Anuj J Kapadia

Anuj J Kapadia

Associate Professor in Radiology

Associate Professor of Physics (Secondary)

Office Location: 
2424 Erwin Road, Suite 302, Ravin Advanced Imaging Laboratories, Durham, NC 27705
Front Office Address: 
Box 2731 Med Ctr, Duke University Medical Center, Durham, NC 27710
(919) 684-1442


My research focuses on developing an innovative imaging modality - Neutron Stimulated Emission Computed Tomography (NSECT), that uses inelastic scattering through fast neutrons to generate tomographic images of the body's element composition. Such information is vital in diagnosing a variety of disorders ranging from iron and copper overload in the liver to several cancers. Specifically, there are two ongoing projects:

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.

Education & Training

  • Ph.D., Duke University 2007

Selected Grants

Simulation Tools for 3D and 4D CT and Dosimetry awarded by National Institutes of Health (Investigator). 2007 to 2024

Characterizing, modeling, and mitigating texturing in X-ray diffraction imaging Phase 2, Year 6 awarded by Northeastern University (Co-Principal Investigator). 2017 to 2020

Rapid X-ray diffraction imaging for improved tissue analysis in pathologic applications awarded by North Carolina Biotechnology Center (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 United States Army Medical Research and Materiel Command (PI-Fellow). 2006 to 2009

Breast Elemental Composition Imaging awarded by National Institutes of Health (Graduate Student). 2004 to 2007

Sharma, Shobhit, et al. “A real-time Monte Carlo tool for individualized dose estimations in clinical CT.Phys Med Biol, vol. 64, no. 21, Nov. 2019, p. 215020. Pubmed, doi:10.1088/1361-6560/ab467f. Full Text

Hoye, Jocelyn, et al. “Organ doses from CT localizer radiographs: Development, validation, and application of a Monte Carlo estimation technique.Med Phys, vol. 46, no. 11, Nov. 2019, pp. 5262–72. Pubmed, doi:10.1002/mp.13781. Full Text Open Access Copy

Abadi, E., et al. “Development of a scanner-specific simulation framework for photon-counting computed tomography.” Biomedical Physics and Engineering Express, vol. 5, no. 5, Aug. 2019. Scopus, doi:10.1088/2057-1976/ab37e9. Full Text

Abadi, Ehsan, et al. “DukeSim: A Realistic, Rapid, and Scanner-Specific Simulation Framework in Computed Tomography.Ieee Trans Med Imaging, vol. 38, no. 6, June 2019, pp. 1457–65. Pubmed, doi:10.1109/TMI.2018.2886530. Full Text

Abadi, Ehsan, et al. “Modeling "Textured" Bones in Virtual Human Phantoms.Ieee Trans Radiat Plasma Med Sci, vol. 3, no. 1, Jan. 2019, pp. 47–53. Pubmed, doi:10.1109/TRPMS.2018.2828083. Full Text

Zhu, Zheyuan, et al. “X-ray diffraction tomography with limited projection information.Sci Rep, vol. 8, no. 1, Jan. 2018, p. 522. Pubmed, doi:10.1038/s41598-017-19089-w. Full Text

Odinaka, Ikenna, et al. “Joint System and Algorithm Design for Computationally Efficient Fan Beam Coded Aperture X-Ray Coherent Scatter Imaging.” Ieee Transactions on Computational Imaging, vol. 3, no. 4, Institute of Electrical and Electronics Engineers (IEEE), Dec. 2017, pp. 506–21. Crossref, doi:10.1109/tci.2017.2721742. Full Text

Fu, Wanyi, et al. “Breast dose reduction with organ-based, wide-angle tube current modulated CT.J Med Imaging (Bellingham), vol. 4, no. 3, July 2017, p. 031208. Pubmed, doi:10.1117/1.JMI.4.3.031208. Full Text

Hoye, Jocelyn, et al. “Organ dose variability and trends in tomosynthesis and radiography.J Med Imaging (Bellingham), vol. 4, no. 3, July 2017, p. 031207. Pubmed, doi:10.1117/1.JMI.4.3.031207. Full Text

Lakshmanan, Manu N., et al. “Accuracy assessment and characterization of x-ray coded aperture coherent scatter spectral imaging for breast cancer classification.J Med Imaging (Bellingham), vol. 4, no. 1, Jan. 2017, p. 013505. Pubmed, doi:10.1117/1.JMI.4.1.013505. Full Text


Samei, E., et al. “Virtual imaging trials: An emerging experimental paradigm in imaging research and practice.” Progress in Biomedical Optics and Imaging  Proceedings of Spie, vol. 11312, 2020. Scopus, doi:10.1117/12.2549818. Full Text

Abadi, E., et al. “Trade-off between spatial details and motion artifact in multi-detector CT: A virtual clinical trial with 4D textured human models.” Progress in Biomedical Optics and Imaging  Proceedings of Spie, vol. 10948, 2019. Scopus, doi:10.1117/12.2512891. Full Text

Sharma, S., et al. “A comprehensive GPU-based framework for scatter estimation in single source, dual source, and photon-counting CT.” Progress in Biomedical Optics and Imaging  Proceedings of Spie, vol. 10948, 2019. Scopus, doi:10.1117/12.2513198. Full Text

Abadi, E., et al. “A framework for realistic virtual clinical trials in photon counting computed tomography.” Progress in Biomedical Optics and Imaging  Proceedings of Spie, vol. 10948, 2019. Scopus, doi:10.1117/12.2512898. Full Text

Fu, W., et al. “Multi-organ segmentation in clinical-computed tomography for patient-specific image quality and dose metrology.” Progress in Biomedical Optics and Imaging  Proceedings of Spie, vol. 10948, 2019. Scopus, doi:10.1117/12.2512883. Full Text

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.