Dr. Joseph M. Harms Profile

Dr. Joseph M. Harms

at Winship Cancer Institute of Emory Univ

SPIE Involvement:

Author

Publications (9)

Proceedings Article | 15 February 2021 Presentation + Paper

Accurate characterization of metal implants and human materials using novel proton counting detector for Monte Carlo dose calculation in proton therapy

Serdar Charyyev, Chih-Wei Chang, Joseph Harms, Cristina Oancea, S. Tim Yoon, Xiaofeng Yang, Tiezhi Zhang, Jun Zhou, Shuai Leng, Liyong Lin

Proceedings Volume 11595, 115950B (2021) https://doi.org/10.1117/12.2581751

KEYWORDS: Metals, Sensors, Monte Carlo methods, Spine, Tissues, Spectral imaging cameras, Particles, Aluminum

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Owing to poor characterization of implant and adjacent human tissues, the presence of metal implants has been shown to be a risk factor for clinical results for proton therapy. In this project we have developed a way of characterizing implant and human materials in terms of water-equivalent thicknesses (WET) and relative stopping power (RSP) using a novel proton counting detector. We tracked each proton using a fast spectral imaging camera AdvaPIX-TPX3 which operated in energy mode measures collected energy per-voxel to derive the deposited energy along the particle track across the voxelated sensor. We considered three scenarios: sampling of WET of a CIRS M701 Adult Phantom (CMAP) at different locations; measurements of energy perturbations in the CMAP implanted with metal rods; sampling of WET of a more complex spine phantom. WET and RSP information were extracted from energy spectra at position along the central axis by using the shift in the most probable energy (MPE) from the reference energy (either initial incident energy or energy without a metal implant). Measurements were compared to TOPAS simulation results. Measured WET of the CMAP ranged from 18.63 to 25.23 cm depending on the location of the sampling which agreed with TOPAS simulation results within 1.6%. The RSPs of metals from CMAP perturbation measurements were determined as 1.97, 2.98, and 5.44 for Al, Ti and CoCr, respectively, which agreed with TOPAS within 2.3%. RSPs for material composition of a more complex spine phantom yielded 1.096, 1.309 and 1.001 for Acrylic, PEEK and PVC, respectively. In summary, this work has shown a method to accurately characterize RSPs of metal and human materials of CMAP implanted with metals and a complex spine phantom. Using the data obtained by the proposed method, it may be possible to validate RSP maps provided by conventional photon computed tomography techniques. Owing to poor characterization of implant and adjacent human tissues, the presence of metal implants has been shown to be a risk factor for clinical results for proton therapy. In this project we have developed a way of characterizing implant and human materials in terms of water-equivalent thicknesses (WET) and relative stopping power (RSP) using a novel proton counting detector. We tracked each proton using a fast spectral imaging camera AdvaPIX-TPX3 which operated in energy mode measures collected energy per-voxel to derive the deposited energy along the particle track across the voxelated sensor. We considered three scenarios: sampling of WET of a CIRS M701 Adult Phantom (CMAP) at different locations; measurements of energy perturbations in the CMAP implanted with metal rods; sampling of WET of a more complex spine phantom. WET and RSP information were extracted from energy spectra at position along the central axis by using the shift in the most probable energy (MPE) from the reference energy (either initial incident energy or energy without a metal implant). Measurements were compared to TOPAS simulation results. Measured WET of the CMAP ranged from 18.63 to 25.23 cm depending on the location of the sampling which agreed with TOPAS simulation results within 1.6%. The RSPs of metals from CMAP perturbation measurements were determined as 1.97, 2.98, and 5.44 for Al, Ti and CoCr, respectively, which agreed with TOPAS within 2.3%. RSPs for material composition of a more complex spine phantom yielded 1.096, 1.309 and 1.001 for Acrylic, PEEK and PVC, respectively. In summary, this work has shown a method to accurately characterize RSPs of metal and human materials of CMAP implanted with metals and a complex spine phantom. Using the data obtained by the proposed method, it may be possible to validate RSP maps provided by conventional photon computed tomography techniques.

Proceedings Article | 16 March 2020 Paper

Deep learning-based relative stopping power mapping generation with cone-beam CT in proton radiation therapy

Tonghe Wang, Joseph Harms, Yang Lei, Beth Ghavidel, William Stokes, Tian Liu, Walter Curran, Mark McDonald, Jun Zhou, Xiaofeng Yang

Proceedings Volume 11312, 113124L (2020) https://doi.org/10.1117/12.2549275

KEYWORDS: Computed tomography, Radiotherapy, Therapeutics, Cancer, Tissues, Calibration, Medical physics, Monte Carlo methods, Image quality, Image fusion

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Proceedings Article | 16 March 2020 Paper

Organ-at-Risk (OAR) segmentation in head and neck CT using U-RCNN

Yang Lei, Joseph Harms, Xue Dong, Tonghe Wang, Xiangyang Tang, David Yu, Jonathan Beitler, Walter Curran, Tian Liu, Xiaofeng Yang

Proceedings Volume 11314, 1131444 (2020) https://doi.org/10.1117/12.2549782

KEYWORDS: Image segmentation, Computed tomography, Cancer, Head, Neck, Radiotherapy, Tissues, Oncology, Tumors, 3D modeling

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Proceedings Article | 16 March 2020 Paper

Stopping power map estimation from dual-energy CT using deep convolutional neural network

Tonghe Wang, Yang Lei, Joseph Harms, Yingzi Liu, Beth Ghavidel, Liyong Lin, Jonathan Beilter, Walter Curran, Tian Liu, Jun Zhou, Xiaofeng Yang

Proceedings Volume 11312, 113124M (2020) https://doi.org/10.1117/12.2549357

KEYWORDS: Computed tomography, Radiotherapy, Image segmentation, Tissues, X-ray computed tomography, Medical physics, Dual energy imaging, Physics, Biology, Image fusion

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Proceedings Article | 16 March 2020 Paper

Weakly supervised multi-needle detection in 3D ultrasound images with bidirectional convolutional sparse coding

Yupei Zhang, Joseph Harms, Yang Lei, Tonghe Wang, Tian Liu, Ashesh Jani, Walter Curran, Pretesh Patel, Xiaofeng Yang

Proceedings Volume 11319, 1131914 (2020) https://doi.org/10.1117/12.2549283

KEYWORDS: Computed tomography, Associative arrays, 3D image processing, Prostate, 3D modeling, Process modeling, Reconstruction algorithms, Ultrasonography, High dynamic range imaging, Visualization

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Proceedings Article | 28 February 2020 Paper

Left ventricular myocardium segmentation in coronary computed tomography angiography using 3D deep attention u-net

Xiuxiu He, Bangjun Guo, Yang Lei, Joseph Harms, Tonghe Wang, Tian Liu, Longjiang Zhang, Xiaofeng Yang

Proceedings Volume 11317, 113172H (2020) https://doi.org/10.1117/12.2559638

KEYWORDS: Image segmentation, Computed tomography, Medical imaging, Chemical vapor deposition, Angiography, Heart, 3D image processing, Magnetic resonance imaging, Prostate, Arteries

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Proceedings Article | 15 March 2019 Paper

A learning-based automatic segmentation method on left ventricle in SPECT imaging

Tonghe Wang, Yang Lei, Haipeng Tang, Joseph Harms, Cheng Wang, Tian Liu, Walter Curran, Weihua Zhou, Dianfu Li, Xiaofeng Yang

Proceedings Volume 10953, 109531M (2019) https://doi.org/10.1117/12.2512554

KEYWORDS: Image segmentation, Single photon emission computed tomography, Magnetic resonance imaging, Gated imaging, Machine learning, Computed tomography, Binary data, Heart, Cancer, 3D image processing

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Proceedings Article | 1 March 2019 Paper

MRI-based pseudo CT generation using classification and regression random forest

Yang Lei, Tonghe Wang, Joseph Harms, Ghazal Shafai-Erfani, Sibo Tian, Kristin Higgins, Hui-Kuo Shu, Hyunsuk Shim, Hui Mao, Walter Curran, Tian Liu, Xiaofeng Yang

Proceedings Volume 10948, 1094843 (2019) https://doi.org/10.1117/12.2512560

KEYWORDS: Magnetic resonance imaging, Computed tomography, Bone, Radiotherapy, Image segmentation, Feature extraction, X-ray computed tomography, Medical physics, Radiation oncology, Classification systems

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Proceedings Article | 1 March 2019 Presentation + Paper

Image quality improvement in cone-beam CT using deep learning

Yang Lei, Tonghe Wang, Joseph Harms, Ghazal Shafai-Erfani, Xue Dong, Jun Zhou, Pretesh Patel, Xiangyang Tang, Tian Liu, Walter Curran, Kristin Higgins, Xiaofeng Yang

Proceedings Volume 10948, 1094827 (2019) https://doi.org/10.1117/12.2512545

KEYWORDS: Computed tomography, Image quality, Radiotherapy, Image restoration, Convolution, Cancer, Gallium nitride, Neural networks, Signal to noise ratio, Data acquisition

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Showing 5 of 9 publications

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