Anuj J Kapadia
Associate Professor in Radiology
Associate Professor of Physics (Secondary)
1) Experimental Implementation of NSECT
Neutron spectroscopy techniques are showing significant promise in determining element concentrations in the human body. We have developed a tomographic imaging system capable of generating tomographic images of the element concentration within a body through a single non-invasive in-vivo scan. This system has been implemented using a Van-de-Graaf accelerator fast neutron source and high-purity germanium gamma detectors at the Triangle Universities Nuclear Laboratory. This setup has been used to obtain NSECT scans for several samples such as bovine liver, mouse specimens and human breast tissue. In order to extract maximum information about a target sample with the lowest possible levels of dose, it is essential to maximize the sensitivity of the scanning system. In other words, the signal to noise ratio for the experimental setup must be maximized. This project aims at increasing the sensitivity of the NSECT system by understanding the various sources of noise and implementing techniques to reduce their effect. Noise in the system may originate from several factors such as the radiative background in the scanning room, and neutron scatter off of components of the system other than the target. Some of these effects can be reduced by using Time-of-Flight background reduction, while others can be reduced by acquiring a separate sample-out scan. Post processing background reduction techniques are also being developed for removing detector efficiency dependent noise. At this point we have acquired element information from whole mouse specimens and iron-overloaded liver models made of bovine liver tissue artificially injected with iron. Tomographic images have been generated from a solid iron and copper phantom. Our final goal is to implement a low-dose non-invasive scanning system for diagnosis of iron overload and breast cancer.
2) Monte-Carlo simulations in GEANT4
For each tomographic scan of a sample using NSECT, there are several acquisition parameters that can be varied. These parameters can broadly be classified into three categories: (i) Neutron Beam parameters: neutron flux, energy and beam width, (ii) Detector parameters: detector type, size, efficiency and location; (iii) Scanning Geometry: spatial and angular sampling rates. Due to the enormous number of combinations possible using these parameters, it is not feasible to investigate the effects of each parameter on the reconstructed image using a real neutron beam in the limited beam time available. A feasible alternative to this is to use Monte-Carlo simulations to reproduce the entire experiment in a virtual world. The effect of each individual parameter can then be studied using only computer processing time and resources. We use the high energy physics Monte-Carlo software package GEANT4, developed by CERN, which incorporates numerous tools required for building particle sources and detectors, and tracking particle interactions within them. The simulations built so far include the neutron source, HPGE and BGO gamma detectors, and several target materials such as iron, liver and breast tissue.
Characterizing, modeling, and mitigating texturing in X-ray diffraction imaging Phase 2, Year 6 awarded by Northeastern University (Co-Principal Investigator). 2017 to 2020
Simulation Tools for 3D and 4D CT and Dosimetry awarded by National Institutes of Health (Investigator). 2007 to 2019
Rapid X-ray diffraction imaging for improved tissue analysis in pathologic applications awarded by (Principal Investigator). 2017 to 2019
International Workshop on the Next Generation Gamma-ray Sources awarded by Department of Energy (Co-Principal Investigator). 2015 to 2017
Characterizing, modeling, and mitigating texturing in X-ray diffraction imaging awarded by Northeastern University (Co-Principal Investigator). 2017
Cross-disciplinary Training in Medical Physics awarded by National Institutes of Health (Mentor). 2007 to 2013
Stimulation to Evaluate Accuracy and Patient Dose in Neutron Stimulated Emission Computed Tomography (NSECT) awarded by (PI-Fellow). 2006 to 2009
Breast Elemental Composition Imaging awarded by National Institutes of Health (Graduate Student). 2004 to 2007
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Nacouzi, D., et al. “Smarter Cancer Detection Through Machine-Learning Applied to High-Resolution Diffraction Tissue Scanning.” Medical Physics, vol. 45, no. 6, WILEY, 2018, pp. E503–E503.
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