Caballero, L., Albiol, F., Corbi Bellot, A., Domingo-Pardo, C., Leganes Nieto, J. L., Agramunt Ros, J., et al. (2018). Gamma-ray imaging system for real-time measurements in nuclear waste characterisation. J. Instrum., 13, P03016–23pp.
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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Magan, D. L. P., Caballero, L., Domingo-Pardo, C., Agramunt-Ros, J., Albiol, F., Casanovas, A., et al. (2016). First tests of the applicability of gamma-ray imaging for background discrimination in time-of-flight neutron capture measurements. Nucl. Instrum. Methods Phys. Res. A, 823, 107–119.
Abstract: In this work we explore for the first time the applicability of using gamma-ray imaging in neutron capture measurements to identify and suppress spatially localized background. For this aim, a pinhole gamma camera is assembled, tested and characterized in terms of energy and spatial performance. It consists of a monolithic CeBr3 scintillating crystal coupled to a position-sensitive photomultiplier and readout through an integrated circuit AMIC2GR. The pinhole collimator is a massive carven block of lead. A series of dedicated measurements with calibrated sources and with a neutron beam incident on a Au-197 sample have been carried out at n_TOF, achieving an enhancement of a factor of two in the signal-to-background ratio when selecting only those events coming from the direction of the sample.
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Babiano, V., Balibrea, J., Caballero, L., Calvo, D., Ladarescu, I., Mira Prats, S., et al. (2020). First i-TED demonstrator: A Compton imager with Dynamic Electronic Collimation. Nucl. Instrum. Methods Phys. Res. A, 953, 163228–9pp.
Abstract: i-TED consists of both a total energy detector and a Compton camera primarily intended for the measurement of neutron capture cross sections by means of the simultaneous combination of neutron time-of-flight (TOF) and gamma-ray imaging techniques. TOF allows one to obtain a neutron-energy differential capture yield, whereas the imaging capability is intended for the discrimination of radiative background sources, that have a spatial origin different from that of the capture sample under investigation. A distinctive feature of i-TED is the embedded Dynamic Electronic Collimation (DEC) concept, which allows for a trade-off between efficiency and image resolution. Here we report on some general design considerations and first performance characterization measurements made with an i-TED demonstrator in order to explore its gamma-ray detection and imaging capabilities.
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Schaffter, T. et al, Albiol, F., & Caballero, L. (2020). Evaluation of Combined Artificial Intelligence and Radiologist Assessment to Interpret Screening Mammograms. JAMA Netw. Open, 3(3), e200265–15pp.
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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Perez-Cerdan, A. B., Rubio, B., Gelletly, W., Algora, A., Agramunt, J., Nacher, E., et al. (2013). Deformation of Sr and Rb isotopes close to the N = Z line via beta-decay studies using the total absorption technique. Phys. Rev. C, 88(1), 014324–15pp.
Abstract: A study of the Gamow-Teller strength distributions B(GT) in the beta decay of Sr-78 and Rb-76,Rb-78 has been made using a total absorption spectrometer (TAS). Following the success in deducing the sign of the deformation for Sr-76, a similar approach is adopted for Sr-78 based on a comparison of the measured B(GT) with quasiparticle random-phase approximation calculations. This work confirms its previously expected prolate deformation in the ground state. Conclusions about the structure of the odd-odd Rb-76,Rb-78 isotopes have been drawn based on their measured B(GT) distributions.
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