Note to Physicians and Sonographers on Potential Underestimation of Acoustic Safety Indexes for Diagnostic Array Transducers

Perception, Knowledge, Indications, and Future Prospects of Point-of-Care Ultrasound Among Medical Students in Saudi Arabia

Introduction Point-of-care ultrasound (POCUS) has become integral across medical specialties globally, addressing clinical queries, guiding procedures, and bridging the gap between physical examination and advanced imaging. Early ultrasound training for medical students enhances clinical decision-making and reduces diagnostic errors. Aims To evaluate the knowledge and attitude of senior medical students towards POCUS and to assess knowledge gaps and difficulties encountered by senior medical students to assist in the development of future curricula. Methodology This is an observational, cross-sectional approach to evaluate knowledge, attitude, and practice of POCUS among senior medical students in the Kingdom of Saudi Arabia. The study was conducted from January to September 2023. An electronic questionnaire was distributed through online platforms utilizing medical school databases across various regions. The survey encompassed sociodemographics, training methods, diagnostic indications, and participants' self-reported proficiency and attitudes toward POCUS. The data was chiefly collected using the Likert scale. Descriptive statistics were used to describe the quantitative and categorical variables. Bivariate and multivariate analyses were used to examine correlations. Results A total of 359 senior medical students completed the survey. Most responders were females (57.9%) with the predominating age group being ≤ 24 years (83.6%). The students predominantly were from the Central region of Saudi Arabia (75.5%). Ultrasound training varied among responders; 31.5% received formal courses (median duration: two hours) and 23.4% informal courses (median duration: four hours). Around 17.3% practiced POCUS self-teaching (median duration: four hours). A total of 3.6% had formal POCUS accreditation. A gargantuan 82.2% never used POCUS in their attached hospital for a variety of reasons. Multivariable logistic binary regression analysis showed a positive correlation between students' self-teaching of POCUS and their perceived difficulty performing an ultrasound examination for patients in daily practice. Discussion A comparable study was done at King Saud bin Abdulaziz University for Health Sciences (KSAU-HS) in 2022 surveying 229 senior medical students by Rajendram et al. In their study, 21.4% completed formal courses and 12.7% took informal courses. While many students in our study were not exposed to POCUS (82.2%), KSAU-HS reported a higher percentage reaching 94.8%. A study by Russel et al. demonstrated more than half of 154 surveyed medical schools in the United States have implemented POCUS into their students' curriculum. Conclusion POCUS stands as a valuable skill that can enhance the educational journey of undergraduate medical students. Considering that a significant number of participants haven't yet taken formal medical school courses suggests a lack of awareness about its significance in the medical field. Offering additional courses with practical components could enhance the proficiency, confidence, and outlook of medical students toward POCUS.

Nominal Versus Actual Spatial Resolution: Comparison of Directivity and Frequency-Dependent Effective Sensitive Element Size for Membrane, Needle, Capsule, and Fiber-Optic Hydrophones

Frequency-dependent effective sensitive element radius [Formula: see text] is a key parameter for elucidating physical mechanisms of hydrophone operation. In addition, it is essential to know [Formula: see text] to correct for hydrophone output voltage reduction due to spatial averaging across the hydrophone sensitive element surface. At low frequencies, [Formula: see text] is greater than geometrical sensitive element radius a_g . Consequently, at low frequencies, investigators can overrate their hydrophone spatial resolution. Empirical models for [Formula: see text] for membrane, needle, and fiber-optic hydrophones have been obtained previously. In this article, an empirical model for [Formula: see text] for capsule hydrophones is presented, so that models are now available for the four most common hydrophone types used in biomedical ultrasound. The [Formula: see text] value was estimated from directivity measurements (over the range from 1 to 20 MHz) for five capsule hydrophones (three with [Formula: see text] and two with [Formula: see text]). The results suggest that capsule hydrophones behave according to a "rigid piston" model for k a _g ≥ 0.7 ( k = 2π /wavelength). Comparing the four hydrophone types, the low-frequency discrepancy between [Formula: see text] and a_g was found to be greatest for membrane hydrophones, followed by capsule hydrophones, and smallest for needle and fiber-optic hydrophones. Empirical models for [Formula: see text] are helpful for choosing an appropriate hydrophone for an experiment and for correcting for spatial averaging (over the sensitive element surface) in pressure and beamwidth measurements. When reporting hydrophone-based pressure measurements, investigators should specify [Formula: see text] at the center frequency (which may be estimated from the models presented here) in addition to a_g .

Hydrophone Measurements for Biomedical Ultrasound Applications: A Review

This article presents basic principles of hydrophone measurements, including mechanisms of action for various hydrophone designs, sensitivity and directivity calibration procedures, practical considerations for performing measurements, signal processing methods to correct for both frequency-dependent sensitivity and spatial averaging across the hydrophone sensitive element, uncertainty in hydrophone measurements, special considerations for high-intensity therapeutic ultrasound, and advice for choosing an appropriate hydrophone for a particular measurement task. Recommendations are made for information to be included in hydrophone measurement reporting.

Spatiotemporal Deconvolution of Hydrophone Response for Linear and Nonlinear Beams-Part I: Theory, Spatial-Averaging Correction Formulas, and Criteria for Sensitive Element Size

This article reports spatiotemporal deconvolution methods and simple empirical formulas to correct pressure and beamwidth measurements for spatial averaging across a hydrophone sensitive element. Readers who are uninterested in hydrophone theory may proceed directly to Appendix A for an easy method to estimate spatial-averaging correction factors. Hydrophones were modeled as angular spectrum filters. Simulations modeled nine circular transducers (1-10 MHz; F/1.4-F/3.2) driven at six power levels and measured with eight hydrophones (432 beam/hydrophone combinations). For example, the model predicts that if a 200- [Formula: see text] membrane hydrophone measures a moderately nonlinear 5-MHz beam from an F/1 transducer, spatial-averaging correction factors are 33% (peak compressional pressure or p_c ), 18% (peak rarefactional pressure or p ), and 18% (full width half maximum or FWHM). Theoretical and experimental estimates of spatial-averaging correction factors to were in good agreement (within 5%) for linear and moderately nonlinear signals. Criteria for maximum appropriate hydrophone sensitive element size as functions of experimental parameters were derived. Unlike the oft-cited International Electrotechnical Commission (IEC) criterion, the new criteria were derived for focusing rather than planar transducers and can accommodate nonlinear signals in addition to linear signals. Responsible reporting of hydrophone-based pressure and beamwidth measurements should always acknowledge spatial-averaging considerations.

Spatiotemporal Deconvolution of Hydrophone Response for Linear and Nonlinear Beams-Part II: Experimental Validation

This article reports experimental validation for spatiotemporal deconvolution methods and simple empirical formulas to correct pressure and beamwidth measurements for spatial averaging across a hydrophone sensitive element. The method was validated using linear and nonlinear beams transmitted by seven single-element spherically focusing transducers (2-10 MHz; F /#: 1-3) and measured with five hydrophones (sensitive element diameters d_g : 85-1000 [Formula: see text]), resulting in 35 transducer/hydrophone combinations. Exponential functions, exp( -αx ), where x = d_g /( λ₁F /#) and λ₁ is the fundamental wavelength, were used to model focal pressure ratios p'/p (where p' is the measured value subjected to spatial averaging and p is the true axial value that would be obtained with a hypothetical point hydrophone). Spatiotemporal deconvolution reduced α (followed by root mean squared difference between data and fit) from 0.29-0.30 (7%) to 0.01 (8%) (linear signals) and from 0.29-0.40 (8%) to 0.04 (14%) (nonlinear signals), indicating successful spatial averaging correction. Linear functions, Cx + 1, were used to model FWHM'/FWHM, where FWHM is full-width half-maximum. Spatiotemporal deconvolution reduced C from 9% (4%) to -0.6% (1%) (linear signals) and from 30% (10%) to 6% (5%) (nonlinear signals), indicating successful spatial averaging correction. Spatiotemporal deconvolution resulted in significant improvement in accuracy even when the hydrophone geometrical sensitive element diameter exceeded the beam FWHM. Responsible reporting of hydrophone-based pressure measurements should always acknowledge spatial averaging considerations.