Focused ultrasound technology is a non-invasive therapy procedure that employs ultrasound energy to treat neurological problems by focusing it on a few millimetres of the brain, even deep regions, without opening the skull. It has been used to treat a variety of refractory brain illnesses, including depression and Alzheimer’s disease because it has a low impact on surrounding healthy tissue and lowers side effects like problems and infections.
However, its application has been limited thus far since it is difficult to reflect the distortion of ultrasound waves generated by the varied shapes of patients’ skulls in real time. A research team led by Dr. Kim, Hyungmin of the Bionics Research Center at the Korea Institute of Science and Technology (KIST) has developed a real-time acoustic simulation technology based on generative AI to predict and correct the distortion of the ultrasound focus position caused by the skull in real-time during focused ultrasound therapy. Until now, the clinical applicability of AI simulation models in the field of non-invasive focused ultrasound therapy technology has not been validated.
However, its application has been limited thus far since it is difficult to reflect the distortion of ultrasound waves generated by the varied shapes of patients’ skulls in real time.
A research team led by Dr. Kim, Hyungmin of the Bionics Research Center at the Korea Institute of Science and Technology (KIST) has developed a real-time acoustic simulation technology based on generative AI to predict and correct the distortion of the ultrasound focus position caused by the skull in real-time during focused ultrasound therapy. Until now, the clinical applicability of AI simulation models in the field of non-invasive focused ultrasound therapy technology has not been validated.
To predict the location of the invisible acoustic focus, navigation systems based on medical images taken before treatment are currently utilised, which provide information about the relative position of the patient and the ultrasound transducer. However, they are limited by their inability to account for the distortion of ultrasound waves caused by the skull, and while various simulation techniques have been used to compensate for this, they still require significant computational time, making them difficult to apply in actual clinical practice.
The research team developed a real-time focused ultrasound simulation technology through an artificial intelligence model based on a generative adversarial neural network (GAN), a deep learning model widely used for image generation in the medical field. The technology reduces the update time of three-dimensional simulation information reflecting changes in ultrasound acoustic waves from 14 s to 0.1 s, while showing an average maximum acoustic pressure error of less than 7 per cent and a focal position error of less than 6mm, both of which are within the error range of existing simulation technologies, increasing the possibility of clinical application.
The research team also developed a medical image-based navigation system to verify the performance of the developed technology in order to rapidly deploy it to real-world clinical practice. The system can provide real-time acoustic simulations at the rate of 5 Hz depending on the position of the ultrasound transducer, and succeeded in predicting the position of the ultrasound energy and focus within the skull in real-time during focused ultrasound therapy.
Previously, due to the long calculation time, the ultrasound transducer had to be precisely positioned in a pre-planned location to utilise the simulation results. However, with the newly developed simulation-guided navigation system, it is now possible to adjust the ultrasound focus based on the acoustic simulation results obtained in real-time. In the future, it is expected to improve the accuracy of focused ultrasound and provide safe treatment for patients by being able to quickly respond to unexpected situations that may occur during the treatment process.
“As the accuracy and safety of focused ultrasound brain disease treatment has been improved through this research, more clinical applications will emerge,” said Dr. Kim, Hyungmin of KIST. “For practical use, we plan to verify the system by diversifying the ultrasound sonication environment, such as multi-array ultrasound transducers.”
(With inputs from ANI)
