Conventional High Intensity Focused Ultrasound (HIFU) is a therapeutic modality which is extracorporeally administered. In applications where a relatively small HIFU lesion is required, an intravascular HIFU probe can be deployed to the ablation site. In this paper, we demonstrate the design and implementation a fully integrated HIFU drive system on a chip to be placed on a 6 Fr catheter probe. An 8-element capacitive micromachined ultrasound transducer (CMUT) ring array of 2 mm diameter has been used as the ultrasound source. The driver chip is fabricated in 0.35 μm AMS high-voltage CMOS technology and comprises eight continuous-wave (CW) high-voltage CMUT drivers (10.9 ns and 9.4 ns rise and fall times at 20 V output into a 15 pF), an eight-channel digital beamformer (8-12 MHz output frequency with 11.25 phase accuracy) and a phase locked loop with an integrated VCO as a tunable clock source (128-192 MHz). The chip occupies 1.85 × 1.8 mm area including input and output (I/O) pads. When the transducer array is immersed in sunflower oil and driven by the IC with eight 20 V CW pulses at 10 MHz, real-time thermal images of the HIFU beam indicate that the focal temperature rises by 16.8 C in 11 seconds. Each HV driver consumes around 67 mW of power when driving the CMUT array at 10 MHz, which adds up to 560 mW for the whole chip. FEM based analysis reveals that the outer surface temperature of the catheter is expected to remain below the 42C tissue damage limit during therapy.
Gastrointestinal (GI) tract diseases are responsible for substantial morbidity and mortality worldwide, including colorectal cancer, which has shown a rising incidence among adults younger than 50. Although this could be alleviated by regular screening, only a small percentage of those at risk are screened comprehensively, due to shortcomings in accuracy and patient acceptance. To address these challenges, we designed an artificial intelligence (AI)-empowered wireless video endoscopic capsule that surpasses the performance of the existing solutions by featuring, among others: (1) real-time image processing using onboard deep neural networks (DNN), (2) enhanced visualization of the mucous layer by deploying both white-light and narrow-band imaging, (3) on-the-go task modification and DNN update using over-the-air-programming and (4) bi-directional communication with patient’s personal electronic devices to report important findings. We tested our solution in an in vivo setting, by administrating our endoscopic capsule to a pig under general anesthesia. All novel features, successfully implemented on a single platform, were validated. Our study lays the groundwork for clinically implementing a new generation of endoscopic capsules, which will significantly improve early diagnosis of upper and lower GI tract diseases.
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