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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 |
Type |
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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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 |
Babiano-Suarez, V. et al; Lerendegui-Marco, J.; Balibrea-Correa, J.; Caballero, L.; Calvo, D.; Ladarescu, I.; Real, D.; Domingo-Pardo, C. |
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Title |
Imaging neutron capture cross sections: i-TED proof-of-concept and future prospects based on Machine-Learning techniques |
Type |
Journal Article |
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Year |
2021 |
Publication |
European Physical Journal A |
Abbreviated Journal |
Eur. Phys. J. A |
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Volume |
57 |
Issue |
6 |
Pages |
197 - 17pp |
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Abstract |
i-TED is an innovative detection system which exploits Compton imaging techniques to achieve a superior signal-to-background ratio in (n, gamma) cross-section measurements using time-of-flight technique. This work presents the first experimental validation of the i-TED apparatus for high-resolution time-of-flight experiments and demonstrates for the first time the concept proposed for background rejection. To this aim, the Au-197(n, gamma) and Fe-56(n, gamma) reactions were studied at CERN n_TOF using an i-TED demonstrator based on three position-sensitive detectors. Two C6D6 detectors were also used to benchmark the performance of i-TED. The i-TED prototype built for this study shows a factor of similar to 3 higher detection sensitivity than state-of-the-art C6D6 detectors in the 10 keV neutron-energy region of astrophysical interest. This paper explores also the perspectives of further enhancement in performance attainable with the final i-TED array consisting of twenty position-sensitive detectors and newanalysis methodologies based on Machine-Learning techniques. |
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ISSN |
1434-6001 |
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Notes |
WOS:000662881100001 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
4875 |
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Author |
n_TOF Collaboration (Domingo-Pardo, C. et al); Babiano-Suarez, V.; Balibrea-Correa, J.; Caballero, L.; Ladarescu, I.; Lerendegui-Marco, J.; Tain, J.L.; Tarifeño-Saldivia, A. |
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Title |
Advances and new ideas for neutron-capture astrophysics experiments at CERN n_TOF |
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Journal Article |
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Year |
2023 |
Publication |
European Physical Journal A |
Abbreviated Journal |
Eur. Phys. J. A |
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Volume |
59 |
Issue |
1 |
Pages |
8 - 11pp |
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Abstract |
This article presents a few selected developments and future ideas related to the measurement of (n, gamma ) data of astrophysical interest at CERN n_TOF. The MC-aided analysis methodology for the use of low-efficiency radiation detectors in time-of-flight neutron-capture measurements is discussed, with particular emphasis on the systematic accuracy. Several recent instrumental advances are also presented, such as the development of total-energy detectors with gamma- ray imaging capability for background suppression, and the development of an array of small-volume organic scintilla tors aimed at exploiting the high instantaneous neutron-flux of EAR2. Finally, astrophysics prospects related to the intermediate i neutron-capture process of nucleosynthesis are discussed in the context of the new NEAR activation area. |
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Address |
[Domingo-Pardo, C.; Babiano-Suarez, V.; Balibrea-Correa, J.; Caballero, L.; Ladarescu, I.; Lerendegui-Marco, J.; Tain, J. L.; Tarifeno-Saldivia, A.] Univ Valencia, CSIC, Inst Fis Corpuscular, Valencia, Spain, Email: domingo@ific.uv.es |
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Publisher |
Springer |
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Language |
English |
Summary Language |
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Series Volume |
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ISSN |
1434-6001 |
ISBN |
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Notes |
WOS:000926364900001 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
5479 |
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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 |
Type |
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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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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Author |
Caballero, L.; Albiol, F.; Corbi Bellot, A.; Domingo-Pardo, C.; Leganes Nieto, J.L.; Agramunt Ros, J.; Contreras, P.; Monserrate, M.; Olleros Rodriguez, P.; Perez Magan, D.L. |
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Title |
Gamma-ray imaging system for real-time measurements in nuclear waste characterisation |
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 |
P03016 - 23pp |
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Keywords |
Inspection with gamma rays; Radiation monitoring |
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Abstract |
Acompact, portable and large field-of-viewgamma camera that is able to identify, locate and quantify gamma-ray emitting radioisotopes in real-time has been developed. The device delivers spectroscopic and imaging capabilities that enable its use it in a variety of nuclear waste characterisation scenarios, such as radioactivity monitoring in nuclear power plants and more specifically for the decommissioning of nuclear facilities. The technical development of this apparatus and some examples of its application in field measurements are reported in this article. The performance of the presented gamma-camera is also benchmarked against other conventional techniques. |
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Address |
[Caballero, L.] Univ Valencia, CSIC, Inst Fis Corpuscular, C Catedrat Jose Beltran 2, E-46980 Paterna, Spain, Email: Luis.Caballero@ific.uv.es |
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Publisher |
Iop Publishing Ltd |
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English |
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ISSN |
1748-0221 |
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Notes |
WOS:000428146300006 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
no |
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Call Number |
IFIC @ pastor @ |
Serial |
3540 |
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