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Author Ros Garcia, A.; Barrio, J.; Etxebeste, A.; Garcia-Lopez, J.; Jimenez-Ramos, M.C.; Lacasta, C.; Muñoz, E.; Oliver, J.F.; Roser, J.; Llosa, G.
Title MACACO II test-beam with high energy photons Type Journal Article
Year 2020 Publication Physics in Medicine and Biology Abbreviated Journal Phys. Med. Biol.
Volume 65 Issue 24 Pages 245027 - 12pp
Keywords Compton imaging; Compton camera; proton therapy; LaBr3; test-beam; image reconstruction
Abstract The IRIS group at IFIC Valencia is developing a three-layer Compton camera for treatment monitoring in proton therapy. The system is composed of three detector planes, each made of a LaBr3<i monolithic crystal coupled to a SiPM array. Having obtained successful results with the first prototype (MACACO) that demonstrated the feasibility of the proposed technology, a second prototype (MACACO II) with improved performance has been developed, and is the subject of this work. The new system has an enhanced detector energy resolution which translates into a higher spatial resolution of the telescope. The image reconstruction method has also been improved with an accurate model of the sensitivity matrix. The device has been tested with high energy photons at the National Accelerator Centre (CNA, Seville). The tests involved a proton beam of 18 MeV impinging on a graphite target, to produce 4.4 MeV photons. Data were taken at different system positions of the telescope with the first detector at 65 and 160 mm from the target, and at different beam intensities. The measurements allowed successful reconstruction of the photon emission distribution at two target positions separated by 5 mm in different telescope configurations. This result was obtained both with data recorded in the first and second telescope planes (two interaction events) and, for the first time in beam experiments, with data recorded in the three planes (three interaction events).
Address [Ros Garcia, A.; Barrio, J.; Etxebeste, A.; Lacasta, C.; Munoz, E.; Oliver, J. F.; Roser, J.; Llosa, G.] Univ Valencia, CSIC, Inst Fis Corpuscular, Valencia, Spain, Email: arosgar@ific.uv.es
Corporate Author (up) Thesis
Publisher Iop Publishing Ltd Place of Publication Editor
Language English Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0031-9155 ISBN Medium
Area Expedition Conference
Notes WOS:000600803000001 Approved no
Is ISI yes International Collaboration no
Call Number IFIC @ pastor @ Serial 4654
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Author Balibrea-Correa, J.; Lerendegui-Marco, J.; Babiano-Suarez, V.; Caballero, L.; Calvo, D.; Ladarescu, I.; Olleros-Rodriguez, P.; Domingo-Pardo, C.
Title Machine Learning aided 3D-position reconstruction in large LaCl3 crystals Type Journal Article
Year 2021 Publication Nuclear Instruments & Methods in Physics Research A Abbreviated Journal Nucl. Instrum. Methods Phys. Res. A
Volume 1001 Issue Pages 165249 - 17pp
Keywords Gamma-ray; Position sensitive detectors; Monolithic crystals; Compton imaging; Machine Learning; Convolutional Neural Networks; Total Energy Detector; Neutron capture cross-section
Abstract We investigate five different models to reconstruct the 3D gamma-ray hit coordinates in five large LaCl3(Ce) monolithic crystals optically coupled to pixelated silicon photomultipliers. These scintillators have a base surface of 50 x 50 mm(2) and five different thicknesses, from 10 mm to 30 mm. Four of these models are analytical prescriptions and one is based on a Convolutional Neural Network. Average resolutions close to 1-2 mm fwhm are obtained in the transverse crystal plane for crystal thicknesses between 10 mm and 20 mm using analytical models. For thicker crystals average resolutions of about 3-5 mm fwhm are obtained. Depth of interaction resolutions between 1 mm and 4 mm are achieved depending on the distance of the interaction point to the photosensor surface. We propose a Machine Learning algorithm to correct for linearity distortions and pin-cushion effects. The latter allows one to keep a large field of view of about 70%-80% of the crystal surface, regardless of crystal thickness. This work is aimed at optimizing the performance of the so-called Total Energy Detector with Compton imaging capability (i-TED) for time-of-flight neutron capture cross-section measurements.
Address [Balibrea-Correa, J.; Lerendegui-Marco, J.; Babiano-Suarez, V.; Caballero, L.; Calvo, D.; Ladarescu, I.; Olleros-Rodriguez, P.; Domingo-Pardo, C.] Univ Valencia, CSIC, Inst Fis Corpuscular, Valencia, Spain, Email: javier.balibrea@ific.uv.es
Corporate Author (up) Thesis
Publisher Elsevier Place of Publication Editor
Language English Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0168-9002 ISBN Medium
Area Expedition Conference
Notes WOS:000641308300007 Approved no
Is ISI yes International Collaboration no
Call Number IFIC @ pastor @ Serial 4803
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Author Stoppa, F.; Vreeswijk, P.; Bloemen, S.; Bhattacharyya, S.; Caron, S.; Johannesson, G.; Ruiz de Austri, R.; van den Oetelaar, C.; Zaharijas, G.; Groot, P.J.; Cator, E.; Nelemans, G.
Title AutoSourceID-Light Fast optical source localization via U-Net and Laplacian of Gaussian Type Journal Article
Year 2022 Publication Astronomy & Astrophysics Abbreviated Journal Astron. Astrophys.
Volume 662 Issue Pages A109 - 8pp
Keywords astronomical databases; miscellaneous; methods; data analysis; stars; imaging; techniques; image processing
Abstract Aims. With the ever-increasing survey speed of optical wide-field telescopes and the importance of discovering transients when they are still young, rapid and reliable source localization is paramount. We present AutoSourceID-Light (ASID-L), an innovative framework that uses computer vision techniques that can naturally deal with large amounts of data and rapidly localize sources in optical images. Methods. We show that the ASID-L algorithm based on U-shaped networks and enhanced with a Laplacian of Gaussian filter provides outstanding performance in the localization of sources. A U-Net network discerns the sources in the images from many different artifacts and passes the result to a Laplacian of Gaussian filter that then estimates the exact location. Results. Using ASID-L on the optical images of the MeerLICHT telescope demonstrates the great speed and localization power of the method. We compare the results with SExtractor and show that our method outperforms this more widely used method. ASID-L rapidly detects more sources not only in low- and mid-density fields, but particularly in areas with more than 150 sources per square arcminute. The training set and code used in this paper are publicly available.
Address [Stoppa, F.; Vreeswijk, P.; Bloemen, S.; Groot, P. J.; Nelemans, G.] Radboud Univ Nijmegen, Dept Astrophys, IMAPP, POB 9010, NL-6500 GL Nijmegen, Netherlands, Email: f.stoppa@astro.ru.nl
Corporate Author (up) Thesis
Publisher Edp Sciences S A Place of Publication Editor
Language English Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0004-6361 ISBN Medium
Area Expedition Conference
Notes WOS:000818665600009 Approved no
Is ISI yes International Collaboration yes
Call Number IFIC @ pastor @ Serial 5291
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Author Roser, J.; Barrientos, L.; Bernabeu, J.; Borja-Lloret, M.; Muñoz, E.; Ros, A.; Viegas, R.; Llosa, G.
Title Joint image reconstruction algorithm in Compton cameras Type Journal Article
Year 2022 Publication Physics in Medicine and Biology Abbreviated Journal Phys. Med. Biol.
Volume 67 Issue 15 Pages 155009 - 15pp
Keywords Compton camera; compton imaging; hadron therapy; image reconstruction; LM-MLEM; Monte Carlo simulations; multi-layer compton telescope
Abstract Objective. To demonstrate the benefits of using an joint image reconstruction algorithm based on the List Mode Maximum Likelihood Expectation Maximization that combines events measured in different channels of information of a Compton camera. Approach. Both simulations and experimental data are employed to show the algorithm performance. Main results. The obtained joint images present improved image quality and yield better estimates of displacements of high-energy gamma-ray emitting sources. The algorithm also provides images that are more stable than any individual channel against the noisy convergence that characterizes Maximum Likelihood based algorithms. Significance. The joint reconstruction algorithm can improve the quality and robustness of Compton camera images. It also has high versatility, as it can be easily adapted to any Compton camera geometry. It is thus expected to represent an important step in the optimization of Compton camera imaging.
Address [Roser, J.; Barrientos, L.; Bernabeu, J.; Borja-Lloret, M.; Munoz, E.; Ros, A.; Viegas, R.; Llosa, G.] CSIC UV, Inst Fis Corpuscular IFIC, Valencia, Spain, Email: Jorge.Roser@ific.uv.es
Corporate Author (up) Thesis
Publisher IOP Publishing Ltd Place of Publication Editor
Language English Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0031-9155 ISBN Medium
Area Expedition Conference
Notes WOS:000827830200001 Approved no
Is ISI yes International Collaboration no
Call Number IFIC @ pastor @ Serial 5298
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Author Borys, D. et al; Brzezinski, K.
Title ProTheRaMon-a GATE simulation framework for proton therapy range monitoring using PET imaging Type Journal Article
Year 2022 Publication Physics in Medicine and Biology Abbreviated Journal Phys. Med. Biol.
Volume 67 Issue 22 Pages 224002 - 15pp
Keywords proton therapy; GATE; Monte Carlo simulations; J-PET; medical imaging
Abstract Objective. This paper reports on the implementation and shows examples of the use of the ProTheRaMon framework for simulating the delivery of proton therapy treatment plans and range monitoring using positron emission tomography (PET). ProTheRaMon offers complete processing of proton therapy treatment plans, patient CT geometries, and intra-treatment PET imaging, taking into account therapy and imaging coordinate systems and activity decay during the PET imaging protocol specific to a given proton therapy facility. We present the ProTheRaMon framework and illustrate its potential use case and data processing steps for a patient treated at the Cyclotron Centre Bronowice (CCB) proton therapy center in Krakow, Poland. Approach. The ProTheRaMon framework is based on GATE Monte Carlo software, the CASToR reconstruction package and in-house developed Python and bash scripts. The framework consists of five separated simulation and data processing steps, that can be further optimized according to the user's needs and specific settings of a given proton therapy facility and PET scanner design. Main results. ProTheRaMon is presented using example data from a patient treated at CCB and the J-PET scanner to demonstrate the application of the framework for proton therapy range monitoring. The output of each simulation and data processing stage is described and visualized. Significance. We demonstrate that the ProTheRaMon simulation platform is a high-performance tool, capable of running on a computational cluster and suitable for multi-parameter studies, with databases consisting of large number of patients, as well as different PET scanner geometries and settings for range monitoring in a clinical environment. Due to its modular structure, the ProTheRaMon framework can be adjusted for different proton therapy centers and/or different PET detector geometries. It is available to the community via github (Borys et al 2022).
Address [Borys, Damian] Silesian Tech Univ, Dept Syst Biol & Engn, Gliwice, Poland, Email: damin.borys@polsl.pl
Corporate Author (up) Thesis
Publisher IOP Publishing Ltd Place of Publication Editor
Language English Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0031-9155 ISBN Medium
Area Expedition Conference
Notes WOS:000885248200001 Approved no
Is ISI yes International Collaboration no
Call Number IFIC @ pastor @ Serial 5416
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Author Borja-Lloret, M.; Barrientos, L.; Bernabeu, J.; Lacasta, C.; Muñoz, E.; Ros, A.; Roser, J.; Viegas, R.; Llosa, G.
Title Influence of the background in Compton camera images for proton therapy treatment monitoring Type Journal Article
Year 2023 Publication Physics in Medicine and Biology Abbreviated Journal Phys. Med. Biol.
Volume 68 Issue 14 Pages 144001 - 16pp
Keywords Compton imaging; Compton camera; proton therapy; treatment monitoring; Monte Carlo simulation; image reconstruction; background
Abstract Objective. Background events are one of the most relevant contributions to image degradation in Compton camera imaging for hadron therapy treatment monitoring. A study of the background and its contribution to image degradation is important to define future strategies to reduce the background in the system. Approach. In this simulation study, the percentage of different kinds of events and their contribution to the reconstructed image in a two-layer Compton camera have been evaluated. To this end, GATE v8.2 simulations of a proton beam impinging on a PMMA phantom have been carried out, for different proton beam energies and at different beam intensities. Main results. For a simulated Compton camera made of Lanthanum (III) Bromide monolithic crystals, coincidences caused by neutrons arriving from the phantom are the most common type of background produced by secondary radiations in the Compton camera, causing between 13% and 33% of the detected coincidences, depending on the beam energy. Results also show that random coincidences are a significant cause of image degradation at high beam intensities, and their influence in the reconstructed images is studied for values of the time coincidence windows from 500 ps to 100 ns. Significance. Results indicate the timing capabilities required to retrieve the fall-off position with good precision. Still, the noise observed in the image when no randoms are considered make us consider further background rejection methods.
Address [Borja-Lloret, M.; Barrientos, L.; Bernabeu, J.; Lacasta, C.; Munoz, E.; Ros, A.; Roser, J.; Viegas, R.; Llosa, G.] Inst Fis Corpuscular IFIC, CSIC UV, Valencia, Spain, Email: Marina.Borja@csic.es
Corporate Author (up) Thesis
Publisher IOP Publishing Ltd Place of Publication Editor
Language English Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0031-9155 ISBN Medium
Area Expedition Conference
Notes WOS:001022671300001 Approved no
Is ISI yes International Collaboration no
Call Number IFIC @ pastor @ Serial 5571
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Author Stoppa, F.; Ruiz de Austri, R.; Vreeswijk, P.; Bhattacharyya, S.; Caron, S.; Bloemen, S.; Zaharijas, G.; Principe, G.; Vodeb, V.; Groot, P.J.; Cator, E.; Nelemans, G.
Title AutoSourceID-FeatureExtractor Optical image analysis using a two-step mean variance estimation network for feature estimation and uncertainty characterisation Type Journal Article
Year 2023 Publication Astronomy & Astrophysics Abbreviated Journal Astron. Astrophys.
Volume 680 Issue Pages A108 - 14pp
Keywords astronomical databases: miscellaneous; methods: data analysis; stars: imaging; techniques: image processing
Abstract Aims. In astronomy, machine learning has been successful in various tasks such as source localisation, classification, anomaly detection, and segmentation. However, feature regression remains an area with room for improvement. We aim to design a network that can accurately estimate sources' features and their uncertainties from single-band image cutouts, given the approximated locations of the sources provided by the previously developed code AutoSourceID-Light (ASID-L) or other external catalogues. This work serves as a proof of concept, showing the potential of machine learning in estimating astronomical features when trained on meticulously crafted synthetic images and subsequently applied to real astronomical data.Methods. The algorithm presented here, AutoSourceID-FeatureExtractor (ASID-FE), uses single-band cutouts of 32x32 pixels around the localised sources to estimate flux, sub-pixel centre coordinates, and their uncertainties. ASID-FE employs a two-step mean variance estimation (TS-MVE) approach to first estimate the features and then their uncertainties without the need for additional information, for example the point spread function (PSF). For this proof of concept, we generated a synthetic dataset comprising only point sources directly derived from real images, ensuring a controlled yet authentic testing environment.Results. We show that ASID-FE, trained on synthetic images derived from the MeerLICHT telescope, can predict more accurate features with respect to similar codes such as SourceExtractor and that the two-step method can estimate well-calibrated uncertainties that are better behaved compared to similar methods that use deep ensembles of simple MVE networks. Finally, we evaluate the model on real images from the MeerLICHT telescope and the Zwicky Transient Facility (ZTF) to test its transfer learning abilities.
Address [Stoppa, F.; Vreeswijk, P.; Bloemen, S.; Groot, P. J.; Nelemans, G.] Radboud Univ Nijmegen, Dept Astrophys, IMAPP, POB 9010, NL-6500 GL Nijmegen, Netherlands, Email: f.stoppa@astro.ru.nl
Corporate Author (up) Thesis
Publisher Edp Sciences S A Place of Publication Editor
Language English Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0004-6361 ISBN Medium
Area Expedition Conference
Notes WOS:001131898100003 Approved no
Is ISI yes International Collaboration yes
Call Number IFIC @ pastor @ Serial 5887
Permanent link to this record
 

 
Author Stoppa, F.; Bhattacharyya, S.; Ruiz de Austri, R.; Vreeswijk, P.; Caron, S.; Zaharijas, G.; Bloemen, S.; Principe, G.; Malyshev, D.; Vodeb, V.; Groot, P.J.; Cator, E.; Nelemans, G.
Title AutoSourceID-Classifier Star-galaxy classification using a convolutional neural network with spatial information Type Journal Article
Year 2023 Publication Astronomy & Astrophysics Abbreviated Journal Astron. Astrophys.
Volume 680 Issue Pages A109 - 16pp
Keywords methods: data analysis; techniques: image processing; astronomical databases: miscellaneous; stars: imaging; Galaxies: statistics
Abstract Aims. Traditional star-galaxy classification techniques often rely on feature estimation from catalogs, a process susceptible to introducing inaccuracies, thereby potentially jeopardizing the classification's reliability. Certain galaxies, especially those not manifesting as extended sources, can be misclassified when their shape parameters and flux solely drive the inference. We aim to create a robust and accurate classification network for identifying stars and galaxies directly from astronomical images.Methods. The AutoSourceID-Classifier (ASID-C) algorithm developed for this work uses 32x32 pixel single filter band source cutouts generated by the previously developed AutoSourceID-Light (ASID-L) code. By leveraging convolutional neural networks (CNN) and additional information about the source position within the full-field image, ASID-C aims to accurately classify all stars and galaxies within a survey. Subsequently, we employed a modified Platt scaling calibration for the output of the CNN, ensuring that the derived probabilities were effectively calibrated, delivering precise and reliable results.Results. We show that ASID-C, trained on MeerLICHT telescope images and using the Dark Energy Camera Legacy Survey (DECaLS) morphological classification, is a robust classifier and outperforms similar codes such as SourceExtractor. To facilitate a rigorous comparison, we also trained an eXtreme Gradient Boosting (XGBoost) model on tabular features extracted by SourceExtractor. While this XGBoost model approaches ASID-C in performance metrics, it does not offer the computational efficiency and reduced error propagation inherent in ASID-C's direct image-based classification approach. ASID-C excels in low signal-to-noise ratio and crowded scenarios, potentially aiding in transient host identification and advancing deep-sky astronomy.
Address [Stoppa, F.; Vreeswijk, P.; Bloemen, S.; Groot, P. J.; Nelemans, G.] Radboud Univ Nijmegen, Dept Astrophys IMAPP, POB 9010, NL-6500 GL Nijmegen, Netherlands, Email: f.stoppa@astro.ru.nl
Corporate Author (up) Thesis
Publisher Edp Sciences S A Place of Publication Editor
Language English Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0004-6361 ISBN Medium
Area Expedition Conference
Notes WOS:001131898100001 Approved no
Is ISI yes International Collaboration yes
Call Number IFIC @ pastor @ Serial 5888
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Author Lerendegui-Marco, J.; Babiano-Suarez, V.; Balibrea-Correa, J.; Caballero, L.; Calvo, D.; Ladarescu, I.; Domingo-Pardo, C.
Title Simultaneous Gamma-Neutron Vision device: a portable and versatile tool for nuclear inspections Type Journal Article
Year 2024 Publication EPJ Techniques and Instrumentation Abbreviated Journal EPJ Tech. Instrum.
Volume 11 Issue 1 Pages 2 - 17pp
Keywords Gamma imaging; Neutron imaging; Nuclear inspections; Homeland security; Nuclear waste characterization
Abstract This work presents GN-Vision, a novel dual gamma-ray and neutron imaging system, which aims at simultaneously obtaining information about the spatial origin of gamma-ray and neutron sources. The proposed device is based on two position sensitive detection planes and exploits the Compton imaging technique for the imaging of gamma-rays. In addition, spatial distributions of slow- and thermal-neutron sources (<100 eV) are reconstructed by using a passive neutron pin-hole collimator attached to the first detection plane. The proposed gamma-neutron imaging device could be of prime interest for nuclear safety and security applications. The two main advantages of this imaging system are its high efficiency and portability, making it well suited for nuclear applications were compactness and real-time imaging is important. This work presents the working principle and conceptual design of the GN-Vision system and explores, on the basis of Monte Carlo simulations, its simultaneous gamma-ray and neutron detection and imaging capabilities for a realistic scenario where a Cf-252 source is hidden in a neutron moderating container.
Address [Lerendegui-Marco, Jorge; Babiano-Suarez, Victor; Balibrea-Correa, Javier; Caballero, Luis; Calvo, David; Ladarescu, Ion; Domingo-Pardo, Cesar] Univ Valencia, Inst Fis Corpuscular, CSIC, Valencia, Spain, Email: jorge.lerendegui@ific.uv.es
Corporate Author (up) Thesis
Publisher Springer Place of Publication Editor
Language English Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 2195-7045 ISBN Medium
Area Expedition Conference
Notes WOS:001171512700001 Approved no
Is ISI yes International Collaboration no
Call Number IFIC @ pastor @ Serial 5975
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