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This project focuses on the development of a novel method of breast cancer detection called Gamma Stimulated Spectroscopy (GSS). This technique is highly sensitive to early stage metabolic changes in tumors and has the ability to quantify specific malignancy-related elements in a tumor through a quick and low dose scan. In this project, we aim to design, implement and optimize a prototype of the GSS imaging system and determine its accuracy and efficacy in breast cancer detection. In the first year of the project, we designed and assembled a preliminary prototype of the GSS system using the free-electron laser at Duke University and acquired experimental spectra from calibration phantoms of elements associated with breast cancer. In addition, we initiated the development of a Monte-Carlo simulation model of the GSS imaging system including the gamma source and detectors. In the second year of the project, we continued the development of the GEANT4 simulation to model and refine the physics interactions governing GSS and investigated advanced detector systems to improve system sensitivity. In addition, we investigated the GSS radiation dose using whole body and breast dosimetry models irradiated with gamma beams. Simulated populations of breast tumor patients were scanned virtually to generate images of six individual malignancyrelated trace elements per phantom. The images showed excellent correlation between the location of the tumor and the concentration of the cancer-marking element in the breast. This data will now be used to identify the best candidate elements for cancer detection. In addition, we also initiated the development of a classifier algorithm to identify an optimal signature of cancer based on a combination of elements. We will now begin to optimize the GSS acquisition system for cancer detection through a targeted element search using the computer classifier algorithm., The original document contains color images.

We are developing a tomographic technique called Neutron Stimulated Emission Computed Tomography (NSECT) for early detection of breast cancer. NSECT is sensitive to metabolic changes in trace element concentrations that are seen in tumors at very early stages of development. Detecting and measuring these element concentrations has the potential to detect breast cancer very early. Using neutrons as the imaging radiation leads to significant concerns about patient dose due to their higher weighting factor. While preliminary experiments show that it is possible to perform NSECT scans with patient dose comparable to mammography, one of the key aspects of successful clinical translation is to deliver the minimum dose possible. This project aims at evaluating the effects of NSECT dose-reduction techniques on the accuracy of detecting breast cancer. Four dose-reduction techniques are under evaluation: reducing neutron flux; reducing spatial projections; reducing angular positions; and using multiple detectors. As separate evaluation of each factor using experimental studies is prohibitively time consuming, these evaluations are performed using Monte-Carlo simulations as a feasible alternative. In the first year of the study, we developed a Monte Carlo simulation of the NSECT tomographic scanning system in GEANT4 along with phantoms of benign and malignant breast tissue. The simulated system has now been used to generate simulated data from NSECT scans of the benign and malignant breast. The simulations have been tested and validated against experimentally acquired data from several different types of phantoms. Tomographic images are reconstructed using a maximum-likelihood algorithm, which has been used to generate diagnostic images of a single-element disease model based on iron distribution. These results demonstrate that NSECT has the ability to detect cancer-marking elements in the breast at reasonable levels of patient dose.

We are developing a tomographic technique called Neutron Stimulated Emission Computed Tomography (NSECT) for early detection of breast cancer. NSECT is sensitive to metabolic changes in trace element concentrations that are seen in tumors at very early stages of development. Detecting and measuring these element concentrations has the potential to detect breast cancer early. Using neutrons as the imaging radiation leads to significant concerns about patient dose. While preliminary experiments show that it is possible to perform NSECT scans with dose similar to that of an abdominal CT scan, one of our primary goals in this project is to reduce dose further to allow NSECT to be more easily accepted as a screening tool. This project aims at evaluating the effects of NSECT dose-reduction techniques on the accuracy of cancer diagnosis. There are 4 dose reduction techniques under evaluation: reducing neutron flux; using fewer spatial projections; fewer angles; and using multiple detectors. As evaluation of each technique individually using experimental studies is not feasible, this study uses Monte-Carlo simulations as an alternative. In the first part of the study, we have designed and built a Monte Carlo simulation of the NSECT tomographic scanning system using GEANT4. We have designed the scanning system as a 3 part system consisting of: (a) a neutron beam source with user-defined neutron flux and neutron beam width, (b) a gantry with user-defined spatial and angular beam sampling rates, and (c) gamma detectors with user-defined detector material, radius, efficiency and location. We have also built GEANT4 phantoms for benign and malignant breast tissue based on element concentrations reported in various experimental studies. Finally, we have integrated the phantoms and tomographic system to simulate a complete NSECT scan of the human breast., The original document contains color images.