Accuracy of lung nodule density on HRCT: analysis by PSF‐based image simulation

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‐

Generation of realistic virtual nodules based on three‐dimensional spatial resolution in lung computed tomography: A pilot phantom study

“…This method potentially overcomes the limitations of previous simulation techniques for pulmonary nodules. [4][5][6][7][8][9][10][11][13][14][15][16][17][18]21 Our proposed method includes direct deconvolution by division of spatial frequencies. This analysis is essentially the same as a common image deblurring (or restoration).…”

“…The authors have explored and developed another approach to the computer generation of virtual nodules . In this method, virtual nodules are computed from object functions based on the spatial resolution characteristics measured for each CT system .…”

“…4,10,11 The authors have explored and developed another approach to the computer generation of virtual nodules. [15][16][17][18] In this method, virtual nodules are computed from object functions based on the spatial resolution characteristics measured for each CT system. [19][20][21] Once the object functions have been prepared, virtual nodules that correspond to nodule images obtained at each screening site can be generated in terms of the spatial resolution.…”

Feasible Method to Assess the Performance of a Lung Cancer CT Screening CAD System in Clinical Practice: Dependence on Nodule Size and Density

“…The virtual nodules were calculated exactly based on the characteristics of the spatial resolution in a CT system, from object functions that were numerically generated by assuming solitary nodules having spherical shape and uniform density. By inserting the virtual nodules into the clinical lung images and applying them to a computer-aided volumetry analysis, the accuracy of the volumetry was investigated [17]. By using those lung images including virtual nodules, it will be possible to evaluate the CAD performance for specific size/density of nodules; for obtaining such the specific performance, the settings of object functions as uniform spheres are appropriate because of the known size and density.…”

A method for evaluating the performance of computer-aided detection of pulmonary nodules in lung cancer CT screening: detection limit for nodule size and density

“…Subsequently, the computer-simulated nodule is resampled in three dimensions with intervals equal to the pixel size and slice interval found in clinical CT images (Figure 1c). 19 The resultant image is a virtual nodule that can be fused into practical images by the process shown in Figure 1d and can be used for clinical evaluation. 19,20 In image fusion, the nodule is added into multiple slices to cover the whole virtual nodule in the z direction (only the centre slice is shown in Figure 1d).…”