Size and density measurements of objects undertaken using computed tomography (CT) are clinically significant for diagnosis. To evaluate the accuracy of these quantifications, we simulated three-dimensional (3D) CT image blurring; this involved the calculation of the convolution of the 3D object function with the measured 3D point spread function (PSF). We initially validated the simulation technique by performing a phantom experiment. Blurred computed images showed good 3D agreement with measured images of the phantom. We used this technique to compute the 3D blurred images from the object functions, in which functions are determined to have the shape of an ideal sphere of varying diameter and assume solitary pulmonary nodules with a uniform density. The accuracy of diameter and density measurements was determined. We conclude that the proposed simulation technique enables us to estimate the image blurring precisely of any 3D structure and to analyze clinical images quantitatively.

A method for verifying the point spread function (PSF) measured by computed tomography has been previously reported [Med. Phys. 33, 2757-2764 (2006)]; however, this additional PSF verification following measurement is laborious. In the present study, the previously described verification method was expanded to PSF determination. First, an image was obtained by scanning a phantom. The image was then two-dimensionally deconvolved with the object function corresponding to the phantom structure, thus allowing the PSF to be obtained. Deconvolution is implemented simply by division of spatial frequencies (corresponding to inverse filtering), in which two parameters are used as adjustable ones. Second, an image was simulated by convolving the object function with the obtained PSF, and the simulated image was compared to the above-measured image of the phantom. The difference indicates the inaccuracy of the PSF obtained by deconvolution. As a criterion for evaluating the difference, the authors define the mean normalized standard deviation (SD) in the difference between simulated and measured images. The above two parameters for deconvolution can be adjusted by referring to the subsequent mean normalized SD (i.e., the PSF is determined so that the mean normalized SD is decreased). In this article, the parameters were varied in a fixed range with a constant increment to find the optimal parameter setting that minimizes the mean normalized SD. Using this method, PSF measurements were performed for various types of image reconstruction kernels (21 types) in four kinds of scanners. For the 16 types of kernels, the mean normalized SDs were less than 2.5%, indicating the accuracy of the determined PSFs. For the other five kernels, the mean normalized SDs ranged from 3.7% to 4.8%. This was because of a large amount of noise in the measured images, and the obtained PSFs would essentially be accurate. The method effectively determines the PSF, with an accompanying verification, after one scanning of a phantom.

This study discusses a method of CT image quality standardization that uses a point-spread function (PSF) in MDCT. CT image I(x,y,z) is represented by the following formula: I(x,y,z) = O(x,y,z)***PSF(x,y,z). Standardization was performed by measuring the three-dimensional (3-D) PSFs of two CT images with different image qualities. The image conversion method was constructed and tested using the 3-D PSFs and CT images of the CT scanners of three different manufacturers. The CT scanners used were Lightspeed QX/i, Somatom Volume Zoom, and Brilliance-40. To obtain the PSF(x,y) of these CT scanners, the line spread functions of the respective reconstruction kernels were measured using a phantom described by J.M. Boone. The kernels for each scanner were: soft, standard, lung, bone, and bone plus (GE); B20f, B40f, B41f, B50f, and B60f (Siemens); and B, C, D, E, and L (Philips). Slice sensitivity profile (SSP) were measured using a micro-disk phantom (50 µ m* φ 1 mm) with 5 mm slice thickness and beam pitch of 1.5 (GE, Siemens) and 0.626 (Philips). 3-D PSF was verified using an MDCT QA phantom. Real chest CT images were converted to images with contrasting standard image quality. Comparison between the converted CT image and the original standard image showed good agreement. The usefulness of the image conversion method is discussed using clinical CT images acquired by CT scanners produced by different manufacturers.