Evaluation of clinical full field digital mammography with the task specific system-model-based Fourier Hotelling observer (SMFHO) SNR.

Purpose: The purpose of this work is to evaluate the performance of the image acquisition chain of clinical full field digital mammography (FFDM) systems by quantifying their image quality, and how well the desired information is captured by the images.

Methods: The authors present a practical methodology to evaluate FFDM using the task specific system-model-based Fourier Hotelling observer (SMFHO) signal to noise ratio (SNR), which evaluates the signal and noise transfer characteristics of FFDM systems in the presence of a uniform polymethyl methacrylate phantom that models the attenuation of a 6 cm thick 20/80 breast (20% glandular/80% adipose). The authors model the system performance using the generalized modulation transfer function, which accounts for scatter blur and focal spot unsharpness, and the generalized noise power spectrum, both estimated with the phantom placed in the field of view. Using the system model, the authors were able to estimate system detectability for a series of simulated disk signals with various diameters and thicknesses, quantified by a SMFHO SNR map. Contrast-detail (CD) curves were generated from the SNR map and adjusted using an estimate of the human observer efficiency, without performing time-consuming human reader studies. Using the SMFHO method the authors compared two FFDM systems, the GE Senographe DS and Hologic Selenia FFDM systems, which use indirect and direct detectors, respectively.

Results: Even though the two FFDM systems have different resolutions, noise properties, detector technologies, and antiscatter grids, the authors found no significant difference between them in terms of detectability for a given signal detection task. The authors also compared the performance between the two image acquisition modes (fine view and standard) of the GE Senographe DS system, and concluded that there is no significant difference when evaluated by the SMFHO. The estimated human observer efficiency was 30 ± 5% when compared to the SMFHO. The results showed good agreement when compared to other model observers as well as previously published human observer data.

Conclusions: This method generates CD curves from the SMFHO SNR that can be used as figures of merit for evaluating the image acquisition performance of clinical FFDM systems. It provides a way of creating an empirical model of the FFDM system that accounts for patient scatter, focal spot unsharpness, and detector blur. With the use of simulated signals, this method can predict system performance for a signal known exactly/background known exactly detection task with a limited number of images, therefore, it can be readily applied in a clinical environment.