A learning-based image reconstruction method for skull-induced aberrations compensation in transcranial photoacoustic computed tomography

Hsuan Kai Huang, Joseph Kuo, Seonyeong Park, Umberto Villa, Lihong V. Wang, Mark A. Anastasio

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Transcranial photoacoustic computed tomography (PACT) is an emerging human neuroimaging modality that holds significant potential for clinical and scientific applications. However, accurate image reconstruction remains challenging due to skull-induced aberration of the measurement data. Model-based image reconstruction methods have been proposed that are based on the elastic wave equation. To be effective, such methods require that the elastic and acoustic properties of the skull are known accurately, which can be difficult to achieve in practice. Additionally, such methods are computationally burdensome. To address these challenges, a novel learning-based image reconstruction was proposed. The method involves the use of a deep neural network to map a preliminary image that was computed by use of a computationally efficient but approximate reconstruction method to a high-quality, de-aberrated, estimate of the induced initial pressure distribution within the cortical region of the brain. The method was systematically evaluated via computer-simulations that involved realistic, full-scale, three-dimensional stochastic head phantoms. The phantoms contained physiologically relevant optical and acoustic properties and stochastically synthesized vasculature. The results demonstrated that the learning-based method could achieve comparable performance to a state-of-the-art model-based method when the assumed skull parameters were accurate, and significantly outperformed the model-based method when uncertainty in the skull parameters was present. Additionally, the method can reduce image reconstruction times from days to tens of minutes. This study represents an important contribution to the development of transcranial PACT and will motivate the exploration of learning-based methods to help advance this important technology.

Original languageEnglish (US)
Title of host publicationPhotons Plus Ultrasound
Subtitle of host publicationImaging and Sensing 2024
EditorsAlexander A. Oraevsky, Lihong V. Wang
PublisherSPIE
ISBN (Electronic)9781510669437
DOIs
StatePublished - 2024
EventPhotons Plus Ultrasound: Imaging and Sensing 2024 - San Francisco, United States
Duration: Jan 28 2024Jan 31 2024

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume12842
ISSN (Print)1605-7422

Conference

ConferencePhotons Plus Ultrasound: Imaging and Sensing 2024
Country/TerritoryUnited States
CitySan Francisco
Period1/28/241/31/24

Keywords

  • deep learning
  • Photoacoustic computed tomography
  • transcranial imaging

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Atomic and Molecular Physics, and Optics
  • Biomaterials
  • Radiology Nuclear Medicine and imaging

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