior knowledge. Secondly, use the support vector regression as the age estimation model to minimize the difference between the estimated age and the real age plus deviation rather than the real age itself. Thirdly, design the fitness function based on the correlation criterion. Fourthly, conduct age estimation on the validation dataset using the trained age estimation model, put the estimated age into the fitness function, and obtain the fitness value of the deviation candidate. Fifthly, repeat the iteration until all the deviation candidates are involved and get the optimal deviation with maximum fitness values. The real age plus the optimal deviation is taken as the brain pathological age. RESULTS: The experimental results showed that the separability of the samples was apparently improved. For normal control- Alzheimer's disease (NC-AD), normal control- mild cognition impairment (NC-MCI), and mild cognition impairment-Alzheimer's disease (MCI-AD), the average improvements were 0.164 (31.66%), 0.1284 (34.29%), and 0.0206 (7.1%), respectively. For NC-MCI-AD, the average improvement was 0.2002 (50.39%). The estimated brain pathological age could be not only more helpful for the classification of AD but also more precisely reflect the accelerated brain aging. CONCLUSION: In conclusion, this paper proposes a new kind of brain age-brain pathological age and offers an estimation method for it that can distinguish different states of AD, thereby better reflecting accelerated brain aging. Besides, the brain pathological age is most helpful for feature reduction, thereby simplifying the relevant classification algorithm.

Cortical folding in post-traumatic stress disorder after motor vehicle accidents: Regional differences in gyrification.

AIM: Structural and functional magnetic resonance imaging (MRI) studies have revealed evidence of brain abnormalities in post-traumatic stress disorder (PTSD) patients. Cortical complexity and local gyrification index (lGI) reflect potential biological processes associated with normal or abnormal cognitive functioning. In the current study, lGI was used to explore cortical folding in PTSD patients involved in motor vehicle accidents (MVA). METHODS: MRI brain scans were acquired from 18 PTSD patients who had suffered MVA at least 6 months previously and 18 healthy control subjects. All MRI images were obtained on a 3-T Siemens MRI machine and the cortical folding was analyzed using the workflow provided by software FreeSurfer. A general FreeSurfer's general linear model was used in the group analysis. In addition, correlation analysis was performed between the average of lGI extracted from the significantly different areas and the data for the clinical scale. RESULTS: The PTSD patients had significantly greater Clinician-Administered PTSD Scale scores than the control group. The patients showed significantly reduced lGI in the left lateral orbitofrontal cortex, consistent with findings of previous volumetric studies on PTSD. But there were no significant correlations in the left lateral orbitofrontal cortex between Clinician-Administered PTSD Scale scores and lGI. CONCLUSION: We suggest that abnormal gyrification in PTSD patients can be an important indicator of neurodevelopment deficits and may indeed be a biological marker for PTSD.

[Detection algorithm of amyloid ß-protein deposition in magnetic resonance image based on pixel feature learning method].

Amyloid ß-protein (Aß) deposition is an important prevention and treatment target for Alzheimer's disease (AD), and early detection of Aß deposition in the brain is the key to early diagnosis of AD. Magnetic resonance imaging (MRI) is the perfect imaging technology for the clinical diagnosis of AD, but it cannot display the plaque deposition directly. In this paper, based on two feature selection modes-filter and wrapper, chain-like agent genetic algorithm (CAGA), principal component analysis (PCA), support vector machine (SVM) and random forest (RF), we designed six kinds of feature learning classification algorithms to detect the information (distribution) of Aß deposition through magnetic resonance image pixels selection. Firstly, we segmented the brain region from brain MR images. Secondly, we extracted the pixels in the segmented brain region as a feature vector (features) according to rows. Thirdly, we conducted feature learning on the extracted features, and obtained the final optimal feature subset by voting mechanism. Finally, using the final optimal selected features, we could find and mark the corresponding pixels on the MR images to show the information about Aß plaque deposition by elastic mapping. According to the experimental results, the proposed pixel features learning methods in this paper could extract and reflect Aß plaque deposition, and the best classification accuracy could be as high as 80%, thereby showing the effectiveness of the methods. The proposed methods can precisely detect the information of the Aß plaque deposition, thereby being helpful for improving classification accuracy of diagnosis of AD.

Relationship between functional connectivity and motor function assessment in stroke patients with hemiplegia: a resting-state functional MRI study.

INTRODUCTION: Resting-state functional magnetic resonance imaging (fMRI) has been used to examine the brain mechanisms of stroke patients with hemiplegia, but the relationship between functional connectivity (FC) and treatment-induced motor function recovery has not yet been fully investigated. This study aimed to identify the brain FC changes in stroke patients and study the relationship between FC and motor function assessment using the resting-state fMRI. METHODS: Seventeen stroke patients with hemiplegia and fifteen healthy control subjects (HCSs) were recruited in this study. We compared the FC between the ipsilesional primary motor cortex (M1) and the whole brain of the patients with the FC of the HCSs and studied the FC changes in the patients before and after conventional rehabilitation and motor imagery therapy. Additionally, correlations between the FC change and motor function of the patients were studied. RESULTS: Compared to the HCSs, the FC in the patient group was significantly increased between the ipsilesional M1 and the ipsilesional inferior parietal cortex, frontal gyrus, supplementary motor area (SMA), and contralesional angular and decreased between the ipsilesional M1 and bilateral M1. After the treatment, the FC between the ipsilesional M1 and contralesional M1 increased while the FC between the ipsilesional M1 and ipsilesional SMA and paracentral lobule decreased. A statistically significant correlation was found between the FC change in the bilateral M1 and the Fugl-Meyer assessment (FMA) score change. CONCLUSION: Our results revealed an abnormal motor network after stroke and suggested that the FC could serve as a biomarker of motor function recovery in stroke patients with hemiplegia.

Detection of Aß plaque deposition in MR images based on pixel feature selection and class information in image level.

BACKGROUND: Amyloid ß-protein (Aß) plaque deposition is an important prevention and treatment target for Alzheimer's disease (AD). As a noninvasive, nonradioactive and highly cost-effective clinical imaging method, magnetic resonance imaging (MRI) is the perfect imaging technology for the clinical diagnosis of AD, but it cannot display the plaque deposition directly. This paper resolves this problem based on pixel feature selection algorithms at the image level. METHODS AND RESULTS: Firstly, the brain region was segmented from mouse model brain MR images. Secondly, the pixels in the segmented brain region were extracted as a feature vector (features). Thirdly, feature selection was conducted on the extracted features, and the optimal feature subset was obtained. Fourthly, the various optimal feature subsets were obtained by repeating the same processing above. Fifthly, based on the optimal feature subsets, the final optimal feature subset was obtained by voting mechanism. Finally, using the final optimal selected features, the corresponding pixels on the MR images could be found and marked to show the information about Aß plaque deposition. The MR images and brain histological image slices of twenty-two model mice were used in the experiments. Four feature selection algorithms were used on the MR images and six kinds of classification experiments are conducted, thereby choosing a pixel feature selection algorithm for further study. The experimental results showed that by using the pixel features selected by the algorithms in this paper, the best classification accuracy between early AD and control slides could be as high as 80 %. The selected and marked MR pixels could show information of Aß plaque deposition without missing most of the Aß plaque deposition compared with brain histological slice images. The hit rate is over than 90 %. CONCLUSIONS: According to the experimental results, the proposed detection algorithm of the Aß plaque deposition based on MR pixel feature selection algorithm is effective. The proposed algorithm can detect the information of the Aß plaque deposition on MR images and the information can be useful for improving the classification accuracy as assistant MR biomarker. Besides, these findings firstly show the feasibility of detection of the Aß plaque deposition on MR images and provide reference method for interested relevant researchers in public.

Classification of Parkinson's disease utilizing multi-edit nearest-neighbor and ensemble learning algorithms with speech samples.

BACKGROUND: The use of speech based data in the classification of Parkinson disease (PD) has been shown to provide an effect, non-invasive mode of classification in recent years. Thus, there has been an increased interest in speech pattern analysis methods applicable to Parkinsonism for building predictive tele-diagnosis and tele-monitoring models. One of the obstacles in optimizing classifications is to reduce noise within the collected speech samples, thus ensuring better classification accuracy and stability. While the currently used methods are effect, the ability to invoke instance selection has been seldomly examined. METHODS: In this study, a PD classification algorithm was proposed and examined that combines a multi-edit-nearest-neighbor (MENN) algorithm and an ensemble learning algorithm. First, the MENN algorithm is applied for selecting optimal training speech samples iteratively, thereby obtaining samples with high separability. Next, an ensemble learning algorithm, random forest (RF) or decorrelated neural network ensembles (DNNE), is used to generate trained samples from the collected training samples. Lastly, the trained ensemble learning algorithms are applied to the test samples for PD classification. This proposed method was examined using a more recently deposited public datasets and compared against other currently used algorithms for validation. RESULTS: Experimental results showed that the proposed algorithm obtained the highest degree of improved classification accuracy (29.44%) compared with the other algorithm that was examined. Furthermore, the MENN algorithm alone was found to improve classification accuracy by as much as 45.72%. Moreover, the proposed algorithm was found to exhibit a higher stability, particularly when combining the MENN and RF algorithms. CONCLUSIONS: This study showed that the proposed method could improve PD classification when using speech data and can be applied to future studies seeking to improve PD classification methods.

[Research on Diagnosis Algorithm of Parkinson's Disease Based on Speech Sample Multi-edit and Random Forest].

Parkinson's disease(PD)diagnosis based on speech data has been proved to be an effective way in recent years.There are still some problems on preprocessing samples,ensemble learning,and so on.The problems can further cause misleading of classifiers,unsatisfactory classification accuracy and stability.This paper proposed a new diagnosis algorithm of PD by combining multi-edit sample selection method and random forest.At the end of it,this paper presents a group of experiments carried out with the newest public datasets.Experimental results showed that this proposed algorithm realized the classification of the samples and the subjects of PD.Furthermore,it achieved average classification accuracy of 100% and obtained improvement of up to 29.44% compared to those provided by the subjects.This paper proposes a new speech diagnosis algorithm for PD based on instance selection;and the method algorithm has a higher and more stable classification accuracy,compared with the other algorithms.

Characterization of post-traumatic stress disorder using resting-state fMRI with a multi-level parametric classification approach.

Functional neuroimaging studies have found intra-regional activity and inter-regional connectivity alterations in patients with post-traumatic stress disorder (PTSD). However, the results of these studies are based on group-level statistics and therefore it is unclear whether PTSD can be discriminated at single-subject level, for instance using the machine learning approach. Here, we proposed a novel framework to identify PTSD using multi-level measures derived from resting-state functional MRI (fMRI). Specifically, three levels of measures were extracted as classification features: (1) regional amplitude of low-frequency fluctuations (univariate feature), which represents local spontaneous synchronous neural activity; (2) temporal functional connectivity (bivariate feature), which represents the extent of similarity of local activity between two regions, and (3) spatial functional connectivity (multivariate feature), which represents the extent of similarity of temporal correlation maps between two regions. Our method was evaluated on 20 PTSD patients and 20 demographically matched healthy controls. The experimental results showed that the features of each level could successfully discriminate PTSD patients from healthy controls. Furthermore, the combination of multi-level features using multi-kernel learning can further improve the classification performance. Specifically, the classification accuracy obtained by the proposed framework was 92.5 %, which was an increase of at least 5 and 17.5 % from the two-level and single-level feature based methods, respectively. Particularly, the limbic structure and prefrontal cortex provided the most discriminant features for classification, consistent with results reported in previous studies. Together, this study demonstrated for the first time that patients with PTSD can be identified at the individual level using resting-state fMRI data. The promising classification results indicated that this method may provide a complementary approach for improving the clinical diagnosis of PTSD.

Automatic Articular Cartilage Segmentation Based on Pattern Recognition from Knee MRI Images.

An automatic method for cartilage segmentation using knee MRI images is described. Three binary classifiers with integral and partial pixel features are built using the Bayesian theorem to segment the femoral cartilage, tibial cartilage and patellar cartilage separately. First, an iterative procedure based on the feedback of the number of strong edges is designed to obtain an appropriate threshold for the Canny operator and to extract the bone-cartilage interface from MRI images. Second, the different edges are identified based on certain features, which allow for different cartilage to be distinguished synchronously. The cartilage is segmented preliminarily with minimum error Bayesian classifiers that have been previously trained. According to the cartilage edge and its anatomic location, the speed of segmentation is improved. Finally, morphological operations are used to improve the primary segmentation results. The cartilage edge is smooth in the automatic segmentation results and shows good consistency with manual segmentation results. The mean Dice similarity coefficient is 0.761.

Age-specific activation of cerebral areas in motor imagery--a fMRI study.

INTRODUCTION: The objectives of this study were to study the age-specific activation patterns of cerebral areas during motor execution (ME) and motor imaging (MI) of the upper extremities and to discuss the age-related neural mechanisms associated with ME or MI. METHODS: The functional magnetic resonance imaging technique was used to monitor the pattern and intensity of brain activation during the ME and MI of the upper extremities in 20 elderly (>50 years) and 19 young healthy subjects (<25 years). RESULTS: No major differences were identified regarding the activated brain areas during ME or MI between the two groups; however, a minor difference was noted. The intensity of the activated brain area during ME was stronger in the older group than in the younger group, while the results with MI were the opposite. The posterior central gyrus and supplementary motor area during MI were more active in the younger group than in the older group. The putamen, lingual, and so on demonstrated stronger activation during dominant hand MI in the older group. CONCLUSION: The results of this study revealed that the brain structure was altered and that neuronal activity was attenuated with age, and the cerebral cortex and subcortical tissues were found to be over-activated to achieve the same level of ME and MI, indicating that the activating effects of the left hemisphere enhanced with age, whereas the inhibitory effects declined during ME, and activation of the right hemisphere became more difficult during MI.

A digital interactive human brain atlas based on Chinese visible human datasets for anatomy teaching.

As we know, the human brain is one of the most complicated organs in the human body, which is the key and difficult point in neuroanatomy and sectional anatomy teaching. With the rapid development and extensive application of imaging technology in clinical diagnosis, doctors are facing higher and higher requirement on their anatomy knowledge. Thus, to cultivate medical students to meet the needs of medical development today and to improve their ability to read and understand radiographic images have become urgent challenges for the medical teachers. In this context, we developed a digital interactive human brain atlas based on the Chinese visible human datasets for anatomy teaching (available for free download from http://www.chinesevisiblehuman.com/down/DHBA.rar). The atlas simultaneously provides views in all 3 primary planes of section. The main structures of the human brain have been anatomically labeled in all 3 views. It is potentially useful for anatomy browsing, user self-testing, and automatic student assessment. In a word, it is interactive, 3D, user friendly, and free of charge, which can provide a new, intuitive means for anatomy teaching.

The Injury Progression in Acute Blast-Induced Mild Traumatic Brain Injury in Rats Reflected by Diffusion Tensor Imaging and Immunohistochemical Examination.

Diffusion tensor imaging (DTI) has emerged as a promising neuroimaging tool for detecting blast-induced mild traumatic brain injury (bmTBI). However, lack of refined acute-phase monitoring and reliable imaging biomarkers hindered its clinical application in early diagnosis of bmTBI, leading to potential long-term disability of patients. In this study, we used DTI in a rat model of bmTBI generated by exposing to single lateral blast waves (151.16 and 349.75 kPa, lasting 47.48 ms) released in a confined bioshock tube, to investigate whole-brain DTI changes at 1, 3, and 7 days after injury. Combined assessment of immunohistochemical analysis, transmission electron microscopy, and behavioral readouts allowed for linking DTI changes to synchronous cellular damages and identifying stable imaging biomarkers. The corpus callosum (CC) and brainstem were identified as predominantly affected regions, in which reduced fractional anisotropy (FA) was detected as early as the first day after injury, with a maximum decline occurring at 3 days post-injury before returning to near normal levels by 7 days. Axial diffusivity (AD) values within the CC and brainstem also significantly reduced at 3 days post-injury. In contrast, the radial diffusivity (RD) in the CC showed acute elevation, peaking at 3 days after injury before normalizing by the 7-day time point. Damages to nerve fibers, including demyelination and axonal degeneration, progressed in lines with changes in DTI parameters, supporting a real-time macroscopic reflection of microscopic neuronal fiber injury by DTI. The most sensitive biomarker was identified as a decrease in FA, AD, and an increase in RD within the CC on the third day after injury, supporting the diagnostic utility of DTI in cases of bmTBI in the acute phase.

Progressive alteration of dynamic functional connectivity patterns in subcortical ischemic vascular cognitive impairment patients.

Alterations in the temporal evolution of brain states in the process of cognitive impairment aggravation due to subcortical ischemic vascular disease (SIVD) is not understood. The dynamic functional connectivity was investigated to identify the abnormal temporal properties of brain states associated with cognitive impairment caused by SIVD. Eighteen patients with subcortical ischemic vascular cognitive impairment with no dementia (SIVCIND), 19 dementia patients (SIVaD) and 26 normal controls were enrolled. We found that the occupancy rate and mean lifetime of brain states were associated with cognitive performance. SIVCIND had a higher occupancy rate and longer mean lifetime in weakly connected states than normal controls. SIVaD had similar but more extensive changes in the temporal properties of brain states. In addition, switching from weakly connected states to more strongly connected states was more difficult in SIVCIND and SIVaD patients than in normal controls, especially in SIVaD patients. The results revealed that not only the transition to but also maintenance in strongly connected states became increasingly difficult when SIVD-related cognitive impairment progressed into a more severe stage.

Diffusion tensor imaging-based research on human white matter anatomy.

The aim of this study is to investigate the white matter by the diffusion tensor imaging and the Chinese visible human dataset and to provide the 3D anatomical data of the corticospinal tract for the neurosurgical planning by studying the probabilistic maps and the reproducibility of the corticospinal tract. Diffusion tensor images and high-resolution T1-weighted images of 15 healthy volunteers were acquired; the DTI data were processed using DtiStudio and FSL software. The FA and color FA maps were compared with the sectional images of the Chinese visible human dataset. The probability maps of the corticospinal tract were generated as a quantitative measure of reproducibility for each voxel of the stereotaxic space. The fibers displayed by the diffusion tensor imaging were well consistent with the sectional images of the Chinese visible human dataset and the existing anatomical knowledge. The three-dimensional architecture of the white matter fibers could be clearly visualized on the diffusion tensor tractography. The diffusion tensor tractography can establish the 3D probability maps of the corticospinal tract, in which the degree of intersubject reproducibility of the corticospinal tract is consistent with the previous architectonic report. DTI is a reliable method of studying the fiber connectivity in human brain, but it is difficult to identify the tiny fibers. The probability maps are useful for evaluating and identifying the corticospinal tract in the DTI, providing anatomical information for the preoperative planning and improving the accuracy of surgical risk assessments preoperatively.

Effects of acute high-altitude exposure on working memory: A functional near-infrared spectroscopy study.

INTRODUCTION: Inadequate oxygen availability may lead to impairment of neurocognitive functions. The aim of the present study was to investigate the effect of acute high-altitude exposure on the cerebral hemodynamic response and working memory. METHODS: The same subjects performed working memory exercises with forward and backward digit span tasks both under normal oxygen conditions and in large simulated hypobaric hypoxia chambers, and a series of physiological parameters were evaluated. Functional near-infrared spectroscopy was used to measure cerebral blood flow changes in the dorsolateral prefrontal cortex (DLPFC) during the tasks. RESULTS: Compared with normoxic conditions, under hypoxic conditions, the heart rate and blood pressure increased, blood oxygen saturation decreased significantly, and the forward task had similar accuracy and response time, while the backward task had lower accuracy and longer response time. Neuroimaging analysis showed increased activation in the DLPFC during the forward task and deactivation during the backward task under hypobaric hypoxia conditions. CONCLUSION: Acute high-altitude exposure leads to physiological adaptations. The abnormal hemodynamic responses of the DLPFC to hypoxia at low pressure reveal the disruption of neurocognitive function by acute high-altitude exposure, which compromises complex cognitive functions, and provides a promising application for functional near infrared spectroscopy in the exploration of neural mechanisms in the brain during high-altitude exposure.

Changes of brain structure and function in ADHD children.

To explore the changes of brain structure and function in attention-deficit/hyperactivity disorder (ADHD), fifteen ADHD patients (inattention subtype) and 15 normal control participants were recruited, the brain structure and function of these subjects were investigated by combining structural magnetic resonance imaging (MRI), diffusion tensor imaging and resting-state functional MRI. The results showed that ADHD patients had a significant decrease in the volume of the white matter (P = 0.04), and a trend toward decreased volume of brain structures except for the putamen and globus pallidus. The visualization of statistical difference maps of the cortical thickness showed that ADHD patients had focal thinning in bilateral frontal regions and the right cingulate cortex (P < 0.05 uncorrected, except for a cluster threshold of 10 voxels). Statistical analysis of the FA maps revealed that ADHD patients had significantly decreased FA in the forceps minor, the internal capsule, the corona radiata, the splenium of the corpus callosum, and the bilateral basal ganglia (P < 0.05 uncorrected as above). ADHD patients had significantly decreased functional connectivity in the anterior cingulate cortex, posterior cingulate cortex, lateral prefrontal cortex, left precuneus and thalamus, but increased functional connectivity in bilateral posterior medial frontal cortex in the default mode network (P < 0.05 uncorrected as above). Our results provide new insights into the changes of the brain structure and function in ADHD, which suggests that alterations in the brain structural and functional connectivity might implicate the pathophysiology of ADHD.

A novel technique for three-dimensional reconstruction for surgical simulation around the craniocervical junction region.

Performing surgeries on the craniocervical junction presents a technical challenge for operating surgeons. Three-dimensional (3D) reconstruction and surgical simulation have improved the efficacy and success rate of surgeries. The aim of this study was to create a 3D, digitized visible model of the craniocervical junction region to help realize accurate simulation of craniocervical surgery on a graphic workstation. Transverse sectional anatomy data for the study were chosen from the first Chinese visible human. Manual axial segmentation of the skull base, cervical spine, cerebellum, vertebral artery, internal carotid artery, sigmoid sinus, internal jugular vein, brain stem, and spinal cord were carried out by using Photoshop software. The segmented structures were reconstructed in 3 dimensions with surface and volume rendering to accurately display 3D models spatially. In contrast to conventional 3D reconstruction techniques that are based on computed tomography and magnetic resonance imaging Digital Imaging and Communications in Medicine (DICOM) inputs and provide mostly osseous details, this technique can help to illustrate the surrounding soft tissue structure and provide a realistic surgical simulation. The reconstructed 3D model was successfully used in simulating complex procedures in the virtual environment, including the transoral approach, bone drillings, and clivus resection.

Creation of the digital three-dimensional model of the prostate and its adjacent structures based on Chinese visible human.

We managed to provide three-dimensional digitized visible model of the prostate and its adjacent structures and to provide morphological data for imaging diagnosis and male urological surgery. With 3D-DOCTOR software, the contour line of prostate and its adjacent structures including rectum, bladder, male urethra, ureter, seminal vesicle, ductus deferens, ejaculatory ducts, obturator internus, levator ani, coccygeus, male pelvis, femur, prostatic nervous and venous plexus, internal and external iliac arteries were segmented from the Chinese visible human (CVH)-1 data set and the three-dimensional surfaces of intrapelvic visceras were successfully and accurately reconstructed via surface rendering, which can also be manipulated individually and interactively. Combined with AMIRA software, surface rendering reconstructed model of male urological organs and its adjacent structures via volume rendering reconstruction can be displayed together clearly and actually. It provides a learning tool of practicing virtual anatomy and virtual urological surgery for medical students and younger surgeons.

A Lightweight Internet Sharing Scheme for Sectional Medical Images according to Existing Hospital Network Facilities and Basic Information Security Rules.

Background: With the outbreak of COVID-19, large-scale telemedicine applications can play an important role in the epidemic areas or less developed areas. However, the transmission of hundreds of megabytes of Sectional Medical Images (SMIs) from hospital's Intranet to the Internet has the problems of efficiency, cost, and security. This article proposes a novel lightweight sharing scheme for permitting Internet users to quickly and safely access the SMIs from a hospital using an Internet computer anywhere but without relying on a virtual private network or another complex deployment. Methods: A four-level endpoint network penetration scheme based on the existing hospital network facilities and information security rules was proposed to realize the secure and lightweight sharing of SMIs over the Internet. A "Master-Slave" interaction to the interactive characteristics of multiplanar reconstruction and maximum/minimum/average intensity projection was designed to enhance the user experience. Finally, a prototype system was established. Results: When accessing SMIs with a data size ranging from 251.6 to 307.04 MB with 200 kBps client bandwidth (extreme test), the network response time to each interactive request remained at approximately 1 s, the original SMIs were kept in the hospital, and the deployment did not require a complex process; the imaging quality and interactive experience were recognized by radiologists. Conclusions: This solution could serve Internet medicine at a low cost and may promote the diversified development of mobile medical technology. Under the current COVID-19 epidemic situation, we expect that it could play a low-cost and high-efficiency role in remote emergency support.