12 May 2004 Exact consideration of data redundancies for spiral cone-beam CT
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In multi-slice spiral computed tomography (CT) there is an obvious trend in adding more and more detector rows. The goals are numerous: volume coverage, isotropic spatial resolution, and speed. Consequently, there will be a variety of scan protocols optimizing clinical applications. Flexibility in table feed requires consideration of data redundancies to ensure efficient detector usage. Until recently this was achieved by approximate reconstruction algorithms only. However, due to the increasing cone angles there is a need of exact treatment of the cone beam geometry. A new, exact and efficient 3-PI algorithm for considering three-fold data redundancies was derived from a general, theoretical framework based on 3D Radon inversion using Grangeat's formula. The 3-PI algorithm possesses a simple and efficient structure as the 1-PI method for non-redundant data previously proposed. Filtering is one-dimensional, performed along lines with variable tilt on the detector. This talk deals with a thorough evaluation of the performance of the 3-PI algorithm in comparison to the 1-PI method. Image quality of the 3-PI algorithm is superior. The prominent spiral artifacts and other discretization artifacts are significantly reduced due to averaging effects when taking into account redundant data. Certainly signal-to-noise ratio is increased. The computational expense is comparable even to that of approximate algorithms. The 3-PI algorithm proves its practicability for applications in medical imaging. Other exact n-PI methods for n-fold data redundancies (n odd) can be deduced from the general, theoretical framework.
© (2004) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Guenter Lauritsch, Guenter Lauritsch, Alexander Katsevich, Alexander Katsevich, Michael Hirsch, Michael Hirsch, } "Exact consideration of data redundancies for spiral cone-beam CT", Proc. SPIE 5370, Medical Imaging 2004: Image Processing, (12 May 2004); doi: 10.1117/12.535249; https://doi.org/10.1117/12.535249

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