Application of boundary detection information in breast tomosynthesis reconstruction
Abstract
Digital tomosynthesis mammography (DTM) is one of the most promising techniques that can potentially improve early detection of breast cancers. DTM can provide three‐dimensional (3D) structural information by reconstructing the whole imaged volume from a sequence of projection‐view (PV) mammograms that are acquired at a small number of projection angles over a limited angular range. Our previous study showed that simultaneous algebraic reconstruction technique (SART) can produce satisfactory tomosynthesized image quality compared to maximum likelihood‐type algorithms. To improve the efficiency of DTM reconstruction and address the problem of boundary artifacts, we have developed methods to incorporate both two‐dimensional (2D) and 3D breast boundary information within the SART reconstruction algorithm in this study. A second generation GE prototype tomosynthesis mammography system with a stationary digital detector was used for PV image acquisition from 21 angles in 3° increments over a angular range. The 2D breast boundary curves on all PV images were obtained by automated segmentation and were used to restrict the SART reconstruction to be performed only within the breast volume. The computation time of SART reconstruction was reduced by 76.3% and 69.9% for cranio‐caudal and mediolateral oblique views, respectively, for the chosen example. In addition, a 3D conical trimming method was developed in which the 2D breast boundary curves from all PVs were back projected to generate the 3D breast surface. This 3D surface was then used to eliminate the multiple breast shadows outside the breast volume due to reconstruction by setting these voxels to a constant background value. Our study demonstrates that, by using the 2D and 3D breast boundary information, all breast boundary and most detector boundary artifacts can be effectively removed on all tomosynthesized slices.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Aug 22, 2007
- Source ID
- 10.1118/1.2761968
Entities
People
- Berkman Sahiner
- Chuan Zhou
- Heang‐ping Chan
- Jun Ge
- Jun Wei
- Lubomir M. Hadjiiski
- Yiheng Zhang
- Yi‐ta Wu
Organizations
- United States Army Medical Research and Development Command
- United States Public Health Service