Computational Imaging Group

Brief description

Matteo Maspero is an assistant professor investigating adaptive Radiotherapy, image acquisition/reconstruction, registration and segmentation with the aid of deep learning. Since 2023, he moved a step closer to the clinic with a training as medical physicist in the Radiotherapy department of UMC Utrecht. He is involved in research activities translating computational methods in clinical settings.

Background

Matteo is born Como (Italy) in May 1989 and studied Physics at the Insubria University graduating (cum laude) in March 2014, where he gained interest in experimental Physics, with a predilection for particle detectors. Following this interest, he had the opportunity to perform profilometry measurements of an anti-proton beam and measure the diameter of the Sun with a pixelated silicon detector and develop a detector for spectral measurements of neutrons produced by an in-hospital linear accelerator. After getting in touch with the hospital environment and feeling the need of broadening his horizons, he decided to step into the field of medical imaging, where, by chance, detectors are somehow involved. Form May 2014 to May 2018 he was enrolled as a PhD candidate at the Radiotherapy Department of the Universitair Medisch Centrum Utrecht (The Netherlands). During his PhD project, he had the luck to discover the beauty of magnetic resonance imaging in the practical realm of radiotherapy. Since 2018, Matteo has been a Postdoc dedicating his efforts to translate deep learning for adaptive radiotherapy clinically. He has been appointed assistant professor, from January 2022 gaining the opportunity to facilitate clinical translation further and keeping an eye on cutting-edge software and its integration and development in the healthcare continuum. Nowadays, medical imaging and radiotherapy are still Matteo’s passions and from 2023 Matteo is following a training to become medical physicist (radiotherapy pathway) along with translational research.

Projects

Key publications

Spadea MF, Maspero M, Zaffino P, Seco J. Deep learning-based synthetic-CT generation in radiotherapy and PET: a review. Med Phys. 2021 Nov; 48(11); https://doi.org/10.1002/mp.15150, https://arxiv.org/2102.02734.
Maspero M, Savenije MHF, Dinkla AM, Seevinck PR, Intven MPW, Juergenliemk-Schulz IM, Kerkmeijer LGW, Van den Berg CAT. Dose evaluation of fast synthetic-CT generation using a generative adversarial network for general pelvis MR-only radiotherapy. Phys Med Biol, 2018 Aug; 63(18):185001-13 https://doi.org/10.1088/1361-6560/aada6d.
Maspero M, Houweling AC, Savenije MH, van Heijst TC, Verhoeff JJ, Kotte AN, van den Berg CA. A single neural network for cone-beam computed tomography-based radiotherapy of head-and-neck, lung and breast cancer. Physics and Imaging in Radiation Oncology. 2020 Apr; 14:24-31. https://doi.org/10.1016/j.phro.2020.04.002.
Savenije MH & Maspero M, Sikkes GG, van der Voort VZ, TJ KA, Bol GH, T van den Berg CA. Clinical implementation of MRI-based organs-at-risk auto-segmentation with convolutional networks for prostate radiotherapy. Radiation Oncology. 2020 May; 15(1):104. https://doi.org/10.1186/s13014-020-01528-0.
Terpstra ML, Maspero M, Bruijnen T, Verhoeff JJ, Lagendijk JJ, van den Berg CA. Real‐time 3D motion estimation from undersampled MRI using multi‐resolution neural networks. Medical Physics. 2021 Nov;48(11):6597-613; https://doi.org/10.1002/mp.15217, code https://gitlab.com/computational-imaging-lab/tempest.

PhD thesis MR-only Radiotherapy of prostate cancer, 1 May 2014 - 28 April 2018, Utrecht, The Netherlands, ISBN: 978-90-393-6953-1, Utrecht University.

Publications

2023

Kolenbrander I, Maspero M, Hendriksen A, Pollitt R, van der Voort van Zyp J, van den Berg CAT, Pluim J, van Eijnatten M. Deep learning-based joint rigid and deformable contour propagation for magnetic resonance imaging-guided prostate radiotherapy. Submitted to Med Phys, 2023 Jun.
Reinders FCJ, Savenije MHF, de Ridder M, Maspero M, Doornaert PAH, Terhaard CHJ, Raaijmakers CPJ, Philippens MEP. Deep learning based segmentation of individual lymph nodes on MRI in head and neck cancer patients. Submitted to CTRo, 2023.
Jacobs L, Mandija S, Hongyan L, van den Berg CAT, Sbrizzi A, Maspero M. Generalizable synthetic MRI with physics-informed convolutional networks. Under submission, 2023, arXiv:2305.12570.
Nijskens L, van den Berg CAT, Verhoeff JJC, Maspero M. Investigating contrast generalisation in deep learning-based brain MRI-to-CT synthesis. Physica Medica, 2023 Aug; 112:102642, https://doi.org/10.1016/j.ejmp.2023.102642; arXiv:arXiv.2303.10202; Zenodo:10.5281/zenodo.7742642.
Terpstra M, Maspero M, Verhoeff JJC, van den Berg CAT. Accelerated respiratory-resolved 4D-MRI with separable spatio-temporal neural networks. Med Phys, 2023, arXiv:2211.05678.
Thummerer A, van der Bijl E, Galapon Jr A, Verhoeff JJC, Langendijk JA, Both S, van den Berg CAT, Maspero, M. SynthRAD2023 Grand Challenge dataset: Generating synthetic CT for radiotherapy. Med Phys. 2023 Jul;50(7):4664-4674; https://doi.org/10.1002/mp.16529; arXiv:2303.16320.

2022

Seravalli E, Sierts M, Brand E, Maspero M, David S, Philippens MEP, Voormolen EHJ, Verhoeff JJC. Dosimetric feasibility of direct post-operative MR-Linac-based stereotactic radiosurgery for resection cavities of brain metastases. Radiother Oncol, 2022 Dec; 179:109456; https://doi.org/10.1016/j.radonc.2022.109456.
Terpstra M, Maspero M, Verhoeff JJC, van den Berg CAT. Accelerated respiratory-resolved 4D-MRI with separable spatio-temporal neural networks. Submitted to Medical Physics, 2022 Nov, arXiv:2211.05678.
Jacobs L, Mandija S, Hongyan L, van den Berg CAT, Sbrizzi A, Maspero M. Generalizable synthetic multi-contrast MRI generation using physics-informed convolutional networks. In Proc Intl Soc Mag Reson Med. 2022: 30, 2850; https://archive.ismrm.org/2022/2850.html.
Terpstra ML, Maspero M, Sbrizzi A, van den Berg CAT. ⊥-loss: a symmetric loss function for magnetic resonance imaging reconstruction and image registration with deep learning. Medical Image Analysis. 2022 Aug; 102509; https://doi.org/10.1016/j.media.2022.102509.
Maspero M, Keijnemans K, Hackett SL, Raaymakers BW, Verhoeff JJC,Fast MF, van den Berg CAT. OC-0772 Deep learning-based 4D synthetic CT for lung radiotherapy. ESTRO2022, May 8-12.

2021

Terpstra ML, Maspero M, Bruijnen T, Verhoeff JJC, Lagendijk JJ, van den Berg CA. Real‐time 3D motion estimation from undersampled MRI using multi‐resolution neural networks. Medical Physics. 2021 Nov;48(11):6597-613; https://doi.org/10.1002/mp.15217, code https://gitlab.com/computational-imaging-lab/tempest.
Spadea MF & Maspero M, Zaffino P, Seco J. Deep learning-based synthetic-CT generation in radiotherapy and PET: a review. Med Phys. 2021 Nov; 48(11);https://doi.org/10.1002/mp.15150, https://arxiv.org/2102.02734.
Hoeben BAW, Seravalli E, Wood AML, Bosman M, Matysiak WP, Maduro JH, van Lier ALHMW, Maspero M, Bol GH, Janssens GO. Influence of eye movement on lens dose and optic nerve target coverage during craniospinal irradiation. Clin Transl Radiat Oncol. 2021 Aug; 31:28-33 https://doi.org/10.1016/J.CTRO.2021.08.009.
Koerkamp ML, de Hond YJ, Maspero M, Kontaxis C, Mandija S, Vasmel JE, Charaghvandi RK, Philippens ME, van Asselen B, van den Bongard HD, Hackett SS. Synthetic CT for single-fraction neoadjuvant partial breast irradiation on an MRI-linac. Phys Med Biol. 2021 Mar; https://doi.org/10.1088/1361-6560/abf1ba.

2020

Maspero M, Bentvelzen LG, Savenije MHF, Guerreiro F, Seravalli E, Janssens GOR, van den Berg CAT, Philippens MEP. Deep learning-based synthetic CT generation fo paediatric brain MR-only photon and proton radiotherapy. Radiother Oncol. 2020 Dec; 153:197:204. https://doi.org/10.1016/j.radonc.2020.09.029.
Terpstra ML, Maspero M, D’Agata F, Stemkens B, Intven MP, Lagendijk JJ, Van den Berg CA, Tijssen RH. Deep learning-based image reconstruction and motion estimation from undersampled radial k-space for real-time MRI-guided radiotherapy. Phys Med Biol. 2020 May; 65(15):5015. https://doi.org/10.1088/1361-6560/ab9358.
Savenije MH, Maspero M, Sikkes GG, van der Voort VZ, TJ KA, Bol GH, T van den Berg CA. Clinical implementation of MRI-based organs-at-risk auto-segmentation with convolutional networks for prostate radiotherapy. Radiation Oncology. 2020 May; 15(1):104. https://doi.org/10.1186/s13014-020-01528-0.
Maspero M, Houweling AC, Savenije MH, van Heijst TC, Verhoeff JJ, Kotte AN, van den Berg CA. A single neural network for cone-beam computed tomography-based radiotherapy of head-and-neck, lung and breast cancer. Physics and Imaging in Radiation Oncology. 2020 Apr; 14:24-31. https://doi.org/10.1016/j.phro.2020.04.002.
Florkow MC, Zijlstra F, Willemsen K, Maspero M, van den Berg CAT, Kerkmeijer LGW, Castelein RM, Weinas H, Viergever MA, van Stralen M, Seevinck PR. Deep learning-based MR-to-CT synthesis: the influence of varying gradient echo-based MR images as input channels. Mag Res Med. 2020 Apr; 83(4):1429-41. https://doi.org/10.1002/mrm.28008.
Eppenhof KA, Maspero M, Savenije MH, de Boer JC, van der Voort van Zyp JR, Raaymakers BW, Raaijmakers AJ, Veta M, van den Berg CA, Pluim JP. Fast contour propagation for MR‐guided prostate radiotherapy using convolutional neural networks. Medical Physics. 2020 Mar; 47(3):1238-48. https://doi.org/10.1002/mp.13994.
Meliado EF, Raaijmakers AJE, Sbrizzi A, Steensma BR, Maspero M, Savenije MHF, Luijten PR, van den Berg CAT. A deep learning method for image-based subject-specific local SAR assessment. Mag Res Med. 2020 Feb; 83(2):695-711. https://doi.org/10.1002/mrm.27948.

2019

de Muinck Keizer D, Kerkmeijer LWG, Maspero M, Andreychenko A, van der Voort van Zyp J, van den Berg CAT, Raaymakers B, Lagendijk JJW, de Boer J. Soft-tissue prostate intrafraction motion tracking in 3D cine-MR for MR-guided radiotherapy. Phys Med Biol, 2019 Dec; 64(23):235008. https://doi.org/10.1088/1361-6560/ab5539.
Kurz C, Maspero M, Savenije MHF, Landry G, Kamp F, Li M, Pinto M, Parodi K, Belka C, van den Berg CAT. CBCT correction using a cycle-consistent generative adversarial network and unpaired training to enable photon and proton dose calculation. Phys Med Biol, 2019 Nov; 64(22):225004. https://doi.org/10.1088/1361-6560/ab4d8c.
Dinkla AM, Florkow MC, Maspero M, Savenije MHF, Zijlstra F, Doornaert PAH, van Stralen M, Philippens MEP, van den Berg CAT, Seevinck PR. Dosimetric evaluation of synthetic CT for head and neck radiotherapy generated by a patch-based 3D convolutional neural network. Med Phys, 2019 Sep; 46(9):4095-4104. https://doi.org/10.1002/mp.13663.

2018

Dinkla AM, Wolterink JM, Maspero M, Savenije MHF, Verhoeff JJC, Seravalli E, Išgum I, Seevinck PR, van den Berg CAT. MR-Only Brain Radiation Therapy: Dosimetric Evaluation of Synthetic CTs Generated by a Dilated Convolutional Neural Network. Int J Radiat Oncol Biol Phys, 2018 Nov; 102(4):801-812. 10.1016/j.ijrobp.2018.05.058.
Maspero M, Tyyger M, Tijssen RHN, Seevinck PR, Intven M, van den Berg CAT. Feasibility of magnetic resonance imaging-only rectum radiotherapy with a commercial synthetic-computed tomography generation solution. Phys Imaging Radiat Oncol, 2018 (7):58-64. https://doi.org/10.1016/j.phro.2018.09.002.
Kerkmeijer LGW, Maspero M, Meijer GJ, van der Voort van Zyp JRN, de Boer HCJ, van den Berg CAT. Magnetic Resonance Imaging Only Workflow for Radiotherapy Simulation and Planning in Prostate Cancer. Clin Oncol, 2018; 30(11):692-701. https://doi.org/10.1016/j.clon.2018.08.009.
Maspero M, Savenije MHF, Dinkla AM, Seevinck PR, Intven MPW, Juergenliemk-Schulz IM, Kerkmeijer LGW, Van den Berg CAT. Dose evaluation of fast synthetic-CT generation using a generative adversarial network for general pelvis MR-only radiotherapy. Phys Med Biol, 2018 Aug; 63(18):185001-13 https://doi.org/10.1088/1361-6560/aada6d.
Maspero M, Seevinck PR, Willems NJW, Sikkes GG, de Kogel GJ, de Boer HCJ, van der Voort van Zyp JRN, van den Berg CAT. Evaluation of gold fiducial marker manual localisation for magnetic resonance-only prostate radiotherapy. Radiat Oncol, 2018 Jun; 13(1):105. https://doi.org/10.1186/s13014-018-1029-7 Code available at https://doi.org/10.24433/CO.9de8bf35-71a7-40ed-8470-bf89536a348d

2017

Maspero M, van den Berg CAT, Landry G, Belka C, Parodi K, Seevinck PR, Raaymakers BW, Kurz C. Feasibility of MR-only proton dose calculations for prostate cancer radiotherapy using a commercial pseudo-CT generation method. Phys Med Biol, 2017 Nov; 62(24):9159-9176. https://doi.org/10.1088/1361-6560/aa9677. Code available at https://doi.org/10.24433/CO.763408fd-9964-4fe8-9689-39fe12937930 or https://matteomaspero.github.io/MRonlyProton-pCTwithAir/.
Maspero M, van den Berg CAT, Zijlstra F, Sikkes GG, de Boer HCJ, Meijer GJ, Kerkmeijer LGW, Viergever MA, Lagendijk JJW, Seevinck PR. Evaluation of an automatic MR-based gold fiducial marker localisation method for MR-only prostate radiotherapy. Phys Med Biol, 2017 Oct; 62(20):7981-8002. https://doi.org/10.1088/1361-6560/aa875f.
Andreychenko A, Kroon PS, Maspero M, Jürgenliemk-Schulz I, De Leeuw AA, Lam MG, Lagendijk JJ, van den Berg CA. The feasibility of semi-automatically generated red bone marrow segmentations based on MR-only for patients with gynecologic cancer. Radiother Oncol, 2017 Apr; 123(1):164-168. https://doi.org/10.1016/j.radonc.2017.01.020.
Maspero M, Seevinck PR, Schubert G, Hoesl MA, van Asselen B, Viergever MA, Lagendijk JJ, Meijer GJ, van den Berg CA. Quantification of confounding factors in MRI-based dose calculations as applied to prostate IMRT. Phys Med Biol, 2017 Feb; 62(3):948-965. https://doi.org/10.1088/1361-6560/aa4fe7; Code available at [https://doi.org/10.24433/CO.763408fd-9964-4fe8-9689-39fe12937930] or https://matteomaspero.github.io/pseudo-CT_generation/.

2015

Maspero M, Berra A, Conti V, Giannini G, Ostinelli A, Prest M, et al. A real time scintillating fiber Time of Flight spectrometer for LINAC photoproduced neutrons. Nucl Ins Meth Phys Res A, 2015 Mar; 777:154–60. https://doi.org/10.1016/j.nima.2014.12.101.

2012

Faurobert M, Fang C, Corbard T, Sigismondi C, Raponi A, De Rosi G, Bianda M, Ramelli R, Caccia M, Maspero M, Negrini L, Wang X. Atmospheric fluctuations below 0.1 Hz during drift-scan solar diameter measurements, 2012, EAS Publication Series 55:381-383. https://doi.org/10.1051/eas/1255054.