The reference publication for RTK is
Rit, S., Vila Oliva, M., Brousmiche, S., Labarbe, R., Sarrut, D., & Sharp, G. C. (2014). The Reconstruction Toolkit (RTK), an open-source cone-beam CT reconstruction toolkit based on the Insight Toolkit (ITK). Journal of Physics: Conference Series, 489, 012079. https://doi.org/10.1088/1742-6596/489/1/012079
The following articles have used and cited RTK
Ivy Chan, Y. C., Li, M., Thummerer, A., Parodi, K., Belka, C., Kurz, C., & Landry, G. (2024). Minimum imaging dose for deep learning-based pelvic synthetic computed tomography generation from cone beam images. Physics and Imaging in Radiation Oncology, 100569. https://doi.org/10.1016/j.phro.2024.100569
Keeler, A., Lehmann, M., Luce, J., Kaur, M., Roeske, J., & Kang, H. (2024). Technical note: TIGRE‐DE for the creation of virtual monoenergetic images from dual‐energy cone‐beam CT. Medical Physics. Portico. https://doi.org/10.1002/mp.17002
Wei, C., Albrecht, J., Rit, S., Laurendeau, M., Thummerer, A., Corradini, S., Belka, C., Steininger, P., Ginzinger, F., Kurz, C., Riboldi, M., & Landry, G. (2024). Reduction of cone‐beam CT artifacts in a robotic CBCT device using saddle trajectories with integrated infrared tracking. Medical Physics, 51(3), 1674–1686. Portico. https://doi.org/10.1002/mp.16943
Belotti, G., Fattori, G., Baroni, G., & Rit, S. (2023). Extension of the cone‐beam CT field‐of‐view using two complementary short scans. Medical Physics. Portico. https://doi.org/10.1002/mp.16869
Martín-Luna, P., Esperante, D., Fernández Prieto, A., Fuster-Martínez, N., García Rivas, I., Gimeno, B., Ginestar, D., González-Iglesias, D., Hueso, J. L., Llosá, G., Martinez-Reviriego, P., Meneses-Felipe, A., Riera, J., Vázquez Regueiro, P., & Hueso-González, F. (2024). Simulation of electron transport and secondary emission in a photomultiplier tube and experimental validation. Sensors and Actuators A: Physical, 365, 114859. https://doi.org/10.1016/j.sna.2023.114859
Messner, I. M., Keuschnigg, P., Stöllinger, B., Kraihamer, M., Coste‐Marin, J., Huber, P., Kellner, D., Kreuzeder, E. M., Steininger, P., & Deutschmann, H. (2023). Investigating focal spot position drift in a mobile imaging system equipped with a monobloc‐based x‐ray generator. Medical Physics. Portico. https://doi.org/10.1002/mp.16859
Belotti, G., Rossi, M., Pella, A., Cerveri, P., & Baroni, G. (2023). A new system for in-room image guidance in particle therapy at CNAO. Physica Medica, 114, 103162. https://doi.org/10.1016/j.ejmp.2023.103162
Hellwege, L., Schaar, M., Buzug, T. M., & Stille, M. (2023). Enhancing virtual monoenergetic images for non-congruent dual-energy CT projection data. Current Directions in Biomedical Engineering, 9(1), 674–677. https://doi.org/10.1515/cdbme-2023-1169
Dillon, O., Reynolds, T., & O’Brien, R. T. (2023). X-ray source arrays for volumetric imaging during radiotherapy treatment. Scientific Reports, 13(1). https://doi.org/10.1038/s41598-023-36708-x
Clark, D. P., & Badea, C. T. (2023). MCR toolkit: A GPU‐based toolkit for multi‐channel reconstruction of preclinical and clinical x‐ray CT data. Medical Physics, 50(8), 4775–4796. Portico. https://doi.org/10.1002/mp.16532
Vijayakumar, J., Goudarzi, N. M., Eeckhaut, G., Schrijnemakers, K., Cnudde, V., & Boone, M. N. (2023). Characterization of Pharmaceutical Tablets by X-ray Tomography. Pharmaceuticals, 16(5), 733. https://doi.org/10.3390/ph16050733
Rossi, M., Belotti, G., Baroni, G., & Cerveri, P. (2023). Feasibility of Proton Dosimetry Overriding Planning CT with Daily CBCT Elaborated through Generative Artificial Intelligence Tools. https://doi.org/10.20944/preprints202304.0596.v1
Lee, H., Cheon, B.-W., Feld, J. W., Grogg, K., Perl, J., Ramos-Méndez, J. A., Faddegon, B., Min, C. H., Paganetti, H., & Schuemann, J. (2023). TOPAS-imaging: extensions to the TOPAS simulation toolkit for medical imaging systems. Physics in Medicine & Biology, 68(8), 084001. https://doi.org/10.1088/1361-6560/acc565
Mouchet, M., Létang, J. M., Lesaint, J., & Rit, S. (2023). Cone-Beam Pair-Wise Data Consistency Conditions in Helical CT. IEEE Transactions on Medical Imaging, 42(10), 2853–2864. https://doi.org/10.1109/tmi.2023.3265812
Noblet, B., Chabanas, M., Rouzé, S., & Voros, S. (2023). Registration of 2D monocular endoscopy to 3D CBCT for video-assisted thoracoscopic surgery. Medical Imaging 2023: Image-Guided Procedures, Robotic Interventions, and Modeling. https://doi.org/10.1117/12.2655786
Wei, R., Liu, Y., Chen, X., Zhu, J., Yang, B., Men, K., & Dai, J. (2023). A projection‐domain correction method in CBCT reconstruction for head and neck radiotherapy using cycle‐GAN and nonlocal means filter. Medical Physics, 50(8), 5045–5060. Portico. https://doi.org/10.1002/mp.16322
Gardner, M., Bouchta, Y. B., Mylonas, A., Mueller, M., Cheng, C., Chlap, P., Finnegan, R., Sykes, J., Keall, P. J., & Nguyen, D. T. (2023). Realistic CT data augmentation for accurate deep‐learning based segmentation of head and neck tumors in kV images acquired during radiation therapy. Medical Physics, 50(7), 4206–4219. Portico. https://doi.org/10.1002/mp.16388
Schmitz, H., Rabe, M., Janssens, G., Rit, S., Parodi, K., Belka, C., Kamp, F., Landry, G., & Kurz, C. (2023). Scatter correction of 4D cone beam computed tomography to detect dosimetric effects due to anatomical changes in proton therapy for lung cancer. Medical Physics, 50(8), 4981–4992. Portico. https://doi.org/10.1002/mp.16335
Blake, S. J., Dillon, O., Byrne, H. L., & O’Brien, R. T. (2023). Thoracic motion‐compensated cone‐beam computed tomography in under 20 seconds on a fast‐rotating linac: A simulation study. Journal of Applied Clinical Medical Physics, 24(3). Portico. https://doi.org/10.1002/acm2.13909
Rodesch, P.-A., Richtsmeier, D., Guliyev, E., Iniewski, K., & Bazalova-Carter, M. (2023). Comparison of Threshold Energy Calibrations of a Photon-Counting Detector and Impact on CT Reconstruction. IEEE Transactions on Radiation and Plasma Medical Sciences, 7(3), 263–272. https://doi.org/10.1109/trpms.2022.3233323
Saporta, A., Etxebeste, A., Kaprelian, T., Létang, J. M., & Sarrut, D. (2022). Modeling families of particle distributions with conditional GAN for Monte Carlo SPECT simulations. Physics in Medicine & Biology, 67(23), 234001. https://doi.org/10.1088/1361-6560/aca068
Charles, M., Clackdoyle, R., & Rit, S. (2022). Cone-beam reconstruction for a circular trajectory with transversely-truncated projections based on the virtual fan-beam method. 7th International Conference on Image Formation in X-Ray Computed Tomography. https://doi.org/10.1117/12.2646482
Du, Y., Wang, R., Biguri, A., Zhao, X., Peng, Y., & Wu, H. (2022). TIGRE-VarianCBCT for on-board cone-beam computed tomography, an open-source toolkit for imaging, dosimetry and clinical research. Physica Medica, 102, 33–45. https://doi.org/10.1016/j.ejmp.2022.08.013
Robert, A., Rit, S., Baudier, T., Jomier, J., & Sarrut, D. (2022). Data-Driven Respiration-Gated SPECT for Liver Radioembolization. IEEE Transactions on Radiation and Plasma Medical Sciences, 6(7), 778–787. https://doi.org/10.1109/trpms.2021.3137990
Thummerer, A., Seller Oria, C., Zaffino, P., Visser, S., Meijers, A., Guterres Marmitt, G., Wijsman, R., Seco, J., Langendijk, J. A., Knopf, A. C., Spadea, M. F., & Both, S. (2022). Deep learning–based 4D‐synthetic CTs from sparse‐view CBCTs for dose calculations in adaptive proton therapy. Medical Physics, 49(11), 6824–6839. Portico. https://doi.org/10.1002/mp.15930
Maloney, B. W., Streeter, S. S., Jermyn, M., Kempner, J., Gesner, M., Meganck, J., Paulsen, K. D., & Pogue, B. W. (2022). Design and analysis of a combined micro-computed tomography and optical structured light system for breast conserving surgery specimen margin imaging. Multimodal Biomedical Imaging XVII. https://doi.org/10.1117/12.2605825
van der Heyden, B., Roden, S., Dok, R., Nuyts, S., & Sterpin, E. (2022). Virtual monoenergetic micro-CT imaging in mice with artificial intelligence. Scientific Reports, 12(1). https://doi.org/10.1038/s41598-022-06172-0
Ben Mosbah, M., Eleon, C., Tisseur, D., Doghmane, A., & Bakhabba, H. (2022). Boron-Coated Straws Imaging Panel Capability for Neutron Emission Computed Tomography for Source Localization Inside Radioactive Drums. IEEE Transactions on Nuclear Science, 69(4), 804–810. https://doi.org/10.1109/tns.2022.3140864
Rabbani, H., Teyfouri, N., & Jabbari, I. (2022). Low-dose cone-beam computed tomography reconstruction through a fast three-dimensional compressed sensing method based on the three-dimensional pseudo-polar fourier transform. Journal of Medical Signals & Sensors, 12(1), 8. https://doi.org/10.4103/jmss.jmss_114_21
Champley, K. M., Willey, T. M., Kim, H., Bond, K., Glenn, S. M., Smith, J. A., Kallman, J. S., Brown, W. D., Seetho, I. M., Keene, L., Azevedo, S. G., McMichael, L. D., Overturf, G., & Martz, H. E. (2022). Livermore tomography tools: Accurate, fast, and flexible software for tomographic science. NDT & E International, 126, 102595. https://doi.org/10.1016/j.ndteint.2021.102595
Dhou, S., Alkhodari, M., Ionascu, D., Williams, C., & Lewis, J. H. (2021). Fluoroscopic 3D Image Generation from Patient-Specific PCA Motion Models Derived from 4D-CBCT Patient Datasets: A Feasibility Study. https://doi.org/10.20944/preprints202111.0519.v1
Mo, Y., Liu, J., Li, Q., Yu, J., Zhang, K., Gao, Y., & Zhang, H. (2021). Joint Motion Estimation and Compensation for Four-Dimensional Cone-Beam Computed Tomography Image Reconstruction. IEEE Access, 9, 147559–147569. https://doi.org/10.1109/access.2021.3110861
Brown, R., Kolbitsch, C., Delplancke, C., Papoutsellis, E., Mayer, J., Ovtchinnikov, E., Pasca, E., Neji, R., da Costa-Luis, C., Gillman, A. G., Ehrhardt, M. J., McClelland, J. R., Eiben, B., & Thielemans, K. (2021). Motion estimation and correction for simultaneous PET/MR using SIRF and CIL. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 379(2204), 20200208. https://doi.org/10.1098/rsta.2020.0208
Peter, J. (2021). Musiré: multimodal simulation and reconstruction framework for the radiological imaging sciences. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 379(2204), 20200190. https://doi.org/10.1098/rsta.2020.0190
Niu, P., Lihuiwang, Xie, B., Robini, M., Boussel, L., Douek, P., Zhu, Y., & Yang, F. (2021). Improved Image Reconstruction Using Multi-Energy Information in Spectral Photon-Counting CT. IEEE Access, 9, 97981–97989. https://doi.org/10.1109/access.2021.3083505
Lau, B. K. F., Reynolds, T., Wallis, A., Smith, S., George, A., Keall, P. J., Sonke, J.-J., Vinod, S. K., Dillon, O., & O’Brien, R. T. (2021). Reducing 4DCBCT scan time and dose through motion compensated acquisition and reconstruction. Physics in Medicine & Biology, 66(7), 075002. https://doi.org/10.1088/1361-6560/abebfb
Liu, P. Z. Y., Gardner, M., Heng, S. M., Shieh, C.-C., Nguyen, D. T., Debrot, E., O’Brien, R., Downes, S., Jackson, M., & Keall, P. J. (2021). Pre-treatment and real-time image guidance for a fixed-beam radiotherapy system. Physics in Medicine & Biology, 66(6), 064003. https://doi.org/10.1088/1361-6560/abdc12
Reynolds, T., Dillon, O., Prinable, J., Whelan, B., Keall, P. J., & O’Brien, R. T. (2021). Adaptive CaRdiac cOne BEAm computed Tomography (ACROBEAT): Developing the next generation of cardiac cone beam CT imaging. Medical Physics, 48(5), 2543–2552. Portico. https://doi.org/10.1002/mp.14811
Kim, C., Jeong, C., Park, M.-J., Cho, B., Song, S. Y., Lee, S., & Kwak, J. (2021). A feasibility study of data redundancy based on-line geometric calibration without dedicated phantom on Varian OBI CBCT system. Medical Imaging 2021: Physics of Medical Imaging. https://doi.org/10.1117/12.2581793
Sarrut, D., Etxebeste, A., Krah, N., & Létang, J. (2021). Modeling complex particles phase space with GAN for Monte Carlo SPECT simulations: a proof of concept. Physics in Medicine & Biology, 66(5), 055014. https://doi.org/10.1088/1361-6560/abde9a
abbas, marwa, Youness, H., & Hassan, A. (2021). An Acceleration Strategy for Generating Cone-Beam CT Images Based on “Multi-core” Systems. Journal of Advanced Engineering Trends, 40(2), 117–126. https://doi.org/10.21608/jaet.2020.39654.1029
Ruf, M., & Steeb, H. (2020). An open, modular, and flexible micro X-ray computed tomography system for research. Review of Scientific Instruments, 91(11). https://doi.org/10.1063/5.0019541
Andersen, A. G., Park, Y.-K., Elstrøm, U. V., Petersen, J. B. B., Sharp, G. C., Winey, B., Dong, L., & Muren, L. P. (2020). Evaluation of an a priori scatter correction algorithm for cone-beam computed tomography based range and dose calculations in proton therapy. Physics and Imaging in Radiation Oncology, 16, 89–94. https://doi.org/10.1016/j.phro.2020.09.014
den Otter, L. A., Chen, K., Janssens, G., Meijers, A., Both, S., Langendijk, J. A., Rosen, L. R., Wu, H. T., & Knopf, A. (2020). Technical Note: 4D cone‐beam CT reconstruction from sparse‐view CBCT data for daily motion assessment in pencil beam scanned proton therapy (PBS‐PT). Medical Physics, 47(12), 6381–6387. Portico. https://doi.org/10.1002/mp.14521
Dillon, O., Keall, P. J., Shieh, C.-C., & O’Brien, R. T. (2020). Evaluating reconstruction algorithms for respiratory motion guided acquisition. Physics in Medicine & Biology, 65(17), 175009. https://doi.org/10.1088/1361-6560/ab98d3
Reynolds, T., Dillon, O., Prinable, J., Whelan, B., Keall, P. J., & O’Brien, R. T. (2020). Toward improved 3D carotid artery imaging with Adaptive CaRdiac cOne BEAm computed Tomography (ACROBEAT). Medical Physics, 47(11), 5749–5760. Portico. https://doi.org/10.1002/mp.14462
Gaudreault, D., Rossignol, J., Berube-Lauziere, Y., & Fontaine, R. (2021). Comparative Study of Image Quality in Time-Correlated Single-Photon Counting Computed Tomography. IEEE Transactions on Radiation and Plasma Medical Sciences, 5(3), 343–349. https://doi.org/10.1109/trpms.2020.3017702
Cai, M., Byrne, M., Archibald-Heeren, B., Metcalfe, P., Rosenfeld, A., & Wang, Y. (2020). Decoupling of bowtie and object effects for beam hardening and scatter artefact reduction in iterative cone-beam CT. Physical and Engineering Sciences in Medicine, 43(4), 1161–1170. https://doi.org/10.1007/s13246-020-00918-8
Madesta, F., Sentker, T., Gauer, T., & Werner, R. (2020). Self‐contained deep learning‐based boosting of 4D cone‐beam CT reconstruction. Medical Physics, 47(11), 5619–5631. Portico. https://doi.org/10.1002/mp.14441
Lalonde, A., Winey, B., Verburg, J., Paganetti, H., & Sharp, G. C. (2020). Evaluation of CBCT scatter correction using deep convolutional neural networks for head and neck adaptive proton therapy. Physics in Medicine & Biology, 65(24), 245022. https://doi.org/10.1088/1361-6560/ab9fcb
Langer, M., Cen, Z., Rit, S., & Létang, J. M. (2020). Towards Monte Carlo simulation of X-ray phase contrast using GATE. Optics Express, 28(10), 14522. https://doi.org/10.1364/oe.391471
Dhou, S., Lewis, J., Cai, W., Ionascu, D., & Williams, C. (2020). Quantifying day-to-day variations in 4DCBCT-based PCA motion models. Biomedical Physics & Engineering Express, 6(3), 035020. https://doi.org/10.1088/2057-1976/ab817e
Khellaf, F., Krah, N., Létang, J. M., & Rit, S. (2020). 2D directional ramp filter. Physics in Medicine & Biology, 65(8), 08NT01. https://doi.org/10.1088/1361-6560/ab7875
Collins-Fekete, C.-A., Dikaios, N., Royle, G., & Evans, P. M. (2020). Statistical limitations in proton imaging. Physics in Medicine & Biology, 65(8), 085011. https://doi.org/10.1088/1361-6560/ab7972
Kostenko, A., Palenstijn, W. J., Coban, S. B., Hendriksen, A. A., van Liere, R., & Batenburg, K. J. (2020). Prototyping X-ray tomographic reconstruction pipelines with FleXbox. SoftwareX, 11, 100364. https://doi.org/10.1016/j.softx.2019.100364
Konopka, J. K., Poinapen, D., Gariepy, T., Holdsworth, D. W., & McNeil, J. N. (2020). Timing of failed parasitoid development in Halyomorpha halys eggs. Biological Control, 141, 104124. https://doi.org/10.1016/j.biocontrol.2019.104124
Soubies, E., Soulez, F., McCann, M. T., Pham, T., Donati, L., Debarre, T., Sage, D., & Unser, M. (2019). Pocket guide to solve inverse problems with GlobalBioIm. Inverse Problems, 35(10), 104006. https://doi.org/10.1088/1361-6420/ab2ae9
Shieh, C., Gonzalez, Y., Li, B., Jia, X., Rit, S., Mory, C., Riblett, M., Hugo, G., Zhang, Y., Jiang, Z., Liu, X., Ren, L., & Keall, P. (2019). SPARE: Sparse‐view reconstruction challenge for 4D cone‐beam CT from a 1‐min scan. Medical Physics, 46(9), 3799–3811. Portico. https://doi.org/10.1002/mp.13687
Reynolds, T., Shieh, C., Keall, P. J., & O’Brien, R. T. (2019). Dual cardiac and respiratory gated thoracic imaging via adaptive gantry velocity and projection rate modulation on a linear accelerator: A Proof‐of‐Concept Simulation Study. Medical Physics, 46(9), 4116–4126. Portico. https://doi.org/10.1002/mp.13670
Pfeiffer, T., Frysch, R., Bismark, R. N., & Rose, G. (2019). CTL: modular open-source C++-library for CT-simulations. 15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine. https://doi.org/10.1117/12.2534517
Iuso, D., Frysch, R., Pfeiffer, T., & Rose, G. (2019). Analysis of scatter artifacts in cone-beam CT due to scattered radiation of metallic objects. 15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine. https://doi.org/10.1117/12.2534465
Krah, N., & Rit, S. (2019). Optimized conversion from CT numbers to proton relative stopping power based on proton radiography and scatter corrected cone-beam CT images. 15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine. https://doi.org/10.1117/12.2534898
Riblett, M. J., Christensen, G. E., Weiss, E., & Hugo, G. D. (2018). Data‐driven respiratory motion compensation for four‐dimensional cone‐beam computed tomography (4D‐
Landry, G., Dörringer, F., Si‐Mohamed, S., Douek, P., Abascal, J. F. P. J., Peyrin, F., Almeida, I. P., Verhaegen, F., Rinaldi, I., Parodi, K., & Rit, S. (2019). Technical Note: Relative proton stopping power estimation from virtual monoenergetic images reconstructed from dual‐layer computed tomography. Medical Physics, 46(4), 1821–1828. Portico. https://doi.org/10.1002/mp.13404
Madesta, F., Gauer, T., Sentker, T., & Werner, R. (2019). Self-consistent deep learning-based boosting of 4D cone-beam computed tomography reconstruction. Medical Imaging 2019: Image Processing. https://doi.org/10.1117/12.2512980
Landry, G., Hansen, D., Kamp, F., Li, M., Hoyle, B., Weller, J., Parodi, K., Belka, C., & Kurz, C. (2019). Comparing Unet training with three different datasets to correct CBCT images for prostate radiotherapy dose calculations. Physics in Medicine & Biology, 64(3), 035011. https://doi.org/10.1088/1361-6560/aaf496
Cooper, B. J., O’Brien, R. T., Shieh, C.-C., & Keall, P. J. (2019). Real-time respiratory triggered four dimensional cone-beam CT halves imaging dose compared to conventional 4D CBCT. Physics in Medicine & Biology, 64(7), 07NT01. https://doi.org/10.1088/1361-6560/ab065d
Fournier, D. E., Norley, C. J. D., Pollmann, S. I., Bailey, C. S., Al Helal, F., Willmore, K. E., Holdsworth, D. W., Dixon, S. J., & Séguin, C. A. (2019). Ectopic spinal calcification associated with diffuse idiopathic skeletal hyperostosis (DISH): A quantitative micro‐ct analysis. Journal of Orthopaedic Research, 37(3), 717–726. Portico. https://doi.org/10.1002/jor.24247
Schyns, L. E., Eekers, D. B., van der Heyden, B., Almeida, I. P., Vaniqui, A., & Verhaegen, F. (2019). Murine vs human tissue compositions: implications of using human tissue compositions for photon energy absorption in mice. The British Journal of Radiology, 92(1095), 20180454. https://doi.org/10.1259/bjr.20180454
Brun, F. (2018). From Projections to the 3D Analysis of the Regenerated Tissue. Fundamental Biomedical Technologies, 69–90. https://doi.org/10.1007/978-3-030-00368-5_5
Niepel, K., Kamp, F., Kurz, C., Hansen, D., Rit, S., Neppl, S., Hofmaier, J., Bondesson, D., Thieke, C., Dinkel, J., Belka, C., Parodi, K., & Landry, G. (2019). Feasibility of 4DCBCT-based proton dose calculation: An ex vivo porcine lung phantom study. Zeitschrift Für Medizinische Physik, 29(3), 249–261. https://doi.org/10.1016/j.zemedi.2018.10.005
Vaniqui, A., Schyns, L. E. J. R., Almeida, I. P., van der Heyden, B., Podesta, M., & Verhaegen, F. (2019). The effect of different image reconstruction techniques on pre-clinical quantitative imaging and dual-energy CT. The British Journal of Radiology, 92(1095), 20180447. https://doi.org/10.1259/bjr.20180447
Merlin, T., Stute, S., Benoit, D., Bert, J., Carlier, T., Comtat, C., Filipovic, M., Lamare, F., & Visvikis, D. (2018). CASToR: a generic data organization and processing code framework for multi-modal and multi-dimensional tomographic reconstruction. Physics in Medicine & Biology, 63(18), 185005. https://doi.org/10.1088/1361-6560/aadac1
Hansen, D. C., Landry, G., Kamp, F., Li, M., Belka, C., Parodi, K., & Kurz, C. (2018). ScatterNet: A convolutional neural network for cone‐beam CT intensity correction. Medical Physics, 45(11), 4916–4926. Portico. https://doi.org/10.1002/mp.13175
van der Heyden, B., Podesta, M., Eekers, D. B., Vaniqui, A., Almeida, I. P., Schyns, L. E., van Hoof, S. J., & Verhaegen, F. (2019). Automatic multiatlas based organ at risk segmentation in mice. The British Journal of Radiology, 92(1095), 20180364. https://doi.org/10.1259/bjr.20180364
Rodriguez-Alvarez, M. J., Sanchez, F., Soriano, A., Moliner, L., Sanchez, S., & Benlloch, J. (2018). QR-Factorization Algorithm for Computed Tomography (CT): Comparison With FDK and Conjugate Gradient (CG) Algorithms. IEEE Transactions on Radiation and Plasma Medical Sciences, 2(5), 459–469. https://doi.org/10.1109/trpms.2018.2843803
Kim, J., Park, Y.-K., Edmunds, D., Oh, K., Sharp, G. C., & Winey, B. (2018). Kilovoltage projection streaming-based tracking application (KiPSTA): First clinical implementation during spine stereotactic radiation surgery. Advances in Radiation Oncology, 3(4), 682–692. https://doi.org/10.1016/j.adro.2018.06.002
Castonguay-Henri, A., Matenine, D., Schmittbuhl, M., & de Guise, J. A. (2018). Image Quality Optimization and Soft Tissue Visualization in Cone-Beam CT Imaging. World Congress on Medical Physics and Biomedical Engineering 2018, 283–288. https://doi.org/10.1007/978-981-10-9035-6_51
van der Heyden, B., Schyns, L. E. J. R., Podesta, M., Vaniqui, A., Almeida, I. P., Landry, G., & Verhaegen, F. (2018). VOXSI: A voxelized single- and dual-energy CT scenario generator for quantitative imaging. Physics and Imaging in Radiation Oncology, 6, 47–52. https://doi.org/10.1016/j.phro.2018.05.004
Cajgfinger, T., Rit, S., Létang, J. M., Halty, A., & Sarrut, D. (2018). Fixed forced detection for fast SPECT Monte-Carlo simulation. Physics in Medicine & Biology, 63(5), 055011. https://doi.org/10.1088/1361-6560/aa9e32
Tisseur, D., Bhatia, N., Estre, N., Berge, L., Eck, D., & Payan, E. (2018). Evaluation of a scattering correction method for high energy tomography. EPJ Web of Conferences, 170, 06006. https://doi.org/10.1051/epjconf/201817006006
Liu, Yu. (2017). Improve Industrial Cone-Beam Computed Tomography by Integrating Prior Information [ETH Zurich]. https://doi.org/10.3929/ETHZ-B-000219410
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