Abstract
BackgroundThe aim of this work was to evaluate detection of low-contrast objects and image quality in computed tomography (CT) phantom images acquired at different tube loadings (i.e. mAs) and reconstructed with different algorithms, in order to find appropriate settings to reduce the dose to the patient without any image detriment.MethodsImages of supraslice low-contrast objects of a CT phantom were acquired using different mAs values. Images were reconstructed using filtered back projection (FBP), hybrid and iterative model-based methods. Image quality parameters were evaluated in terms of modulation transfer function; noise, and uniformity using two software resources. For the definition of low-contrast detectability, studies based on both human (i.e. four-alternative forced-choice test) and model observers were performed across the various images.ResultsCompared to FBP, image quality parameters were improved by using iterative reconstruction (IR) algorithms. In particular, IR model-based methods provided a 60% noise reduction and a 70% dose reduction, preserving image quality and low-contrast detectability for human radiological evaluation. According to the model observer, the diameters of the minimum detectable detail were around 2 mm (up to 100 mAs). Below 100 mAs, the model observer was unable to provide a result.ConclusionIR methods improve CT protocol quality, providing a potential dose reduction while maintaining a good image detectability. Model observer can in principle be useful to assist human performance in CT low-contrast detection tasks and in dose optimisation.
Highlights
The aim of this work was to evaluate detection of low-contrast objects and image quality in computed tomography (CT) phantom images acquired at different tube loadings and reconstructed with different algorithms, in order to find appropriate settings to reduce the dose to the patient without any image detriment
The purpose of this work was to evaluate image quality and low-contrast object detectability in CT phantom images acquired at different tube loadings and reconstructed with different algorithms in order to reduce mAs and CT dose, with respect to a standard reference value, without detriment of the images
Numerical results of both software systems resulted to be comparable in terms of noise analysis, whereas a difference arose for uniformity
Summary
The aim of this work was to evaluate detection of low-contrast objects and image quality in computed tomography (CT) phantom images acquired at different tube loadings (i.e. mAs) and reconstructed with different algorithms, in order to find appropriate settings to reduce the dose to the patient without any image detriment. The overall per caput mean effective dose per year to the population in European countries, due to X-ray procedures, is about 1.05 mSv. Computed tomography (CT), which is a key medical imaging modality within clinical diagnostic applications, contributes, on average, to 57% of this dose (range 5.31–83.1%) [1], with a mean value of 7.44 mSv [2]. In Switzerland, through 2013 the number of CT exams was 117 per 1000 inhabitants, with an average dose per exam of 8.54 mSv. CT alone contributed to about 70% of the collective dose, with an average annual effective dose of 1 mSv per inhabitant [3]. CT alone contributed to about 70% of the collective dose, with an average annual effective dose of 1 mSv per inhabitant [3] In light of these data, reduction of radiation dose from CT has become an essential field of study. The advent of faster microprocessors, CT iterative reconstruction (IR) methods were launched to integrate already existing algorithms such as filtered back projection (FBP) as a way to reduce patient radiation exposure while maintaining high-contrast spatial resolution
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