Acoustic Simulation for Transcranial Focused Ultrasound Using GAN-Based Synthetic CT

Heekyung Koh, Tae Young Park, Yong An Chung, Jong Hwan Lee, Hyungmin Kim

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

Transcranial focused ultrasound (tFUS) is a promising non-invasive technique for treating neurological and psychiatric disorders. One of the challenges for tFUS is the disruption of wave propagation through the skull. Consequently, despite the risks associated with exposure to ionizing radiation, computed tomography (CT) is required to estimate the acoustic transmission through the skull. This study aims to generate synthetic CT (sCT) from T1-weighted magnetic resonance imaging (MRI) and investigate its applicability to tFUS acoustic simulation. We trained a 3D conditional generative adversarial network (3D-cGAN) with 15 subjects. We then assessed image quality with 15 test subjects: mean absolute error (MAE) = 85.72±9.50 HU (head) and 280.25±24.02 HU (skull), dice coefficient similarity (DSC) = 0.88±0.02 (skull). In terms of skull density ratio (SDR) and skull thickness (ST), no significant difference was found between sCT and real CT (rCT). When the acoustic simulation results of rCT and sCT were compared, the intracranial peak acoustic pressure ratio was found to be less than 4%, and the distance between focal points less than 1 mm.

Original languageEnglish
Pages (from-to)161-171
Number of pages11
JournalIEEE Journal of Biomedical and Health Informatics
Volume26
Issue number1
DOIs
Publication statusPublished - 2022 Jan 1

Keywords

  • MRI-only
  • Transcranial focused ultrasound
  • acoustic simulation
  • conditional GAN
  • generative adversarial network
  • single-element transducer
  • synthetic CT

ASJC Scopus subject areas

  • Biotechnology
  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Health Information Management

Fingerprint

Dive into the research topics of 'Acoustic Simulation for Transcranial Focused Ultrasound Using GAN-Based Synthetic CT'. Together they form a unique fingerprint.

Cite this