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

OBJECTIVE: Image 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. METHODS: Fifty-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 (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 ). RESULTS: The 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