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Alfonso Rodriguez-Molares

Bio: Alfonso Rodriguez-Molares is an academic researcher from Norwegian University of Science and Technology. The author has contributed to research in topics: Beamforming & Specular reflection. The author has an hindex of 12, co-authored 36 publications receiving 542 citations. Previous affiliations of Alfonso Rodriguez-Molares include University of Adelaide & University of Oslo.

Papers
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Journal ArticleDOI
TL;DR: This work generalizes the definition of CNR based on the overlap area between two probability density functions and proposes a new metric, gCNR, which is robust against dynamic range alterations and allows us to assess the relevance of new imaging algorithms.
Abstract: In the last 30 years, the contrast-to-noise ratio (CNR) has been used to estimate the contrast and lesion detectability in ultrasound images. Recent studies have shown that the CNR cannot be used with modern beamformers, as dynamic range alterations can produce arbitrarily high CNR values with no real effect on the probability of lesion detection. We generalize the definition of CNR based on the overlap area between two probability density functions. This generalized CNR (gCNR) is robust against dynamic range alterations; it can be applied to all kind of images, units, or scales; it provides a quantitative measure for contrast; and it has a simple statistical interpretation, i.e., the success rate that can be expected from an ideal observer at the task of separating pixels. We test gCNR on several state-of-the-art imaging algorithms and, in addition, on a trivial compression of the dynamic range. We observe that CNR varies greatly between the state-of-the-art methods, with improvements larger than 100%. We observe that trivial compression leads to a CNR improvement of over 200%. The proposed index, however, yields the same value for compressed and uncompressed images. The tested methods showed mismatched performance in terms of lesion detectability, with variations in gCNR ranging from −0.08 to +0.29. This new metric fixes a methodological flaw in the way we study contrast and allows us to assess the relevance of new imaging algorithms.

224 citations

Proceedings ArticleDOI
01 Sep 2016
TL;DR: PICMUS, the Plane-Wave Imaging Challenge in Medical Ultrasound aims to provide tools to compensate for the lack of transmit focusing in plane-Wave imaging, and its motivation, implementation, and metrics.
Abstract: Plane-Wave imaging enables very high frame rates, up to several thousand frames per second. Unfortunately the lack of transmit focusing leads to reduced image quality, both in terms of resolution and contrast. Recently, numerous beamforming techniques have been proposed to compensate for this effect, but comparing the different methods is difficult due to the lack of appropriate tools. PICMUS, the Plane-Wave Imaging Challenge in Medical Ultrasound aims to provide these tools. This paper describes the PICMUS challenge, its motivation, implementation, and metrics.

166 citations

Proceedings ArticleDOI
01 Sep 2017
TL;DR: The PICMUS challenge was a pioneering step that made clear that two things are required to establish a fair comparison: a common data format, and a body of methods to process that data.
Abstract: We present the UltraSound ToolBox (USTB), a processing framework for ultrasound signals. USTB aims to facilitate the comparison of imaging techniques and the dissemination of research results. It fills the void of tools for algorithm sharing and verification, and enables a solid assessment of the correctness and relevance of new approaches. It also aims to boost research productivity by cutting down implementation time and code maintenance. USTB is a MATLAB toolbox for processing 2D and 3D ultrasound data, supporting both MATLAB and C++ implementations. Channel data from any origin, simulated and experimental, and using any kind of sequence, e.g. synthetic transmit aperture imaging (STAI) or coherent plane-wave compounding (CPWC), can be processed with USTB. Here we describe some of the elements of USTB such as: the ultrasound file format, the concept of the general beamformer, and the signal processing pipeline. We also show a minimal code example, and demonstrate that USTB can be used with the most used transmit sequences: STAI, CPWC, diverging wave imaging (DWI), focused imaging (FI), and retrospective transmit beamforming (RTB).

61 citations

Proceedings ArticleDOI
01 Oct 2018
TL;DR: A new image quality index, the generalized contrast-to-noise ratio (GCNR), based on the overlap area of the probability density function inside and outside the target area is proposed, and allows us to assess the significance of contrast enhancing effects in ultrasound imaging.
Abstract: Many adaptive algorithms claim to provide higher contrast than delay-and-sum (DAS). These claims are often backed by estimations of the contrast-to-noise ratio (CNR). Intuitively, we assume that higher CNR leads to higher probability of lesion detection, and this is indeed the case for DAS. However, non-linear processing can arbitrarily alter CNR, and yet yield no improvement in the detection probability. We propose a new image quality index, the generalized contrast-to-noise ratio (GCNR), based on the overlap area of the probability density function inside and outside the target area. GCNR can be used with non-linear beamforming algorithms, remaining unaltered if the dynamic range is changed. We demonstrate that GCNR is proportional to the maximum success rate that can be expected from the algorithm. Using Field II, we compare the performance of CNR and GCNR in 6 imaging algorithms. While CNR varies significantly between the 6 algorithms, we do not observe notable variations in GCNR (<10%), which means that the 6 algorithms have similar lesion detection capabilities. GCNR fixes the methodological flaw of using CNR with algorithms that alter the probability density function of the ultrasound signal, and allows us to assess the significance of contrast enhancing effects in ultrasound imaging.

56 citations

Journal ArticleDOI
TL;DR: A potential for US as a future modality for 4D cardiac vector flow imaging was demonstrated and measurements in a healthy LV showed good agreement with PC-MRI, which will be further evaluated in clinical studies.
Abstract: In vivo characterization of intracardiac blood velocity vector fields may provide new clinical information but is currently not available for bedside evaluation. In this paper, 4-D vector flow imaging for intracardiac flow assessment is demonstrated using a clinical ultrasound (US) system and a matrix array transducer, without the use of contrast agent. Two acquisition schemes were developed, one for full volumetric coverage of the left ventricle (LA) at 50 vps and a 3-D thick-slice setup with continuous frame acquisition (4000 vps), both utilizing ECG-gating. The 3-D vector velocity estimates were obtained using a novel method combining phase and envelope information. In vitro validation in a rotating tissue-mimicking phantom revealed velocity estimates in compliance with the ground truth, with a linear regression slope of 0.80, 0.77, and 1.03 for the ${x}$ , ${y}$ , and ${z}$ velocity components, and with standard deviations of 2.53, 3.19, and 0.95 cm/s, respectively. In vivo measurements in a healthy LV showed good agreement with PC-MRI. Quantitative analysis of energy loss (EL) and kinetic energy (KE) further showed similar trends, with peak KE at 1.5 and 2.4 mJ during systole and 3.6 and 3.1 mJ for diastole for US and PC-MRI. Similar for EL, 0.15– 0.2 and 0.7 mW was found during systole and 0.6 and 0.7 mW during diastole, for US and PC-MRI, respectively. Overall, a potential for US as a future modality for 4D cardiac vector flow imaging was demonstrated, which will be further evaluated in clinical studies.

54 citations


Cited by
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Journal ArticleDOI
TL;DR: This work generalizes the definition of CNR based on the overlap area between two probability density functions and proposes a new metric, gCNR, which is robust against dynamic range alterations and allows us to assess the relevance of new imaging algorithms.
Abstract: In the last 30 years, the contrast-to-noise ratio (CNR) has been used to estimate the contrast and lesion detectability in ultrasound images. Recent studies have shown that the CNR cannot be used with modern beamformers, as dynamic range alterations can produce arbitrarily high CNR values with no real effect on the probability of lesion detection. We generalize the definition of CNR based on the overlap area between two probability density functions. This generalized CNR (gCNR) is robust against dynamic range alterations; it can be applied to all kind of images, units, or scales; it provides a quantitative measure for contrast; and it has a simple statistical interpretation, i.e., the success rate that can be expected from an ideal observer at the task of separating pixels. We test gCNR on several state-of-the-art imaging algorithms and, in addition, on a trivial compression of the dynamic range. We observe that CNR varies greatly between the state-of-the-art methods, with improvements larger than 100%. We observe that trivial compression leads to a CNR improvement of over 200%. The proposed index, however, yields the same value for compressed and uncompressed images. The tested methods showed mismatched performance in terms of lesion detectability, with variations in gCNR ranging from −0.08 to +0.29. This new metric fixes a methodological flaw in the way we study contrast and allows us to assess the relevance of new imaging algorithms.

224 citations

Journal ArticleDOI
TL;DR: This paper synthesizes and updates a number of previous review articles on various aspects of this multi-barrier approach to reduce the risk from toxic cyanobacterial blooms in drinking water, to provide a holistic resource for researchers, water managers and engineers, as well as water treatment plant operators.
Abstract: Blooms of toxic cyanobacteria in water supply systems are a global issue affecting water supplies on every major continent except Antarctica. The occurrence of toxic cyanobacteria in freshwater is increasing in both frequency and distribution. The protection of water supplies has therefore become increasingly more challenging. To reduce the risk from toxic cyanobacterial blooms in drinking water, a multi-barrier approach is needed, consisting of prevention, source control, treatment optimization, and monitoring. In this paper, current research on some of the critical elements of this multi-barrier approach are reviewed and synthesized, with an emphasis on the effectiveness of water treatment technologies for removing cyanobacteria and related toxic compounds. This paper synthesizes and updates a number of previous review articles on various aspects of this multi-barrier approach in order to provide a holistic resource for researchers, water managers and engineers, as well as water treatment plant operators.

211 citations

Journal ArticleDOI
TL;DR: Deep neural networks were trained to suppress off-axis scattering in sub-band ultrasound channel data in the frequency domain using an inverse short-time Fourier transform to reconstruct channel data.
Abstract: We investigate the use of deep neural networks (DNNs) for suppressing off-axis scattering in ultrasound channel data. Our implementation operates in the frequency domain via the short-time Fourier transform. The inputs to the DNN consisted of the separated real and imaginary components (i.e. in-phase and quadrature components) observed across the aperture of the array, at a single frequency and for a single depth. Different networks were trained for different frequencies. The output had the same structure as the input and the real and imaginary components were combined as complex data before an inverse short-time Fourier transform was used to reconstruct channel data. Using simulation, physical phantom experiment, and in vivo scans from a human liver, we compared this DNN approach to standard delay-and-sum (DAS) beamforming and an adaptive imaging technique that uses the coherence factor. For a simulated point target, the side lobes when using the DNN approach were about 60 dB below those of standard DAS. For a simulated anechoic cyst, the DNN approach improved contrast ratio (CR) and contrast-to-noise (CNR) ratio by 8.8 dB and 0.3 dB, respectively, compared with DAS. For an anechoic cyst in a physical phantom, the DNN approach improved CR and CNR by 17.1 dB and 0.7 dB, respectively. For two in vivo scans, the DNN approach improved CR and CNR by 13.8 dB and 9.7 dB, respectively. We also explored methods for examining how the networks in this paper function.

146 citations

Journal ArticleDOI
TL;DR: The present paper is a thorough review of the recent work done in the fields of cavitation-assisted microorganism's destruction and aims to serve as a foundation to build on in the next years.
Abstract: A sudden decrease in pressure triggers the formation of vapour and gas bubbles inside a liquid medium (also called cavitation). This leads to many (key) engineering problems: material loss, noise, and vibration of hydraulic machinery. On the other hand, cavitation is a potentially useful phenomenon: the extreme conditions are increasingly used for a wide variety of applications such as surface cleaning, enhanced chemistry, and wastewater treatment (bacteria eradication and virus inactivation). Despite this significant progress, a large gap persists between the understanding of the mechanisms that contribute to the effects of cavitation and its application. Although engineers are already commercializing devices that employ cavitation, we are still not able to answer the fundamental question: What precisely are the mechanisms how bubbles can clean, disinfect, kill bacteria and enhance chemical activity? The present paper is a thorough review of the recent (from 2005 onward) work done in the fields of cavitation-assisted microorganism's destruction and aims to serve as a foundation to build on in the next years.

139 citations

Journal ArticleDOI
01 Sep 2020-ACS Nano
TL;DR: This review aims at analyzing the state of the art of microrobots imaging by critically discussing the potentialities and limitations of the techniques employed in this field and highlighting the existing challenges and perspective solutions which could be promising for future in vivo applications.
Abstract: Medical microrobots (MRs) have been demonstrated for a variety of non-invasive biomedical applications, such as tissue engineering, drug delivery, and assisted fertilization, among others. However, most of these demonstrations have been carried out in in vitro settings and under optical microscopy, being significantly different from the clinical practice. Thus, medical imaging techniques are required for localizing and tracking such tiny therapeutic machines when used in medical-relevant applications. This review aims at analyzing the state of the art of microrobots imaging by critically discussing the potentialities and limitations of the techniques employed in this field. Moreover, the physics and the working principle behind each analyzed imaging strategy, the spatiotemporal resolution, and the penetration depth are thoroughly discussed. The paper deals with the suitability of each imaging technique for tracking single or swarms of MRs and discusses the scenarios where contrast or imaging agent's inclusion is required, either to absorb, emit, or reflect a determined physical signal detected by an external system. Finally, the review highlights the existing challenges and perspective solutions which could be promising for future in vivo applications.

121 citations