CT方案优化中检测指标的自动计算和对比细节曲线的生成。

IF 3.4 3区 医学 Q2 ENGINEERING, BIOMEDICAL
Choirul Anam, Ariij Naufal, Heri Sutanto, Kusworo Adi, Chai Hong Yeong, Geoff Dougherty
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引用次数: 0

摘要

目的:本研究的目的是开发一种自动生成可检测性指数(d')的对比细节(C-D)曲线的方法,并在不同的管电流设置和重建滤波器类型下评估其性能。方法:为了计算给定物体尺寸和对比度的d',使用Edge, Lung和Soft滤波器类型,从80,120,160和200ma的管电流下获取的ACR 464 CT幻象图像中获得任务传递函数(TTF)和噪声功率谱(NPS)。任务对象的尺寸(1-15 mm)和对比度(1-15 HU)不同,有平面信号和高斯信号类型。对于每个定义的任务对象,使用非预白化(NPW)模型观测器计算d'。该过程在多个对象大小和对比度上迭代每个预定义的任务函数,从而得到与合成的低对比度图像相对应的d' map。然后使用用户定义的d'截止值生成C-D曲线。为了进行比较,在5名人类观察者(HOs)的视觉评估基础上,生成了一条单独的C-D曲线。主要结果:自动化方法成功地计算了d'值,并根据物体大小和对比度将合成的低对比度图像排列成网格。d' cut-off值为3或4的C-D曲线最能反映HOs的性能。对于管电流变化,增加电流导致更高的可检测性。对于过滤器类型的变化,与边缘和软过滤器相比,肺过滤器的可检测性相对较低。意义:开发了一种自动化方法来计算大范围物体尺寸和对比度下的d‘,并生成基于d’的C-D曲线,用于CT方案优化。结果与HO趋势一致,并有效捕获了不同成像参数下可检测性的变化。 。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Automated computation of detectability index and generation of contrast-detail curves for CT protocol optimization.

Objective.The aim of this study was to develop an automatic method for generating a detectability index (d')-based contrast-detail (C-D) curve across multiple object sizes and contrasts, and to evaluate its performance under varying tube current settings and reconstruction filter types.Approach.To computed'for a given object size and contrast, the task-transfer function and noise power spectrum were obtained from ACR 464 computed tomography (CT) phantom images acquired at tube currents of 80, 120, 160 and 200 mA, using Edge, Lung, and Soft filter types. The task objects were varied in size (1-15 mm) and contrast levels (1-15 HU) with both flat and Gaussian signal types. For each defined task object,d'was calculated using a non-prewhitening model observer. This process was iterated for every predefined task function across multiple object sizes and contrasts, resulting in ad'map corresponding to the synthetic low-contrast images. AC-Dcurve was then generated using ad'cut-off value defined by the user. For comparison, a separateC-Dcurve was generated based on visual assessment by five human observers (HOs).Main results.The automated method successfully computedd'values and arranged synthetic low-contrast images into a grid according to object size and contrast.C-Dcurves usingd'cut-off values of 3 or 4 most closely reflected HOs performance. For tube current variations, increasing the current led to higher detectability. For filter type variations, the Lung filter resulted in relatively lower detectability compared to the Edge and Soft filters.Significance. An automated method to calculated'across a wide range of object sizes and contrasts, and to generate ad'-basedC-Dcurve for CT protocol optimization was developed. The results were consistent with HO trends and effectively captured detectability changes across different imaging parameters.

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来源期刊
Physics in medicine and biology
Physics in medicine and biology 医学-工程:生物医学
CiteScore
6.50
自引率
14.30%
发文量
409
审稿时长
2 months
期刊介绍: The development and application of theoretical, computational and experimental physics to medicine, physiology and biology. Topics covered are: therapy physics (including ionizing and non-ionizing radiation); biomedical imaging (e.g. x-ray, magnetic resonance, ultrasound, optical and nuclear imaging); image-guided interventions; image reconstruction and analysis (including kinetic modelling); artificial intelligence in biomedical physics and analysis; nanoparticles in imaging and therapy; radiobiology; radiation protection and patient dose monitoring; radiation dosimetry
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