Room: Exhibit Hall | Forum 1
Purpose: mammography is the first choice of breast cancer screening, which has proven to be the most effective screening method. An anti-scatter grid between the patient and the image detector is usually employed to enhance the contrast and quality of image by absorbing unexpected scattered signals. However, the grid pattern casts shadows and produces artifacts, which severely degrade the image quality. To solve the problem, we propose patch based fast frequency signal filtering for grid artifacts suppressing
Methods: The proposed method divides image into several blocks for tuning filter simultaneously, which reduces the frequency interference among image blocks as well as saves computation time by multithread processing. Moreover, characteristic peak detection is employed in each block automatically, which is robust to the accuracy of the anti-scatter grid and its motion. Specifically, the method consists of six steps: 1) image block processing; 2) two-dimension Fast Fourier Transformation (FFT); 3) characteristic frequency detection; 4) filter processing; 5) two-dimension Inverse Fast Fourier Transformation (IFFT); 6) image block integrating. To evaluate the proposed method, both qualitative and quantitative studies were performed on simulated and realistic phantom data
Results: In the simulated experiment, the proposed method could suppress more grid artifacts and the profile of results is closer to the reference profile than the conventional method. The result of the realistic phantom data is also consistent with the simulation phantom.
Conclusion: The proposed method, which has been tested in simulated and the realistic data study, shows great potential for achieving high quality of mammography with grid artifact suppressing.
Funding Support, Disclosures, and Conflict of Interest: National Natural Science Foundation of China (81301940 and 81428019), National Key Research and Development Program (2016YFA0202003), Guangdong Natural Science Foundation of China (2016A030310388 and 2017A030313692), and Southern Medical University Startup fund (LX2016N003).
Not Applicable / None Entered.