Low-dissipation optimization of the prefrontal cortex in the -12° head-down tilt position: A functional near-infrared spectroscopy study.

Introduction: Our present study set out to investigate the instant state of the prefrontal cortex (PFC) in healthy subjects before and after placement in the -12°head-down tilt (HDT) position in order to explore the mechanism behind the low-dissipation optimization state of the PFC. Methods: 40 young, right-handed healthy subjects (male: female = 20: 20) were enrolled in this study. Three resting state positions, 0°initial position, -12°HDT position, and 0°rest position were sequentially tested, each for 10 minutes. A continuous-wave functional near-infrared spectroscopy (fNIRS) instrument was used to assess the resting state hemodynamic data of the PFC. After preprocessing the hemodynamics data, we evaluated changes in resting-state functional connectivity (rsFC) level and beta values of PFC. The subjective visual analogue scale (VAS) was applied before and after the experiment. The presence of sleep changes or adverse reactions were also recorded. Results: Pairwise comparisons of the concentrations of oxyhemoglobin (HbO), deoxyhemoglobin (HbR), and hemoglobin (HbT) revealed significant differences in the aforementioned positions. Specifically, the average rsFC of PFC showed a gradual increase throughout the whole process. In addition, based on graph theory, the topological properties of brain network, such as small-world network and nodal degree centrality were analyzed. The results show that global efficiency and small-world sigma (σ) value were differences between 0°initial and 0°rest. Discussion: In this study, placement in the -12°HDT had a significant effect on PFC function, mainly manifested as self-inhibition, decreased concentration of HbO in the PFC, and improved rsFC, which may provide ideas to the understanding and explanation of neurological diseases.

Dataset on blood flow and instantaneous wave-free ratio in normal and stenosed coronary arteries.

Instantaneous wave-free ratio (iFR) has been proposed as a hemodynamic parameter that can reliably reflect the blood flow in stenosed coronary arteries. Currently, there are few investigations on the quantitative analysis of iFR in the patients regarding the variation of microcirculatory resistance (MR). The data aim to provide geometric (cross-section area of branches) and hemodynamic (flow rate and iFR of branches) parameters of normal and stenosed coronary arteries derived from CFD simulation. The CFD simulation was performed on the three-dimensional artery models reconstructed from computed tomography (CT) images of four subjects. The hemodynamic parameters were obtained in six situations of MR to simulate coronary microvascular dysfunction (CMD). This dataset could be used as the reference to estimate the iFR and flow rate in patients with CMD and stenosis in coronary arteries. The geometric parameters could be used in the modelling of coronary arteries.

Effect of microcirculatory resistance on coronary blood flow and instantaneous wave-free ratio: A computational study.

BACKGROUND AND OBJECTIVE: The instantaneous wave-free ratio (iFR) has been proposed to estimate the hemodynamic severity of atherosclerotic stenosis in coronary arteries. The atherosclerotic stenosis in a proximal coronary artery could change its distal microcirculatory resistance (MR). However, there is a lack of investigation about the effect of MR variation on the blood flow and iFR of stenotic coronary arteries. We aim to investigate the changes of blood flow and iFR caused by distal MR variation. METHODS: Four three-dimensional models of coronary arteries were reconstructed from the computed tomography images of two normal cases and two cases with 74.9% and 96.4% (in area) stenoses in a large branch of left anterior descending artery (LAD). Computational fluid dynamics simulation was performed on each model under 6 MR situations: hyperemia as the reference situation, resting when MR was multiplied by 8/3 in all outlet branches, h-one-1.5 and h-one-2 when MR was multiplied by 1.5 and 2.0 in one branch (the stenotic, or the corresponding branch in normal case) of LAD, h-branches-1.5 and h-branches-2 when MR was multiplied by 1.5 and 2.0 in the stenotic/corresponding and its cognate branches. Flow rate and iFR of each outlet branch were then calculated and compared between different MR situations to investigate the effect of MR variation on flow rate and iFR. RESULTS: In the 74.9% stenosed and normal cases, referring to the hyperemia situation, the increase of MR in any branch significantly decreased its flow rate and increased its iFR, with limited effect on the flow rate (<3%) and iFR (<0.01) of other branches. However, in the 96.4% stenosed case, the doubled MR in the stenosed branch (h-one-2) significantly increased the flow rate (>10%) and iFR (>0.05) of its cognate branches. CONCLUSION: The increase of MR in a normal or mildly stenosed branch of coronary artery decreases its blood flow and increases its iFR, with limited effect on other branches. Whereas, the increase of MR in a severely stenotic large branch could significantly increase the flow velocity and iFR of its cognate branches.

Effect of gender-related depression on heart rate variability during an autonomic nervous test.

Patients with depression have lower heart rate variability (HRV) compared with controls. However, studies have indicated HRV difference between male and female controls. The gender effect might be interactive with the depression effect on the HRV, resulting in a low accuracy of recognising the patients with depression from the controls. Our study explores the effect of gender-related depression on HRV. Four ANS tests including resting, deep breathing, Valsalva, and orthostatic test are employed as stimuli. HRV were collected from 182 subjects comprising 91 depressive patients (33 females/58 males) and 91 controls (33 females/58 males) in the four tests. Time and frequency domains and nonlinear parameters are employed to quantify HRV. Two-way ANOVA is applied to evaluate the effect of gender-related depression. Most HRV parameters of the patients significantly differ from those of the controls, but some parameters indicate different depression effect between the males and females in the deep breathing and Valsalva test. Some HRV parameters illustrate significant difference between the male and female controls. Therefore, the effect of depression on HRV of each gender should be investigated.

The predictive value of Cardiodynamicsgram in myocardial perfusion abnormalities.

Myocardial perfusion abnormalities are the first sign of the ischemic cascade in the development of coronary artery disease (CAD). Thus, the early detection of myocardial perfusion abnormalities is significant for the prevention of CAD. Recently, a novel noninvasive method named Cardiodynamicsgram (CDG) has been proposed for early detection of CAD. This study aims to evaluate the predictive value of CDG in myocardial perfusion abnormalities for suspected ischemic heart disease. In the study, 86 suspected patients were enrolled. Standard 12-lead ECG and CDG were performed simultaneously before single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI). Diagnostic accuracy of CDG for myocardial perfusion abnormalities detection is assessed using SPECT MPI as the reference standard. Of these 86 suspected patients, 37 patients were positive in CDG, 49 patients were negative in CDG. Diagnostic accuracy of CDG at presentation for myocardial perfusion abnormalities was 84.9%, sensitivity 84.0%, and specificity 89.4%. Furthermore, of the 10 patients whose SPECT MPI results are reverse redistribution, 9 patients were positive in CDG. Underlying causes of false positive CDG findings included the factors that can change the stability of cardiac electrical conduction and measurement noise. Myocardial remodeling in patients with old myocardial infarction might be the major cause of false negative findings. Results show a good consistency between the CDG and SPECT MPI in evaluating myocardial perfusion abnormalities. It suggests that CDG might be used as a cost-effective tool for assessing the myocardial perfusion abnormalities in the clinic.

Depression recognition according to heart rate variability using Bayesian Networks.

BACKGROUND: Doctors mainly use scale tests and subjective judgment in the clinical diagnosis of depression. Researches have demonstrated that depression is associated with the dysfunction of the autonomic nervous system (ANS), where its modulation can be evaluated by heart rate variability (HRV). Depression patients have lower HRV than healthy subjects. Therefore, HRV may be used to distinguish depression patients from healthy people. METHODS: HRV signals were collected from 76 female subjects composed of 38 depression patients and 38 healthy people. Time domain, frequency domain, and non-linear features were extracted from the HRV signals of these subjects, who were subjected to the Ewing test as an ANS stimulus. Then, these multiple features were input into Bayesian networks, served as a classifier, to distinguish depression patients from healthy people. Hence, accuracy, sensitivity, and specificity were calculated to evaluate the performance of the classifier. RESULTS: Recognition results indicate 86.4% accuracy, 89.5% sensitivity, and 84.2% specificity. The individuals subjected to the Ewing test showed better recognition results than those at individual test states (resting state, deep breathing state, Valsalva state, and standing state) of the Ewing test. The root mean square of successive differences (RMSSD) of the HRV exhibits a significant relevance with recognition. CONCLUSION: Bayesian networks can be applied to the recognition of depression patients from healthy people and the recognition results demonstrate the significant association between depression and HRV. The Ewing test is a good ANS stimulus for acquiring the difference of HRV between depression patients and healthy people to recognize depression. The RMSSD of the HRV is important in recognition and may be a significant index in distinguishing depression patients from healthy people.

Heart rate variability in patients with major depression disorder during a clinical autonomic test.

Major depression disorder (MDD) patients express dysfunction autonomic nervous system (ANS) and reduced heart rate variability (HRV). However, previous researches mainly focused on examining resting state without considering a series of stimulation tests of ANS between MDD patients and healthy people. For this purpose, 40 healthy people and 40 MDD patients participated and finished 10min clinical autonomic test-Ewing test. Parameters of HRV such as time domain, frequency domain, and nonlinear dynamical parameters were calculated. Most of HRV parameters during Ewing test of MDD patients are lower than healthy people, and inversely, the ratio of low frequency to high frequency is higher when standing up. In addition, heart rate of healthy people in deep breathing and Valsalva test states are higher than that in resting state, nonlinear dynamical parameter (RCMSE1) of healthy people in standing up state is higher than that in Valsalva test state. The experimental results suggest that parasympathetic nervous system of MDD patients is influenced by mental state and become dysfunction under long-term depression, it cannot exert normal physiological functions when it is activated. Sympathetic activity increases on MDD patients, and this enhancement is more obvious which expresses higher complexity of HRV time series.

Nonrigid registration with corresponding points constraint for automatic segmentation of cardiac DSCT images.

BACKGROUND: Dual-source computed tomography (DSCT) is a very effective way for diagnosis and treatment of heart disease. The quantitative information of spatiotemporal DSCT images can be important for the evaluation of cardiac function. To avoid the shortcoming of manual delineation, it is imperative to develop an automatic segmentation technique for 4D cardiac images. METHODS: In this paper, we implement the heart segmentation-propagation framework based on nonrigid registration. The corresponding points of anatomical substructures are extracted by using the extension of n-dimensional scale invariant feature transform method. They are considered as a constraint term of nonrigid registration using the free-form deformation, in order to restrain the large variations and boundary ambiguity between subjects. RESULTS: We validate our method on 15 patients at ten time phases. Atlases are constructed by the training dataset from ten patients. On the remaining data the median overlap is shown to improve significantly compared to original mutual information, in particular from 0.4703 to 0.5015 ([Formula: see text]) for left ventricle myocardium and from 0.6307 to 0.6519 ([Formula: see text]) for right atrium. CONCLUSIONS: The proposed method outperforms standard mutual information of intensity only. The segmentation errors had been significantly reduced at the left ventricle myocardium and the right atrium. The mean surface distance of using our framework is around 1.73 mm for the whole heart.

Dynamic updating atlas for heart segmentation with a nonlinear field-based model.

BACKGROUND: Segmentation of cardiac computed tomography (CT) images is an effective method for assessing the dynamic function of the heart and lungs. In the atlas-based heart segmentation approach, the quality of segmentation usually relies upon atlas images, and the selection of those reference images is a key step. The optimal goal in this selection process is to have the reference images as close to the target image as possible. METHODS: This study proposes an atlas dynamic update algorithm using a scheme of nonlinear deformation field. The proposed method is based on the features among double-source CT (DSCT) slices. The extraction of these features will form a base to construct an average model and the created reference atlas image is updated during the registration process. A nonlinear field-based model was used to effectively implement a 4D cardiac segmentation. RESULTS: The proposed segmentation framework was validated with 14 4D cardiac CT sequences. The algorithm achieved an acceptable accuracy (1.0-2.8 mm). CONCLUSION: Our proposed method that combines a nonlinear field-based model and dynamic updating atlas strategies can provide an effective and accurate way for whole heart segmentation. The success of the proposed method largely relies on the effective use of the prior knowledge of the atlas and the similarity explored among the to-be-segmented DSCT sequences.

Heterogeneity in Spinal Bone Mineral Density Among Young Adults From Three Eastern Provincial Capital Cities in Mainland China.

This study compares spinal volumetric bone mineral density (vBMD) with spinal areal bone mineral density (aBMD) among young adults from 3 eastern provincial capital cities in Mainland China. A total of 416 young adults (age range: 20-40 yr) from 3 eastern provincial capital cities (Beijing, Shanghai, and Guangzhou) in Mainland China were recruited in this study. From each subject, the vBMD of the lumbar spine was measured by the Mindways quantitative computed tomography system. Moreover, the aBMD of the lumbar spine, measured by the dual-energy X-ray absorptiometry, was extracted from a previous multicenter large-scale study, and the 420 participants were matched by age, gender, height, weight, as well as geographic territory. The vBMD and the aBMD values were further compared and analyzed. Generally, the bone mineral density (BMD) results were significantly different among participants from the 3 cities (p <0.05). Specifically, both vBMD and aBMD values of participants from Beijing were significantly different from those from Guangzhou (p <0.05). Additionally, a statistically significant difference in aBMD values was also found between participants from Beijing and Shanghai (p <0.05). However, no significant differences were found between participants from Shanghai and Guangzhou in terms of the aBMD and vBMD values (p1 > 0.05 and p2 > 0.05). Interestingly, the overall mean vBMD value was 5.9% greater in women than those in men for all the 3 cities (p <0.001). This study demonstrated an overall heterogeneity in spinal BMD among young adults from 3 eastern provincial capital cities in Mainland China. Specifically, the taller and heavier young adults from the northern part of China have smaller spinal vBMD but higher spinal aBMD values than those who were shorter and lighter from the southern part of China.

Segmentation of pulmonary nodules using adaptive local region energy with probability density function-based similarity distance and multi-features clustering.

BACKGROUND: Pulmonary nodules in computerized tomography (CT) images are potential manifestations of lung cancer. Segmentation of potential nodule objects is the first necessary and crucial step in computer-aided detection system of pulmonary nodules. The segmentation of various types of nodules, especially for ground-glass opacity (GGO) nodules and juxta-vascular nodules, present various challenges. The nodule with GGO characteristic possesses typical intensity inhomogeneity and weak edges, which is difficult to define the boundary; the juxta-vascular nodule is connected to a vessel, and they have very similar intensities. Traditional segmentation methods may result in the problems of boundary leakage and a small volume over-segmentation. This paper deals with the above mentioned problems. METHODS: A novel segmentation method for pulmonary nodules is proposed, which uses an adaptive local region energy model with probability density function (PDF)-based similarity distance and multi-features dynamic clustering refinement method. Our approach has several novel aspects: (1) in the proposed adaptive local region energy model, the local domain for local energy model is selected adaptively based on k-nearest-neighbour (KNN) estimate method, and measurable distances between probability density functions of multi-dimension features with high class separability are used to build the cost function. (2) A multi-features dynamic clustering method is used for the segmentation refinement of juxta-vascular nodules, which is based on the nodule segmentation using active contour model (ACM) with adaptive local region energy and vessel segmentation using flow direction feature (FDF)-based region growing method. (3) it handles various types of nodules under a united framework. RESULTS: The proposed method has been validated on a clinical dataset of 113 chest CT scans that contain 157 nodules determined by a ground truth reading process, and evaluating the algorithm on the provided data leads to an average Tanimoto/Jaccard error of 0.17, 0.20 and 0.24 for GGO, juxta-vascular and GGO juxta-vascular nodules, respectively. CONCLUSIONS: Experimental results show desirable performances of the proposed method. The proposed segmentation method outperforms the traditional methods.

Near-Infrared Camera Calibration for Optical Surgical Navigation.

Near-infrared optical tracking devices, which are important components of surgical navigation systems, need to be calibrated for effective tracking. The calibration results has a direct influence on the tracking accuracy of an entire system. Therefore, the study of calibration techniques is of theoretical significance and practical value. In the present work, a systematic calibration method based on movable plates is established, which analyzes existing calibration theories and implements methods using calibration reference objects. First, the distortion model of near-infrared cameras (NICs) is analyzed in the implementation of this method. Second, the calibration images from different positions and orientations are used to establish the required linear equations. The initial values of the NIC parameters are calculated with the direct linear transformation method. Finally, the accurate internal and external parameters of the NICs are obtained by conducting nonlinear optimization. Analysis results show that the relative errors of the left and right NICs in the tracking system are 0.244 and 0.282 % for the focal lengths and 0.735 and 1.111 % for the principal points, respectively. The image residuals of the left and right image sets are both less than 0.01 pixel. The standard error of the calibration result is lower than 1, and the measurement error of the tracking system is less than 0.3 mm. The experimental data show that the proposed method of calibrating NICs is effective and can generate favorable calibration results.

Tetralogy of Fallot Cardiac Function Evaluation and Intelligent Diagnosis Based on Dual-Source Computed Tomography Cardiac Images.

Tetralogy of Fallot (TOF) is the most common complex congenital heart disease (CHD) of the cyanotic type. Studies on ventricular functions have received an increasing amount of attention as the development of diagnosis and treatment technology for CHD continues to advance. Reasonable options for imaging examination and accurate assessment of preoperative and postoperative left ventricular functions of TOF patients are important in improving the cure rate of TOF radical operation, therapeutic evaluation, and judgment prognosis. Therefore, with the aid of dual-source computed tomography (DSCT), cardiac images with high temporal resolution and high definition, we measured the left ventricular time-volume curve using image data and calculating the left ventricular function parameters to conduct the preliminary evaluation on TOF patients. To comprehensively evaluate the cardiac function, the segmental ventricular wall function parameters were measured, and the measurement results were mapped to a bull's eye diagram to realize the standardization of segmental ventricular wall function evaluation. Finally, we introduced a new clustering method based on auto-regression model parameters and combined this method with Euclidean distance measurements to establish an intelligent diagnosis of TOF. The results of this experiment show that the TOF evaluation and the intelligent diagnostic methods proposed in this article are feasible.

Synchronization Design and Error Analysis of Near-Infrared Cameras in Surgical Navigation.

The accuracy of optical tracking systems is important to scientists. With the improvements reported in this regard, such systems have been applied to an increasing number of operations. To enhance the accuracy of these systems further and to reduce the effect of synchronization and visual field errors, this study introduces a field-programmable gate array (FPGA)-based synchronization control method, a method for measuring synchronous errors, and an error distribution map in field of view. Synchronization control maximizes the parallel processing capability of FPGA, and synchronous error measurement can effectively detect the errors caused by synchronization in an optical tracking system. The distribution of positioning errors can be detected in field of view through the aforementioned error distribution map. Therefore, doctors can perform surgeries in areas with few positioning errors, and the accuracy of optical tracking systems is considerably improved. The system is analyzed and validated in this study through experiments that involve the proposed methods, which can eliminate positioning errors attributed to asynchronous cameras and different fields of view.

Simulation and Visualization of Liver Cancer Ablation Focus in Optical Surgical Navigation.

Radiofrequency ablation therapy of liver cancer is a local mini-invasive treatment technology with several advantages, such as low trauma, safety, effectiveness, and quick postoperative recovery. The application of the optical surgical navigation system in radiofrequency ablation therapy can realize the real-time positioning of surgical instruments and focus. The positioning results can be displayed on the computer, thereby guiding doctors to accurately insert the radiofrequency electrode into the focus and improving surgical efficiency. Meanwhile, the accurate evaluation of the form and size of the ablation focus by the navigation system is the key to realizing the complete ablation of liver cancer. Therefore, based on the heat conduction equation, this paper simplifies the simulation process of the ablation focus, calculates the volume of the ablation focus by distinguishing boundary points and internal points, achieves the effective simulation of the ablation results in the surgery, and reconstructs the ablation focus by using ray casting algorithm and mobile cube algorithm for 3D visualization processing, thereby providing doctors the convenience of being able to simulate the radiofrequency ablation surgery before the actual surgery.

An Ensemble Learning Based Framework for Traditional Chinese Medicine Data Analysis with ICD-10 Labels.

OBJECTIVE: This study aims to establish a model to analyze clinical experience of TCM veteran doctors. We propose an ensemble learning based framework to analyze clinical records with ICD-10 labels information for effective diagnosis and acupoints recommendation. METHODS: We propose an ensemble learning framework for the analysis task. A set of base learners composed of decision tree (DT) and support vector machine (SVM) are trained by bootstrapping the training dataset. The base learners are sorted by accuracy and diversity through nondominated sort (NDS) algorithm and combined through a deep ensemble learning strategy. RESULTS: We evaluate the proposed method with comparison to two currently successful methods on a clinical diagnosis dataset with manually labeled ICD-10 information. ICD-10 label annotation and acupoints recommendation are evaluated for three methods. The proposed method achieves an accuracy rate of 88.2% ± 2.8% measured by zero-one loss for the first evaluation session and 79.6% ± 3.6% measured by Hamming loss, which are superior to the other two methods. CONCLUSION: The proposed ensemble model can effectively model the implied knowledge and experience in historic clinical data records. The computational cost of training a set of base learners is relatively low.

A Semi-Automatic Coronary Artery Segmentation Framework Using Mechanical Simulation.

CVD (cardiovascular disease) is one of the biggest threats to human beings nowadays. An early and quantitative diagnosis of CVD is important in extending lifespan and improving people's life quality. Coronary artery stenosis can prevent CVD. To diagnose the degree of stenosis, the inner diameter of coronary artery needs to be measured. To achieve such measurement, the coronary artery is segmented by using a method that is based on morphology and the continuity between computed tomography image slices. A centerline extraction method based on mechanical simulation is proposed. This centerline extraction method can figure out a basic framework of the coronary artery by simulating pixel dots of the artery image into mass points. Such mass points have tensile forces, with which the outer pixel dots can be drawn to the center. Subsequently, the centerline of the coronary artery can be outlined by using the local line-fitting method. Finally, the nearest point method is adopted to measure the inner diameter. Experimental results showed that the methods proposed in this paper can precisely extract the centerline of the coronary artery and can accurately measure its inner diameter, thereby providing a basis for quantitative diagnosis of coronary artery stenosis.

Dual-energy CT angiography for the diagnosis of intracranial dural arteriovenous fistula.

PURPOSE: To investigate the clinical utility of dual-source dual-energy CT angiography (DSDECTA) for diagnosing intracranial dural arteriovenous fistula (DAVF). METHODS: Nine intracranial DAVF patients were examined using Siemens DSDECTA and cerebral digital subtraction angiography (DSA). Imaging data were retrospectively analyzed to evaluate the concordance between the imaging modalities. RESULTS: DSDECTA examination showed that the blood-supplying arteries were thickened and the draining veins and dural sinuses were expanded in all 9 patients. The presence and characteristics of intracranial DAVF were confirmed using DSA. Head CT showed subarachnoid hemorrhage in 4 cases and intracerebral hematoma in 3 cases. CONCLUSION: Although DSA is the gold standard for DAVF diagnosis, DSDECTA is less invasive and more suitable for revealing the three-dimensional structure of secondary intracranial lesions as well as other DAVF characteristics. Thus, DSDECTA may be a new alternative for noninvasive screening of suspected DAVF patients before interventional embolization and surgical resection.

Nonrigid Registration Regularized by Shape Information: Application to Atlas Construction of Cardiac CT Images.

Cardiac atlases play an important role in the computer-aided diagnosis of cardiovascular diseases, in particular they need to deal with large and highly variable image datasets. In this paper, we propose a new nonrigid registration algorithm incorporating shape information, to produce comprehensive atlases. For one thing, the multiscale gradient orientation features of images are combined to form the construction of multifeature mutual information. Additionally, the shape information of multiple-objects in images is incorporated into the cost function for registration. We demonstrate the merits of the new registration algorithm on the 3D data sets of 15 patients. The experimental results show that the new registration algorithm can outperform the conventional intensity-based registration method. The obtained atlas can represent the cardiac structures more accurately.

Augmenting multi-instance multilabel learning with sparse bayesian models for skin biopsy image analysis.

Skin biopsy images can reveal causes and severity of many skin diseases, which is a significant complement for skin surface inspection. Automatic annotation of skin biopsy image is an important problem for increasing efficiency and reducing the subjectiveness in diagnosis. However it is challenging particularly when there exists indirect relationship between annotation terms and local regions of a biopsy image, as well as local structures with different textures. In this paper, a novel method based on a recent proposed machine learning model, named multi-instance multilabel (MIML), is proposed to model the potential knowledge and experience of doctors on skin biopsy image annotation. We first show that the problem of skin biopsy image annotation can naturally be expressed as a MIML problem and then propose an image representation method that can capture both region structure and texture features, and a sparse Bayesian MIML algorithm which can produce probabilities indicating the confidence of annotation. The proposed algorithm framework is evaluated on a real clinical dataset containing 12,700 skin biopsy images. The results show that it is effective and prominent.