Channelized hotelling observer‐based low‐contrast detectability on the ACR CT accreditation phantom: Part II. Repeatability study

Abstract

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.