Nonprewhitening model observers in the Fourier and spatial domain: a comparison of predictions for iterative and deep learning reconstruction in computed tomography

The nonprewhitening matched filter (NPWMF) is frequently used to assess task-based image quality in computed tomography (CT). However, modern reconstruction algorithms, based on iterative reconstruction (IR) or Deep Learning image reconstruction (DLIR), exhibit properties that undermine Fourier domain approaches. One alternative is to abandon the NPWMF. Here, instead, calculation of the NPWMF in the spatial domain is explored with and without assumption of Gaussian observer response. Model observer predictions of area-under-the-curve were determined for a Revolution CT scanner (GE Healthcare) and a NAEOTOM Alpha scanner (Siemens Healthineers). For the former, the vendor's IR and DLIR were investigated. For the latter, the vendor's IR was used and compared to results from a reader study. Results support the conclusion that Fourier domain calculations can exaggerate benefits of denoising and that spatial domain calculations can provide good agreement with human observers. Assumption of Gaussian observer response did not lead to substantial errors.