Computation of Time-Constant Histograms from the Forced Expired Volume-Time Curves

An efficient BASIC program was established on Apple-Ⅱmicrocomputer with the CHESTAC-35F pulmonary function testing system tocalculate the time-constant histogram from the spirogram.By a set of analogousstudies,we confirmed that the recovered time-constant histogram was within onecompartment of the assumed one and that the root of mean square values of theresiduals between the experimental and model volume-time curves was 0.02.Infour healthy adult subjects,the mean value of time-constant histograms(TCx)hadthe variation coefficient 6.6％.The results show that our method is reliable andreproducible,and it can be used for clinical investigation.     

医学图像增强方法综述

医学图像增强的目的是通过图像增强的方法得到优化的医学图像,以帮助医生从图像中获得更多细节信息,进一步做出更加客观的诊断及制定更全面的治疗方案,在一定程度上可提高临床诊断的准确性。本文首先归纳总结当前应用较为广泛的医学图像增强处理技术,包括传统的图像增强方法、改进的图像增强方法、融合的图像增强方法以及深度学习方法,然后对这些方法的原理、优缺点加以分析和总结。最后指出无论是传统方法还是现代图像增强方法,都应在最大限度保留其优势的情况下进行融合,取长补短,注重简单化和时效性,使提高图像的视觉质量同时更具有实用性。     

Predominantβ-adrenoceptor blocking effect of xamoterol averaged over the day in patients with mild to moderate heart failure: insight into the mechanism of its long-term clinical efficacy

Summary Xamoterol acts as a ₁-adrenoceptor agonist at low sympathetic activity and as an antagonist at high activity. Although its long-term efficacy has been proven in patients with mild to moderate heart failure, it remains unclear which effect, agonism or antagonism, accounts for its long-term activity.To clarify the effect of xamoterol on cardiac sympathetic activity in daily life, 24-h R-R interval histograms were obtained during administration of xamoterol 100 mg b. d. for 1 week to 10 patients with mild to moderate heart failure. Eight normal subjects were also studied as controls. To examine the relation between the effect of xamoterol and sympathetic activity, plasma noradrenaline (NA) levels were measured under 5 graded conditions simulating daily living.Xamoterol administration significantly decreased the standard deviation of the R-R interval, both in patients with heart failure and in normal subjects. The mean R-R interval, however, was increased in patients with heart failure, relative to normal subjects.In both groups, the R-R interval histograms had two peaks, i. e. a short daytime peak and a long night-time peak. Xamoterol decreased the median of the night-time peak without changing the daytime peak in normal subjects. In contrast, it increased the median of the daytime peak without producing a significant change in the nighttime peak in patients with heart failure. Levels of plasma NA were significantly higher in patients than in normal subjects under all conditions.Thus, in normal subjects xamoterol predominantly increased the slower heart rate at night with only a minor effect on the higher heart rate in the daytime, whereas it predominantly attenuated the daytime tachycardia induced by sympathetic stimulation in patients with heart failure.It is concluded that xamoterol tends overall to act as a-adrenoceptor antagonist during the day, especially in the daytime in patients with mild to moderate heart failure. Its antagonist rather than its agonist effect may account for the long-term efficacy of xamoterol in patients with mild to moderate heart failure.     

超声医学图像灰度校正方法的改进

介绍了超声医学图像灰度校正常用方法的基本原理,这些方法主要有灰度级校正、灰度变换及直方图修正。对广泛应用且效果较好的直方图均衡化法作了较详细阐述,并针对其对于局部细节增强不显著及不具交互特征等缺点提出了改进方法,并将几种灰度校正方法级联起来对超声图像进行处理,实现了有选择地增强某个灰度值范围对比度的功能,增强了超声诊断图像的实用性。     

基于对称边缘方向直方图自动提取主要道路

提出一种从航空影像中自动提取主要道路的方法。以对称边缘方向直方图作为主要道路特征矢量,先采用一种顾及数据空间分布特性的模糊C均值聚类算法分割提取主要道路种子段,然后利用Snakes算法跟踪提取完整主要道路网络。实验结果表明,所提方法能有效地从航空影像中自动提取主要道路网。     

直方图诊断脂肪肝的应用探讨

目的 探讨直方图对脂肪肝诊断的价值。方法 应用超声诊断仪中直方图功能,在70例脂肪肝患者的增强回声区中取样,获得直方图M值,与正常组50例健康人进行对照,判断有无数值上的差异。结果 正常组M值为25～49,70例脂肪肝患者M值为50～120,与正常组未显示有交叉。结论 直方图对脂肪肝的诊断具有临床价值。     

Exploratory Study of Apparent Diffusion Coefficient Histogram Metrics in Assessing Pancreatic Malignancy

PurposeTo evaluate whole-lesion 3D-histogram apparent diffusion coefficient (ADC) metrics for assessment of pancreatic malignancy.MethodsForty-two pancreatic malignancies (36 pancreatic adenocarcinoma [PDAC], 6 pancreatic neuroendocrine [PanNET]) underwent abdominal magnetic resonance imaging (MRI) with diffusion-weighted imaging before endoscopic ultrasound biopsy or surgical resection. Two radiologists independently placed 3D volumes of interest to derive whole-lesion histogram ADC metrics. Mann-Whitney tests and receiver operating characteristic analyses were used to assess metrics’ diagnostic performance for lesion histology, T-stage, N-stage, and grade.ResultsWhole-lesion ADC histogram metrics lower in PDACs than PanNETs for both readers (P ≤ .026) were mean ADC (area under the curve [AUC] = 0.787-0.792), mean of the bottom 10th percentile (mean_0-10) (AUC = 0.787-0.880), mean of the 10th-25th percentile (mean_10-25) (AUC = 0.884-0.917) and mean of the 25th-50th percentile (mean_25-50) (AUC = 0.829-0.829). For mean_10-25 (metric with highest AUC for identifying PDAC), for reader 1 a threshold > 0.94 × 10^?3 mm²/s achieved sensitivity 94% and specificity 83%, and for reader 2 a threshold > 0.82 achieved sensitivity 97% and specificity 67%. Metrics lower in nodal status ≥ N₁ than N₀ for both readers (P ≤ .043) were mean_0-10 (AUC = 0.789-0.822) and mean_10-25 (AUC = 0.800-0.822). For mean_10-25 (metric with highest AUC for identifying N₀), for reader 1 a threshold <1.17 achieved sensitivity 87% and specificity 67%, and for reader 2 a threshold <1.04 achieved sensitivity 87% and specificity 83%. No metric was associated with T-stage (P > .195) or grade (P > .215).ConclusionVolumetric ADC histogram metrics may serve as non-invasive biomarkers of pancreatic malignancy. Mean_10-25 outperformed standard mean for lesion histology and nodal status, supporting the role of histogram analysis.     

机器学习在肺癌VMAT计划中对危及器官剂量预测的可行性

目的:探讨机器学习在肺癌容积旋转调强（VMAT）治疗计划对心脏和肺的剂量体积直方图（DVH）预测的可行性。方法:选取51例肺癌VMAT计划,随机选取其中43例为训练组,剩余8例为验证组。分析训练组中患者的解剖信息与两侧肺V5、V20和心脏V30、V40的相关性。采用机器学习方法,以解剖信息为输入、危及器官（OAR）的DVH为输出,分别构建并训练关于两侧肺以及心脏的人工神经网络模型。将验证组中8例VMAT计划中的解剖信息分别输入到已经构建好的人工神经网络模型,分别预测OAR的DVH。结果:两侧肺V5、V20和心脏V30、V40受自身体积大小影响可忽略,受OAR与靶区的空间相对位置关系影响较大。患侧肺、对侧肺、心脏的人工神经网络结构模型中隐藏层分别含有41、38、34个神经结点,线性回归系数分别为0.994、0.975、0.986。对验证组中患侧肺和对侧肺的V5、V20的预测误差分别为2.70%[±]1.83%、2.84[%±]1.97%和13.7%[±]7.8%、0.72[%±]0.75%,对心脏V30、V40的预测误差分别为3.20[%±]0.63%、2.1[%±]1.5%,仅对侧肺V5的预测值和实际值差异有统计学意义（P<0.05）。结论:采用人工神经网络方法可以对肺癌VMAT计划中解剖信息与OAR的DVH数据进行学习,构建的人工神经网络模型可预测出患侧肺、心脏V25[~]V60和对侧肺V20的DVH数据,可为临床计划设计提供参考。