A multimodal breast cancer imaging system using coregistered dynamic diffuse optical tomography and digital breast tomosynthesis
Author(s)
Zimmermann, Bernhard B
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Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Advisor
David A. Boas and Elfar Adalsteinsson.
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Diffuse optical tomography (DOT) is an emerging noninvasive functional imaging method for breast cancer diagnosis and neoadjuvant chemotherapy monitoring. In particular, the multimodal approach of combining DOT with x-ray digital breast tomosynthesis (DBT) is especially synergistic as DBT prior information can be used to enhance the DOT reconstruction. DOT, in turn, provides a functional information overlay onto the mammographic images, increasing sensitivity and specificity to cancer pathology. We describe a dynamic DOT apparatus designed for tight integration with commercial DBT scanners and providing a fast (1 Hz+) image acquisition rate to enable tracking of hemodynamic changes induced by the mammographic breast compression. The most significant advance enabling fast acquisition was the design and construction of a direct analog-to-digital conversion frequency-domain near-infrared spectroscopy (FD-NIRS) component. It achieves simultaneous dual wavelength operation at 685 nm and 830 nm by concurrent 67.5 MHz and 75 MHz frequency modulation of each laser source, respectively, followed by digitization using a high-speed analog to digital converter and real-time hybrid FPGA-assisted demodulation by discrete Fourier transform (DFT). The overall DOT system integrates 96 CW-NIRS and 24 FD-NIRS source locations, as well as 32 CW-NIRS and 20 FD-NIRS detection locations into low-profile plates that mate to the DBT compression paddle and x-ray detector cover, respectively. The plates and the embedded optical fibers are made of plastic to minimize x-ray absorption and thus allow true simultaneous acquisition of the DBT image. We first characterize each major system component individually, and then demonstrate overall performance using static and dynamic tissue-like phantoms, as well as in vivo images acquired from the pool of patients recalled for breast biopsies at the Massachusetts General Hospital Breast Imaging Division.
Description
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017. Cataloged from PDF version of thesis. Page 102 blank. Includes bibliographical references (pages 87-101).
Date issued
2017Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer SciencePublisher
Massachusetts Institute of Technology
Keywords
Electrical Engineering and Computer Science.