A deep learning-based approach to identify in vivo catheter tips during photoacoustic-guided cardiac interventions

Derek Allman, Fabrizio Assis, Jonathan Chrispin, Muyinatu A. Lediju Bell

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

Abstract

Interventional cardiac procedures often require ionizing radiation to guide cardiac catheters to the heart. To reduce the associated risks of ionizing radiation, our group is exploring photoacoustic imaging in conjunc- tion with robotic visual servoing, which requires segmentation of catheter tips. However, typical segmentation algorithms are susceptible to reflection artifacts. To address this challenge, signal sources can be identified in the presence of reflection artifacts using a deep neural network, as we previously demonstrated with a linear array ultrasound transducer. This paper extends our previous work to detect photoacoustic sources received by a phased array transducer, which is more common in cardiac applications. We trained a convolutional neural network (CNN) with simulated photoacoustic channel data to identify point sources. The network was tested with an independent simulated validation data set not included during training as well as in vivo data acquired during a pig catheterization procedure. When tested on the independent simulated validation data set, the CNN correctly classified 84.2% of sources with a misclassification rate of 0.01%, and the mean absolute location error of correctly classified sources was 0.095 mm and 0.462 mm in the axial and lateral dimensions, respectively. When applied to in vivo data, the network correctly classified 91.4% of sources with a 7.86% misclassification rate. These results indicate that a CNN is capable of identifying photoacoustic sources recorded by phased array transducers, which is promising for cardiac applications.

Original languageEnglish (US)
Title of host publicationPhotons Plus Ultrasound
Subtitle of host publicationImaging and Sensing 2019
EditorsAlexander A. Oraevsky, Lihong V. Wang
PublisherSPIE
ISBN (Electronic)9781510623989
DOIs
Publication statusPublished - Jan 1 2019
EventPhotons Plus Ultrasound: Imaging and Sensing 2019 - San Francisco, United States
Duration: Feb 3 2019Feb 6 2019

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume10878
ISSN (Print)1605-7422

Conference

ConferencePhotons Plus Ultrasound: Imaging and Sensing 2019
CountryUnited States
CitySan Francisco
Period2/3/192/6/19

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ASJC Scopus subject areas

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

Cite this

Allman, D., Assis, F., Chrispin, J., & Lediju Bell, M. A. (2019). A deep learning-based approach to identify in vivo catheter tips during photoacoustic-guided cardiac interventions. In A. A. Oraevsky, & L. V. Wang (Eds.), Photons Plus Ultrasound: Imaging and Sensing 2019 [108785E] (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 10878). SPIE. https://doi.org/10.1117/12.2510993