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首页> 外文期刊>Medical image analysis >Personalising population-based respiratory motion models of the heart using neighbourhood approximation based on learnt anatomical features
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Personalising population-based respiratory motion models of the heart using neighbourhood approximation based on learnt anatomical features

机译：使用基于学习到的解剖特征的邻域逼近来个性化基于人群的心脏呼吸运动模型

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Respiratory motion models have been proposed for the estimation and compensation of respiratory motion during image acquisition and image-guided interventions on organs in the chest and abdomen. However, such techniques are not commonly used in the clinic. Subject-specific motion models require a dynamic calibration scan that interrupts the clinical workflow and is often impractical to acquire, while population-based motion models are not as accurate as subject-specific motion models. To address this lack of accuracy, we propose a novel personalisation framework for population-based respiratory motion models and demonstrate its application to respiratory motion of the heart. The proposed method selects a subset of the population sample which is more likely to represent the cardiac respiratory motion of an unseen subject, thus providing a more accurate motion model. The selection is based only on anatomical features of the heart extracted from a static image. The features used are learnt using a neighbourhood approximation technique from a set of training datasets for which respiratory motion estimates are available. Results on a population sample of 28 adult healthy volunteers show average improvements in estimation accuracy of 20% compared to a standard population-based motion model, with an average value for the 50th and 95th quantiles of the estimation error of 1.6 mm and 4.7 mm respectively. Furthermore, the anatomical features of the heart most strongly correlated to respiratory motion are investigated for the first time, showing the features on the apex in proximity to the diaphragm and the rib cage, on the left ventricle and interventricular septum to be good predictors of the similarity in cardiac respiratory motion.
机译：已经提出了呼吸运动模型，用于估计和补偿对胸部和腹部器官的图像获取和图像引导的干预过程中的呼吸运动。但是，这种技术在临床中并不常用。特定于对象的运动模型需要动态校准扫描，该扫描会打断临床工作流程并且通常不实用，而基于人群的运动模型不如特定于对象的运动模型那么准确。为了解决这种缺乏准确性的问题，我们为基于人群的呼吸运动模型提出了一种新颖的个性化框架，并展示了其在心脏呼吸运动中的应用。所提出的方法选择总体样本的子集，该子集更可能代表看不见的对象的心脏呼吸运动，从而提供更准确的运动模型。该选择仅基于从静态图像提取的心脏的解剖特征。使用邻域逼近技术从一组训练数据集中学习使用的特征，对于这些训练数据集可以进行呼吸运动估计。对28位成年健康志愿者进行的人口抽样结果显示，与基于标准人群的运动模型相比，估计准确性平均提高了20％，第50和第95个分位数的估计误差平均值分别为1.6 mm和4.7 mm 。此外，首次对与呼吸运动最密切相关的心脏解剖特征进行了研究，显示出靠近the肌和肋廓的心尖，左心室和室间隔的特征可以很好地预测心脏呼吸运动的相似性。

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