Convolutional autoencoder-based reconstruction of vascular structures in photoacoustic images

Israr Ul Haq, Yoshinobu Kawahara

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

Abstract

In medical imaging, a number of imaging modalities have been used to visualize the structural information of different internal organs in the human body and some modalities can even visualize structures at a cellular level for diagnostic and treatment purposes. Optical resolution photoacoustic microscopy (OR-PAM) is one of the emerging imaging modalities to analyze the anatomy and functionality of tissues non-invasive. It is a hybrid imaging technology that combines photoacoustic (PA) contrast and acoustic resolution to reconstruct images of tissues in humans and animals. However, in OR-PAM the received ultrasonic signal by the thermal expansion of tissues has a low signal-to-noise ratio because most of the signal power is lost in the conversion process from light to acoustic waves which makes it difficult to visualize the structural information in the PA images. Traditional denoising methods such as wiener filter, bandpass filter, wavelet-based denoising, noise reduction by SVD and dictionary-based denoising methods can denoise PA images to some extent but it is still a difficult task to preserve structural information in the images by such methods. In this research, a convolutional autoencoder (CAE) based model is proposed to denoise and learn the structural patterns of blood vessels in PA images. To achieve this task, a CAE model is first trained between noisy and Gabor filtered sub-images, those contain the patterns of different vascular structures. Then, the trained model is used to approximate the denoise version of the input noisy sub-images of blood vessels. The proposed model is trained and tested on PA images of blood vessels of a mouse ear, acquired by the OR-PAM imaging system and the results show that our proposed method can effectively approximate and reconstruct the noisy vascular structures than traditional images denoising filtering methods.

Original languageEnglish
Title of host publicationBiomedical Spectroscopy, Microscopy, and Imaging
EditorsJurgen Popp, Csilla Gergely
PublisherSPIE
ISBN (Electronic)9781510634909
DOIs
Publication statusPublished - 2020
EventBiomedical Spectroscopy, Microscopy, and Imaging 2020 - Virtual, Online, France
Duration: Apr 6 2020Apr 10 2020

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume11359
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

ConferenceBiomedical Spectroscopy, Microscopy, and Imaging 2020
CountryFrance
CityVirtual, Online
Period4/6/204/10/20

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'Convolutional autoencoder-based reconstruction of vascular structures in photoacoustic images'. Together they form a unique fingerprint.

Cite this