The team led by Professor Hwang Jae-Yoon of the DGIST Department of Electrical Engineering and Computer Science created a deep learning-based ultrasonic hologram generating framework technology that allows for the free configuration of focused ultrasound in real-time based on holograms. In the future, it will serve as a fundamental technology for precise brain stimulation and therapy.

Even for prenatal examinations, ultrasound is a safe tool. Ultrasound techniques for brain stimulation and therapy have lately been researched since they can activate deep locations without requiring surgery. According to earlier studies, ultrasonic brain stimulation can cure ailments including Alzheimer’s disease, depression, and pain.

DGIST To overcome these constraints, Professor Hwang Jae-team Yoon suggested a deep learning-based learning architecture that can encapsulate free and accurate ultrasound focusing in real-time. As a consequence, Professor Hwang’s team showed that focusing ultrasound into the required form more precisely was achievable in a hologram production time that was nearly real-time and up to 400 times quicker than the current ultrasonic hologram generating algorithm approach.

The study team’s deep learning-based learning framework develops ultrasonic hologram generation skills through self-supervised learning. Self-supervised learning is a technique for teaching a computer to learn by itself to find a rule for data that has no solution. The study team suggested an approach for learning to create ultrasonic holograms, a deep learning network tailored for creating ultrasonic holograms, and a new loss function while demonstrating the reliability and superiority of each element through simulations and actual trials.

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Problem and Solution

The issue is that the current technology concentrates ultrasound into a single tiny point or a huge circle for stimulation, which makes it challenging to selectively activate relevant portions of the brain when several areas interact with each other at the same time. A system that uses the holographic concept to focus ultrasound freely on a specific location has been presented as a solution to this problem. Still, it has drawbacks, including poor precision and a lengthy computation process to create a hologram.

To sum it up –

Acoustic holography is gaining popularity for various applications. However, there are still few studies on how to create acoustic holograms. Even traditional acoustic hologram algorithms need more efficiency in producing acoustic holograms quickly and accurately, impeding the creation of new applications. The DGIST Professor Hwang Jae-Yoon team proposes a deep learning-based system to create acoustic holograms quickly and accurately. The framework’s autoencoder-like design allows for the realization of unsupervised training without the need for ground truth. The holographic ultrasonic generating network (HU-Net), a newly created hologram generator network ideal for unsupervised learning of hologram creation, and a unique loss function designed for energy-efficient holograms are demonstrated for the framework.

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