The objective of the project is to develop a digital specimen tomosynthesis (DST) system, based upon the clinical specimen radiography (SR) system for performing rapid, volumetric imaging of breast lumpectomy specimen in or near a surgical suite. Even though SR is used currently for surgical positive-margin evaluation, pathological examination guidance, and other clinical/preclinical applications, its utility is limied by the lack of volumetric information about specimens in SR images. The proposed DST can yield useful volumetric information about the specimen by virtue of which overlapping anatomic structures can be discerned, yet involving only minimal modifications to clinical SR. It can accomplish the imaging task within 1-2 minutes, an acceptable clinical time for a rapid evaluation of the specimen in identifying positive margins in and near an operating suite, thus reducing the frequency of positive margins being identified, and of consequent re- excisions, post surgically. It can also be used for improving accuracy in pathologic examinations by providing guidance to the pathologist to focus on regions in the specimen with the highest probability of positivity, thus increasing the sensitivity of the pathology process by reducing the incidence of missed positive regions due to suboptimal sampling. Because multiple SR images, including the ones used in current clinic evaluation, are collected in DST imaging, the DST thus retains the full functionality of current SR, and would not alter the workflow in the operating room, but only enhance the quality of information obtained from imaging, leading to improved decision making. Because the DST is designed based upon an inexpensive SR system that is used widely in breast-surgery suites, it provides an economical, practical solution which can readily be disseminated. Knowledge gained in the project can be exploited for the design and development of advanced, application- specific emerging X-ray-based tomographic imaging systems in image-guided surgery and radiation therapy, and in extremity imaging.
The specific aims of the project are (1) to develop DST for rapid, volumetric imaging of lumpectomy specimen; (2) to develop algorithms for enabling DST imaging of lumpectomy specimen; and (3) to evaluate the performance of DST-scan configurations/algorithms.

Public Health Relevance

The objective of the program is to develop a digital specimen tomosynthesis (DST) imaging system for acquiring volumetric information of lumpectomy and surgical specimens. The DST can add significant value to the intra-operative evaluation of surgical specimens by enhancing immediate, accurate identification of positive margins in and near surgical suites, thereby reducing the frequency of positive margins being found postoperatively and consequent post-surgical re-excision. It can also find other clinical and preclinical applications, including guidance to pathologists to target on regions within a specimen of high likelihood of being positive margins, thus improving the sensitivity of the pathologic examination, by reducing the incidence of missed positive regions due to current suboptimal sampling in lumpectomy and surgical specimens.

Agency
National Institute of Health (NIH)
Institute
National Institute of Biomedical Imaging and Bioengineering (NIBIB)
Type
Research Project (R01)
Project #
5R01EB018102-03
Application #
9085109
Study Section
Biomedical Imaging Technology Study Section (BMIT)
Program Officer
Shabestari, Behrouz
Project Start
2014-09-01
Project End
2018-05-31
Budget Start
2016-06-01
Budget End
2017-05-31
Support Year
3
Fiscal Year
2016
Total Cost
Indirect Cost
Name
University of Chicago
Department
Radiation-Diagnostic/Oncology
Type
Schools of Medicine
DUNS #
005421136
City
Chicago
State
IL
Country
United States
Zip Code
60637
Xia, Dan; Chang, Yu-Bing; Manak, Joe et al. (2018) Reduction of Angularly-Varying-Data Truncation in C-Arm CBCT Imaging. Sens Imaging 19:
Zhang, Zheng; Rose, Sean; Ye, Jinghan et al. (2018) Optimization-Based Image Reconstruction From Low-Count, List-Mode TOF-PET Data. IEEE Trans Biomed Eng 65:936-946
Chen, Buxin; Zhang, Zheng; Xia, Dan et al. (2018) Algorithm-enabled partial-angular-scan configurations for dual-energy CT. Med Phys 45:1857-1870
Aggrawal, Hari Om; Andersen, Martin S; Rose, Sean et al. (2018) A Convex Reconstruction Model for X-ray Tomographic Imaging with Uncertain Flat-fields. IEEE Trans Comput Imaging 4:17-31
Rose, Sean D; Sanchez, Adrian A; Sidky, Emil Y et al. (2017) Investigating simulation-based metrics for characterizing linear iterative reconstruction in digital breast tomosynthesis. Med Phys 44:e279-e296
Schmidt, Taly Gilat; Barber, Rina Foygel; Sidky, Emil Y (2017) A Spectral CT Method to Directly Estimate Basis Material Maps From Experimental Photon-Counting Data. IEEE Trans Med Imaging 36:1808-1819
Chen, Buxin; Zhang, Zheng; Sidky, Emil Y et al. (2017) Image reconstruction and scan configurations enabled by optimization-based algorithms in multispectral CT. Phys Med Biol 62:8763-8793
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
Pearson, Erik; Pan, Xiaochuan; Pelizzari, Charles (2016) Dynamic intensity-weighted region of interest imaging for conebeam CT. J Xray Sci Technol 24:361-77
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

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