Characterization of Medical Ultrasound Transducers

Tomov, Borislav Gueorguiev; Diederichsen, Søren Elmin; Thomsen, Erik Vilain; Jensen, Jørgen Arendt

doi:10.1109/ultsym.2018.8579798

Cited by 6 publications

(3 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…One frame was created by transmitting three steered plane waves after placing 100 point targets randomly within a region of 6.4 × 6.4 mm 2 (an average target density of 2.44 mm −2 ) where the center was 18 mm away from a transducer. The transducer was modeled after a commercial 192-element linear array, and the measured impulse response [10], [11] was applied to make the RF data as close to real measured data as possible. The parameters used in simulation are listed in Table I. The simulated raw RF data were not beamformed but delayed, based on the time-of-flight calculated by…”

Section: Methodsmentioning

confidence: 99%

Ultrasound Multiple Point Target Detection and Localization using Deep Learning

Youn

Ommen

Stuart

et al. 2019

2019 IEEE International Ultrasonics Symposium (IUS)

Self Cite

View full text Add to dashboard Cite

Super-resolution imaging (SRI) can achieve subwavelength resolution by detecting and tracking intravenously injected microbubbles (MBs) over time. However, current SRI is limited by long data acquisition times since the MB detection still relies on diffraction-limited conventional ultrasound images. This limits the number of detectable MBs in a fixed time duration. In this work, we propose a deep learning-based method for detecting and localizing high-density multiple point targets from radio frequency (RF) channel data. A Convolutional Neural Network (CNN) was trained to return confidence maps given RF channel data, and the positions of point targets were estimated from the confidence maps. RF channel data for training and evaluation were simulated in Field II by placing point targets randomly in the region of interest and transmitting three steered plane waves. The trained CNN achieved a precision and recall of 0.999 and 0.960 on a simulated test dataset. The localization errors after excluding outliers were within ± 46 µm and ± 27 µm in the lateral and axial directions. A scatterer phantom was 3-D printed and imaged by the Synthetic Aperture Real-time Ultrasound System (SARUS). On measured data, a precision and recall of 0.976 and 0.998 were achieved, and the localization errors after excluding outliers were within ± 101 µm and ± 75 µm in the lateral and axial directions. We expect that this method can be extended to highly concentrated microbubble (MB) detection in order to accelerate SRI.

show abstract

Section: Methodsmentioning

confidence: 99%

Ultrasound Multiple Point Target Detection and Localization using Deep Learning

Youn

Ommen

Stuart

et al. 2019

2019 IEEE International Ultrasonics Symposium (IUS)

Self Cite

View full text Add to dashboard Cite

show abstract

“…The parameters for the simulation are listed in Table I. The transducer was modeled after a commercial 5.2 MHz 192element linear array transducer, and a measured impulse response [37] was applied to make the simulated RF channel data as close to measured data as possible [38].…”

Section: A Rf Channel Data Simulationmentioning

confidence: 99%

Detection and Localization of Ultrasound Scatterers Using Convolutional Neural Networks

Youn

Ommen

Stuart

et al. 2020

IEEE Trans. Med. Imaging

Self Cite

View full text Add to dashboard Cite

Delay-and-sum (DAS) beamforming is unable to identify individual scatterers when their density is so high that their point spread functions overlap each other. This paper proposes a convolutional neural network (CNN)-based method to detect and localize high-density scatterers, some of which are closer than the resolution limit of DAS beamforming. A CNN was designed to take radio frequency channel data and return nonoverlapping Gaussian confidence maps. The scatterer positions were estimated from the confidence maps by identifying local maxima. On simulated test sets, the CNN method with three plane waves achieved a precision of 1.00 and a recall of 0.91. Localization uncertainties after excluding outliers were ± 46 µm (outlier ratio: 4%) laterally and ± 26 µm (outlier ratio: 1%) axially. To evaluate the proposed method on measured data, two phantoms containing cavities were 3-D printed and imaged. For phantom study, training data were modified according to the physical properties of the phantoms and a new CNN was trained. On an uniformly spaced scatterer phantom, a precision of 0.98 and a recall of 1.00 were achieved with the localization uncertainties of ± 101 µm (outlier ratio: 1%) laterally and ± 37 µm (outlier ratio: 1%) axially. On a randomly spaced scatterer phantom, a precision of 0.59 and a recall of 0.63 were achieved. The localization uncertainties were ± 132 µm (outlier ratio: 0%) laterally and ± 44 µm with a bias of 22 µm (outlier ratio: 0%) axially. This method can potentially be extended to detect highly concentrated microbubbles in order to shorten data acquisition times of super-resolution ultrasound imaging.

show abstract

“…Local disbonds or thickness variations could be hard to notice if a transducer performance is evaluated by the static capacitance, pulse shape or frequency response measurements [6,7]. Furthermore, while in design or manufacturing technology adjustment stage, or even in production, it is desirable to spot even the slightest discrepancies in performance [8].…”

Section: Introductionmentioning

confidence: 99%

High frequency focused imaging for ultrasonic probe integrity inspection

Svilainis

Kybartas

Aleksandrovas

et al. 2020

NDT & E International

View full text Add to dashboard Cite

Most ultrasonic probes present a layered structure that may comprise delay lines, acoustic lens, wear plates, matching layers, electrodes, piezoelectric layers, and a backing block. Probe integrity can be compromised by different defects, such as deficient bonding, presence of air bubbles or porosity, lack of electrical conductivity in electrodes, and deviations in the layers' acoustic thicknesses, impedances or attenuation values. Conventional testing procedures are based on the integral properties of the probe, such as acoustic directivity, sensitivity, and electrical impedance. An alternative technique for the non-destructive inspection of the probe's integrity is presented in this study. The proposed technique is based on imaging of the internal structure using a higher frequency focused ultrasound. Scanning the probe's surface using this focused beam provides two imaging modes: reflection and transmission. In the reflection mode, the higher intensity of the echoes indicates the presence of the defects, such as disbonds, poor acoustic matching or porosity. The main novelty of the proposed technique is in the transmission mode: the probe's electric response to the high frequency focused beam is used to get a 2D distribution of transduction efficiency. Moreover, the probe's response can be gated to separate, both front and back side, responses in the time domain, so the local integrity of an electroacoustic structure is evaluated. To verify the proposed technique, a focused 20 MHz transducer with a focal spot diameter of 0.35 mm was mounted on an ultrasonic scanner and immersed in water above the surface of the probe to be tested. Signals were gated by the layers' thickness, giving the C-scan images corresponding to the different material interfaces inside the tested probes: front face, matching layers, piezoelement and backing. It was demonstrated that the resulting C-scan images are informative in evaluating probe's integrity and layers' acoustic thickness. Comparison of the C-scan images obtained in the reflection and transmission modes shows a good agreement in the structure deficiencies evaluation. The technique proposed is attractive due to its simplicity. It can be useful in several applications: i) the probes already in use can be verified and if any deficiencies are detected, the information obtained can be used to evaluate and guide repair; ii) fabrication quality can be verified by confirming the adhesion of layers and parameters in 2D; and iii) the development of new fabrication routes can be guided, where adherence, compatibility, homogeneity, etc. of new materials or new technologies can be evaluated.

show abstract

Characterization of Medical Ultrasound Transducers

Cited by 6 publications

References 14 publications

Ultrasound Multiple Point Target Detection and Localization using Deep Learning

Ultrasound Multiple Point Target Detection and Localization using Deep Learning

Detection and Localization of Ultrasound Scatterers Using Convolutional Neural Networks

High frequency focused imaging for ultrasonic probe integrity inspection

Contact Info

Product

Resources

About