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Albiol, F., Corbi, A., & Albiol, A. (2017). 3D measurements in conventional X-ray imaging with RGB-D sensors. Med. Eng. Phys., 42, 73–79.
Abstract: A method for deriving 3D internal information in conventional X-ray settings is presented. It is based on the combination of a pair of radiographs from a patient and it avoids the use of X-ray-opaque fiducials and external reference structures. To achieve this goal, we augment an ordinary X-ray device with a consumer RGB-D camera. The patient' s rotation around the craniocaudal axis is tracked relative to this camera thanks to the depth information provided and the application of a modern surface-mapping algorithm. The measured spatial information is then translated to the reference frame of the X-ray imaging system. By using the intrinsic parameters of the diagnostic equipment, epipolar geometry, and X-ray images of the patient at different angles, 3D internal positions can be obtained. Both the RGB-D and Xray instruments are first geometrically calibrated to find their joint spatial transformation. The proposed method is applied to three rotating phantoms. The first two consist of an anthropomorphic head and a torso, which are filled with spherical lead bearings at precise locations. The third one is made of simple foam and has metal needles of several known lengths embedded in it. The results show that it is possible to resolve anatomical positions and lengths with a millimetric level of precision. With the proposed approach, internal 3D reconstructed coordinates and distances can be provided to the physician. It also contributes to reducing the invasiveness of ordinary X-ray environments and can replace other types of clinical explorations that are mainly aimed at measuring or geometrically relating elements that are present inside the patient's body.
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Albiol, A., Albiol, F., Paredes, R., Plasencia-Martinez, J. M., Blanco Barrio, A., Garcia Santos, J. M., et al. (2022). A comparison of Covid-19 early detection between convolutional neural networks and radiologists. Insights Imaging, 13(1), 122–12pp.
Abstract: Background The role of chest radiography in COVID-19 disease has changed since the beginning of the pandemic from a diagnostic tool when microbiological resources were scarce to a different one focused on detecting and monitoring COVID-19 lung involvement. Using chest radiographs, early detection of the disease is still helpful in resource-poor environments. However, the sensitivity of a chest radiograph for diagnosing COVID-19 is modest, even for expert radiologists. In this paper, the performance of a deep learning algorithm on the first clinical encounter is evaluated and compared with a group of radiologists with different years of experience. Methods The algorithm uses an ensemble of four deep convolutional networks, Ensemble4Covid, trained to detect COVID-19 on frontal chest radiographs. The algorithm was tested using images from the first clinical encounter of positive and negative cases. Its performance was compared with five radiologists on a smaller test subset of patients. The algorithm's performance was also validated using the public dataset COVIDx. Results Compared to the consensus of five radiologists, the Ensemble4Covid model achieved an AUC of 0.85, whereas the radiologists achieved an AUC of 0.71. Compared with other state-of-the-art models, the performance of a single model of our ensemble achieved nonsignificant differences in the public dataset COVIDx. Conclusion The results show that the use of images from the first clinical encounter significantly drops the detection performance of COVID-19. The performance of our Ensemble4Covid under these challenging conditions is considerably higher compared to a consensus of five radiologists. Artificial intelligence can be used for the fast diagnosis of COVID-19.
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Tortajada, S., Albiol, F., Caballero, L., Albiol, A., & Leganes-Nieto, J. L. (2023). A portable geometry-independent tomographic system for gamma-ray, a next generation of nuclear waste characterization. Sci Rep, 13(1), 12284–10pp.
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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Albiol, A., Corbi, A., & Albiol, F. (2017). Automatic intensity windowing of mammographic images based on a perceptual metric. Med. Phys., 44(4), 1369–1378.
Abstract: Purpose: Initial auto-adjustment of the window level WL and width WW applied to mammographic images. The proposed intensity windowing (IW) method is based on the maximization of the mutual information (MI) between a perceptual decomposition of the original 12-bit sources and their screen displayed 8-bit version. Besides zoom, color inversion and panning operations, IW is the most commonly performed task in daily screening and has a direct impact on diagnosis and the time involved in the process. Methods: The authors present a human visual system and perception-based algorithm named GRAIL (Gabor-relying adjustment of image levels). GRAIL initially measures a mammogram's quality based on the MI between the original instance and its Gabor-filtered derivations. From this point on, the algorithm performs an automatic intensity windowing process that outputs the WL/WW that best displays each mammogram for screening. GRAIL starts with the default, high contrast, wide dynamic range 12-bit data, and then maximizes the graphical information presented in ordinary 8-bit displays. Tests have been carried out with several mammogram databases. They comprise correlations and an ANOVA analysis with the manual IW levels established by a group of radiologists. A complete MATLAB implementation of GRAIL is available at . Results: Auto-leveled images show superior quality both perceptually and objectively compared to their full intensity range and compared to the application of other common methods like global contrast stretching (GCS). The correlations between the human determined intensity values and the ones estimated by our method surpass that of GCS. The ANOVA analysis with the upper intensity thresholds also reveals a similar outcome. GRAIL has also proven to specially perform better with images that contain micro-calcifications and/or foreign X-ray-opaque elements and with healthy BI-RADS A-type mammograms. It can also speed up the initial screening time by a mean of 4.5 s per image. Conclusions: A novel methodology is introduced that enables a quality-driven balancing of the WL/WW of mammographic images. This correction seeks the representation that maximizes the amount of graphical information contained in each image. The presented technique can contribute to the diagnosis and the overall efficiency of the breast screening session by suggesting, at the beginning, an optimal and customized windowing setting for each mammogram.
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Agramunt, J. et al, Tain, J. L., Albiol, F., Algora, A., Domingo-Pardo, C., Jordan, M. D., et al. (2016). Characterization of a neutron-beta counting system with beta-delayed neutron emitters. Nucl. Instrum. Methods Phys. Res. A, 807, 69–78.
Abstract: A new detection system for the measurement of beta-delayed neutron emission probabilities has been characterized using fission products with well known beta-delayed neutron emission properties. The setup consists of BELEN-20, a 4 pi-neutron counter with twenty He-3 proportional tubes arranged inside a large polyethylene neutron moderator, a thin Si detector for beta counting and a self-triggering digital data acquisition system. The use of delayed-neutron precursors with different neutron emission windows allowed the study of the effect of energy dependency on neutron, beta and beta-neutron rates. The observed effect is well reproduced by Monte Carlo simulations. The impact of this dependency on the accuracy of neutron emission probabilities is discussed. A new accurate value of the neutron emission probability for the important delayed-neutron precursor I-137 was obtained, P-n = 7.76(14)%.
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