arXiv subjects

Marco Crocco

Publications and source records attributed to Marco Crocco.

7 recordsLinked to original sources

arXiv2025

tiDAS: a time invariant approximation of the Delay and Sum algorithm for biomedical ultrasound PSF reconstructions

Ultrasound imaging is a real-time diagnostic modality that reconstructs acoustic signals into visual representations of internal body structures. A key component in this process is beamforming, with the Delay and Sum (DAS) algorithm being a standard due to its balance between simplicity and effectiveness. However, the computational cost of DAS can be a limiting factor, especially in real-time scenarios where fast frame reconstruction is essential. In this work, we introduce a time-invariant approximation of the DAS algorithm (tiDAS), designed to accelerate the reconstruction process without compromising image quality. By adopting a one-dimensional, row-wise convolutional formulation, tiDAS significantly reduces computational complexity while preserving the core properties of the original model. This approach not only enables faster image reconstruction but also provides a structured foundation for the application of deconvolution methods aimed at enhancing resolution. Synthetic experiments demonstrate that tiDAS achieves a favorable trade-off between speed and accuracy, making it a promising tool for improving the efficiency of real-time ultrasound imaging.

math.NA↗

arXiv2025

Compound Mask for Divergent Wave Imaging in Medical Ultrasound

Divergent wave imaging with coherent compounding allows obtaining broad field of view and higher frame rate with respect to line by line insonification. However, the spatial and contrast resolution crucially depends on the weights applied in the compound phase, whose optimization is often cumbersome and based on trial and error. This study addresses these limitations by introducing a closed-form approach that maps the transmit apodization weights used in synthetic aperture imaging into the compound mask applied to divergent wave imaging. The approach draws inspiration from a successful technique developed for plane wave imaging, leveraging synthetic aperture imaging as a reference due to its superior image quality. It works for both linear and convex geometries and arbitrary spatial arrangements of virtual sources generating divergent waves. The approach has been validated through simulated data using both linear and convex probes, demonstrating that the Full Width at Half Maximum (FWHM) in Divergent Wave Linear Array (DWLA) increased by 7.5% at 20 mm and 9% at 30 mm compared to Synthetic Aperture Linear Array (SALA). For Divergent Wave Convex Array (DWCA), the increase was 1.64% at 20 mm and 26.56% at 30 mm compared to Synthetic Aperture Convex Array (SACA), witnessing the method's effectiveness.

physics.med-ph↗

arXiv2023

A hybrid time-frequency parametric modelling of medical ultrasound signal transmission

Medical ultrasound imaging is the most widespread real-time non-invasive imaging system and its formulation comprises signal transmission, signal reception, and image formation. Ultrasound signal transmission modelling has been formalized over the years through different approaches by exploiting the physics of the associated wave problem. This work proposes a novel computational framework for modelling the ultrasound signal transmission step in the time-frequency domain for a linear-array probe. More specifically, from the impulse response theory defined in the time domain, we derived a parametric model in the corresponding frequency domain, with appropriate approximations for the narrowband case. To validate the model, we implemented a numerical simulator and tested it with synthetic data. Numerical experiments demonstrate that the proposed model is computationally feasible, efficient, and compatible with realistic measurements and existing state-of-the-art simulators. The formulated model can be employed for analyzing how the involved parameters affect the generated beam pattern, and ultimately for optimizing measurement settings in an automatic and systematic way.

math.NA↗

arXiv2022

A stochastic approach to delays optimization for narrowband transmit beam pattern in medical ultrasound

Ultrasound imaging is extensively employed in clinical settings due to its non-ionizing nature and real-time capabilities. The beamformer represents a crucial component of an ultrasound machine, playing a significant role in shaping the ultimate quality of the reconstructed image. Therefore, Transmit Beam Pattern (TBP) optimization is an important task in medical ultrasound, but state-of-the-art TBP optimization has well-known drawbacks like non-uniform beam width over depth, presence of significant side lobes, and quick energy drop out after the focal depth. To overcome these limitations, we developed a novel optimization approach for TBP by focusing the analysis on its narrowband approximation, particularly suited for Acoustic Radiation Force Impulse (ARFI) elastography, and considering transmit delays as free variables instead of linked to a specific focal depth. We formulate the problem as a non linear Least Squares problem to minimize the difference between the TBP corresponding to a set of delays and the desired one, modeled as a 2D rectangular shape elongated in the direction of the beam axis. In order to quantitatively evaluate the results, we define three quality metrics based on main lobe width, side lobe level, and central line power. Results obtained by our synthetic software simulation show that the main lobe width is considerably more intense and uniform over the whole depth range with respect to classical focalized Beam Patterns, and our optimized delay profile results in a combination of standard delay profiles at different focal depths. The application of the proposed method to ARFI elastography shows improvements in the concentration of the ultrasound energy along a desired axis.

math.OC↗

arXiv2016

Uncalibrated 3D Room Reconstruction from Sound

This paper presents a method to reconstruct the 3D structure of generic convex rooms from sound signals. Differently from most of the previous approaches, the method is fully uncalibrated in the sense that no knowledge about the microphones and sources position is needed. Moreover, we demonstrate that it is possible to bypass the well known echo labeling problem, allowing to reconstruct the room shape in a reasonable computation time without the need of additional hypotheses on the echoes order of arrival. Finally, the method is intrinsically robust to outliers and missing data in the echoes detection, allowing to work also in low SNR conditions. The proposed pipeline formalises the problem in different steps such as time of arrival estimation, microphones and sources localization and walls estimation. After providing a solution to these different problems we present a global optimization approach that links together all the problems in a single optimization function. The accuracy and robustness of the method is assessed on a wide set of simulated setups and in a challenging real scenario. Moreover we make freely available for a challenging dataset for 3D room reconstruction with accurate ground truth in a real scenario.

cs.SD↗

arXiv2015

3D Pose from Detections

We present a novel method to infer, in closed-form, a general 3D spatial occupancy and orientation of a collection of rigid objects given 2D image detections from a sequence of images. In particular, starting from 2D ellipses fitted to bounding boxes, this novel multi-view problem can be reformulated as the estimation of a quadric (ellipsoid) in 3D. We show that an efficient solution exists in the dual-space using a minimum of three views while a solution with two views is possible through the use of regularization. However, this algebraic solution can be negatively affected in the presence of gross inaccuracies in the bounding boxes estimation. To this end, we also propose a robust ellipse fitting algorithm able to improve performance in the presence of errors in the detected objects. Results on synthetic tests and on different real datasets, involving real challenging scenarios, demonstrate the applicability and potential of our method.

cs.CV↗

arXiv2014

Audio Surveillance: a Systematic Review

Despite surveillance systems are becoming increasingly ubiquitous in our living environment, automated surveillance, currently based on video sensory modality and machine intelligence, lacks most of the time the robustness and reliability required in several real applications. To tackle this issue, audio sensory devices have been taken into account, both alone or in combination with video, giving birth, in the last decade, to a considerable amount of research. In this paper audio-based automated surveillance methods are organized into a comprehensive survey: a general taxonomy, inspired by the more widespread video surveillance field, is proposed in order to systematically describe the methods covering background subtraction, event classification, object tracking and situation analysis. For each of these tasks, all the significant works are reviewed, detailing their pros and cons and the context for which they have been proposed. Moreover, a specific section is devoted to audio features, discussing their expressiveness and their employment in the above described tasks. Differently, from other surveys on audio processing and analysis, the present one is specifically targeted to automated surveillance, highlighting the target applications of each described methods and providing the reader tables and schemes useful to retrieve the most suited algorithms for a specific requirement.

cs.SD↗