CT scout z-resolution improvement with image restoration methods Yufeng Zheng, Xiaohui Cui, Mark P. Wachowiak, and Adel S. Elmaghraby Dept. of Computer Science and Engineering, University of Louisville, Louisville, Kentucky, USA ABSTRACT Currently, new applications demand utilizing CT scout images for diagnostic purposes. However, many CT scout images cannot be used diagnostically due to their poor resolution, particularly in the direction of table movement. Spatial resolution generally can be improved with image restoration techniques. Based on the principles of Wiener filtering and inverse filtering, this paper presents a modified Wiener filtering approach in the frequency domain. The concept of equivalent target point spread function is introduced, which makes the restoration process steerable. Consequently, balancing resolution improvement with noise suppression is facilitated. Experiments compare the image quality with traditional inverse filtering and Wiener filtering. The modified Wiener filtering method has been shown to restore the scout image with higher resolution and lower noise. Keywords: CT scout, image restoration, inverse filtering, resolution improvement, Wiener filtering 1. INTRODUCTION A CT scout image is obtained with a moving table and a stationary gantry. The current scout image generation process is designed for providing landmarks for CT scan ranges (like axial scans and helical scans), such as the GE Medical Systems LightSpeed TM scanner. The only requirement for the scout is that object boundaries are clear. However, new applications, such as utilizing scout image to replace x-ray film for diagnostic purposes in urology applications, demand higher image quality. Other applications may use scout images for assisted diagnosis instead of x-ray film. Patients are not exposed to additional radiation, and no extra operations are required, because the scout scan is a standard and necessary operation in most CT scans. But in current scout imaging, the spatial resolution in the table translation direction (z) is always lower than that of the channel direction (x). For instance, the spatial resolution is 0.58mm in the x direction and 1.25mm in the z direction for LightSpeed TM scanners. Therefore, the objective is to improve z-axis resolution to be close to that of the x-axis while minimizing noise. Following z-resolution improvement, it is desirable to enhance the scout image details and contrast. In general, a scout image has a wide dynamic range that is usually due to the large difference in absorbing x-rays between bones and soft tissue. For example, a scout from human subject by LightSpeed TM scanner has dynamic range of 1.38×10 5 intensities, which is displayed after preprocessing. A CT axial image is usually observed by changing the window width and location, actually changing the CT number and its display range 1 , which results in many views of the same image. For a scout image, it is inconvenient to observe the image by changing the window width and location because the CT number is not applicable to these images, and because the whole image should be examined with one view. Thus, the scout image has such a wide range that many important details will be lost when directly mapped to 256 intensities. In other words, the question is how to display a wide dynamic image within 256 gray levels. A fusion enhancement algorithm (FEA) is under development to resolve this problem, and some primary results are demonstrated in Section 5. A full discussion of this enhancement algorithm will be presented in a subsequent paper. In this paper, the z-resolution improvement will be discussed in detail. A general solution is to deconvolve the degraded image with the system point spread function (PSF). The PSF can be measured or computed for the specified system. The PSF in the z-axis for scout scanning is calculated as shown in Fig. 1. Deconvolution can be performed in the spatial or frequency domains. The frequency domain is preferred for convenient filter design and analysis. In this domain, inverse filtering and Wiener filtering are standard restoration methods for low-level noise systems such as CT Medical Imaging 2003: Image Processing, Milan Sonka, J. Michael Fitzpatrick, Editors, Proceedings of SPIE Vol. 5032 (2003) © 2003 SPIE · 1605-7422/03/$15.00 1851 Downloaded from SPIE Digital Library on 15 Mar 2010 to 128.219.49.8. Terms of Use: http://spiedl.org/terms