Compressed-sensing (CS)-based Image Deblurring Scheme with a Total Variation Regularization Penalty for Improving Image Characteristics in Digital Tomosynthesis (DTS)

In this work, we considered a compressed-sensing (CS)-based image deblurring scheme with a total-variation (TV) regularization penalty for improving image characteristics in digital tomosynthesis (DTS). We implemented the proposed image deblurring algorithm and performed a systematic simulation to demonstrate its viability. We also performed an experiment by using a table-top setup which consists of an x-ray tube operated at 90 kV_p, 6 mAs and a CMOS-type flat-panel detector having a 198-μm pixel resolution. In the both simulation and experiment, 51 projection images were taken with a tomographic angle range of θ=60^o and an angle step of Δθ=1.2^o and then deblurred by using the proposed deblurring algorithm before performing the common filtered-backprojection (FBP)-based DTS reconstruction. According to our results, the image sharpness of the recovered x-ray images and the reconstructed DTS images were significantly improved and the cross-plane spatial resolution in DTS was also improved by a factor of about 1.4. Thus the proposed deblurring scheme appears to be effective for the blurring problems in both conventional radiography and DTS and is applicable to improve the present image characteristics.