Breast density is an independent risk factor for breast cancer, where women with denser breasts are more likely to
develop cancer. By identifying women at higher risk, healthcare providers can suggest screening at a younger age to
effectively diagnose and treat breast cancer in its earlier stages. Clinical risk assessment models currently do not
incorporate breast density, despite its strong correlation with breast cancer. Current methods to measure breast density rely
on mammography and MRI, both of which may be difficult to use as a routine risk assessment tool. We propose to use
diffuse optical tomography with structured-light to measure the dense, fibroglandular (FGT) tissue volume, which has a
different chromophore signature than the surrounding adipose tissue. To test the ability of this technique, we performed
simulations by creating numerical breast phantoms from segmented breast MR images. We looked at two different cases,
one with a centralized FGT distribution and one with a dispersed distribution. As expected, the water and lipid volumes
segmented at half-maximum were overestimated for the dispersed case. However, it was noticed that the recovered water
and lipid concentrations were lower and higher, respectively, than the centralized case. This information may provide
insight into the morphological distribution of the FGT and can be a correction in estimating the breast density.
We are developing a ballistic-photon based approach for improving the spatial resolution of fluorescence tomography
using time-domain measurements. This approach uses early photon information contained in measured time-of-fight
distributions originating from fluorescence emission. The time point spread functions (TPSF) from both excitation light
and emission light are acquired with gated single photon Avalanche detector (SPAD) and time-correlated single photon
counting after a short laser pulse. To determine the ballistic photons for reconstruction, the lifetime of the fluorophore
and the time gate from the excitation profiles will be used for calibration, and then the time gate of the fluorescence
profile can be defined by a simple time convolution. By mimicking first generation CT data acquisition, the sourcedetector
pair will translate across and also rotate around the subject. The measurement from each source-detector
position will be reshaped into a histogram that can be used by a simple back-projection algorithm in order to reconstruct
high resolution fluorescence images. Finally, from these 2D sectioning slides, a 3D inclusion can be reconstructed
accurately. To validate the approach, simulation of light transport is performed for biological tissue-like media with
embedded fluorescent inclusion by solving the diffusion equation with Finite Element Method using COMSOL
Multiphysics simulation. The reconstruction results from simulation studies have confirmed that this approach
drastically improves the spatial resolution of fluorescence tomography. Moreover, all the results have shown the
feasibility of this technique for high resolution small animal imaging up to several centimeters.
Fluorescence tomography is a non invasive, non ionizing imaging technique able to provide a 3D distribution of fluorescent
agents within thick highly scattering mediums, using low cost instrumentation. However, its low spatial resolution due to
undetermined and ill-posed nature of its inverse problem has delayed its integration into the clinical settings. In addition,
the quality of the fluorescence tomography images is degraded due to the excitation light leakage contaminating the
fluorescence measurements. This excitation light leakage results from the excitation photons that cannot be blocked by the
fluorescence filters. In this contribution, we present a new method to remove this excitation light leakage noise based on
the use of a temperature sensitive fluorescence agents. By performing different sets of measurements using this temperature
sensitive agents at multiple temperatures, the excitation light leakage can be estimated and then removed from the
measured fluorescence signals . The results obtained using this technique demonstrate its potential for use in in-vivo small
animal imaging.
Diffuse optical imaging with structured-light illumination and detection can provide rapid, wide-field anatomical and functional imaging of the breast with an application for breast cancer screening. Our aims for this study were to test the feasibility of structured-light, test our pattern set, and develop and optimize our image reconstruction algorithm. For our phantom studies, we created an agar phantom with dimensions similar to a compressed breast. A cubic inclusion of 30mm by 30mm by 25mm with twice the amount of absorption contrast than the background was placed at the center. Near-infrared light of eleven patterns including a full illumination and single stripes was illuminated onto the breast phantom and detected with a CCD camera, with integration of the signals according to the patterns performed post-data acquisition, with a total of 121 measurements. These measurements were then used in our reconstruction algorithm that iteratively minimized the difference between the collected data and the estimation from our FEM-based forward model of photon diffusion to calculate the absorption values. Reconstructions of the 3D absorption maps detect an inclusion at the center and indicate that our selected set of patterns may be sufficient for structured-light imaging. We are currently improving our instrumentation and testing with additional phantom studies, while also performing simulations of numerical breast phantoms created from MR images to test structured-light’s ability to image complex and realistic breast tissue composition. We hope to use this technique as optical method to image molecular markers, such as hemoglobin, water and lipid, within the breast.
Breast density is a risk factor for breast cancer and we propose using diffuse optical tomography with structured light
illuminations (SLI) to quantify the percentage of the fibroglandular (dense) tissue within the breast. Segmentations of
dense tissue from breast MRI cases were used to create a geometric model of the breast. COMSOL-generated Finite
Element Method (FEM) meshes were used for simulating photon migration through the breast tissue and reconstructing
the absorption maps. In these preliminary simulations, the absorption coefficients of the non-dense and dense tissue were
assigned using literature values based on their concentrations of water, lipid, oxy- and deoxyhemoglobin as they are the
main chromophores, or absorbers of light, within the breast. Synthetic SLI measurements were obtained using a FEMbased
forward solver. During the simulation, 12 distinct patterns consisting of vertical stripes, horizontal stripes, and
checkerboard patterns were used for illumination and detection. Using these simulated measurements, FEM-based
inverse solvers were used to reconstruct the 3D absorption maps. In this study, the methods are applied to reconstruct
the absorption maps for multiple wavelengths (780nm, 830nm, 900nm, 1000nm) using one case as an example. We are
currently continuing these simulations with additional cases and reconstructing 3D concentration maps of the
chromophores within the dense and non-dense breast tissue.
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