Premium
A novel pre‐processing technique for improving image quality in digital breast tomosynthesis
Author(s) -
Kim Hyeongseok,
Lee Taewon,
Hong Joonpyo,
Sabir Sohail,
Lee JungRyun,
Choi Young Wook,
Kim Hak Hee,
Chae Eun Young,
Cho Seungryong
Publication year - 2017
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 - 10.1002/mp.12078
Subject(s) - artificial intelligence , histogram , projection (relational algebra) , computer vision , iterative reconstruction , pixel , computer science , image processing , image quality , tomosynthesis , data set , offset (computer science) , pattern recognition (psychology) , mammography , algorithm , image (mathematics) , breast cancer , medicine , cancer , programming language
Purpose Nonlinear pre‐reconstruction processing of the projection data in computed tomography ( CT ) where accurate recovery of the CT numbers is important for diagnosis is usually discouraged, for such a processing would violate the physics of image formation in CT . However, one can devise a pre‐processing step to enhance detectability of lesions in digital breast tomosynthesis ( DBT ) where accurate recovery of the CT numbers is fundamentally impossible due to the incompleteness of the scanned data. Since the detection of lesions such as micro‐calcifications and mass in breasts is the purpose of using DBT , it is justified that a technique producing higher detectability of lesions is a virtue. Methods A histogram modification technique was developed in the projection data domain. Histogram of raw projection data was first divided into two parts: One for the breast projection data and the other for background. Background pixel values were set to a single value that represents the boundary between breast and background. After that, both histogram parts were shifted by an appropriate amount of offset and the histogram‐modified projection data were log‐transformed. Filtered‐backprojection ( FBP ) algorithm was used for image reconstruction of DBT . To evaluate performance of the proposed method, we computed the detectability index for the reconstructed images from clinically acquired data. Results Typical breast border enhancement artifacts were greatly suppressed and the detectability of calcifications and masses was increased by use of the proposed method. Compared to a global threshold‐based post‐reconstruction processing technique, the proposed method produced images of higher contrast without invoking additional image artifacts. Conclusions In this work, we report a novel pre‐processing technique that improves detectability of lesions in DBT and has potential advantages over the global threshold‐based post‐reconstruction processing technique. The proposed method not only increased the lesion detectability but also reduced typical image artifacts pronounced in conventional FBP ‐based DBT .