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Breast Tissue Decomposition Using Spectral CT After Distortion Correction

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H Ding

H Ding*, B Zhao , M Klopfer , F Masaki , P Baturin , S Molloi , University of California, Irvine, CA


TU-F-18C-1 Tuesday 4:30PM - 6:00PM Room: 18C

Purpose: To investigate the feasibility of accurate breast tissue compositional characterization by using spectral-distortion-corrected dual energy images from a photon-counting spectral CT.

Methods: Thirty eight postmortem breasts were imaged with a Cadmium-Zinc-Telluride (CZT)-based photon-counting spectral CT system at beam energy of 100 kVp. The energy-resolved detector sorted photons into low and high energy bins with a splitting energy of 42 keV. The estimated mean glandular dose (MGD) for each breast was approximately 2.0 mGy. Dual energy technique was used to decompose breast tissue into water, lipid, and protein contents. Two image-based methods were investigated to improve the accuracy of tissue compositional characterization. The first method simply limited the recorded spectra up to 90 keV. This reduced the pulse pile-up artifacts but it has some dose penalty. The second method corrected the spectral information of all measured photons by using a spectral distortion correction technique. Breasts were then chemically decomposed into their respective water, lipid, and protein contents, which was used as the reference standard. The accuracy of the tissue compositional measurement with spectral CT was evaluated by the root-mean-square (RMS) errors in percentage composition.

Results: The errors in quantitative material decomposition were significantly reduced after the appropriate image processing methods. As compared to the chemical analysis as the reference standard, the averages of the RMS errors were estimated to be 15.5%, 3.3%, and 2.8% for the raw, energy-limited, and spectral-corrected images, respectively.

Conclusion: Spectral CT can be used to accurately quantify the water, lipid, and protein contents in breast tissues by implementing a spectral distortion correction algorithm. The tissue compositional information can potentially improve the sensitivity and specificity for breast cancer diagnosis.

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