Publications

Peer-reviewed journals

[1]          H. Lin, T. Liu, C. Shi, S. Petillion, I. Kindts, C. Weltens, T. Depuydt, Y. Song, Z. Saleh, X. G. Xu, and X. Tang, “Feasibility study of individualized optimal positioning selection for left-sided whole breast radiotherapy: DIBH or prone (submitted),” Journal of Applied Clinical Medical Physics, (2017).

[2]          T. Liu, N. Wolfe, C. D. Carothers, W. Ji, and X. G. Xu, “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]

[3]          T. Liu, X. G. Xu, and C. D. Carothers, “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]

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

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

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

[7]          A. Ding, M. Mille, T. Liu, P. F. Caracappa, and X. G. Xu, “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]

 

Conference abstracts (oral presentations and posters)

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

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

[3]          T. Liu, H. Lin, B. Bednarz, C. Shi, X. Tang, and X. G. Xu, “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).

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

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

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

[7]          L. Mao, T. Liu, H. Lin, P. F. Caracappa, Y. Gao, L. T. Dauer, and X. G. Xu, “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).

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

[9]          L. Yang, T. Liu, H. Lin, H. Liu, Z. Wang, X. Pei, Z. Chen, and X. G. Xu, “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).

[10]        H. Lin, T. Liu, C. Shi, S. Petillion, I. Kindts, X. Tang, and X. G. Xu, “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).

[11]        H. Lin, T. Liu, L. Su, B. Bednarz, P. Caracappa, and X. G. Xu, “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).

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

[13]        T. Liu, H. Lin, L. Su, C. Shi, X. Tang, B. Bednarz, and X. G. Xu, “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).

[14]        T. Liu, N. Wolfe, H. Lin, K. Zieb, W. Ji, P. Caracappa, C. D. Carothers, and X. G. Xu, “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).

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

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

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

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

[19]      T. Liu, H. Lin, P. F. Caracappa, and X. G. Xu, “Extension of a GPU/MIC based Monte Carlo Code, ARCHER, to internal radiation dose calculations,” Health Physics, 109(S1), p. S56, (2015).

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

[21]        T. Liu, L. Su, X. Du, H. Lin, K. Zieb, W. Ji, P. Caracappa, and X. G. Xu, “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).

[22]        T. Liu, N. Wolfe, C. D. Carothers, W. Ji, and X. G. Xu, “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).

[23]        T. Liu, N. Wolfe, C. D. Carothers, W. Ji, and X. G. Xu, “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).

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

[25]        T. Liu, N. Wolfe, L. Su, C. D. Carothers, B. Bednarz, and X. G. Xu, “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).

[26]        N. Wolfe, C. D. Carothers, T. Liu, and X. G. Xu, “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).

[27]        X. Du, T. Liu, L. Su, P. F. Caracappa, and X. G. Xu, “Extension of ARCHER Monte Carlo code to health physics dosimetry and shielding design: preliminary results,” Health Physics, 107(S1), p. S38, (2014).

[28]        X. Du, T. Liu, L. Su, W. Ji, P. F. Caracappa, and X. G. Xu, “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).

[29]        W. Huo, T. Liu, L. Su, X. Du, Z. Chen, and X. G. Xu, “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).

[30]        H. Lin, T. Liu, L. Su, X. Du, Y. Gao, P. F. Caracappa, and X. G. Xu, “Formation of computational phantoms from CT numbers for use in the ARCHER Monte Carlo code,” Health Physics, 107(S1), p. S98, (2014).

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

[32]        T. Liu, X. Du, L. Su, Y. Gao, W. Ji, D. Zhang, J. Q. Shi, B. Liu, M. K. Kalra, and X. G. Xu, “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).

[33]        T. Liu, X. Du, L. Su, W. Ji, and X. G. Xu, “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).

[34]        T. Liu, L. Su, X. Du, P. F. Caracappa, and X. G. Xu, “Comparison of accuracy and speed of ARCHER with MCNP for organ dose calculations from external photon beams under standard irradiation geometries,” Health Physics, 107(S1), p. S114, (2014).

[35]        T. Liu, L. Su, X. Du, H. Lin, K. Zieb, W. Ji, P. Caracappa, and X. G. Xu, “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).

[36]        N. Wolfe, T. Liu, C. Carothers, and X. G. Xu, “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.

[37]        X. Du, T. Liu, W. Ji, X. G. Xu, and F. B. Brown, “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.

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

[39]        T. Liu, X. Du, W. Ji, X. G. Xu, and F. B. Brown, “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).

[40]        T. Liu, X. Du, and X. G. Xu, “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).

[41]        T. Liu, W. Ji, and X. G. Xu, “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).

[42]        T. Liu, X. G. Xu, and C. D. Carothers, “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).

[43]        M. J. Riblett, T. Liu, W. Ji, and X. G. Xu, “Use of hardware accelerators for Monte Carlo-based neutron radiation transport: a preliminary study,” Health Physics, 105(S1), p. S99, (2013).

[44]        L. Su, X. Du, T. Liu, and X. G. Xu, “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).

[45]        L. Su, X. Du, T. Liu, and X. G. Xu, “A fast Monte Carlo electron transport code for dose calculations using the GPU accelerator,” Health Physics, 105(S1), p. S41, (2013).

[46]        L. Su, X. Du, T. Liu, and X. G. Xu, “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).

[47]        X. G. Xu, T. Liu, L. Su, X. Du, M. Riblett, W. Ji, and F. B. Brown, “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).

[48]        X. G. Xu, T. Liu, L. Su, X. Du, M. J. Riblett, W. Ji, D. Gu, C. D. Carothers, M. S. Shephard, F. B. Brown, M. K. Kalra, and B. Liu, “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).

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

[50]        T. Liu, A. Ding, W. Ji, X. G. Xu, C. D. Carothers, and F. B. Brown, “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).

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

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

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

[54]      L. Su, T. Liu, A. Ding, and X. G. Xu, “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).

[55]        L. Su, T. Liu, A. Ding, and X. G. Xu, “GPU/CUDA-based Monte Carlo methods for radiation protection dose calculations involving X-ray and proton sources,” Health Physics, 103(S1), p. S78, (2012).

[56]        X. G. Xu, L. Su, T. Liu, and A. Ding, “GPU-based Monte Carlo method for medical physics applications: preliminary results for x-ray and proton applications,” in World Congress on Medical Physics and Biomedical Engineering (WC 2012), Beijing, China, (2012).

[57]        A. Ding, T. Liu, C. Liang, W. Ji, M. S. Shepard, X. G. Xu, and F. B. Brown, “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).

[58]        T. Liu, A. Ding, P. F. Caracappa, and X. G. Xu, “Modeling of obese individuals using automatic deformation of mesh-based computational phantoms,” Health Physics, 101(S1), p. S34, (2011).

[59]        M. Mille, A. Ding, T. Liu, Y. Na, P. F. Caracappa, and X. G. Xu, “The effect of patient obesity on PET/CT imaging dose using a phantom with a body mass index of 45,” Health Physics, 101 (S1), p. S31, (2011).

[60]        X. G. Xu and T. Liu, “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).

[61]        T. Liu, M. Mille, P. F. Caracappa, X. G. Xu, S. Nour, and K. Inn, “A software solution to bioassay detector calibration using a library of virtual phantoms,” Health Physics, 99(S1), p. S78, (2010).

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