Although conventional mammography is currently the best modality to detect early breast cancer, it is limited in that the recorded image represents the superposition of a 3D object onto a 2D plane. As an alternative, cone-beam CT breast imaging with a CsI based flat-panel imager (CTBI) has been proposed with the ability to provide 3D visualization of breast tissue. To investigate possible improvements in lesion detection accuracy using CTBI over digital mammography (DM), a computer simulation study was conducted using simulated lesions embedded into a structured 3D breast model. The computer simulation realistically modeled x-ray transport through a breast model, as well as the signal and noise propagation through the flat-panel imager. Polyenergetic x-ray spectra of W/Al 50 kVp for CTBI and Mo/Mo 28 kVp for DM were modeled. For the CTBI simulation, the intensity of the x-ray spectra for each projection view was determined so as to provide a total mean glandular dose (MGD) of 4 mGy, which is approximately equivalent to that given in a conventional two-view screening mammography study. Since only one DM view was investigated here, the intensity of the DM x-ray spectra was defined to give 2 mGy MGD. Irregular lesions were simulated by using a stochastic growth algorithm providing lesions with an effective diameter of 5 mm. Breast tissue was simulated by generating an ensemble of backgrounds with a power law spectrum. To evaluate lesion detection accuracy, a receiver operating characteristic (ROC) study was performed with 4 observers reading an ensemble of images for each case. The average area under the ROC curves (Az) was 0.94 for CTBI, and 0.81 for DM. Results indicate that a 5 mm lesion embedded in a structured breast phantom can be detected by CT breast imaging with statistically significant higher confidence than with digital mammography.
The purpose of this study is to investigate the detectability of microcalcification clusters (MCCs) using CT mammography with a flat-panel detector. Compared with conventional mammography, CT mammography can provide improved discrimination between malignant and benign cases as it can provide the radiologist with more accurate morphological information on MCCs. In this study, two aspects of MCC detection with flat-panel CT mammography were examined: (1) the minimal size of MCCs detectable with mean glandular dose (MGD) used in conventional mammography; (2) the effect of different detector pixel size on the detectability of MCCs. A realistic computer simulation modeling x-ray transport through the breast, as well as both signal and noise propagation through the flat-panel imager, was developed to investigate these questions. Microcalcifications were simulated as calcium carbonate spheres with diameters set at the levels of 125, 150 and 175 μm. Each cluster consisted of 10 spheres spread randomly in a 6×6 mm2 region of interest (ROI) and the detector pixel size was set to 100×100, 200×200, or 300×300μm2. After reconstructing 100 projection sets for each case (half with signal present) with the cone-beam Feldkamp (FDK) algorithm, a localization receiver operating characteristic (LROC) study was conducted to evaluate the detectability of MCCs. Five observers chose the locations of cluster centers with correspondent confidence ratings. The average area under the LROC curve suggested that the 175 μm MCCs can be detected at a high level of confidence. Results also indicate that flat-panel detectors with pixel size of 200×200 μm2 are appropriate for detecting small targets, such as MCCs.
Software has been developed to simulate a cone-beam CT mammography imaging system that consists of an x-ray tube and a flat-panel detector that rotate simultaneously around the pendant breast. The simulation uses an analytical expression or ray-tracing to generate projection sets of breast phantoms at 1 keV intervals dictated by the input x-ray energy spectra. The x-ray focal spot was modeled as having a Gaussian distribution. The detector was modeled as an amorphous silicon (aSi:H) flat-panel imager that uses a structured CsI scintillator. Noise propagation through the detector was simulated by modeling statistical variations of the projection images at each energy interval as a scaled Poisson process. Scintillator blurring was simulated by using an empirically determined modulation transfer function. After introducing noise and detector blur, projection sets simulated at each energy were then combined and reconstructed using Feldkamp's cone-beam reconstruction algorithm. Using this framework, the effects of a number of acquisition and reconstruction parameters can be investigated. Some examples are shown including the impact of the kVp setting and the number of projection angles on the reconstructed image.