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SU‐F‐I‐09: Improvement of Image Registration Using Total‐Variation Based Noise Reduction Algorithms for Low‐Dose CBCT
Author(s) -
Mukherjee S,
Farr J,
Merchant T,
Yao W
Publication year - 2016
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.4955837
Subject(s) - image noise , image registration , noise reduction , noise (video) , reduction (mathematics) , metric (unit) , artificial intelligence , algorithm , computer science , similarity (geometry) , signal to noise ratio (imaging) , mathematics , peak signal to noise ratio , computer vision , image (mathematics) , statistics , operations management , geometry , economics
Purpose: To study the effect of total‐variation based noise reduction algorithms to improve the image registration of low‐dose CBCT for patient positioning in radiation therapy. Methods: In low‐dose CBCT, the reconstructed image is degraded by excessive quantum noise. In this study, we developed a total‐variation based noise reduction algorithm and studied the effect of the algorithm on noise reduction and image registration accuracy. To study the effect of noise reduction, we have calculated the peak signal‐to‐noise ratio (PSNR). To study the improvement of image registration, we performed image registration between volumetric CT and MV‐ CBCT images of different head‐and‐neck patients and calculated the mutual information (MI) and Pearson correlation coefficient (PCC) as a similarity metric. The PSNR, MI and PCC were calculated for both the noisy and noise‐reduced CBCT images. Results: The algorithms were shown to be effective in reducing the noise level and improving the MI and PCC for the low‐dose CBCT images tested. For the different head‐and‐neck patients, a maximum improvement of PSNR of 10 dB with respect to the noisy image was calculated. The improvement of MI and PCC was 9% and 2% respectively. Conclusion: Total‐variation based noise reduction algorithm was studied to improve the image registration between CT and low‐dose CBCT. The algorithm had shown promising results in reducing the noise from low‐dose CBCT images and improving the similarity metric in terms of MI and PCC.