Digital breast tomosynthesis (DBT) is being proposed as a replacement for conventional mammography for breast cancer screening. However, there are limitations to DBT that reduce its effectiveness for screening, principally, difficulty in imaging microcalcifications and increased reading times by radiologists. We propose a method to overcome these limitations.
Our proposed method is to divide the total dose given to the patient unequally such that one projection uses at least half of the dose and the remaining dose is divided over the remaining projections. We assume that in screening with DBT, only a single view is obtained using twice the dose of a conventional mammogram. All the projection images are used in the reconstruction. The 2D projection image that received the highest dose is analyzed by a computer-aided detection (CADe) scheme for microcalcifications. The radiologist views the 3D image set, with mass CADe, principally to search for masses and the 2D image to search for clustered microcalcifications with CADe. Since the 3D image set is for mass detection, the image can be reconstructed using larger sized pixels. This will reduce computation time and image noise. In principle, radiologists can review the tomosynthesis slices faster since they do not have to search for microcalcifications.
We believe that by producing both a high resolution, "standard" dose 2D image and a lower resolution 3D image set, both calcifications and masses can be optimally imaged and detected in a time efficient manner.
Tomosynthesis is emerging as a promising modality for breast imaging. Several manufacturers have developed prototype
units and have acquired clinical and phantom data. Scanning configurations of these prototypes vary. So far, studies
relating scanning configuration to image quality have been limited to those geometries that could be implemented on a
particular prototype. To overcome this limitation, we are developing a model of breast tomosynthesis image acquisition
system, which models the formation of the x-ray image and x-ray detector.
The x-ray image of an object is computed analytically for a polychromatic x-ray beam. Objects consist of volumetric
regions that are bounded by either a planar, ellipsoidal, cylindrical or conical surface, allowing for a variety of objects. xray
scatter is computed by convolving the image with a scatter point-spread function. Poisson noise according to the
entrance exposure is added to the image.
The x-ray detector in this model is composed of a phosphor screen followed by a detector array. X-ray interactions in the
screen are modeled as depth-dependent. The optical output of the screen is converted into digital units using a gain factor
which was assumed to be Gaussian distributed.
To validate this data model, we acquired images of a contrast-detail phantom on a stereotactic biopsy unit. The x-ray
source is mounted on an arm that pivots in a plane about the detector center. The x-ray detector consists of a Min-R type
screen fiber-optically coupled to a CCD camera.
To compare actual and simulated data, we compared line profiles as well as several automatically extracted image
features such as contrast-to-noise ratio, contrast, area and radial gradient index. Good agreement was found between
simulation and physical data, indicating that we can now use this model to explore image quality for various
tomosynthesis scanning configurations.
We have developed a method for producing simulated mammograms from high fidelity breast specimen radiographs. The method has the advantage of having access to all the truth information for the lesions. By modeling different parts of a screen-film system, we simulated the output of the system, and compared it to the real mammography images from the same samples. In this work we show how our simulation program produces realistic mammography images and also the observer study that tests how well the observers can distinguish the real and simulated images. Preliminary results from the ROC study show that the observers could not distinguish the two types of images very well.