journal article·2024-07-01Open access

Acoustically transparent alumina-based cranial implants enhance ultrasound transmission through a combined mechano-acoustic resonant effect

Mario Ibrahin Gutierrez, Pathikumar Sellappan, Elias H Penilla, Irais Poblete-Naredo, Arturo Vera, Lorenzo Leija, Javier E Garay

Journal of Physics: Materials· Volume 7· Issue 3

Abstract

Abstract Therapeutic ultrasound for brain stimulation has increased in the last years. This energy has shown promising results for treating Alzheimer’s disease, Parkinson’s disease and traumatic brain injury, among other conditions. However, the application of ultrasound in the brain should trespass a natural but highly attenuating and distorting barrier, the cranium. Implantable ceramic materials can be used to replace part of the cranium as an alternate method to enhance ultrasound transmission. In this work, it is presented the acoustic characterization of alumina ceramic disks that can be employed as cranial implants for acoustic windows-to-the-brain. Alumina samples were prepared using current-activated pressure-assisted densification and were acoustically characterized. Acoustic impedance and attenuation of the samples were determined for different porosities. Additionally, measured and modeled acoustic fields are presented and analyzed in terms of the total ultrasound transmitted through the ceramics. Results indicate a resonant behavior in the alumina disks when the thickness corresponds to a half-wavelength of ultrasound; this resonance permits a total of 95.4% of ultrasound transmission; for thicknesses out of the resonant zone, transmission is 53.0%. Alumina proves to be an excellent medium for ultrasound transmission that, in conjunction with its mechanical and optical properties, can be useful for cranium replacement in mixed opto-acoustic applications.
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Research context

OpenAlex topics: Photoacoustic and Ultrasonic Imaging · Ultrasound and Hyperthermia Applications · Ultrasound and Cavitation Phenomena

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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