TH‐B‐218‐01: Iterative Reconstruction Methods in Computed Tomography

Abstract

Sophisticated iterative reconstruction methods for computed tomography have seen more widespread adoption with the availability of increased computational power and the desire for improved dose utilization. While improved image quality and dose reduction are possible with iterative approaches, there is a (sometimes confusing) diversity of algorithm choices. This presentation will introduce the basic concepts behind iterative reconstruction and statistical methods, including how such approaches are formed and illustrating different classes of reconstruction algorithm. Differences between iterative approaches and traditional reconstructions will be discussed ‐ both in terms of how data is processed as well as the differing image properties that arise from each type of algorithm. The lecture will highlight advantages and limitations of iterative reconstructions and provide listeners with a basic introduction of the inner workings of the iterative methodology.Learning Objectives:1. Understand the fundamentals of how iterative methods are formed and how to identify particular classes of iterative methods.2. Gain intuition into the flexibility of iterative methods and why these approaches can have an advantage over other techniques.3. Learn about the nature of iteratively reconstructed images and how images produced by these methods can have significantly different image properties from those produced by traditional analytic approaches.This talk may contain research sponsored by Siemens, Varian Medical Systems, and/or Carestream Health.