Strain echocardiography in predicting LV dysfunction in RV apical pacing

Right ventricular (RV) pacing is associated with a reduction in left ventricular (LV) systolic function, thought to be mediated by pacing-induced ventricular dyssynchrony. The prevalence of heart failure after RV pacing is reported to range from 31±3%. We studied 60 subjects with high-grade atrioventricular block and Complete Heart Block (CHB) scheduled to undergo right ventricular apical pacing. 2D echocardiography was done at baseline, 1 month and 12 months. Pacing-induced cardiomyopathy was defined as a reduction in LVEF to <45%. Strain was evaluated off-line from digitally stored images using all advanced software package (cardiac wall motion quantification (CMQ); Toshiba Medical Systems). Longitudinal strain for individual myocardial segments was measured from the apical four-chamber, two-chamber and long axis views (16 segment AHA/ASE model). None had LV dysfunction at baseline based on 2D and strain echo imaging. Subsequently 18 patients were detected to develop low GLS score (less than -14.5) at 1 month. On subsequent follow up at 1 year, all 18 patients developed LV dysfunction on 2D Echocardiography. Thus Strain imaging with GLS score helped in early detection of LV dysfunction in RV apical pacing subjects. Pacing-induced cardiomyopathy had significant association with high grade AV block with pacemaker dependency. It had no significant associations with other comorbidities like diabetes, hypertension, ischemic heart disease or with the type of medication intake. However there was a statistically significant association with heart failure.     

超声三维斑点追踪技术评估左室型单心室Glenn术后心功能变化

目的 应用超声三维斑点追踪技术(three-dimensional speckle tracking imaging,3DSTI)观察左室型单心室患儿Glenn手术前、后左心功能的变化.方法 对17例左室型单心室患儿于Glenn术前及术后分别进行三维超声和心脏磁共振检查,估测左心功能;术前、术后分别与年龄、性别匹配的1...     

Deep reinforcement learning for cerebral anterior vessel tree extraction from 3D CTA images

Extracting the cerebral anterior vessel tree of patients with an intracranial large vessel occlusion (LVO) is relevant to investigate potential biomarkers that can contribute to treatment decision making. The purpose of our work is to develop a method that can achieve this from routinely acquired computed tomography angiography (CTA) and computed tomography perfusion (CTP) images.To this end, we regard the anterior vessel tree as a set of bifurcations and connected centerlines. The method consists of a proximal policy optimization (PPO) based deep reinforcement learning (DRL) approach for tracking centerlines, a convolutional neural network based bifurcation detector, and a breadth-first vessel tree construction approach taking the tracking and bifurcation detection results as input. We experimentally determine the added values of various components of the tracker. Both DRL vessel tracking and CNN bifurcation detection were assessed in a cross validation experiment using 115 subjects. The anterior vessel tree formation was evaluated on an independent test set of 25 subjects, and compared to interobserver variation on a small subset of images.The DRL tracking result achieves a median overlapping rate until the first error (1.8 mm off the reference standard) of 100, [46, 100] % on 8032 vessels over 115 subjects. The bifurcation detector reaches an average recall and precision of 76% and 87% respectively during the vessel tree formation process. The final vessel tree formation achieves a median recall of 68% and precision of 70%, which is in line with the interobserver agreement.     

光学引导跟踪系统在放疗应用中的准确性研究

目的初步探究体表光学引导跟踪系统(OGTS)在肿瘤放射治疗应用中的追踪精度。方法分为模体验证及临床验证, 模体验证采用专用设备, 利用OGTS记录光学标记点在反光球平台上从指定位置移动到目标位置的位移值, 将该位移值与模体中固定距离比较, 以计算系统的准确性与重复性。临床验证通过选取45例放疗患者进行OGTS跟踪准确性和重复性研究, 其中头部、乳腺、直肠肿瘤患者各15例。每例患者均获取随机3个治疗分次下图像引导校正摆位前后图像引导定位系统(IGPS)和OGTS的值, 分别记录每次误差校正的平移值。治疗前用IGPS修正患者摆位误差并获取相关数据, 以IGPS校正平移误差的结果为金标准验证OGTS监测患者位置平移的准确性, 计算综合平移偏差。结果模体测量结果显示, 跟踪准确性的综合平移偏差最大值为0.18 mm, 跟踪重复性综合平移偏差的标准差为0.03 mm。临床试验结果统计显示, IGPS与OGTS追踪精度仅在头部z方向上差异有统计学意义(t=2.21, P<0.05), 而在头部其他方向及乳腺、直肠的3个平移方向差异均无统计学意义(P>0.05)。综合平移偏差分析表明, ...     

Comparison between the Rizzoli and Oxford foot models with independent and clustered tracking markers

BackgroundThe Rizzoli Foot Model (RFM) and Oxford Foot Model (OFM) are used to analyze segmented foot kinematics with independent tracking markers. Alternatively, rigid marker clusters can be used to improve markers’ visualization and facilitate analyzing shod gait.Research questionAre there differences in angles from the RFM and OFM, obtained with independent and clustered tracking markers, during the stance phase of walking?MethodsWalking kinematics of 14 non-disabled participants (25.2 years (SD 2.8)) were measured at self-selected speed. Rearfoot-shank and forefoot-rearfoot angles were measured from two models with two tracking methods: RFM, OFM, RFM-cluster, and OFM-cluster. In RFM-cluster and OFM-cluster, the rearfoot and forefoot tracking markers were rigidly clustered, fixed on rods’ tips attached to a metallic base. Statistical Parametric Mapping (SPM) One-Way Repeated Measures ANOVAs and SPM Paired t-tests were used to compare waveforms. Coefficients of Multiple Correlation (CMC) quantified the similarity between waveforms. One-way Repeated Measures ANOVAs were conducted to compare the ranges of motion (ROMs), and pre-planned contrasts investigated differences between the models and tracking methods. Intraclass Correlation Coefficients (ICC) were computed to verify the similarity between ROMs.ResultsDifferences occurred mostly in small parts of the stance phase for the cluster vs. non-cluster comparisons and the RFM vs. OFM comparisons. ROMs were slightly different between the models and tracking methods in most comparisons. The curves (CMC ≥ 0.71) were highly similar between the models and tracking methods. The ROMs (ICC ≥ 0.67) were moderatetly to highly similar in most comparisons. RFM vs. RFM-cluster (forefoot-rearfoot angle - transverse plane), OFM vs. OFM-cluster and RFM vs. OFM (forefoot-rearfoot angle - frontal plane) were not similar (non-significant).SignificanceRigid clusters are an alternative for tracking rearfoot-shank and forefoot-rearfoot angles during the stance phase of walking. However, specific differences should be considered to contrast results from different models and tracking methods.