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Sergio J. Sanabria

Bio: Sergio J. Sanabria is an academic researcher from University of Zurich. The author has contributed to research in topics: Glued laminated timber & Imaging phantom. The author has an hindex of 15, co-authored 57 publications receiving 646 citations. Previous affiliations of Sergio J. Sanabria include ETH Zurich & University of Deusto.


Papers
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Journal ArticleDOI
TL;DR: In this paper, the influence of moisture content on the elastic characteristics of beech wood (Fagus sylvatica L.) by means of ultrasonic waves was investigated, and a set of elastic engineering parameters (i.e., three Young's moduli, three shear moduli and six Poisson's ratios) was determined at four specific moisture contents.
Abstract: The present study investigates the influence of moisture content on the elastic characteristics of beech wood (Fagus sylvatica L.) by means of ultrasonic waves. A set of elastic engineering parameters (i.e. three Young’s moduli, three shear moduli and six Poisson’s ratios) is determined at four specific moisture contents. The results reveal the significant influence of the moisture content on the elastic behaviour of beech wood. With the exception of some Poisson’s ratios, the engineering parameters decrease with increasing moisture content, indicating a decline in stiffness at higher moisture contents. At the same time, wood anisotropy, displayed by the two-dimensional representation of the velocity surface, remains almost unchanged. The results prove that the ultrasonic technique is suitable for determining the elastic moduli. However, non-diagonal terms of the stiffness matrix must be considered when calculating the Young’s moduli. This is shown experimentally by comparing the ultrasonic Young’s moduli calculated without, and allowing for, the non-diagonal terms. While the ultrasonic technique is found to be reliable to measure the elastic moduli, based on the measured values, its eligibility to measure the Poisson’s ratios remains uncertain.

77 citations

Journal ArticleDOI
TL;DR: A SoS reconstruction strategy, where the forward problem is formulated using differential time-of-flight measurements based on apparent displacements along different ultrasound wave propagation paths, and the inverse problem is solved in spatial-domain using a proposed total-variation scheme with spatially-varying anisotropic weighting to compensate for geometric bias from limited angle imaging setup.
Abstract: Despite many uses of ultrasound, some pathologies such as breast cancer still cannot reliably be diagnosed in either conventional B-mode ultrasound imaging nor with more recent ultrasound elastography methods. Speed-of-sound (SoS) is a quantitative imaging biomarker, which is sensitive to structural changes due to pathology, and hence could facilitate diagnosis. Full-angle ultrasound computed tomography (USCT) was proposed to obtain spatially-resolved SoS images, however, its water-bath setup involves practical limitations. To increase clinical utility and for widespread use, recently, a limited-angle Fourier-domain SoS reconstruction was proposed, however, it suffers from significant image reconstruction artifacts. In this work, we present a SoS reconstruction strategy, where the forward problem is formulated using differential time-of-flight measurements based on apparent displacements along different ultrasound wave propagation paths, and the inverse problem is solved in spatial-domain using a proposed total-variation scheme with spatially-varying anisotropic weighting to compensate for geometric bias from limited angle imaging setup. This is shown to be robust to missing displacement data and easily allow for incorporating any prior geometric information. In numerical simulations, SoS values in inclusions are accurately reconstructed with 90% accuracy up to a noise level of 50%. With respect to Fourier-domain reconstruction, our proposed method improved contrast ratio from 0.37 to 0.67 for even high noise levels such as 50%. Numerical full-wave simulation and our preliminary in vivo results illustrate the clinical applicability of our method in a breast cancer imaging setting. Our proposed method requires single-sided access to the tissue and can be implemented as an add-on to conventional ultrasound equipment, applicable to a range of transducers and applications.

75 citations

Journal ArticleDOI
TL;DR: In this paper, a normal through-transmission set-up with 120 kHz transducers allowed for successful and accurate imaging of the geometry of glued and non-glued areas in all inspected objects.
Abstract: Glued timber products are widely used in construction; therefore, it is necessary to develop non-destructive bonding quality assessment methods for long-term structural health monitoring. Air-coupled ultrasound (ACU) inspection is a novel technique, with phenomenal improvements in reproducibility compared to traditional contact ultrasonics, unlimited scanning possibilities, and a high potential for delamination detection in wood products. As part of an ongoing project, glued timber samples of 10 mm thickness with artificial glue line defects were inspected. A normal through-transmission set-up with 120 kHz transducers allowed for successful and accurate imaging of the geometry of glued and non-glued areas in all inspected objects. The influence of wood heterogeneity and the reproducibility of ACU amplitude measurements were analysed in detail, identifying the main sources of variation. Future work is planned for the inspection of more complex glued timber objects.

42 citations

Journal ArticleDOI
TL;DR: In this article, an air-coupled ultrasound (ACU) 120 kHz normal transmission system enabled successful imaging of bonding and saw cut defects in multilayered glulam beams up to 280 mm in height with a signal-to-noise ratio (SNR) of 40 dB.
Abstract: A novel air-coupled ultrasound (ACU) 120 kHz normal transmission system enabled successful imaging of bonding and saw cut defects in multilayered glulam beams up to 280 mm in height with a signal-to-noise ratio (SNR) of 40 dB. The main wave propagation paths were modeled; quasi-longitudinal and quasi-transverse modes were coupled in each lamella and the sound field was found to be shifted from the insonification axis as a function of the ring angle, leading to interference of wave paths in the receiver and to 15 dB amplitude variability in defect-free glulam. The assessment was improved with spatial processing algorithms that profited from the arbitrary scanning resolution and high reproducibility of ACU. Overlapped averaging reduced in-band noise by 15 dB, amplitude tracking captured only the first incoming oscillation, thus minimizing diffraction around defect regions, and image normalization compensated 6 dB of systematic amplitude variability across the fiber direction. The application of ACU to in situ defect monitoring was demonstrated by using multiparameter difference imaging of measurements of the same sample with and without saw cut defects. The segmentation of the defect geometry was improved significantly and the amplitude variability was reduced by 10 dB. Further work is planned to model additional insonification setups and grain and density heterogeneities.

40 citations

Journal ArticleDOI
TL;DR: There were significant differences of SoS and ΔSoS between young and elderly females, and the novel technique shows potential for sarcopenia quantification using a standard ultrasound machine.
Abstract: To measure speed of sound (SoS) with a novel hand-held ultrasound technique as a quantitative indicator for muscle loss and fatty muscular degeneration. Both calf muscles of 11 healthy, young females (mean age 29 years), and 10 elderly females (mean age 82 years) were prospectively examined with a standard ultrasound machine. A flat Plexiglas® reflector, on the opposite side of the probe with the calf in between, was used as timing reference for SoS (m/s) and ΔSoS (variation of SoS, m/s). Handgrip strength (kPA), Tegner activity scores, and 5-point comfort score (1 = comfortable to 5 = never again) were also assessed. Ultrasound parameters (muscle/adipose thickness, echo intensity) were measured for comparison. Both calves were assessed in less than two minutes. All measurements were successful. The elderly females showed significantly lower SoS (1516 m/s, SD17) compared to the young adults (1545 m/s, SD10; p < 0.01). The ΔSoS of elderly females was significantly higher (12.2 m/s, SD3.6) than for young females (6.4 m/s, SD1.5; p < 0.01). Significant correlations of SoS with hand grip strength (r = 0.644) and Tegner activity score (rs = 0.709) were found, of similar magnitude as the correlation of hand grip strength with Tegner activity score (rs = 0.794). The average comfort score of the elderly was 1.1 and for the young adults 1.4. SoS senior/young classification (AUC = 0.936) was superior to conventional US parameters. There were significant differences of SoS and ΔSoS between young and elderly females. Measurements were fast and well tolerated. The novel technique shows potential for sarcopenia quantification using a standard ultrasound machine. • Speed of sound ultrasound: a novel technique to identify sarcopenia in seniors. • Measurements were fast and well tolerated using a standard ultrasound machine. • The novel technique shows potential for sarcopenia quantification.

37 citations


Cited by
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Journal ArticleDOI
TL;DR: A gentle introduction to deep learning in medical image processing is given, proceeding from theoretical foundations to applications, including general reasons for the popularity of deep learning, including several major breakthroughs in computer science.
Abstract: This paper tries to give a gentle introduction to deep learning in medical image processing, proceeding from theoretical foundations to applications. We first discuss general reasons for the popularity of deep learning, including several major breakthroughs in computer science. Next, we start reviewing the fundamental basics of the perceptron and neural networks, along with some fundamental theory that is often omitted. Doing so allows us to understand the reasons for the rise of deep learning in many application domains. Obviously medical image processing is one of these areas which has been largely affected by this rapid progress, in particular in image detection and recognition, image segmentation, image registration, and computer-aided diagnosis. There are also recent trends in physical simulation, modeling, and reconstruction that have led to astonishing results. Yet, some of these approaches neglect prior knowledge and hence bear the risk of producing implausible results. These apparent weaknesses highlight current limitations of deep ()learning. However, we also briefly discuss promising approaches that might be able to resolve these problems in the future.

339 citations

Journal ArticleDOI
TL;DR: Wang et al. as mentioned in this paper provided a general background, highlighted representative results with an emphasis on medical imaging, and discussed key issues that need to be addressed in this emerging field.
Abstract: Deep-learning-based tomographic imaging is an important application of artificial intelligence and a new frontier of machine learning. Deep learning has been widely used in computer vision and image analysis, which deal with existing images, improve these images, and produce features from them. Since 2016, deep learning techniques have been actively researched for tomographic imaging, especially in the context of biomedicine, with impressive results and great potential. Tomographic reconstruction produces images of multi-dimensional structures from externally measured ‘encoded’ data in the form of various tomographic transforms (integrals, harmonics, echoes and so on). In this Review, we provide a general background, highlight representative results with an emphasis on medical imaging, and discuss key issues that need to be addressed in this emerging field. In particular, tomographic imaging is an integral part of modern medicine, and will play a key role in personalized, preventive and precision medicine and make it intelligent, inexpensive and indiscriminate. The popularity of deep learning is leading to new areas in biomedical applications. Wang and colleagues summarize in this Review the recent development and future directions of deep neural networks for superior image quality in the tomographic imaging field.

250 citations

Journal ArticleDOI
TL;DR: Developments in air-coupled transduction and electronics are briefly treated, although the emphasis here is on methods of characterization and inspection, and in overcoming limitations inherent in the use of such a tenuous sound coupling medium as air.

206 citations