# Cone beam tomographic image reconstruction: Algorithm development and application in small animal imaging

#### Abstract

In order to increase scanning speed, the trend in clinical helical CT has been toward more detector rows, leading naturally to cone beam geometry. Therefore, algorithms accounting properly for the cone beam geometry have to be developed. There are two types of helical cone beam algorithms: one approximate, the other exact. Approximate methods are basically extensions of two dimensional tomography. They are usually efficient but will fail when the cone angle becomes large. Exact methods are based on inverse 3D Radon transform. They are relatively inefficient but their performance is independent of the cone angle. In this dissertation, we focus on the development of an exact helical cone beam algorithm for long object imaging. Our approach is based on the theory of short object imaging by Kudo et al . 1998. One of the key findings in their work is that filtering the truncated projection can be divided into two parts: One, finite in the axial direction, results from ramp filtering the data within the Tam window. The other, infinite in the z direction, results from unbounded filtering of ray sums over PI lines only. We show that for an ROI defined by PI lines emanating from the initial and final source positions on a helical segment, the boundary data which would otherwise contaminates the reconstruction of the ROI can be completely excluded. This novel definition of the ROI leads to a simple algorithm for long object imaging. The overscan of the algorithm is analytically calculated and it is the same as that of the zero boundary method. The reconstructed ROI can be divided into two regions: One is minimally contaminated by the portion outside the ROI, while the other is reconstructed free of contamination. Practical issues regarding algorithm implementation are also discussed. The key operators which need to be implemented in the algorithm are Tam windowing, derivatives and two-dimensional filters. A fractional value has to be assigned to the values straddling the boundary of the Tam window and the two-dimensional filtering has to be implemented with an oversampling technique to avoid the aliasing and DC shift artifact. Derivative can be implemented in either the spatial or frequency domain. Finally, we apply the proposed helical cone beam algorithm to a micro CT system for small animal imaging and show preliminary results. In the dissertation appendix, we also implement a ring artifact reduction algorithm for circular cone beam tomography and propose and implement a respiratory gating method for small animal imaging. (Abstract shortened by UMI.)

#### Recommended Citation

Hu, Jicun, "Cone beam tomographic image reconstruction: Algorithm development and application in small animal imaging" (2003). *Dissertations (1962 - 2010) Access via Proquest Digital Dissertations*. AAI3116859.

https://epublications.marquette.edu/dissertations/AAI3116859