Deep Neural Network for Navigation of a Single-element Transducer during Transcranial Focused Ultrasound Therapy: Proof of Concept.

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Abstract

Transcranial focused ultrasound (tFUS) has gained attention in the field of brain stimulation owing to its non-invasive neurotherapeutic potentials. However, complex interactions between acoustic waves and the cranium may introduce misalignment of the acoustic focus from a geometric target location, thus, necessitate on-site feedback of real-time navigational information of the transducer (spatial coordinates and angular orientation) for the operators to accurately place the acoustic focus to the desired brain area. In this study, we propose a deep-learning-based network model that can provide spatial navigational information of a single-element FUS transducer with respect to the targeted brain region. The training dataset were acquired through forward simulations that reflect the different tFUS transmissions for each skull structure using cranial computed tomography (CT) image data. The performance of the network was evaluated through three ex vivo calvaria. As a result show that the presented neural network-based method can an accurately navigate the FUS transducer with the conformity of ∼ 99.59% in placement of the transducer and ∼74.49% in the focal volume and an average difference of ∼ 0.96 mm in the focal point, all capable of real-time operation (∼10 ms).