Correlation of Automated in Vivo Image Quality With Radiologist's Performance in Abdomen Computed Tomography Across Conventional and Deep Learning Reconstructions.

Abstract

ObjectiveImage quality evaluation in radiology is most relevant when reflects radiologists' performance. This study assessed how image quality measurement in terms of in vivo-characterized detectability index () for low-contrast liver lesion assessment in CT is correlated with radiologists' performance across 2 different CT reconstructions.MethodsFifty-one contrast-enhanced abdominal studies for investigating colorectal liver metastases were prospectively performed using 2 radiation dose exposures and reconstructed with Filtered back projection (FBP) and deep learning image reconstruction (DL) algorithms for a total of 161 noncalcified hypoattenuating lesions for 3 lesion size (D) subsets (<6 mm, 6 to 10 mm, and >10 mm). Images were assessed by expert radiologists for hepatic lesion detection task and likelihood of malignancy across the 2 imaging conditions. All cases were also evaluated automatically in terms of in vivo as a metric of task-based performance, both using a conventional technique and a new formalism of an added frequency term in the internal noise component of to accommodate the nonlinearity of the DL reconstruction (adj).ResultsThe study found conventionally defined d' well-reflective of radiologists' evaluation of FBP images but not well-aligned with that of DL images. The new formalism provided more consistent reflection of performance across reconstruction techniques. In particular, in the lesion group D <=6 mm, the difference between radiologists' accuracy in images acquired with DL and images acquired with FBP was -26%, and the related adj difference was -9%, whereas the was 34%. Analogously, for the lesion group 6 mm < D <=10 mm, the differences were -15%, -13%, and 29%, respectively. Lastly, for the lesion group D>10 mm, radiologists showed the same accuracy in both FPB and DL images, difference in adj was -11%, and difference in was 31%.ConclusionThe new formalism can robustly reflect CT systems clinical performance irrespective of reconstruction algorithm. The methodology can be more readily applied to assess the real-world performance of CT systems.