Fan, Mingdong

AbstractBackgroundObjective and quantitative evaluation for low‐contrast detectability that correlates with human observer performance is lacking for routine CT quality control testing. Channelized Hotelling observer (CHO) is considered a strong candidate to fill the need but has long been deemed impractical to implement due to its requirement of a large number of repeated scans in order to provide accurate and precise estimates of index of detectability (d’). In our previous work, we optimized a CHO model observer on the American College of Radiology (ACR) CT accreditation phantom and achieved accurate measurement of d’ with only 1–3 repeat scans.PurposeIn this work, we aim to validate the repeatability of the proposed CHO‐based low‐contrast evaluation on four scanner models using the ACR CT accreditation phantom.MethodsThe repeatability test was performed on four different scanners from two major CT manufacturers: Siemens Force and Alpha; Canon Prism and Prime SP. An ACR CT phantom was scanned 10 times, each time after repositioning of the phantom. For each repositioning, 3 repeated scans were acquired at 24, 12, and 6 mGy on all four scanner models. CHO was applied at the measured dose levels for different low‐contrast object sizes (4–6 mm). The CHO was also applied to images created using deep learning‐based reconstructions on Canon Prism and to four different scan/reconstruction modes on the Siemens Alpha, a photon‐counting‐detector (PCD)‐CT. The repeatability was evaluated by the probability that a measurement would fall within the ±15% tolerance (P<15%).ResultsWith the CHO setting optimized for the ACR phantom and the use of 3 repeated scans and 9 non‐overlapping slices per scan, the CHO measurement could provide high repeatability with P<15% of 98.8%–99.9% at 12 mGy with IR reconstruction on all four scanners. On scanner A, P<15% were 91.5%–99.9% at the three dose levels and for all three object sizes while the numbers were 93.6%–99.998% on scanner B. P<15% were 96.5%–97.2% for the two deep learning reconstructions and 97.0%–99.97% for the four scan/reconstruction modes on the PCD‐CT.ConclusionThe CHO provided highly repeatable measurements with over 95% probability that a CHO measurement would lie within the ±15% tolerance for most of the dose levels and object sizes on the ACR phantom. The repeatability was maintained when the CHO was applied to images created with a commercial deep learning‐based reconstruction and various scan/reconstruction modes on a PCD‐CT. This study demonstrates that practical implementation of CHO for routine quality control and performance evaluation is feasible.

AbstractBackgroundCurrent 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 nonlinear methods, such as iterative reconstruction (IR) 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 QC 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′).PurposeThe 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 QC tests.MethodsTo 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.ResultsOptimized parameters in CHO calculation were determined: two background ROIs per object, four objects per low‐contrast object size, nine 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.ConclusionWith 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 adoption of CHO‐based low‐contrast evaluation in routine QC tests.