High Resolution, Deep Imaging Using Confocal Time-of-Flight Diffuse Optical Tomography

Yongyi Zhao, Ankit Raghuram, Hyun K. Kim, Andreas H. Hielscher, Jacob T. Robinson, Ashok Veeraraghavan

Research output: Contribution to journalArticlepeer-review


Light scattering by tissue severely limits how deep beneath the surface one can image, and the spatial resolution one can obtain from these images. Diffuse optical tomography (DOT) is one of the most powerful techniques for imaging deep within tissue - well beyond the conventional ∼ 10-15 mean scattering lengths tolerated by ballistic imaging techniques such as confocal and two-photon microscopy. Unfortunately, existing DOT systems are limited, achieving only centimeter-scale resolution. Furthermore, they suffer from slow acquisition times and slow reconstruction speeds making real-time imaging infeasible. We show that time-of-flight diffuse optical tomography (ToF-DOT) and its confocal variant (CToF-DOT), by exploiting the photon travel time information, allow us to achieve millimeter spatial resolution in the highly scattered diffusion regime ( mean free paths). In addition, we demonstrate two additional innovations: focusing on confocal measurements, and multiplexing the illumination sources allow us to significantly reduce the measurement acquisition time. Finally, we rely on a novel convolutional approximation that allows us to develop a fast reconstruction algorithm, achieving a 100× speedup in reconstruction time compared to traditional DOT reconstruction techniques. Together, we believe that these technical advances serve as the first step towards real-time, millimeter resolution, deep tissue imaging using DOT.

Original languageEnglish (US)
Article number9415130
Pages (from-to)2206-2219
Number of pages14
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Issue number7
StatePublished - Jul 1 2021


  • Time-of-flight imaging
  • confocal
  • diffuse optical tomography
  • fluorescence imaging
  • time binning
  • Algorithms
  • Tomography, Optical

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence
  • Applied Mathematics
  • Computer Vision and Pattern Recognition
  • Computational Theory and Mathematics


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