Extending the Utility of the Parabolic Approximation in Medical Ultrasound Using Wide-Angle Diffraction Modeling

Soneson, Joshua E.

doi:10.1109/tuffc.2017.2654125

Cited by 12 publications

(8 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Nevertheless, Table I shows that (12) predicts FWHM consistent with values obtained by simulation [87] and experiment [14]. For smaller f-numbers, alternative diffraction models may be required [85, 86].…”

Section: Theorysupporting

confidence: 64%

“…HIFU Simulator solves the KZK equation, which is the quadratic approximation of the Westervelt equation (not to be confused with the quadratic spatial averaging model discussed previously). The KZK equation is theoretically valid up to angles of about 15-20° from the propagation axis (roughly equivalent to F# > 1.5) [86, 92]. However, it has been reported that the KZK equation can be relatively accurate in the frequency domain about 25° from the transducer axis (F# = 1) [92].…”

Section: Methodsmentioning

confidence: 99%

“…The validity of (12) becomes increasingly challenged as f-number (F# = ratio of focal distance to aperture diameter) decreases [85, 86]. Table I tests criteria for validity [81] of (12) for an F/1 transducer with frequency and dimensions typical for therapeutic applications, such as the Sonic Concepts (Bothell, WA) H-101.…”

Section: Theorymentioning

confidence: 99%

See 2 more Smart Citations

Considerations for Choosing Sensitive Element Size for Needle and Fiber-Optic Hydrophones—Part I: Spatiotemporal Transfer Function and Graphical Guide

Wear

2019

IEEE Trans. Ultrason., Ferroelect., Freq. Contr.

View full text Add to dashboard Cite

The spatiotemporal transfer function for a needle or reflectance-based fiber-optic hydrophone is modeled as separable into the product of two filters corresponding to frequency-dependent sensitivity and spatial averaging. The separable hydrophone transfer function model is verified numerically by comparison to a more general rigid piston spatiotemporal response model that does not assume separability. Spatial averaging effects are characterized by frequency-dependent “effective” sensitive element diameter, which can be more than double the geometrical sensitive element diameter. The transfer function is tested in simulation using a nonlinear focused pressure wave model based on Gaussian harmonic radial pressure distributions. The pressure wave model is validated by comparing to experimental hydrophone scans of nonlinear beams produced by three source transducers. An analytic form for the spatial averaging filter, applicable to Gaussian harmonic beams, is derived. A second analytic form for the spatial averaging filter, applicable to quadratic harmonic beams, is derived by extending the spatial averaging correction recommended by IEC 62127-1 Annex E to nonlinear signals with multiple harmonics. Both forms are applicable to all hydrophones (not just needle and fiber optic hydrophones). Simulation analysis performed for a wide variety of transducer geometries indicates that the Gaussian spatial averaging filter formula is more accurate than the Quadratic form over a wider range of harmonics. Additional experimental validation is provided in Part II. Readers who are uninterested in hydrophone theory may skip the theoretical and experimental sections of this paper and proceed to the Graphical Guide for practical information to inform and support selection of hydrophone sensitive element size (but might be well-advised to read the Introduction).

show abstract

Section: Theorysupporting

confidence: 64%

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Considerations for Choosing Sensitive Element Size for Needle and Fiber-Optic Hydrophones—Part I: Spatiotemporal Transfer Function and Graphical Guide

Wear

2019

IEEE Trans. Ultrason., Ferroelect., Freq. Contr.

View full text Add to dashboard Cite

show abstract

“…Other computational tools to assess specific aspects of device performance or safety that industry can employ include a simulator for high-intensity focused ultrasound (HIFU) beams and heating effects ( 50 , 51 ), benchmarks models for computational fluid dynamics ( 19 ), patient-specific workflows for assessing clot trapping efficiency in IVC filters ( 52 ), surrogate models for predicting device-specific and species-specific hemolysis (Craven et al, under review), optical-thermal light-tissue interactions for photoacoustic breast imaging ( 53 ), and an online app for assessing the safety of color additives ( 54 ).…”

Section: Computational Modeling Researchmentioning

confidence: 99%

Advancing Regulatory Science With Computational Modeling for Medical Devices at the FDA's Office of Science and Engineering Laboratories

et al. 2018

View full text Add to dashboard Cite

Protecting and promoting public health is the mission of the U.S. Food and Drug Administration (FDA). FDA's Center for Devices and Radiological Health (CDRH), which regulates medical devices marketed in the U.S., envisions itself as the world's leader in medical device innovation and regulatory science–the development of new methods, standards, and approaches to assess the safety, efficacy, quality, and performance of medical devices. Traditionally, bench testing, animal studies, and clinical trials have been the main sources of evidence for getting medical devices on the market in the U.S. In recent years, however, computational modeling has become an increasingly powerful tool for evaluating medical devices, complementing bench, animal and clinical methods. Moreover, computational modeling methods are increasingly being used within software platforms, serving as clinical decision support tools, and are being embedded in medical devices. Because of its reach and huge potential, computational modeling has been identified as a priority by CDRH, and indeed by FDA's leadership. Therefore, the Office of Science and Engineering Laboratories (OSEL)—the research arm of CDRH—has committed significant resources to transforming computational modeling from a valuable scientific tool to a valuable regulatory tool, and developing mechanisms to rely more on digital evidence in place of other evidence. This article introduces the role of computational modeling for medical devices, describes OSEL's ongoing research, and overviews how evidence from computational modeling (i.e., digital evidence) has been used in regulatory submissions by industry to CDRH in recent years. It concludes by discussing the potential future role for computational modeling and digital evidence in medical devices.

show abstract

“…Hydrophones can distort pressure signals due to 1) frequency-dependent sensitivity and 2) spatial averaging across the finite sensitive element. These forms of distortion are relevant to HITU signals, which can be highly nonlinear and can therefore have very broad bandwidths due to the presence of multiple harmonics [28,29,[40][41][42][43]. Nonlinearity affects spatial averaging because as harmonic frequency increases, harmonic beam width decreases [40,[44][45][46][47][48][49] and the potential for harmonic spatial averaging increases [50,51].…”

Section: Introductionmentioning

confidence: 99%

Correction for Spatial Averaging Artifacts in Hydrophone Measurements of High-Intensity Therapeutic Ultrasound: An Inverse Filter Approach

Wear

Howard²

2019

IEEE Trans. Ultrason., Ferroelect., Freq. Contr.

View full text Add to dashboard Cite

High-intensity therapeutic ultrasound (HITU) pressure is often measured using a hydrophone. HITU pressure waves typically contain multiple harmonics due to nonlinear propagation. As harmonic frequency increases, harmonic beam width decreases. For sufficiently high harmonic frequency, beam width may become comparable to the hydrophone effective sensitive element diameter, resulting in signal reduction due to spatial averaging. An analytic formula for a hydrophone spatial averaging filter for beams with Gaussian harmonic radial profiles was tested on HITU pressure signals generated by three transducers (1.45 MHz, F/1; 1.53 MHz, F/1.5; 3.91 MHz, F/1) with focal pressures up to 48 MPa. The HITU signals were measured using fiber-optic and needle hydrophones (nominal geometrical sensitive element diameters: 100 μm and 400 μm). Harmonic radial profiles were measured with transverse scans in the focal plane using the fiberoptic hydrophone. Harmonic radial profiles were accurately approximated by Gaussian functions with root-mean-square (RMS) differences between transverse scans and Gaussian fits less than 9% for frequencies up to approximately 50 MHz. The Gaussian harmonic beam width parameter σ n varied with harmonic number n according to a power law, σ n = σ 1 /n q where 0.5 < q < 0.6. RMS differences between experimental and theoretical spatial averaging filters were 11% ± 1% (1.45 MHz), 8% ± 1% (1.53 MHz), and 4% ± 1% (3.91 MHz). For the two more highly focused (F/1) transducers, the effect of spatial averaging was to underestimate peak compressional pressure (pcp), peak rarefactional pressure (prp), and pulse intensity integral (pii) by (mean ± standard deviation) (pcp: 4.9% ± 0.5%, prp: 0.4% ± 0.2% pii: 2.9% ± 1.0%) and (pcp: 28.3% ± 9.6% prp: 6.0% ± 2.4% pii: 24.3% ± 6.7%) for the 100 μm and 400 μm diameter hydrophones respectively. These errors can be suppressed by application of the inverse spatial averaging filter.

show abstract

Extending the Utility of the Parabolic Approximation in Medical Ultrasound Using Wide-Angle Diffraction Modeling

Cited by 12 publications

References 27 publications

Considerations for Choosing Sensitive Element Size for Needle and Fiber-Optic Hydrophones—Part I: Spatiotemporal Transfer Function and Graphical Guide

Considerations for Choosing Sensitive Element Size for Needle and Fiber-Optic Hydrophones—Part I: Spatiotemporal Transfer Function and Graphical Guide

Advancing Regulatory Science With Computational Modeling for Medical Devices at the FDA's Office of Science and Engineering Laboratories

Correction for Spatial Averaging Artifacts in Hydrophone Measurements of High-Intensity Therapeutic Ultrasound: An Inverse Filter Approach

Contact Info

Product

Resources

About