A deep learning-based image reconstruction method for USCT that employs multimodality inputs

Gangwon Jeong, Fu Li, Umberto Villa, Mark A. Anastasio

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

Abstract

Ultrasound computed tomography (USCT) has the potential to detect breast cancer by measuring tissue acoustic properties such as speed-of-sound (SOS). Current USCT image reconstruction methods for SOS fall into two categories, each with its own limitations. Ray-based methods are computationally efficient but suffer from low spatial resolution due to neglecting scattering effects, while full-waveform inversion (FWI) methods offer higher spatial resolution but are computationally intensive, limiting their widespread application. To address these issues, a deep learning (DL)-based method is proposed for USCT breast imaging that achieves SOS reconstruction quality comparable to FWI while remaining computationally efficient. This method leverages the computational efficiency and high-quality image reconstruction capabilities of DL-based methods, which have shown promise in various medical image reconstruction problems. Specifically, low-resolution SOS images estimated by ray-based traveltime tomography and reflectivity images from reflection tomography are employed as inputs to a U-Net-based image reconstruction method. These complementary images provide direct SOS information (via traveltime tomography) and tissue boundary information (via reflectivity tomography). The U-Net is trained in a supervised manner to map the two input images into a single, high-resolution image of the SOS map. Numerical studies using realistic numerical breast phantoms show promise for improving image quality compared to naïve, single-input U-Net-based approaches, using either traveltime or reflection tomography images as inputs. The proposed DL-based method is computationally efficient and may offer a practical solution for enhancing SOS reconstruction quality, which could potentially improve diagnostic accuracy.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2023
Subtitle of host publicationUltrasonic Imaging and Tomography
EditorsChristian Boehm, Nick Bottenus
PublisherSPIE
ISBN (Electronic)9781510660458
DOIs
StatePublished - 2023
Externally publishedYes
EventMedical Imaging 2023: Ultrasonic Imaging and Tomography - San Diego, United States
Duration: Feb 22 2023Feb 23 2023

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume12470
ISSN (Print)1605-7422

Conference

ConferenceMedical Imaging 2023: Ultrasonic Imaging and Tomography
Country/TerritoryUnited States
CitySan Diego
Period2/22/232/23/23

Keywords

  • Ultrasound computed tomography
  • deep learning
  • full-waveform inversion
  • reflection tomography
  • traveltime tomography

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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