Image quality in CT: From physical measurements to model observers

Abstract

Evaluation of image quality (IQ) in Computed Tomography (CT) is important to ensure that diagnostic questions are correctly answered, whilst keeping radiation dose to the patient as low as is reasonably possible. The assessment of individual aspects of IQ is already a key component of routine quality control of medical x-ray devices. These values together with standard dose indicators can be used to give rise to 'figures of merit' (FOM) to characterise the dose efficiency of the CT scanners operating in certain modes. The demand for clinically relevant IQ characterisation has naturally increased with the development of CT technology (detectors efficiency, image reconstruction and processing), resulting in the adaptation and evolution of assessment methods. The purpose of this review is to present the spectrum of various methods that have been used to characterise image quality in CT: from objective measurements of physical parameters to clinically task-based approaches (i.e. model observer (MO) approach) including pure human observer approach. When combined together with a dose indicator, a generalised dose efficiency index can be explored in a framework of system and patient dose optimisation. We will focus on the IQ methodologies that are required for dealing with standard reconstruction, but also for iterative reconstruction algorithms. With this concept the previously used FOM will be presented with a proposal to update them in order to make them relevant and up to date with technological progress. The MO that objectively assesses IQ for clinically relevant tasks represents the most promising method in terms of radiologist sensitivity performance and therefore of most relevance in the clinical environment.

Objective comparison of high‐contrast spatial resolution and low‐contrast detectability for various clinical protocols on multiple CT scanners

“…6 Task-oriented image quality criteria can then be used as surrogates for the assessment of actual clinical image quality. [7][8][9][10] They will necessarily be simple in comparison to the clinical situations, but will make it possible, for example, to predict the ability to detect simple structures of high and low contrast within homogeneous backgrounds. 9,10,11 This represents the most basic task that can be considered a surrogate for measuring clinical image quality.…”

A deep learning‐ and partial least square regression‐based model observer for a low‐contrast lesion detection task in CT

“…Quantitative and objective image quality assessment is critical to optimize radiation dose and scanning protocols in clinical CT. Human observer (HO) studies to determine diagnostic performance and clinical image quality are typically limited by cost, efficiency, and intra-and interobserver variabilities. Traditional image quality metrics are inappropriate for the newer CT systems when iterative reconstruction (IR) or other nonlinear algorithms are used, [1][2][3][4] and thus have limited use in quantifying overall image quality for given diagnostic tasks. The use of mathematical model observers (MOs), as objective and efficient alternatives to HOs, [5][6][7][8][9][10][11][12][13] have become popular in task-based CT image quality assessment, due to the limitations of traditional methods and the growing needs for CT protocol optimization.…”

Correlation between model observers in uniform background and human observer in patient liver background for a low-contrast detection task in CT

“…Diagnostic image quality assessment is the basis for radiation dose and scanning protocol optimization in clinical CT. Task-based approaches using mathematical model-observers (MO) have become popular to characterize CT image quality, since MO provides objective and quantitative image quality assessment that have been demonstrated to well-correlate with human observer (HO) performance [1]. Fourier based MO relies on the assumption of linear shift invariance and noise stationarity [2, 3].…”