Fully iterative scatter corrected digital breast tomosynthesis using GPU-based fast Monte Carlo simulation and composition ratio update.
OBJECTIVE
In digital breast tomosynthesis (DBT), scatter correction is highly desirable, as it improves image quality at low doses. Because the DBT detector panel is typically stationary during the source rotation, antiscatter grids are not generally compatible with DBT; thus, a software-based scatter correction is required. This work proposes a fully iterative scatter correction method that uses a novel fast Monte Carlo simulation (MCS) with a tissue-composition ratio estimation technique for DBT imaging.


METHODS
To apply MCS to scatter estimation, the material composition in each voxel should be known. To overcome the lack of prior accurate knowledge of tissue composition for DBT, a tissue-composition ratio is estimated based on the observation that the breast tissues are principally composed of adipose and glandular tissues. Using this approximation, the composition ratio can be estimated from the reconstructed attenuation coefficients, and the scatter distribution can then be estimated by MCS using the composition ratio. The scatter estimation and image reconstruction procedures can be performed iteratively until an acceptable accuracy is achieved. For practical use, (i) the authors have implemented a fast MCS using a graphics processing unit (GPU), (ii) the MCS is simplified to transport only x-rays in the energy range of 10-50 keV, modeling Rayleigh and Compton scattering and the photoelectric effect using the tissue-composition ratio of adipose and glandular tissues, and (iii) downsampling is used because the scatter distribution varies rather smoothly.


RESULTS
The authors have demonstrated that the proposed method can accurately estimate the scatter distribution, and that the contrast-to-noise ratio of the final reconstructed image is significantly improved. The authors validated the performance of the MCS by changing the tissue thickness, composition ratio, and x-ray energy. The authors confirmed that the tissue-composition ratio estimation was quite accurate under a variety of conditions. Our GPU-based fast MCS implementation took approximately 3 s to generate each angular projection for a 6 cm thick breast, which is believed to make this process acceptable for clinical applications. In addition, the clinical preferences of three radiologists were evaluated; the preference for the proposed method compared to the preference for the convolution-based method was statistically meaningful (p < 0.05, McNemar test).


CONCLUSIONS
The proposed fully iterative scatter correction method and the GPU-based fast MCS using tissue-composition ratio estimation successfully improved the image quality within a reasonable computational time, which may potentially increase the clinical utility of DBT.
Bio Imaging and Signal Processing Laboratory, Department of Bio and Brain Engineering, KAIST 291, Daehak-ro, Yuseong-gu, Daejeon 34141, South Korea.
Kyungsang
K
Kim
Medical Imaging and Radiotherapeutics Laboratory, Department of Nuclear and Quantum Engineering, KAIST 291, Daehak-ro, Yuseong-gu, Daejeon 34141, South Korea.
Taewon
T
Lee
Samsung Advanced Institute of Technology, Samsung Electronics, 130, Samsung-ro, Yeongtong-gu, Suwon-si, Gyeonggi-do, 443-803, South Korea.
Younghun
Y
Seong
Samsung Advanced Institute of Technology, Samsung Electronics, 130, Samsung-ro, Yeongtong-gu, Suwon-si, Gyeonggi-do, 443-803, South Korea.
Jongha
J
Lee
Samsung Advanced Institute of Technology, Samsung Electronics, 130, Samsung-ro, Yeongtong-gu, Suwon-si, Gyeonggi-do, 443-803, South Korea.
Kwang Eun
KE
Jang
Korea Electrotechnology Research Institute (KERI), 111, Hanggaul-ro, Sangnok-gu, Ansan-si, Gyeonggi-do, 426-170, South Korea.
Jaegu
J
Choi
Korea Electrotechnology Research Institute (KERI), 111, Hanggaul-ro, Sangnok-gu, Ansan-si, Gyeonggi-do, 426-170, South Korea.
Young Wook
YW
Choi
Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro, 43-gil, Songpa-gu, Seoul, 138-736, South Korea.
Hak Hee
HH
Kim
Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro, 43-gil, Songpa-gu, Seoul, 138-736, South Korea.
Hee Jung
HJ
Shin
Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro, 43-gil, Songpa-gu, Seoul, 138-736, South Korea.
Joo Hee
JH
Cha
Medical Imaging and Radiotherapeutics Laboratory, Department of Nuclear and Quantum Engineering, KAIST 291, Daehak-ro, Yuseong-gu, Daejeon 34141, South Korea.
Seungryong
S
Cho
Bio Imaging and Signal Processing Laboratory, Department of Bio and Brain Engineering, KAIST 291, Daehak-ro, Yuseong-gu, Daejeon 34141, South Korea.
Jong Chul
JC
Ye
D003196:Computer Graphics; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008327:Mammography / Q000379:methods*; D009010:Monte Carlo Method; D011856:Radiographic Image Enhancement / Q000379:methods*; D012542:Scattering, Radiation; D013997:Time Factors