| Fully automated detection and segmentation of meningiomas using deep learning on routine multiparametric MRI KR Laukamp, F Thiele, G Shakirin, D Zopfs, A Faymonville, M Timmer, ... European radiology 29, 124-132, 2019 | 186 | 2019 |
| On the detector arrangement for in-beam PET for hadron therapy monitoring P Crespo, G Shakirin, W Enghardt Physics in Medicine & Biology 51 (9), 2143, 2006 | 139 | 2006 |
| Implementation and workflow for PET monitoring of therapeutic ion irradiation: a comparison of in-beam, in-room, and off-line techniques G Shakirin, H Braess, F Fiedler, D Kunath, K Laube, K Parodi, M Priegnitz, ... Physics in Medicine & Biology 56 (5), 1281, 2011 | 125 | 2011 |
| Direct time-of-flight for quantitative, real-time in-beam PET: a concept and feasibility study P Crespo, G Shakirin, F Fiedler, W Enghardt, A Wagner Physics in Medicine & Biology 52 (23), 6795, 2007 | 104 | 2007 |
| PET imaging for treatment verification of ion therapy: Implementation and experience at GSI Darmstadt and MGH Boston K Parodi, T Bortfeld, W Enghardt, F Fiedler, A Knopf, H Paganetti, ... Nuclear Instruments and Methods in Physics Research Section A: Accelerators …, 2008 | 94 | 2008 |
| On the effectiveness of ion range determination from in-beam PET data F Fiedler, G Shakirin, J Skowron, H Braess, P Crespo, D Kunath, ... Physics in Medicine & Biology 55 (7), 1989, 2010 | 85 | 2010 |
| Clinical evaluation of a multiparametric deep learning model for glioblastoma segmentation using heterogeneous magnetic resonance imaging data from clinical routine M Perkuhn, P Stavrinou, F Thiele, G Shakirin, M Mohan, D Garmpis, ... Investigative radiology 53 (11), 647-654, 2018 | 81 | 2018 |
| Accuracy of radiomics-based feature analysis on multiparametric magnetic resonance images for noninvasive meningioma grading KR Laukamp, G Shakirin, B Baeßler, F Thiele, D Zopfs, NG Hokamp, ... World Neurosurgery 132, e366-e390, 2019 | 59 | 2019 |
| Co-localisation of hypoxia and perfusion markers with parameters of glucose metabolism in human squamous cell carcinoma (hSCC) xenografts A Yaromina, V Quennet, D Zips, S Meyer, G Shakirin, S Walenta, ... International journal of radiation biology 85 (11), 972-980, 2009 | 51 | 2009 |
| Automated meningioma segmentation in multiparametric MRI: comparable effectiveness of a deep learning model and manual segmentation KR Laukamp, L Pennig, F Thiele, R Reimer, L Görtz, G Shakirin, D Zopfs, ... Clinical neuroradiology 31, 357-366, 2021 | 49 | 2021 |
| Dealing with geometric uncertainties in dose painting by numbers: introducing the ΔVH M Witte, G Shakirin, A Houweling, H Peulen, M van Herk Radiotherapy and Oncology 100 (3), 402-406, 2011 | 42 | 2011 |
| A novel concept for tumour targeting with radiation: Inverse dose-painting or targeting the “Low Drug Uptake Volume” A Yaromina, M Granzier, R Biemans, N Lieuwes, W van Elmpt, G Shakirin, ... Radiotherapy and Oncology 124 (3), 513-520, 2017 | 31 | 2017 |
| Preliminary assessment of dynamic contrast-enhanced CT implementation in pretreatment FDG-PET/CT for outcome prediction in head and neck tumors A Abramyuk, G Wolf, G Shakirin, U Haberland, S Tokalov, A Koch, ... Acta radiologica 51 (7), 793-799, 2010 | 28 | 2010 |
| Validation of functional imaging as a biomarker for radiation treatment response C Jentsch, B Beuthien-Baumann, EGC Troost, G Shakirin The British Journal of Radiology 88 (1051), 2015 | 27 | 2015 |
| An in silico comparison between margin-based and probabilistic target-planning approaches in head and neck cancer patients D Fontanarosa, HP van der Laan, M Witte, G Shakirin, E Roelofs, ... Radiotherapy and Oncology 109 (3), 430-436, 2013 | 21 | 2013 |
| A method for system matrix construction and processing for reconstruction of in-beam PET data G Shakirin, P Crespo, W Enghardt IEEE Transactions on Nuclear Science 54 (5), 1710-1716, 2007 | 10 | 2007 |
| Probabilistic evaluation of target dose deterioration in dose painting by numbers for stage II/III lung cancer D Fontanarosa, M Witte, G Meijer, G Shakirin, J Steenhuijsen, D Schuring, ... Practical radiation oncology 5 (4), e375-e382, 2015 | 7 | 2015 |
| Liver tissue classification in patients with hepatocellular carcinoma by fusing structured and rotationally invariant context representation J Treilhard, S Smolka, L Staib, J Chapiro, MD Lin, G Shakirin, JS Duncan Medical Image Computing and Computer Assisted Intervention− MICCAI 2017 …, 2017 | 6 | 2017 |
| Combining image and non-image clinical data: an infrastructure that allows machine learning studies in a hospital environment R Espanha, F Thiele, G Shakirin, J Roggenfelder, S Zeiter, P Stavrinou, ... Distributed Computing and Artificial Intelligence, 15th International …, 2019 | 4 | 2019 |
| System solution for in-beam positron emission tomography monitoring of radiation therapy G Shakirin | 3 | 2009 |
