TU-AB-207A-03: Image Quality, Dose, and Clinical Applications

Authors


Abstract

Practicing medical physicists are often time charged with the tasks of evaluating and troubleshooting complex image quality issues related to CT scanners. This course will equip them with a solid and practical understanding of common CT imaging chain and its major components with emphasis on acquisition physics and hardware, reconstruction, artifacts, image quality, dose, and advanced clinical applications. The core objective is to explain the effects of these major system components on the image quality. This course will not focus on the rapid-changing advanced technologies given the two-hour time limit, but the fundamental principles discussed in this course may facilitate better understanding of those more complicated technologies.

The course will begin with an overview of CT acquisition physics and geometry. X-ray tube and CT detector are important acquisition hardware critical to the overall image quality. Each of these two subsystems consists of several major components. An in-depth description of the function and failure modes of these components will be provided. Examples of artifacts related to these failure modes will be presented: off-focal radiation, tube arcing, heel effect, oil bubble, offset drift effect, cross-talk effect, and bad pixels.

The fundamentals of CT image reconstruction will first be discussed on an intuitive level. Approaches that do not require rigorous derivation of mathematical formulations will be presented. This is followed by a detailed derivation of the Fourier slice theorem: the foundation of the FBP algorithm. FBP for parallel-beam, fan-beam, and cone-beam geometries will be discussed. To address the issue of radiation dose related to x-ray CT, recent advances in iterative reconstruction, their advantages, and clinical applications will also be described.

Because of the nature of fundamental physics and mathematics, limitations in data acquisition, and non-ideal conditions of major system components, image artifact often arise in the reconstructed images. Because of the limited scope of this course, only major imaging artifacts, their appearance, and possible mitigation and corrections will be discussed.

Assessment of the performance of a CT scanner is a complicated subject. Procedures to measure common image quality metrics such as high contrast spatial resolution, low contrast detectability, and slice profile will be described. The reason why these metrics used for FBP may not be sufficient for statistical iterative reconstruction will be explained.

Optimizing radiation dose requires comprehension of CT dose metrics. This course will briefly describe various dose metrics, and interaction with acquisition parameters and patient habitus.

CT is among the most frequently used imaging tools due to its superior image quality, easy to operate, and a broad range of applications. This course will present several interesting CT applications such as a mobile CT unit on an ambulance for stroke patients, low dose lung cancer screening, and single heartbeat cardiac CT.

Learning Objectives:

  • 1.Understand the function and impact of major components of X-ray tube on the image quality.
  • 2.Understand the function and impact of major components of CT detector on the image quality.
  • 3.Be familiar with the basic procedure of CT image reconstruction.
  • 4.Understand the effect of image reconstruction on CT image quality and artifacts.
  • 5.Understand the root causes of common CT image artifacts.
  • 6.Be familiar with image quality metrics especially high and low contrast resolution, noise power spectrum, slice sensitivity profile, etc.
  • 7.Understand why basic image quality metrics used for FBP may not be sufficient to characterize the performance of advanced iterative reconstruction.
  • 8.Be familiar with various CT dose metrics and their interaction with acquisition parameters.
  • 9.New development in advanced CT clinical applications.

JH: Employee of GE Healthcare.

FD: No disclosure.; J. Hsieh, Jiang Hsieh is an employee of GE Healthcare.

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