Publications

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

Gardner, M., Dillon, O., Byrne, H., Keall, P., & O’Brien, R. (2024). Data-driven rapid 4D cone-beam CT reconstruction for new generation linacs. Physics in Medicine & Biology, 69(18), 18NT02. https://doi.org/10.1088/1361-6560/ad780a

Zakeri, A., Hokmabadi, A., Nix, M. G., Gooya, A., Wijesinghe, I., & Taylor, Z. A. (2024). 4D-Precise: Learning-based 3D motion estimation and high temporal resolution 4DCT reconstruction from treatment 2D+t X-ray projections. Computer Methods and Programs in Biomedicine, 250, 108158. https://doi.org/10.1016/j.cmpb.2024.108158

Wu, D., Chen, P., Wang, X., Lyngaas, I., Miyajima, T., Endo, T., Matsuoka, S., & Wahib, M. (2024). Real-time High-resolution X-Ray Computed Tomography. Proceedings of the 38th ACM International Conference on Supercomputing, 100, 110–123. https://doi.org/10.1145/3650200.3656634

Rodesch, P.-A., Richtsmeier, D., Murphy, K., Iniewski, K., & Bazalova-Carter, M. (2024). Photon-counting detector step-wedge calibration enabling water and iodine material decomposition. Journal of Instrumentation, 19(05), P05030. https://doi.org/10.1088/1748-0221/19/05/p05030

Chan, Y. C. I., 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, 30, 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, 51(4), 2975–2982. 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, 51(5), 3391–3404. 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, 51(5), 3578–3589. Portico. https://doi.org/10.1002/mp.16859

Le Reun, A., Subrin, K., Dubois, A., & Garnier, S. (2024). Improving accuracy reconstruction of parts through a capability study: A methodology for X-ray Computed Tomography Robotic Cell. Robotics and Autonomous Systems, 171, 104564. https://doi.org/10.1016/j.robot.2023.104564

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, 1611, 83. 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, 489, 38. 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, 69, 21. 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, 87. 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, 24, 38. 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, 5745, 34. 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, 489, 96. 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‐CBCT) using groupwise deformable registration. Medical Physics, 45(10), 4471–4482. Portico. https://doi.org/10.1002/mp.13133

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, 489, 1. 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. Advanced High-Resolution Tomography in Regenerative Medicine, 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

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