Abstract
A computed tomography (CT) image simulation technique based on the point spread function (PSF) was applied to analyze the accuracy of CT‐based clinical evaluations of lung nodule density. The PSF of the CT system was measured and used to perform the lung nodule image simulation. Then, the simulated image was resampled at intervals equal to the pixel size and the slice interval found in clinical high‐resolution CT (HRCT) images. On those images, the nodule density was measured by placing a region of interest (ROI) commonly used for routine clinical practice, and comparing the measured value with the true value (a known density of object function used in the image simulation). It was quantitatively determined that the measured nodule density depended on the nodule diameter and the image reconstruction parameters (kernel and slice thickness). In addition, the measured density fluctuated, depending on the offset between the nodule center and the image voxel center. This fluctuation was reduced by decreasing the slice interval (i.e., with the use of overlapping reconstruction), leading to a stable density evaluation. Our proposed method of PSF‐based image simulation accompanied with resampling enables a quantitative analysis of the accuracy of CT‐based evaluations of lung nodule density. These results could potentially reveal clinical misreadings in diagnosis, and lead to more accurate and precise density evaluations. They would also be of value for determining the optimum scan and reconstruction parameters, such as image reconstruction kernels and slice thicknesses/intervals.PACS numbers: 87.57.‐s, 87.57.cf, 87.57.Q‐
Highlights
Lung cancer screening with low dose computed tomography (CT) was shown to be effective for the reduction of lung cancer mortality by the National Lung Screening Trial.[1]. The wide dissemination of high quality screening with multidetector-row CT (MDCT) may further reduce the mortality due to lung cancer
Three types of point spread function (PSF) were obtained for three reconstruction kernels of FC10, FC50, and FC52
Some slices in Id are shown in Fig. 6; Id was obtained by assuming a sphere diameter of 3.0 mm, the FC50 reconstruction kernel, a slice thickness of 1.0 mm, and a slice interval of 1.0 mm (i.e., such that Id was expressed as Id(1.0, 1.0) according to Id(slice thickness, slice interval), as defined previously
Summary
Lung cancer screening with low dose computed tomography (CT) was shown to be effective for the reduction of lung cancer mortality by the National Lung Screening Trial.[1]. In this approach, an arbitrary object function is numerically generated with a fine digital sampling pitch (Fig. 1(a)).
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