Tomosynthesis, also digital tomosynthesis, is a method for performing high-resolution limited-angle tomography at mammographic dose levels.
Digital breast tomosynthesis (DBT) can provide a higher diagnostic accuracy compared to conventional mammography. In DBT, like conventional mammography, compression is used to improve image quality and decreases radiation dose. The laminographic imaging technique dates back to the 1930s and belongs to the category of geometric or linear tomography.
Because the data acquired are very high resolution (85 - 160 micron typical ), much higher than CT, DBT is unable to offer the narrow slice widths that CT offers (typically 1-1.5 mmm). However, the higher resolution detectors permit very high in-plane resolution, even if the Z-axis resolution is less. The primary interest in DBT is in breast imaging, as an extension to mammography, where it offers better detection rates with little extra increase in radiation.
Differences with conventional CT
DBT combines digital image capture and processing with simple tube/detector motion as used in conventional radiographic tomography. Although there are some similarities to CT, it is a separate technique. In CT, the source/detector makes at least a complete 180-degree rotation about the subject obtaining a complete set of data from which images may be reconstructed. In digital tomosynthesis, only a limited rotation angle (e.g., 15-60 degrees) with a lower number of discrete exposures (e.g., 7-51) than CT. This incomplete set of projections is digitally processed to yield images similar to conventional tomography with a limited depth of field. However, because the image processing is digital, a series of slices at different depths and with different thicknesses can be reconstructed from the same acquisition, saving both time and radiation exposure.
Reconstruction algorithms for tomosynthesis are different from those of conventional CT because the conventional filtered back projection algorithm requires a complete set of data. Iterative algorithms based upon expectation maximization are most commonly used, but are computationally intensive. Some manufacturers have produced practical systems using off-the-shelf GPUs to perform the reconstruction in a few seconds.
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