Library based x‐ray scatter correction for dedicated cone beam breast CT. Issue 8 (15th July 2016)

• Record Type: Journal Article
• Authors: Shi, Linxi; Vedantham, Srinivasan; Karellas, Andrew; Zhu, Lei
• Is Part Of: Medical physics. Volume 43:Issue 8(2016)Part 1
• Page Start: 4529
• Page End: 4544
• Publication Date: 2016-07-15
• Subjects: biological organs -- biological tissues -- cancer -- computerised tomography -- image reconstruction -- image representation -- medical image processing -- Monte Carlo methods -- reliability; Computed tomography -- Cancer -- Probability theory, stochastic processes, and statistics; Computerised tomographs -- Biological material, e.g. blood, urine; Haemocytometers -- Digital computing or data processing equipment or methods, specially adapted for specific applications -- Image data processing or generation, in general; breast -- computed tomography -- cone‐beam breast CT -- scatter correction -- Monte Carlo simulation; X‐ray scattering -- Monte Carlo methods -- X‐ray detectors -- Photons -- Computed tomography -- Medical image reconstruction -- Photon scattering -- Medical image artifacts -- Tissues; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1118/1.4955121
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : Purpose: The image quality of dedicated cone beam breast CT (CBBCT) is limited by substantial scatter contamination, resulting in cupping artifacts and contrast‐loss in reconstructed images. Such effects obscure the visibility of soft‐tissue lesions and calcifications, which hinders breast cancer detection and diagnosis. In this work, we propose a library‐based software approach to suppress scatter on CBBCT images with high efficiency, accuracy, and reliability. Methods: The authors precompute a scatter library on simplified breast models with different sizes using the geant4 ‐based Monte Carlo (MC) toolkit. The breast is approximated as a semiellipsoid with homogeneous glandular/adipose tissue mixture. For scatter correction on real clinical data, the authors estimate the breast size from a first‐pass breast CT reconstruction and then select the corresponding scatter distribution from the library. The selected scatter distribution from simplified breast models is spatially translated to match the projection data from the clinical scan and is subtracted from the measured projection for effective scatter correction. The method performance was evaluated using 15 sets of patient data, with a wide range of breast sizes representing about 95% of general population. Spatial nonuniformity (SNU) and contrast to signal deviation ratio (CDR) were used as metrics for evaluation. Results: Since the time‐consuming MC simulation for library generation is precomputed, the authors' method efficiently corrects for scatter with minimal processing time. Furthermore, the authors find that a scatter library on a simple breast model with only one input parameter, i.e., the breast diameter, sufficiently guarantees improvements in SNU and CDR. For the 15 clinical datasets, the authors' method reduces the average SNU from 7.14% to 2.47% in coronal views and from 10.14% to 3.02% in sagittal views. On average, the CDR is improved by a factor of 1.49 in coronal views and 2.12 in sagittal views. Conclusions: The library‐based scatter correction does not require increase in radiation dose or hardware modifications, and it improves over the existing methods on implementation simplicity and computational efficiency. As demonstrated through patient studies, the authors' approach is effective and stable, and is therefore clinically attractive for CBBCT imaging.