Accurate and efficient measurement of channelized hotelling observer-based low-contrast detectability on the ACR CT accreditation phantom.

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Abstract

Current CT quality control (QC) for low-contrast detectability relies on visual inspection and measurement of contrast-to-noise ratio (CNR). However, CNR numbers become unreliable when it comes to non-linear methods, such as iterative reconstruction and deep-learning-based techniques. Image quality metrics using channelized Hotelling observer (CHO) have been validated to be well correlated with human observer performance on phantom-based and patient-based tasks, but it has not been widely used in routine CT quality control mainly because the CHO calculation typically requires a large number of repeated scans in order to provide accurate and precise estimate of index of detectability (d’).The main goal of this work is to optimize channel filters and other CHO parameters and accurately estimate the low-contrast detectability with minimum number of repeated scans for the widely used American College of Radiology (ACR) CT accreditation phantom so that it can become practically feasible for routine CT quality control tests.To provide a converged d’ value, an ACR phantom was repeatedly scanned 100 times at three dose levels (24, 12 and 6 mGy). Images were reconstructed with two kernels (FBP Br44 and IR Br44-3). d’ as a function of number of repeated scans was determined for different number of background regions of interest (ROIs), different number of low-contrast objects, different number of slices per each object, and different channel filter options. A reference d’ was established using the optimized CHO setting, and the bias of d’ was quantified using the d’ calculated from all 100 repeated scans. The variation of d’ at each condition was estimated using a resampling method combining random subsampling among 100 repeated scans and bootstrapping of the ensembles of signal and background ROIs.Optimized parameters in CHO calculation were determined 2 background ROIs per object, 4 objects per low-contrast object size, 9 non-overlapping slices per object, and a 4-channel Gabor filter. The bias and uncertainty were estimated at different numbers of repeated scans using these parameters. When only one single scan was used in the CHO calculation, the bias of d’ was below 6.2% and the uncertainty 15.6-19.6% for the 6, 5, and 4-mm objects, while with three repeated scans the bias was below 2.0% and uncertainty 8.7%-10.9% for the three object sizes.With optimized parameter settings in CHO, efficient and accurate measurement of low-contrast detectability on the commonly used ACR phantom becomes feasible, which could potentially lead to CHO-based low contrast evaluation in routine quality control tests. This article is protected by copyright. All rights reserved.This article is protected by copyright. All rights reserved.