Abstract
In this study, we investigated the effectiveness of reconstructing interior digital tomosynthesis (IDTS) images by using a dual-resolution voxellation method for achieving high-quality IDTS images at reduced computational cost. In the proposed IDTS, the X-ray beam span covered only a small region-of-interest (ROI) containing the diagnosis target to reduce the radiation dose received by the patient, and the voxels inside the target ROI had high resolution while the voxels outside the ROI were binned by 2×2×2 to reduce computational cost. The IDTS reconstruction algorithm was based on compressed-sensing (CS) theory. A systematic simulation and experiment were performed to evaluate the effectiveness of the proposed method. All projection data were taken at a tomographic angle of 40° and an angle step of 4°. The hardware system used in the experiment consisted of an X-ray tube run at 70 kVp and 5 mAs and a flat-panel detector with a pixel resolution of 198 μm. The results indicated that the proposed CS-based IDTS reconstruction method considerably reduced computational cost while still maintaining high fidelity for the reconstructed image of a region inside the target ROI.
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Park, S., Kim, G., Park, C. et al. Iterative Interior Digital Tomosynthesis Reconstruction Using a Dual-Resolution Voxellation Method. J. Korean Phys. Soc. 73, 355–360 (2018). https://doi.org/10.3938/jkps.73.355
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DOI: https://doi.org/10.3938/jkps.73.355