This work examined the impact of the presampling Modulation Transfer Function (MTF) on detectability of lesion-like targets in digital mammography. Two needle CR plates (CR1 and CR2) with different MTF curves but identical detector response (sensitivity) were selected. The plates were characterized by MTF, normalized noise power spectrum (NNPS) and detective quantum efficiency (DQE). Three image quality phantoms were applied to study the impact of the difference in MTF: first, the CDMAM contrast-detail phantom to give gold thickness threshold (T); second, a 3D structured phantom with lesion models (calcifications and masses), evaluated via a 4-alternative forced-choice study to give threshold diameter (d<sub>tr</sub>) and third, a detectability index (d') from a 50 mm PMMA flat field image and an 0.2 mm Al contrast square. MTF coefficient of variation was ~1%, averaged up to 5 mm<sup>-1</sup>. At 5 mm<sup>-1</sup>, a significant 24% reduction in MTF was observed. The lower MTF caused a 12% reduction in NNPS for CR2 compared to CR1 (at detector air kerma 117 μGy). At 5 mm<sup>-1</sup>, there was a drop in DQE of 34% for CR2 compared to CR1. For the test objects, there was a trend to lower detectability for CR2 (lower MTF) for all but one parameter, however none of the changes were significant. The MTF is a sensitive and easily applied means of tracking changes in sharpness before these changes are uncovered using lesion simulating objects in test objects.
Aim: The impact of x-ray system parameters on detectability of specific (clinical) signals can be studied with simulation platforms if these tools are sufficiently accurate and realistic. This work describes the steps taken to verify and confirm the accuracy of a local platform developed for the use in virtual clinical trials of breast tomosynthesis. The (gold standard) reference data will be made available to the community. Materials and methods: Our simulation platform simulates specific targets, including microcalcifications into existing 2D FFDM and DBT background images, a method called partial simulation. There are three steps: (1) creation of a voxel model or 3D analytical object to be inserted into the ‘For Processing’ projections; (2) generation of a realistic object template for the geometry under study and the relevant resolution, scatter and noise properties; (3) insertion of the target into the projections and DBT reconstruction plus image processing. Three objects were simulated as part of the verification: a small high contrast 0.5 mm aluminum (Al) sphere in a poly(methyl methacrylate) (PMMA) stack, a 0.2 mm thick Al sheet in a PMMA stack and a 0.8 mm steel edge. For the small Al sphere, the peak contrast, the signal difference to noise ratio (SDNR), the profile in the (in plane) xy-direction and the artifact spread function (ASF) were compared to results from real acquisitions. Contrast and SDNR were compared to data from a real 0.2 mm Al sheet. Sharpness modelling was verified by comparing the modulation transfer function (MTF) calculated from real and simulated edges. The study was performed for a Siemens Inspiration DBT system. Results: Comparing peak contrast and SDNR for both sphere and sheet showed good agreement (<5% error) in 2D FFDM and DBT. The similarity of the pixel value profiles through the sphere and the sheet in the xy-direction and the ASF for real and simulated Al spheres confirmed accurate geometric modelling. Absolute and relative average deviation between MTF measured from real and simulated edge in the front-back and left-right directions show a good correlation for frequencies up to the Nyquist frequency for 2D FFDM and DBT mode. Real and simulated objects could not be differentiated visually. Conclusion: The close correspondence between simulated and real objects, both visually and quantitatively, indicates that this simulation framework is a strong candidate for use in virtual clinical studies employing 2D FFDM and DBT.
The purpose of this study is comparing the detection performance in 2D full field digital mammography (FFDM) and digital breast tomosynthesis (DBT) using a structured phantom with inserted target objects. The phantom consists of a semi-cylindrical PMMA container, filled with water and PMMA spheres of different diameters. Microcalcifications and 3D printed masses (spiculated and non-spiculated) were inserted. The phantom was imaged ten times in both modes of five systems, using automatic exposure control (AEC) and at half and double the AEC dose. Five readers evaluated target detectability in a four-alternative forced-choice study. The percentage of correct responses (PC) was assessed based on 10 trials of each reader for each object type, size, imaging modality and dose level. Additionally, detection threshold diameters at 62.5 PC were assessed via non-linear regression fitting of the psychometric curve. Evaluation of target detection in FFDM showed that spiculated masses were better detected compared to non-spiculated masses. In DBT, detection of both mass types increased significantly (p=0.0001) compared to FFDM. Microcalcification detection thresholds ranged between 110 and 118 μm and were similar for the five systems in FFDM while larger variations (106-158 μm) were found in DBT. Mass detection was independent of dose in FFDM while weak dependence was seen for DBT. Microcalcification detection increased with increasing dose for both modalities. The phantom was able to show detectability differences between FFDM and DBT mode for five commercial systems in line with the findings from clinical trials. We suggest to use the phantom for task-based assessment methods for acceptance and commissioning testing of DBT systems.
Digital breast tomosynthesis (DBT) is a relatively new 3D mammography technique that promises better detection of low contrast masses than conventional 2D mammography. The parameter space for DBT is large however and finding an optimal balance between dose and image quality remains challenging. Given the large number of conditions and images required in optimization studies, the use of human observers (HO) is time consuming and certainly not feasible for the tuning of all degrees of freedom. Our goal was to develop a model observer (MO) that could predict human detectability for clinically relevant details embedded within a newly developed structured phantom for DBT applications. DBT series were acquired on GE SenoClaire 3D, Giotto Class, Fujifilm AMULET Innovality and Philips MicroDose systems at different dose levels, Siemens Inspiration DBT acquisitions were reconstructed with different algorithms, while a larger set of DBT series was acquired on Hologic Dimensions system for first reproducibility testing. A channelized Hotelling observer (CHO) with Gabor channels was developed The parameters of the Gabor channels were tuned on all systems at standard scanning conditions and the candidate that produced the best fit for all systems was chosen. After tuning, the MO was applied to all systems and conditions. Linear regression lines between MO and HO scores were calculated, giving correlation coefficients between 0.87 and 0.99 for all tested conditions.