Montserrat Parrilla

Early detection of internal heterogeneities is a fundamental objective in industrial quality control processes, particularly for high-value strategic components. Among non-destructive testing (NDT) methods, ultrasonic inspection is one of the most widely applied due to its versatility and its capability to provide information about the internal structure of materials through accurate signal interpretation. In these inspections, each image is reconstructed from numerous ultrasonic signals, which are processed according to specific inspection procedures to generate the graphical representation evaluated by the operator. This inspection approach involves several non-value-adding tasks, such as prolonged inspection times, extensive operator training, and detailed equipment calibration. To address these limitations, a novel technology has been developed that minimizes such dependencies without compromising inspection reliability. The proposed method employs conventional ultrasonic instrumentation but introduces an entirely new concept: instead of relying on complex inspection procedures to guarantee defect detectability, it automatically verifies the internal integrity of the inspected component by comparing it with a reference standard using an artificial intelligence model specifically designed for this purpose. The results demonstrate the capability of this new technique to automatically and instantly validate the quality of inspected components, representing a significant advancement in the efficiency and reliability of real-time inspections.

This paper proposes the use of quantitative methods for the characterization of tissues by linking, into a single approach, ideas coming from the spectral analysis methods commonly used to determine the attenuation coefficient with the envelope statistics formulation. Initially, the Homodyned K-distribution model used to fit data obtained from ultrasound signal envelopes was reviewed, and the necessary equations to further derive the attenuation coefficient from this model were developed. To test and discuss the performance of these methods, experimental work was conducted in phantoms. To this end, a series of tissue-mimicking materials composed of poly-vinyl alcohol (PVA) loaded with different particles (aluminium, alumina, cellulose) at varying concentrations were manufactured. A single-channel scanning system was employed to analyse these samples. It was verified that quantitative images obtained from the attenuation coefficient and from the scatterer clustering μ parameter (associated with scatterer concentration) effectively discriminate materials exhibiting similar echo envelope patterns, enhancing the information obtained in comparison with the conventional analysis based on B-scans. Additionally, the implementation of quantitative bi-parametric imaging mappings based on both the μ parameter and the attenuation coefficient, as a means to rapidly visualize results and identify areas characterized by specific acoustic features, was also proposed.

In the context of a medical imaging application for preclinical research, specifically, erebrovascular imaging in small animals, this work examines the challenges of using a Large-Scale 2D ultrasonic array with 32×32 elements (96×96 wavelengths). The application imposes demanding requirements: very near-field operation, high spatial resolution, high frequency, high frame rate, and imaging in a highly attenuating medium. These needs, combined with current technological limitations, such as element size and constraints on the number of elements that can be driven in parallel, pose significant challenges for system design and implementation. To evaluate system performance, we use plane wave imaging as a reference mode due to its ability to meet high acquisition speed requirements. Our analysis highlights limitations in spatial coverage and image quality when operating the full aperture under planewave transmission constraints. To overcome these limitations, we propose a sparse aperture strategy. When integrated with advanced signal processing techniques, this approach improves both contrast and resolution while maintaining acquisition speed. This makes it a promising solution for high-performance ultrasonic imaging under the demanding conditions of preclinical research.

Industrial robotic arms integrated with server computers, sensors and actuators have revolutionized the way automated non-destructive testing is performed in the aeronautical sector. Currently, there are commercial, industrial robots that have the precision, speed and repetitiveness in their movements that make them suitable for use in numerous non-destructive testing inspections. Automatic ultrasonic inspection of complex geometry parts remains one of the most difficult challenges in the market. The closed configuration, i.e., restricted access to internal motion parameters, of these robotic arms makes it difficult for an adequate synchronism between the movement of the robot and the acquisition of the data. This is a serious problem in the inspection of aerospace components, where high-quality images are necessary to assess the condition of the inspected component. In this paper, we applied a methodology recently patented for the generation of high-quality ultrasonic images of complex geometry pieces using industrial robots. The methodology is based on the calculation of a synchronism map after a calibration experiment and to introduce this corrected map in an autonomous, independent external system developed by the authors to obtain precise ultrasonic images. Therefore, it has been shown that it is possible to establish the synchronization of any industrial robot with any ultrasonic imaging generation system to generate high-quality ultrasonic images.

This work focuses on the use of ultrasound imaging to evaluate the cell concentration of dilute leukocyte suspensions in the range of 10–3000 cells/µL. First, numerical simulations were used to study the influence of the size dispersion and the leukocyte type on the performance of the concentration estimation algorithms, which were developed in previous works assuming single-sized scatterers. From this analysis, corrections to the mentioned algorithms were proposed and then the performance of these corrections was evaluated from experiments. For this, ultrasound images were captured from suspensions of lymphocytes, granulocytes, and their mixtures. These images were obtained using a 20 MHz single-channel scanning system. Results confirmed that concentration estimates provided by conventional algorithms were affected by the size dispersion of cells, leading to a remarkable underestimation of results. The proposed correction to compensate for cell size dispersion obtained from simulations improved the concentration estimation of these algorithms, for the cell suspensions tested, approaching the results to the reference optical characterization. Moreover, it was shown that these models provided a total leukocyte concentration from the ultrasound images which was independent of the relative populations of different white blood cell types.

The analysis of the beampattern is the base of sparse arrays design process. However, in the case of bidimensional arrays, this analysis has a high computational cost, turning the design process into a long and complex task. If the imaging system development is considered a holistic process, the aperture is a sampling grid that must be considered in the spatial domain through the coarray structure. Here, we propose to guide the aperture design process using statistical parameters of the distribution of the weights in the coarray. We have studied three designs of sparse matrix binned arrays with different sparseness degrees. Our results prove that there is a relationship between these parameters and the beampattern, which is valuable and improves the array design process. The proposed methodology reduces the computational cost up to 58 times with respect to the conventional fitness function based on the beampattern analysis.

This work provides a guide to design ultrasonic synthetic aperture systems for non-grid two-dimensional sparse arrays such as spirals or annular segmented arrays. It presents an algorithm that identifies which elements have a more significant impact on the beampattern characteristics and uses this information to reduce the number of signals, the number of emitters and the number of parallel receiver channels involved in the beamforming process. Consequently, we can optimise the 3D synthetic aperture ultrasonic imaging system for a specific sparse array, reducing the computational cost, the hardware requirements and the system complexity. Simulations using a Fermat spiral array and experimental data based on an annular segmented array with 64 elements are used to assess this algorithm.

A methodology for the assessment of cell concentration, in the range 5-100 cells/ μL, suitable for in vivo analysis of serous body fluids is presented in this work. This methodology is based on the quantitative analysis of ultrasound images obtained from cell suspensions and considers applicability criteria, such as short analysis times, moderate frequency, and absolute concentration estimation, all necessary to deal with the variability of tissues among different patients. Numerical simulations provided the framework to analyze the impact of echo overlapping and the polydispersion of scatterer sizes on the cell concentration estimation. The cell concentration range that can be analyzed as a function of the transducer and emitted waveform used was also discussed. Experiments were conducted to evaluate the performance of the method using 7- μm and 12- μm polystyrene particles in water suspensions in the 5-100 particles/ μL range. A single scanning focused transducer working at a central frequency of 20 MHz was used to obtain ultrasound images. The method proposed to estimate the concentration proved to be robust for different particle sizes and variations of gain acquisition settings. The effect of tissues placed in the ultrasound path between the probe and the sample was also investigated using 3-mm-thick tissue mimics. Under this situation, the algorithm was robust for the concentration analysis of 12 μm particle suspensions, yet significant deviations were obtained for the smallest particles.

Current computation capabilities normal, Delay and Sum (DAS) and Total Focusing Method (TFM) allow new definitions for beamformers. In this paper, a new beamforming method is proposed. It exploits diversity across pixel data after focusing process. The method is based on statistical analysis and sparse array concept. It avoids common aberrations introduced by beamforming process without loosing the original image texture, producing a better quality image. We evaluate the proposed method through simulation on standard phantoms. Images resulting from our novel method display better quality and provide localised estimations on image noise.