Model observer task‐based assessment of computed tomography metal artifact reduction using a hip arthroplasty phantom

The United States Food and Drug Administration (FDA) recently published a model observer-based framework for the objective performance assessment of computed tomography (CT) metal artifact reduction (MAR) algorithms and demonstrated the framework's feasibility in the low-contrast detectability (LCD) task-based assessment of MAR performance in a mathematical phantom. This study investigates the feasibility of the model observer-based framework in LCD task-based assessment of MAR performance using a physical arthroplasty phantom, results of which were then compared with the performance of human observers. A phantom simulating a unilateral hip prosthesis was designed with a rotatable insert containing a metal implant (cobalt-chromium spheres attached to titanium rods) and 16 unique low-contrast spherical lesions. Each lesion was scanned 100 times on a CT scanner (Somatom Force, Siemens Healthineers) with standard full-dose and half-dose protocols (140 kVp, 300 and 150 quality reference mAs) in each of four different insert rotations to supply 100 pairs of signal-present (lesion) and signal-absent (background) images needed for model observer analyses. Lesion detectability (d') using channelized Hotelling observers (CHO) was optimized by testing different image transformation techniques and channel selection (Gabor and Laguerre-Gauss [LG]) and calculated for each lesion reconstructed with and without iterative MAR (iMAR, Siemens Healthineers). Linear regression was used to assess the d' in each image set. Spearman's correlation was used to compare d' results to human detectability and confidence scores from a previously published human observer study involving the same phantom. CHO d' measurements using LG channels were less sensitive to artifacts than those using Gabor channels and were therefore selected for the LCD assessment. Image masking and thresholding provided more accurate d' by isolating the signal and minimizing background differences. For all lesions, d' values of full-dose iMAR images were significantly greater than those of filtered back projection (FBP) images at full dose (p < 0.001) and half dose (p < 0.001). Additionally, d' values of half-dose iMAR images were significantly greater than those of FBP images at full dose (p = 0.010) and half dose (p < 0.001). The d' values were not significantly different between full-dose and half-dose FBP (p = 0.620) or between full-dose and half-dose iMAR (p = 0.358). Pooling across all lesions, d' measurements were positively correlated with human detection rate (Spearman correlation coefficient = 0.723; p < 0.001) and confidence scores (Spearman correlation coefficient = 0.727; p < 0.001). The use of CHO in the LCD assessment of MAR performance can be feasibly performed on a physical phantom, and results using this method correlated well with findings from human observers.