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Efficacy evaluation of retrospectively applying the Varian normal breathing predictive filter for volume definition and artifact reduction in 4D CT lung patients

机译:回顾性应用Varian正常呼吸预测过滤器对4D CT肺部患者进行容积定义和减少伪影的疗效评估

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摘要

Phase‐based sorting of four‐dimensional computed tomography (4D CT) datasets is prone to image artifacts due to patient's breathing irregularities that occur during the image acquisition. The purpose of this study is to investigate the effect of the Varian normal breathing predictive filter (NBPF) as a retrospective phase‐sorting parameter in 4D CT. Ten 4D CT lung cancer datasets were obtained. The volumes of all tumors present, as well as the total lung volume, were calculated on the maximum intensity projection (MIP) images as well as each individual phase image. The NBPF was varied retrospectively within the available range, and changes in volume and image quality were recorded. The patients' breathing trace was analysed and the magnitude and location of any breathing irregularities were correlated to the behavior of the NBPF. The NBPF was found to have a considerable effect on the quality of the images in MIP and single‐phase datasets. When used appropriately, the NBPF is shown to have the ability to account for and correct image artifacts. However, when turned off (0%) or set above a critical level (approximately 40%), it resulted in erroneous volume reconstructions with variations in tumor volume up to 26.6%. Those phases associated with peak inspiration were found to be more susceptible to changes in the NBPF. The NBPF settings selected prior to exporting the breathing trace for patients evaluated using 4D CT directly affect the accuracy of the targeting and volume estimation of lung tumors. Recommendations are made to address potential errors in patient anatomy introduced by breathing irregularities, specifically deep breath or cough irregularities, by implementing the proper settings and use of this tool.PACS numbers: 87.57.Q‐, 87.57.C‐, 87.57.N‐, 87.57.nf, 87.55.D‐.
机译:二维计算机断层扫描(4D CT)数据集的基于阶段的排序很容易产生图像伪影,这是由于在图像采集过程中发生了患者的呼吸不规则。这项研究的目的是研究Varian正常呼吸预测滤镜(NBPF)作为4D CT中回顾性相位分类参数的效果。获得了十个4D CT肺癌数据集。在最大强度投影(MIP)图像以及每个单独的相位图像上计算所有存在的肿瘤的体积以及总肺体积。 NBPF在可用范围内进行回顾性更改,并记录音量和图像质量的变化。分析了患者的呼吸轨迹,并将任何呼吸异常的程度和位置与NBPF的行为相关。发现NBPF对MIP和单相数据集中的图像质量有很大影响。如果使用得当,则表明NBPF具有处理和校正图像伪像的能力。但是,当关闭(0%)或将其设置为高于临界水平(大约40%)时,会导致错误的体积重建,而肿瘤体积的变化最高可达26.6%。发现与峰值吸气有关的那些阶段更容易受到NBPF变化的影响。对于使用4D CT评估的患者,在导出呼吸轨迹之前选择的NBPF设置直接影响肺肿瘤靶向和体积估计的准确性。建议采取适当的设置并使用此工具,以解决因呼吸不规则特别是深呼吸或咳嗽不规则而引起的患者解剖结构潜在错误.PACS编号:87.57.Q-,87.57.C-,87.57.N- ,87.57.nf,87.55.D‐。

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