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Author |
Olleros, P.; Caballero, L.; Domingo-Pardo, C.; Babiano, V.; Ladarescu, I.; Calvo, D.; Gramage, P.; Nacher, E.; Tain, J.L.; Tolosa, A. |
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Title |
On the performance of large monolithic LaCl3(Ce) crystals coupled to pixelated silicon photosensors |
Type |
Journal Article |
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Year |
2018 |
Publication |
Journal of Instrumentation |
Abbreviated Journal |
J. Instrum. |
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Volume  |
13 |
Issue |
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Pages |
P03014 - 17pp |
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Keywords |
Compton imaging; Detector modelling and simulations I (interaction of radiation with matter interaction of photons with matter interaction of hadrons with matter etc); Gamma detectors (scintillators CZT HPG HgI etc); Instrumentation and methods for time-of-flight (TOF); spectroscopy |
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Abstract |
We investigate the performance of large area radiation detectors, with high energy-and spatial-resolution, intended for the development of a Total Energy Detector with gamma-ray imaging capability, so-called i-TED. This new development aims for an enhancement in detection sensitivity in time-of-flight neutron capture measurements, versus the commonly used C6D6 liquid scintillation total-energy detectors. In this work, we study in detail the impact of the readout photosensor on the energy response of large area (50 x 50 mm(2)) monolithic LaCl3(Ce) crystals, in particular when replacing a conventional mono-cathode photomultiplier tube by an 8 x 8 pixelated silicon photomultiplier. Using the largest commercially available monolithic SiPM array (25 cm(2)), with a pixel size of 6 x 6 mm(2), we have measured an average energy resolution of 3.92% FWHM at 662 keV for crystal thick-nesses of 10, 20 and 30 mm. The results are confronted with detailed Monte Carlo (MC) calculations, where optical processes and properties have been included for the reliable tracking of the scintillation photons. After the experimental validation of the MC model, we use our MC code to explore the impact of a smaller photosensor segmentation on the energy resolution. Our optical MC simulations predict only a marginal deterioration of the spectroscopic performance for pixels of 3 x 3 mm(2). |
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Address |
[Olleros, P.; Caballero, L.; Domingo-Pardo, C.; Babiano, V.; Ladarescu, I.; Calvo, D.; Gramage, P.; Tain, J. L.; Tolosa, A.] Univ Valencia, CSIC, Inst Fis Corpuscular, C Catedrat Jose Beltran 2, Paterna 46980, Spain, Email: Luis.Caballero@ific.uv.es |
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Iop Publishing Ltd |
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English |
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ISSN |
1748-0221 |
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Notes |
WOS:000428146300004 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
no |
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Call Number |
IFIC @ pastor @ |
Serial |
3542 |
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Author |
Tortajada, S.; Albiol, F.; Caballero, L.; Albiol, A.; Leganes-Nieto, J.L. |
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Title |
A portable geometry-independent tomographic system for gamma-ray, a next generation of nuclear waste characterization |
Type |
Journal Article |
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Year |
2023 |
Publication |
Scientific Reports |
Abbreviated Journal |
Sci Rep |
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Volume |
13 |
Issue |
1 |
Pages |
12284 - 10pp |
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Abstract |
One of the main activities of the nuclear industry is the characterisation of radioactive waste based on the detection of gamma radiation. Large volumes of radioactive waste are classified according to their average activity, but often the radioactivity exceeds the maximum allowed by regulators in specific parts of the bulk. In addition, the detection of the radiation is currently based on static detection systems where the geometry of the bulk is fixed and well known. Furthermore, these systems are not portable and depend on the transport of waste to the places where the detection systems are located. However, there are situations where the geometry varies and where moving waste is complex. This is especially true in compromised situations.We present a new model for nuclear waste management based on a portable and geometry-independent tomographic system for three-dimensional image reconstruction for gamma radiation detection. The system relies on a combination of a gamma radiation camera and a visible camera that allows to visualise radioactivity using augmented reality and artificial computer vision techniques. This novel tomographic system has the potential to be a disruptive innovation in the nuclear industry for nuclear waste management. |
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Address |
[Tortajada, Salvador; Albiol, Francisco; Caballero, Luis] Univ Valencia, CSIC, Inst Fis Corpuscular, E-46980 Paterna Valencia, Spain, Email: s.tortajada@ific.uv.es |
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Publisher |
Nature Portfolio |
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English |
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ISSN |
2045-2322 |
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Notes |
WOS:001041587900052 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
no |
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Call Number |
IFIC @ pastor @ |
Serial |
5612 |
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Author |
Lerendegui-Marco, J.; Babiano-Suarez, V.; Balibrea-Correa, J.; Caballero, L.; Calvo, D.; Ladarescu, I.; Domingo-Pardo, C. |
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Title |
Simultaneous Gamma-Neutron Vision device: a portable and versatile tool for nuclear inspections |
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Journal Article |
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Year |
2024 |
Publication |
EPJ Techniques and Instrumentation |
Abbreviated Journal |
EPJ Tech. Instrum. |
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Volume |
11 |
Issue |
1 |
Pages |
2 - 17pp |
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Keywords |
Gamma imaging; Neutron imaging; Nuclear inspections; Homeland security; Nuclear waste characterization |
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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. |
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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 |
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Publisher |
Springer |
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English |
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ISSN |
2195-7045 |
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Notes |
WOS:001171512700001 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
no |
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Call Number |
IFIC @ pastor @ |
Serial |
5975 |
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Author |
Schaffter, T. et al; Albiol, F.; Caballero, L. |
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Title |
Evaluation of Combined Artificial Intelligence and Radiologist Assessment to Interpret Screening Mammograms |
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Journal Article |
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Year |
2020 |
Publication |
JAMA Network Open |
Abbreviated Journal |
JAMA Netw. Open |
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Volume |
3 |
Issue |
3 |
Pages |
e200265 - 15pp |
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Keywords |
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Abstract |
Importance Mammography screening currently relies on subjective human interpretation. Artificial intelligence (AI) advances could be used to increase mammography screening accuracy by reducing missed cancers and false positives. Objective To evaluate whether AI can overcome human mammography interpretation limitations with a rigorous, unbiased evaluation of machine learning algorithms. Design, Setting, and Participants In this diagnostic accuracy study conducted between September 2016 and November 2017, an international, crowdsourced challenge was hosted to foster AI algorithm development focused on interpreting screening mammography. More than 1100 participants comprising 126 teams from 44 countries participated. Analysis began November 18, 2016. Main Outcomes and Measurements Algorithms used images alone (challenge 1) or combined images, previous examinations (if available), and clinical and demographic risk factor data (challenge 2) and output a score that translated to cancer yes/no within 12 months. Algorithm accuracy for breast cancer detection was evaluated using area under the curve and algorithm specificity compared with radiologists' specificity with radiologists' sensitivity set at 85.9% (United States) and 83.9% (Sweden). An ensemble method aggregating top-performing AI algorithms and radiologists' recall assessment was developed and evaluated. Results Overall, 144231 screening mammograms from 85580 US women (952 cancer positive <= 12 months from screening) were used for algorithm training and validation. A second independent validation cohort included 166578 examinations from 68008 Swedish women (780 cancer positive). The top-performing algorithm achieved an area under the curve of 0.858 (United States) and 0.903 (Sweden) and 66.2% (United States) and 81.2% (Sweden) specificity at the radiologists' sensitivity, lower than community-practice radiologists' specificity of 90.5% (United States) and 98.5% (Sweden). Combining top-performing algorithms and US radiologist assessments resulted in a higher area under the curve of 0.942 and achieved a significantly improved specificity (92.0%) at the same sensitivity. Conclusions and Relevance While no single AI algorithm outperformed radiologists, an ensemble of AI algorithms combined with radiologist assessment in a single-reader screening environment improved overall accuracy. This study underscores the potential of using machine learning methods for enhancing mammography screening interpretation. Question How do deep learning algorithms perform compared with radiologists in screening mammography interpretation? Findings In this diagnostic accuracy study using 144231 screening mammograms from 85580 women from the United States and 166578 screening mammograms from 68008 women from Sweden, no single artificial intelligence algorithm outperformed US community radiologist benchmarks; including clinical data and prior mammograms did not improve artificial intelligence performance. However, combining best-performing artificial intelligence algorithms with single-radiologist assessment demonstrated increased specificity. Meaning Integrating artificial intelligence to mammography interpretation in single-radiologist settings could yield significant performance improvements, with the potential to reduce health care system expenditures and address resource scarcity experienced in population-based screening programs. This diagnostic accuracy study evaluates whether artificial intelligence can overcome human mammography interpretation limits with a rigorous, unbiased evaluation of machine learning algorithms. |
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Address |
[Schaffter, Thomas; Hoff, Bruce; Yu, Thomas; Neto, Elias Chaibub; Friend, Stephen; Guinney, Justin] Sage Bionetworks, Computat Oncol, Seattle, WA USA, Email: gustavo@us.ibm.com |
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Publisher |
Amer Medical Assoc |
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English |
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ISSN |
2574-3805 |
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Notes |
WOS:000519249800002 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
4683 |
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