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End-to-end algorithm research in PACT — from signal processing to reconstruction solution to image processing: A review

  • Jinjin Cheng
  • , Shuilin Wang
  • , Shirui Dong
  • , Yang Wang
  • , Xuyu Fu
  • , Jichao Zhang
  • , Wenkai Li
  • , Siyu Chen
  • , Silue Zeng
  • , Yaguang Ren
  • , Xiaohui Ma
  • , Jinhai Liu
  • , Mingjian Sun*
  • , Rongkang Gao*
  • , Chengbo Liu*
  • *Corresponding author for this work
  • Shenzhen Institute of Advanced Technology
  • Harbin Institute of Technology Weihai
  • Northeastern University China
  • Southern Medical University
  • General Hospital of People's Liberation Army

Research output: Contribution to journalReview articlepeer-review

Abstract

Photoacoustic computed tomography (PACT) is an innovative biomedical imaging technique that has gained significant application in the field of biomedicine due to its ability to visualize optical contrast with high resolution and deep tissue penetration. However, the inherent challenges associated with photoacoustic signal excitation, propagation and detection often result in suboptimal image quality. To overcome these limitations, researchers have developed various advanced algorithms that span the entire image reconstruction pipeline. This review paper aims to present a detailed analysis of the latest advancements in PACT algorithms and synthesize these algorithms into a coherent framework. We provide tripartite analysis — from signal processing to reconstruction solution to image processing, covering a spectrum of techniques. The principles and methodologies, as well as their applicability and limitations, are thoroughly discussed. The primary objective of this study is to provide a thorough review of advanced algorithms applicable to PACT, offering both theoretical foundations and practical guidance for enhancing the imaging effect of PACT.

Original languageEnglish
Article number2530006
JournalJournal of Innovative Optical Health Sciences
Volume18
Issue number4
DOIs
StatePublished - 1 Jul 2025

Keywords

  • Photoacoustic computed tomography
  • deep learning
  • main transformation
  • post-processing
  • pre-processing

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