Digital breast tomosynthesis (DBT) is being pursued by many medical device manufacturers, because its limited tomographic capability directly addresses a major short-coming of full-field digital mammography (FFDM). Namely, due to the nature of projection imaging, false positives can occur because overlapping normal tissue structures sometimes resemble tumors, and false negatives happen because tumors may be masked by normal tissue - particularly in dense breasts of young women. Many studies on DBT have been performed or ongoing and the general sense is that DBT does help for tumor detection, but it may perform worse than FFDM on microcalcification detection and characterization, which are imaging tasks that can indirectly indicate malignancy. The proposed research aims at optimizing image reconstruction algorithms for DBT. Our preliminary results indicate already, for example, that microcalcification imaging in DBT is limited by the sub-optimality of the image reconstruction algorithm: our initial investigations already yield a factor-of-two gain in microcalcification signal-to-noise ratio. As many of the current iterative image reconstruction (IIR) algorithms employed in DBT were essentially borrowed, with little modification, from nuclear medicine imaging there is clearly room for large gains in image quality for tailoring IIR to DBT. The fact that DBT devices are already undergoing clinical trials with sub-optimal image reconstruction algorithms increases the urgency of the proposed research. The goal of the research is to show that clinical utility of DBT can be enhanced significantly by tailoring IIR to application in DBT.
The aims of the proposed research are: (1) Construct a framework for IIR addressing the limited angular scan and fixed-dose trade-off of image noise and number of projections. (2) Design optimization-based approaches robust against projection truncation. (3) Develop data consistency checks and automated correction techniques for DBT projections. (4) Design image quality metrics of DBT images for algorithm optimization. (5) Evaluate algorithm performance with human observers.

Public Health Relevance

The proposed project Limited angle image reconstruction algorithms for digital breast tomosynthesis aims at investigating and developing iterative image reconstruction (IIR) algorithms specific to digital breast tomosynthesis (DBT), which is an unusual X-ray-based tomographic device with highly asymmetric resolution. The project will address projection data issues specific to DBT, such as projection truncation. The algorithms will be validated by human observer studies.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Project (R01)
Project #
5R01CA158446-02
Application #
8441525
Study Section
Special Emphasis Panel (ZRG1-BMIT-J (01))
Program Officer
Zhang, Huiming
Project Start
2012-04-01
Project End
2016-03-31
Budget Start
2013-04-01
Budget End
2014-03-31
Support Year
2
Fiscal Year
2013
Total Cost
$297,899
Indirect Cost
$102,849
Name
University of Chicago
Department
Radiation-Diagnostic/Oncology
Type
Schools of Medicine
DUNS #
005421136
City
Chicago
State
IL
Country
United States
Zip Code
60637
Zhang, Zheng; Ye, Jinghan; Chen, Buxin et al. (2016) Investigation of optimization-based reconstruction with an image-total-variation constraint in PET. Phys Med Biol 61:6055-84
Zhang, Zheng; Han, Xiao; Pearson, Erik et al. (2016) Artifact reduction in short-scan CBCT by use of optimization-based reconstruction. Phys Med Biol 61:3387-406
Xia, Dan; Langan, David A; Solomon, Stephen B et al. (2016) Optimization-based image reconstruction with artifact reduction in C-arm CBCT. Phys Med Biol 61:7300-7333
Rose, Sean; Andersen, Martin S; Sidky, Emil Y et al. (2015) Noise properties of CT images reconstructed by use of constrained total-variation, data-discrepancy minimization. Med Phys 42:2690-8
Sánchez, Adrian A (2015) Estimation of noise properties for TV-regularized image reconstruction in computed tomography. Phys Med Biol 60:7007-33
Jørgensen, Jakob S; Sidky, Emil Y; Hansen, Per Christian et al. (2015) EMPIRICAL AVERAGE-CASE RELATION BETWEEN UNDERSAMPLING AND SPARSITY IN X-RAY CT. Inverse Probl Imaging (Springfield) 9:431-446
Han, Xiao; Pearson, Erik; Pelizzari, Charles et al. (2015) Algorithm-enabled exploration of image-quality potential of cone-beam CT in image-guided radiation therapy. Phys Med Biol 60:4601-33
Graff, Christian G; Sidky, Emil Y (2015) Compressive sensing in medical imaging. Appl Opt 54:C23-44
Sidky, Emil Y; Kraemer, David N; Roth, Erin G et al. (2014) Analysis of iterative region-of-interest image reconstruction for x-ray computed tomography. J Med Imaging (Bellingham) 1:031007
Sanchez, Adrian A; Sidky, Emil Y; Pan, Xiaochuan (2014) Region of interest based Hotelling observer for computed tomography with comparison to alternative methods. J Med Imaging (Bellingham) 1:031010

Showing the most recent 10 out of 19 publications