Validation of algorithmic CT image quality metrics with preferences of radiologists

Abstract

Purpose: Automated assessment of perceptual image quality on clinical Computed Tomography (CT) data by computer algorithms has the potential to greatly facilitate data-driven monitoring and optimization of CT image acquisition protocols. The application of these techniques in clinical operation requires the knowledge of how the output of the computer algorithms corresponds to clinical expectations. This study addressed the need to validate algorithmic image quality measurements on clinical CT images with preferences of radiologists and determine the clinically acceptable range of algorithmic measurements for abdominal CT examinations. Materials and methods: Algorithmic measurements of image quality metrics (organ HU, noise magnitude, and clarity) were performed on a clinical CT image dataset with supplemental measures of noise power spectrum from phantom images using techniques developed previously. The algorithmic measurements were compared to clinical expectations of image quality in an observer study with seven radiologists. Sets of CT liver images were selected from the dataset where images in the same set varied in terms of one metric at a time. These sets of images were shown via a web interface to one observer at a time. First, the observer rank ordered the CT images in a set according to his/her preference for the varying metric. The observer then selected his/her preferred acceptable range of the metric within the ranked images. The agreement between algorithmic and observer rankings of image quality were investigated and the clinically acceptable image quality in terms of algorithmic measurements were determined. Results: The overall rank-order agreements between algorithmic and observer assessments were 0.90, 0.98, and 1.00 for noise magnitude, liver parenchyma HU, and clarity, respectively. The results indicate a strong agreement between the algorithmic and observer assessments of image quality. Clinically acceptable thresholds (median) of algorithmic metric values were (17.8, 32.6) HU for noise magnitude, (92.1, 131.9) for liver parenchyma HU, and (0.47, 0.52) for clarity. Conclusions: The observer study results indicated that these algorithms can robustly assess the perceptual quality of clinical CT images in an automated fashion. Clinically acceptable ranges of algorithmic measurements were determined. The correspondence of these image quality assessment algorithms to clinical expectations paves the way toward establishing diagnostic reference levels in terms of clinically acceptable perceptual image quality and data-driven optimization of CT image acquisition protocols.

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Comparison of two versions of a deep learning image reconstruction algorithm on CT image quality and dose reduction: A phantom study

“…This objective image quality assessment shows a very interesting potential in the evaluation of the performance of CT scans and in dose optimization in clinical practice. However, a subjective analysis of the image quality is complementary and allows appreciating the preference of the radiologists on the images obtained 14 …”

“…However, a subjective analysis of the image quality is complementary and allows appreciating the preference of the radiologists on the images obtained. 14 Recently, a new generation of CT image reconstruction algorithms based on deep learning has been developed. 3,10,12,[15][16][17][18][19][20][21][22][23] The two main Deep Learning Image Reconstruction (DLR) algorithms are Advanced intelligent Clear-IQ Engine (AiCE;Canon Medical Canon Medical Systems Corporation) and TrueFidelity TM (GE HealthCare).…”

Evaluation of a Motion Correction Algorithm for C-Arm Computed Tomography Acquired During Transarterial Chemoembolization

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