Peer-reviewed journals

Liu, T., Hong, G., and Cai, W., “A comparative study of effective atomic number calculations for dual‐energy CT,” Medical Physics, 48(10), pp. 5908-5923, (2021). [download]

Adam, D. P., Liu, T., Caracappa, P. F., Bednarz, B. P., and Xu, X. G., “New capabilities of the Monte Carlo dose engine ARCHER-RT: clinical validation of the Varian TrueBeam machine for VMAT external beam radiotherapy.,” Medical Physics, (2020). [download]

Cai, W., Liu, T., Xue, X., Luo, G., Wang, X., Shen, Y., Fang, Q., Sheng, J., Chen, F., and Liang, T., “CT Quantification and Machine-learning Models for Assessment of Disease Severity and Prognosis of COVID-19 Patients,” Academic Radiology, (2020). [download]

Gao, Y., Mahmood, U., Liu, T., Quinn, B., Gollub, M., Xu, X. G., and Dauer, L. T., “Patient-specific organ and effective dose estimates in adult oncologic CT,” American Journal of Roentgenology, 214(4), pp. 738-746, (2020). [download]

Peng, Z., Fang, X., Yan, P., Shan, H., Liu, T., Pei, X., Wang, G., Liu, B., Kalra, M. K., and Xu, X. G., “A method of rapid quantification of patient‐specific organ doses for CT using deep‐learning based multi‐organ segmentation and GPU‐accelerated Monte Carlo dose computing,” Medical Physics, (2020). [download]

Mao, L., Liu, T., Caracappa, P. F., Lin, H., Gao, Y., Dauer, L. T., and Xu, X. G., “Influences of operator head posture and protective eyewear on eye lens doses in interventional radiology: a Monte Carlo study,” Medical Physics, 46(6), pp. 2744-2751, (2019). [download]

Peng, Z., Shan, H., Liu, T., Pei, X., Wang, G., and Xu, X. G., “MCDNet – a denoising convolutional neural network to accelerate Monte Carlo radiation transport simulations: a proof of principle with patient dose from x-ray CT imaging,” IEEE Access, 7, pp. 76680 – 76689, (2019). [download]

Lin, H., Liu, T., Shi, C., Petillion, S., Kindts, I., Weltens, C., Depuydt, T., Song, Y., Saleh, Z., and Xu, X. G., “Feasibility study of individualized optimal positioning selection for left‐sided whole breast radiotherapy: DIBH or prone,” Journal of applied clinical medical physics, 19(2), pp. 218-229, (2018). [download]

Pi, Y., Liu, T., and Xu, X. G., “Development of a set of mesh-based and age-dependent Chinese phantoms and application for CT dose calculations,” Radiation protection dosimetry, (2018). [download]

Liu, T., Wolfe, N., Carothers, C. D., Ji, W., and Xu, X. G., “Optimizing the Monte Carlo neutron cross-section construction code, XSBench, for MIC and GPU platforms,” Nuclear Science and Engineering, 185(1), pp. 232-242, (2017). [download]

Liu, T., Xu, X. G., and Carothers, C. D., “Comparison of two accelerators for Monte Carlo radiation transport calculations, NVIDIA Tesla M2090 GPU and Intel Xeon Phi 5110p coprocessor: a case study for x-ray CT imaging dose calculation,” Annals of Nuclear Energy, 82, pp. 230-239, (2015). [download]

Xu, X. G., Liu, T., Su, L., Du, X., Riblett, M., Ji, W., Gu, D., Carothers, C. D., Shephard, M. S., Brown, F. B., Kalra, M. K., and Liu, B., “ARCHER, a new Monte Carlo software tool for emerging heterogeneous computing environments,” Annals of Nuclear Energy, 82, pp. 2-9, (2015). [download]

Su, L., Yang, Y., Bednarz, B., Sterpin, E., Du, X., Liu, T., Ji, W., and Xu, X. G., “ARCHER-RT–A GPU-based and photon-electron coupled Monte Carlo dose computing engine for radiation therapy: software development and application to helical tomotherapy,” Medical Physics, 41(7), p. 071709, (2014). [download]

Zhang, D., Padole, A., Li, X., Singh, S., Khawaja, R. D. A., Lira, D., Liu, T., Shi, J. Q., Otrakji, A., Kalra, M. K., Xu, X. G., and Liu, B., “In vitro dose measurements in a human cadaver with abdomen/pelvis CT scans,” Medical Physics, 41(9), p. 091911, (2014). [download]

Ding, A., Mille, M., Liu, T., Caracappa, P. F., and Xu, X. G., “Extension of RPI-adult male and female computational phantoms to obese patients and a Monte Carlo study of the effect on CT imaging dose,” Physics in Medicine and Biology, 57(9), pp. 2441-2459, (2012). [download]

 

PhD thesis

Liu, T., “Development of ARCHER — a parallel Monte Carlo radiation transport code — for x-ray CT dose calculations using GPU and coprocessor technologies,” PhD, Mechanical, Aerospace, and Nuclear Engineering, Rensselaer Polytechnic Institute, Troy, New York, (2014). [download]

 

Conference abstracts and full papers

[1]          Cai, W., Liu, T., Luo, G., Xue, X., Wang, X., and Chen, F., “CT Imaging of COVID-19 Pneumonia and Its Impacts on Patient Management,” in Radiological Society of North America (RSNA) 2020, Chicago, IL, USA, (2020).

[2]          Adam, D., Liu, T., Caracappa, P., Xu, X. G., and Bednarz, B., “Implementation and benchmarking of volumetric modulated Arc therapy (VMAT) modeling in the GPU-based high-performance Monte Carlo code ARCHER (forthcoming),” Medical Physics, (2019).

[3]          Liu, T., Wolfe, N., Lin, H., Carothers, C. D., and Xu, X. G., “Performance study of atomic tally methods for GPU-accelerated Monte Carlo dose calculation,” in 2019 American Nuclear Society (ANS) Annual Meeting, Minneapolis, MN, USA, (2019).

[4]          Peng, Z., Fang, X., Shan, H., Liu, T., Pei, X., Yan, P., Wang, G., Liu, B., and Xu, X. G., “Multi-organ segmentation of CT images using deep-learning for instant and patient-specific dose reporting (forthcoming),” Medical Physics, (2019).

[5]          Adam, D., Lin, H., Liu, T., Caracappa, P., Xu, X., and Bednarz, B., “Implementation of heterogeneous computing methods and development of an EGSnrc-based external beam dose engine for validating a GPU-based Monte Carlo code, ARCHER,” Medical Physics, 45(6), pp. E444-E445, (2018).

[6]          Lin, H., Adam, D. P., Liu, T., Caracappa, P. F., Bednarz, B. P., and Xu, a. X. G., “Development of ARCHER towards clinical use: modeling and simulation of Varian LINAC for radiation therapy dose calculations,” in 20th Topical Meeting of the Radiation Protection and Shielding Division of the American Nuclear Society 2018, Santa Fe, NM, USA, (2018).

[7]          Liu, T., Wolfe, N., Lin, H., Carothers, C. D., and Xu, X. G., “Performance study of atomic tally methods for GPU-accelerated Monte Carlo dose calculation,” in 20th Topical Meeting of the Radiation Protection and Shielding Division of the American Nuclear Society 2018, Santa Fe, NM, USA, (2018). [download]

[8]          Mao, L., Liu, T., Gao, Y., Dauer, L. T., Caracappa, P. F., and Xu, X. G., “Occupational radiation protection of radiologists and technicians performing fluoroscopically-guided interventional procedures – an investigation of posture and movement effects,” Health Physics, 115(Supplement 1 1), p. S5, (2018).

[9]          Lin, H., Liu, T., Shi, C., Tang, X., Pei, X., and Xu, X. G., “Automatic lung cancer detection from CT using a GPU-accelerated deep convolutional neural networks,” Medical Physics, 44(6), p. 3178, (2017).

[10]        Lin, H., Liu, T., Su, L., Shi, C., Tang, X., Adam, D., Bednarz, B., and Xu, X. G., “Monte Carlo modeling and simulation of the Varian TrueBeam LINAC using heterogeneous computing,” Medical Physics, 44(6), p. 3003, (2017).

[11]        Liu, T., Lin, H., Bednarz, B., Shi, C., Tang, X., and Xu, X. G., “Fast Monte Carlo source modeling and dose calculation for magnetic-resonance imaging-guided radiation therapy (MRIgRT),” presented at the 6th International Workshop on Computational Human Phantoms (CP2017), Annapolis, Maryland, USA, (2017).

[12]        Liu, T., Lin, H., Yang, L., Liu, H., Wang, Z., Pei, X., Chen, Z., and Xu, X. G., “Fast dose calculation for magnetic-resonance imaging-guided radiation therapy (MRIgRT) using GPU-based Monte Carlo code ARCHER,” Medical Physics, 44(6), p. 3131, (2017).

[13]        Mao, L., Liu, T., Gao, Y., Dauer, L. T., Caracappa, P. F., and Xu, X. G., “A study of eye lens dose of interventional radiologist wearing protective eye glasses using fast Monte Carlo simulation code — ARCHER,” Health Physics, 113(Supplement 1 1), p. S83, (2017).

[14]        Mao, L., Liu, T., Lin, H., Caracappa, P., Gao, Y., Dauer, L., and Xu, X. G., “A study of dose to the eye Lens of interventional radiologist using MCNP code and multi resolution phantom coupled with eyeglasses model,” Medical Physics, 44(6), p. 3120, (2017).

[15]        Mao, L., Liu, T., Lin, H., Caracappa, P. F., Gao, Y., Dauer, L. T., and Xu, X. G., “Radiologist phantom with a high-resolution eye model for interventional radiology simulation,” presented at the 6th International Workshop on Computational Human Phantoms (CP2017), Annapolis, Maryland, USA, (2017).

[16]        Tang, X., Lin, H., Liu, T., Shi, C., Petillion, S., Kindts, I., and Xu, X. G., “Feasibility study of a feature based prediction for optimal position selection for left-sided breast radiotherapy,” Medical Physics, 44(6), p. 3039, (2017).

[17]        Yang, L., Liu, T., Lin, H., Liu, H., Wang, Z., Pei, X., Chen, Z., and Xu, X. G., “The dosimetric impact of MRI magnetic field on external-beam therapy using GPU-based rapid Monte Carlo code ARCHER,” in 5th Magnetic Resonance (MR) in Radiation Therapy (RT) symposium 2017, Sydney, Australia, (2017).

[18]        Lin, H., Liu, T., Shi, C., Petillion, S., Kindts, I., Tang, X., and Xu, X. G., “Model based classification for optimal position selection for left-sided breast radiotherapy: free breathing, DIBH, or prone,” Medical Physics, 43(6), pp. 3629–3630, (2016).

[19]        Lin, H., Liu, T., Su, L., Bednarz, B., Caracappa, P., and Xu, X. G., “Modeling of radiotherapy Linac source terms using ARCHER Monte Carlo code: performance comparison for GPU and MIC parallel computing devices,” in 13th International Conference on Radiation Shielding & 19th Topical Meeting of the Radiation Protection and Shielding Division (ICRS-13 & RPSD 2016), France, Paris, (2016).

[20]        Liu, T., Lin, H., Gao, Y., Caracappa, P., Wang, G., Cong, W., and Xu, X. G., “Radiation dose simulation for a newly proposed dynamic bowtie filters for CT using fast Monte Carlo methods,” Medical Physics, 43(6), p. 3861, (2016).

[21]        Liu, T., Lin, H., Su, L., Shi, C., Tang, X., Bednarz, B., and Xu, X. G., “Modeling of radiotherapy Linac source terms using ARCHER Monte Carlo code: performance comparison of GPU and MIC computing accelerators,” Medical Physics, 43(6), p. 3732, (2016).

[22]        Liu, T., Wolfe, N., Lin, H., Zieb, K., Ji, W., Caracappa, P., Carothers, C. D., and Xu, X. G., “Performance study of Monte Carlo codes on Xeon Phi coprocessors — testing MCNP 6.1 and profiling ARCHER geometry module on the FS7ONNi problem,” in 13th International Conference on Radiation Shielding & 19th Topical Meeting of the Radiation Protection and Shielding Division (ICRS-13 & RPSD 2016), France, Paris, (2016).

[23]        Gao, Y., Lin, H., Liu, T., Li, X., Liu, B., Khawaja, R., Kalra, M., Caracappa, P., and Xu, X. G., “Simulation study of patient off-centering effect on organ dose in chest CT scan,” Medical Physics, 42(6), p. 3544, (2015).

[24]        Gao, Y., Liu, T., Li, X., Liu, B., Kalra, M., Caracappa, P., and Xu, X. G., “A preliminary method of risk-informed optimization of tube current modulation for dose reduction in CT,” Medical Physics, 42(6), p. 3622, (2015).

[25]        Lin, H., Gao, Y., Liu, T., Gelblum, D., Ho, A., Powell, S., Tang, X., and Xu, X. G., “Towards quantitative clinical decision on Deep Inspiration Breath Hold (DIBH) or prone for left-sided breast irradiation,” Medical Physics, 42(6), p. 3529, (2015).

[26]        Liu, H., Liu, T., Xu, X. G., Wu, J., and Zhuo, W., “Eye lens dose reduction from CT scan using organ based tube current modulation,” Medical Physics, 42(6), p. 3250, (2015).

[27]        Liu, T., Lin, H., Caracappa, P. F., and Xu, X. G., “Extension of a GPU/MIC based Monte Carlo Code, ARCHER, to internal radiation dose calculations,” Health Physics, 109(Supplement 1), p. S56, (2015).

[28]        Liu, T., Lin, H., Xu, X. G., and Stabin, M., “Development of a nuclear medicine dosimetry module for the GPU-based Monte Carlo code ARCHER,” Medical Physics, 42(6), p. 3661, (2015).

[29]        Liu, T., Su, L., Du, X., Lin, H., Zieb, K., Ji, W., Caracappa, P., and Xu, X. G., “Parallel Monte Carlo methods for heterogeneous hardware computer systems using GPUs and coprocessors: recent development of ARCHER code (invited talk),” in American Nuclear Society (ANS) Annual Meeting 2015, San Antonio, TX, USA, (2015).

[30]        Liu, T., Wolfe, N., Carothers, C. D., Ji, W., and Xu, X. G., “Optimizing the Monte Carlo neutron cross-section construction code, XSBench, to MIC and GPU platforms,” in Joint International Conference on Mathematics and Computation (M&C), Supercomputing in Nuclear Applications (SNA) and the Monte Carlo (MC) Method (M&C+SNA+MC 2015), Nashville, TN, USA, (2015).

[31]        Liu, T., Wolfe, N., Carothers, C. D., Ji, W., and Xu, X. G., “Status of ARCHER — A Monte Carlo Code for the High-Performance Heterogeneous Platforms Involving GPU and MIC,” in Joint International Conference on Mathematics and Computation (M&C), Supercomputing in Nuclear Applications (SNA) and the Monte Carlo (MC) Method (M&C+SNA+MC 2015), Nashville, TN, USA, (2015).

[32]        Liu, T., Wolfe, N., Carothers, C. D., and Xu, X. G., “Development of a medical physics Monte Carlo radiation transport code ARCHER,” in GPU Technology Conference 2015, San Jose, CA, USA, (2015).

[33]        Liu, T., Wolfe, N., Su, L., Carothers, C. D., Bednarz, B., and Xu, X. G., “Near real-time GPU and MIC-based Monte Carlo code ARCHER for radiation dose calculations in voxelized and mesh phantoms,” presented at the 5th International Workshop on Computational Human Phantoms (CP2015), Seoul, Korea, (2015).

[34]        Pi, Y., Feng, M., Huo, W., Zhang, L., Liu, T., Lin, H., Yang, L., Zheng, F., Tan, H., Pan, F., Chen, Z., and Xu, X. G., “Development of mesh-based age-dependent family phantoms,” presented at the 5th International Workshop on Computational Human Phantoms (CP2015), Seoul, Korea, (2015).

[35]        Wolfe, N., Carothers, C. D., Liu, T., and Xu, X. G., “Concurrent CPU, GPU and MIC execution algorithms for ARCHER Monte Carlo code involving photon and neutron radiation transport problems,” in Joint International Conference on Mathematics and Computation (M&C), Supercomputing in Nuclear Applications (SNA) and the Monte Carlo (MC) Method (M&C+SNA+MC 2015), Nashville, TN, USA, (2015).

[36]        Du, X., Liu, T., Su, L., Caracappa, P. F., and Xu, X. G., “Extension of ARCHER Monte Carlo code to health physics dosimetry and shielding design: preliminary results,” Health Physics, 107(Supplement 1), p. S38, (2014).

[37]        Du, X., Liu, T., Su, L., Ji, W., Caracappa, P. F., and Xu, X. G., “Development of CSG-based radiation shielding module for ARCHER: preliminary results for photons,” in Radiation Protection and Shielding Division of the American Nuclear Society 2014, Knoxville, TN, USA, (2014).

[38]        Huo, W., Liu, T., Su, L., Du, X., Chen, Z., and Xu, X. G., “Comparisons of dosimetric accuracy and calculation time of ARCHER and MCNP5 codes for the Ir-192 brachytherapy case,” in Radiation Protection and Shielding Division of the American Nuclear Society 2014, Knoxville, TN, USA, (2014).

[39]        Lin, H., Liu, T., Su, L., Du, X., Gao, Y., Caracappa, P. F., and Xu, X. G., “Formation of computational phantoms from CT numbers for use in the ARCHER Monte Carlo code,” Health Physics, 107(Supplement 1), p. S98, (2014).

[40]        Liu, T., Du, X., Su, L., Gao, Y., Ji, W., Zhang, D., Shi, J. Q., Liu, B., Kalra, M. K., and Xu, X. G., “Monte Carlo CT dose calculation: a comparison between experiment and simulation using ARCHER-CT,” Medical Physics, 41(6), p. 424, (2014).

[41]        Liu, T., Du, X., Su, L., Gao, Y., Ji, W., Zhang, D., Shi, J. Q., Liu, B., Kalra, M. K., and Xu, X. G., “Testing of ARCHER-CT, a fast Monte Carlo Code for CT dose calculation: experiment versus simulation,” Transactions of the American Nuclear Society, 110, p. 481, (2014).

[42]        Liu, T., Du, X., Su, L., Ji, W., and Xu, X. G., “Development of ARCHER-CT, a fast Monte Carlo code for patient-specific CT dose calculations using Nvidia GPU and Intel coprocessor technologies,” in GPU Technology Conference 2014, San Jose, CA, USA, (2014).

[43]        Liu, T., Su, L., Du, X., Caracappa, P. F., and Xu, X. G., “Comparison of accuracy and speed of ARCHER with MCNP for organ dose calculations from external photon beams under standard irradiation geometries,” Health Physics, 107(Supplement 1), p. S114, (2014).

[44]        Liu, T., Su, L., Du, X., Lin, H., Zieb, K., Ji, W., Caracappa, P., and Xu, X. G., “Parallel Monte Carlo methods for heterogeneous hardware computer systems using GPUs and coprocessors: recent development of ARCHER code,” in Radiation Protection and Shielding Division of the American Nuclear Society 2014, Knoxville, TN, USA, (2014).

[45]        Wolfe, N., Liu, T., Carothers, C., and Xu, X. G., “Heterogeneous concurrent execution of Monte Carlo photon transport on CPU, GPU and MIC,” in Proceedings of the 4th Workshop on Irregular Applications: Architectures and Algorithms, (2014), pp. 49-52. [download]

[46]        Du, X., Liu, T., Ji, W., Xu, X. G., and Brown, F. B., “Evaluation of vectorized Monte Carlo algorithms on GPUs for a neutron eigenvalue problem,” in Proceedings of International Conference on Mathematics and Computational Methods Applied to Nuclear Science & Engineering (M&C 2013), Sun Valley, Idaho, USA, (2013), pp. 2513-2522.

[47]        Du, X., Liu, T., Su, L., Riblett, M., and Xu, X. G., “A hardware accelerator based fast Monte Carlo code for radiation dosimetry: software design and preliminary results,” Medical Physics, 40(6), p. 475, (2013).

[48]        Liu, T., Du, X., Ji, W., Xu, X. G., and Brown, F. B., “A comparative study of history-based versus vectorized Monte Carlo methods in the GPU/CUDA environment for a simple neutron eigenvalue problem,” in Joint International Conference on Supercomputing in Nuclear Applications and Monte Carlo (SNA & MC 2013), Paris, France, (2013). [download]

[49]        Liu, T., Du, X., and Xu, X. G., “Affordable supercomputer-based Monte Carlo CT dose calculations: a hardware comparison between Nvidia M2090 GPU and Intel Xeon Phi 5110p coprocessor,” Medical Physics, 40(6), p. 459, (2013).

[50]        Liu, T., Ji, W., and Xu, X. G., “Development of GPU-based Monte Carlo code for fast CT imaging dose calculation on CUDA Fermi architecture,” in International Conference on Mathematics and Computational Methods Applied to Nuclear Science and Engineering (M&C 13), Sun Valley, ID, (2013).

[51]        Liu, T., Xu, X. G., and Carothers, C. D., “Comparison of two accelerators for Monte Carlo radiation transport calculations, NVIDIA Tesla M2090 GPU and Intel Xeon Phi 5110p coprocessor: a case study for x-ray CT imaging dose calculation,” in Joint International Conference on Supercomputing in Nuclear Applications and Monte Carlo (SNA & MC 2013), Paris, France, (2013).

[52]        Riblett, M. J., Liu, T., Ji, W., and Xu, X. G., “Use of hardware accelerators for Monte Carlo-based neutron radiation transport: a preliminary study,” Health Physics, 105(Supplement 1), p. S99, (2013).

[53]        Su, L., Du, X., Liu, T., and Xu, X. G., “A fast Monte Carlo electron transport code for dose calculations using the GPU accelerator,” Health Physics, 105(Supplement 1), p. S41, (2013).

[54]        Su, L., Du, X., Liu, T., and Xu, X. G., “Fast Monte Carlo electron-photon transport code using hardware accelerators: preliminary results for brachytherapy and radionuclide therapy cases,” Medical Physics, 40(6), p. 397, (2013).

[55]        Su, L., Du, X., Liu, T., and Xu, X. G., “GPU-accelerated Monte Carlo electron transport methods: development and application for radiation dose calculations using six GPU cards,” in Joint International Conference on Supercomputing in Nuclear Applications and Monte Carlo (SNA & MC 2013), Paris, France, (2013).

[56]        Xu, X. G., Liu, T., Su, L., Du, X., Riblett, M., Ji, W., and Brown, F. B., “An update of ARCHER, a Monte Carlo radiation transport software testbed for emerging hardware such as GPUs,” Transactions of the American Nuclear Society, 108, pp. 433-434, (2013).

[57]        Xu, X. G., Liu, T., Su, L., Du, X., Riblett, M. J., Ji, W., Gu, D., Carothers, C. D., Shephard, M. S., Brown, F. B., Kalra, M. K., and Liu, B., “ARCHER, a new Monte Carlo software tool for emerging heterogeneous computing environments,” in Joint International Conference on Supercomputing in Nuclear Applications and Monte Carlo (SNA & MC 2013), Paris, France, (2013).

[58]        Zhang, D., Cai, W., Li, X., Liu, T., and Liu, B., “A comparison of radiation dose to the colon between single-energy and dual-energy CT colonography,” in Radiological Society of North America (RSNA) 2013, 99th Scientific Assembly and Annual Meeting, Chicago, IL, USA, (2013).

[59]        Liu, T., Ding, A., Ji, W., Xu, X. G., Carothers, C. D., and Brown, F. B., “A Monte Carlo neutron transport code for eigenvalue calculations on a dual-GPU system and CUDA environment,” in International Topical Meeting on Advances in Reactor Physics (PHYSOR 2012), Knoxville, TN, USA, (2012).

[60]        Liu, T., Ding, A., and Xu, X. G., “GPU-based Monte Carlo methods for accelerating radiographic and CT imaging dose calculations: feasibility and scalability,” Medical Physics, 39(6), p. 3876, (2012).

[61]        Liu, T., Ding, A., and Xu, X. G., “Accelerated Monte Carlo methods for photon dosimetry using a dual-GPU system and CUDA,” Medical Physics, 39(6), p. 3818, (2012).

[62]        Liu, T., Su, L., Ding, A., Ji, W., Carothers, C. D., and Xu, X. G., “GPU/CUDA-ready parallel Monte Carlo codes for reactor analysis and other applications,” Transactions of the American Nuclear Society, 106, pp. 378-379, (2012).

[63]        Su, L., Liu, T., Ding, A., and Xu, X. G., “A GPU/CUDA based Monte Carlo code for proton transport: preliminary results of proton depth dose in water,” Medical Physics, 39(6), p. 3945, 2012 (2012).

[64]        Su, L., Liu, T., Ding, A., and Xu, X. G., “GPU/CUDA-based Monte Carlo methods for radiation protection dose calculations involving X-ray and proton sources,” Health Physics, 103(Supplement 1), p. S78, (2012).

[65]        Ding, A., Liu, T., Liang, C., Ji, W., Shepard, M. S., Xu, X. G., and Brown, F. B., “Evaluation of speedup of Monte Carlo calculations of simple reactor physics problems coded for the GPU/CUDA environment,” in International Conference on Mathematics and Computational Methods Applied to Nuclear Science and Engineering (M&C 11), Rio de Janeiro, Brazil, (2011).

[66]        Liu, T., Ding, A., Caracappa, P. F., and Xu, X. G., “Modeling of obese individuals using automatic deformation of mesh-based computational phantoms,” Health Physics, 101(Supplement 1), p. S34, (2011).

[67]        Mille, M., Ding, A., Liu, T., Na, Y., Caracappa, P. F., and Xu, X. G., “The effect of patient obesity on PET/CT imaging dose using a phantom with a body mass index of 45,” Health Physics, 101 (Supplement 1), p. S31, (2011).

[68]        Xu, X. G. and Liu, T., “Quantifying uncertainty in radiation protection dosimetry using statistical phantoms,” in The 3rd International Workshop on Computational Phantoms for Radiation Protection, Imaging and Radiotherapy, Beijing, China, (2011).

[69]        Liu, T., Mille, M., Caracappa, P. F., Xu, X. G., Nour, S., and Inn, K., “A software solution to bioassay detector calibration using a library of virtual phantoms,” Health Physics, 99(Supplement 1), p. S78, (2010).