Matteo Maspero

University Medical Center Utrecht, Department Of Radiotherapy, PhD Candidate

University Medical Center Utrecht, Image Sciences Institute, PhD Candidate

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Matteo is currently employed as a clinical scientist/postdoctoral researcher investigating adaptive Radiotherapy, image acquisition/reconstruction and MR-only Radiotherapy with the aid of deep learning.

Background
Matteo is born Como (Italy) in May 1989 and studied Physics at the Insubria University graduating (cum laude) in March 2014. In those years 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.
Nowadays, medical imaging and Radiotherapy are still some of Matteo's passions.
Supervisors: Cornelis A.T. van den Berg, Peter R. Seevinck, Max A. Viergever, and Jan J.W. Lagendijk

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Nicole Herbots

Arizona State University

Issel Anne Lim, PhD

Johns Hopkins University

Mohammad Mehdi Firoozabadi

The University of Birjand

wan ahmad tajuddin wan abdullah

University of Malaya, Malaysia

Gregory S Fischer

Worcester Polytechnic Institute

Ahmad Lutfi Yusoff

Universiti Sains Malaysia

Karunanithi Rajamanickam

CHETTINAD university

Pedro Guerra

Universidad Politécnica de Madrid

Bartlomiej W Papiez

University of Oxford

Rasheda Chowdhury

University of Montreal

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Papers by Matteo Maspero

Dosimetric feasibility of direct post-operative MR-Linac-based stereotactic radiosurgery for resection cavities of brain metastases

Radiotherapy and Oncology

PO-1698: Deep learning-based sCTs with uncertainty estimation from heterogeneous pediatric brain MRI

Radiotherapy and Oncology, 2020

PO-1624: Fast contour propagation for MR-guided prostate radiotherapy using convolutional networks

Radiotherapy and Oncology, 2020

PO-1699: Clinical implementation of MRI-based prostate OARs auto-segmentation with convolutional networks

Radiotherapy and Oncology, 2020

$Research paper thumbnail of Synthetic CT for single-fraction neoadjuvant partial breast irradiation on an MRI-linac$

Synthetic CT for single-fraction neoadjuvant partial breast irradiation on an MRI-linac

Physics in Medicine & Biology, 2021

A synthetic computed tomography (sCT) is required for daily plan optimization on an MRI-linac. Ye... more A synthetic computed tomography (sCT) is required for daily plan optimization on an MRI-linac. Yet, only limited information is available on the accuracy of dose calculations on sCT for breast radiotherapy. This work aimed to (1) evaluate dosimetric accuracy of treatment plans for single-fraction neoadjuvant partial breast irradiation (PBI) on a 1.5 T MRI-linac calculated on a) bulk-density sCT mimicking the current MRI-linac workflow and b) deep learning-generated sCT, and (2) investigate the number of bulk-density levels required. For ten breast cancer patients we created three bulk-density sCTs of increasing complexity from the planning-CT, using bulk-density for: (1) body, lungs, and GTV (sCTBD1); (2) volumes for sCTBD1 plus chest wall and ipsilateral breast (sCTBD2); (3) volumes for sCTBD2 plus ribs (sCTBD3); and a deep learning-generated sCT (sCTDL) from a 1.5 T MRI in supine position. Single-fraction neoadjuvant PBI treatment plans for a 1.5 T MRI-linac were optimized on each...

Fast synthetic CT generation with deep learning for general pelvis MR-only Radiotherapy

To enable magnetic resonance (MR)-only radiotherapy and facilitate modelling of radiation attenua... more To enable magnetic resonance (MR)-only radiotherapy and facilitate modelling of radiation attenuation in humans, synthetic CT (sCT) images need to be generated. Considering the application of MR-guided radiotherapy, sCT generation should occur within minutes. This work aims at assessing whether an existing deep learning network can rapidly generate sCT images leading to accurate MR-based dose calculations in the entire pelvis. A study was conducted on data of 91 patients with prostate (59), rectal (18) and cervical (14) cancer who underwent external beam radiotherapy acquiring both CT and MRI for patients' simulation. Dixon reconstructed water, fat and in-phase images obtained from a conventional dual gradient-recalled echo sequence were used to generate sCT images. A conditional generative adversarial network (cGAN) was used to generate sCT images. The network was trained in a paired fashion on 2D transverse slices of 32 prostate patients and feed-forward evaluated on the remai...

Matteo Maspero

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