13 March 2017 A simulation study of spectral Čerenkov luminescence imaging for tumour margin estimation
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Abstract
Breast cancer is the most common cancer in women in the world. Breast-conserving surgery (BCS) is a standard surgical treatment for breast cancer with the key objective of removing breast tissue, maintaining a negative surgical margin and providing a good cosmetic outcome. A positive surgical margin, meaning the presence of cancerous tissues on the surface of the breast specimen after surgery, is associated with local recurrence after therapy. In this study, we investigate a new imaging modality based on Cerenkov luminescence imaging (CLI) for the purpose of detecting positive surgical margins during BCS. We develop Monte Carlo (MC) simulations using the Geant4 nuclear physics simulation toolbox to study the spectrum of photons emitted given 18F-FDG and breast tissue properties. The resulting simulation spectra show that the CLI signal contains information that may be used to estimate whether the cancerous cells are at a depth of less than 1 mm or greater than 1 mm given appropriate imaging system design and sensitivity. The simulation spectra also show that when the source is located within 1 mm of the surface, the tissue parameters are not relevant to the model as the spectra do not vary significantly. At larger depths, however, the spectral information varies significantly with breast optical parameters, having implications for further studies and system design. While promising, further studies are needed to quantify the CLI response to more accurately incorporate tissue specific parameters and patient specific anatomical details.
Conference Presentation
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Nick Calvert, Nick Calvert, Yusef Helo, Yusef Helo, Thomy Mertzanidou, Thomy Mertzanidou, David S. Tuch, David S. Tuch, Simon R. Arridge, Simon R. Arridge, Danail Stoyanov, Danail Stoyanov, } "A simulation study of spectral Čerenkov luminescence imaging for tumour margin estimation", Proc. SPIE 10137, Medical Imaging 2017: Biomedical Applications in Molecular, Structural, and Functional Imaging, 1013717 (13 March 2017); doi: 10.1117/12.2254407; https://doi.org/10.1117/12.2254407
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