Tutorial on the Use of Deep Learning in Diffuse Optical Tomography

Ganesh M. Balasubramaniam, Ben Wiesel, Netanel Biton, Rajnish Kumar, Judy Kupferman, Shlomi Arnon

Research output: Contribution to journalReview articlepeer-review

16 Scopus citations


Diffuse optical tomography using deep learning is an emerging technology that has found impressive medical diagnostic applications. However, creating an optical imaging system that uses visible and near-infrared (NIR) light is not straightforward due to photon absorption and multi-scattering by tissues. The high distortion levels caused due to these effects make the image reconstruction incredibly challenging. To overcome these challenges, various techniques have been proposed in the past, with varying success. One of the most successful techniques is the application of deep learning algorithms in diffuse optical tomography. This article discusses the current state-of-the-art diffuse optical tomography systems and comprehensively reviews the deep learning algorithms used in image reconstruction. This article attempts to provide researchers with the necessary background and tools to implement deep learning methods to solve diffuse optical tomography.

Original languageEnglish
Article number305
JournalElectronics (Switzerland)
Issue number3
StatePublished - 1 Feb 2022


  • Deep learning
  • Diffuse optical tomography
  • Inverse problems

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Signal Processing
  • Hardware and Architecture
  • Computer Networks and Communications
  • Electrical and Electronic Engineering


Dive into the research topics of 'Tutorial on the Use of Deep Learning in Diffuse Optical Tomography'. Together they form a unique fingerprint.

Cite this