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CToF-DOT Code

drawing

Attached is the associated code for our CToF-DOT publication [1]. The code for each figure (assuming it required a simulation or image reconstruction) is placed in its own directory. Within each directory, you will find a RUNME program which should should run out-of-the-box. Note, most programs will create a fig/ directory at the same level as this package. In this fig/ directory, you will find the output/plots for the program. Most datasets also require data. This dataset for each program can be found at this link. Please download the file, rename the directory to dat, and place the folder in the top level directory before running.

Contact: Yongyi Zhao ([email protected])

Additional contributors include:

  • Ankit Raghuram (Rice University)
  • Hyun Kim (Columbia University and New York University)
  • Vivek Boominathan (Rice University)

If you end up using our code or data, please also cite us, thank you!

@article{Zhao2021,
  author={Zhao, Yongyi and Raghuram, Ankit and Kim, Hyun K. and Hielscher, Andreas H. and Robinson, Jacob T. and Veeraraghavan, Ashok},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, 
  title={High Resolution, Deep Imaging Using Confocal Time-of-Flight Diffuse Optical Tomography}, 
  year={2021},
  volume={43},
  number={7},
  pages={2206-2219},
  doi={10.1109/TPAMI.2021.3075366}}

Misc. Notes:

  1. Our image reconstructions use the FISTA linear inverse solver [2].
  2. To run our code, you must separately download certain plotting programs. The copyright notices for these codes are included in the file copyright_info.txt They are listed below along with the directory in which they should be placed:
  1. Please note, a small modification was made to the vol3d function, and so it is included in this package

References:

[1] Y. Zhao, A. Raghuram, H. Kim, A. Hielscher, J. T. Robinson and A. N. Veeraraghavan, "High Resolution, Deep Imaging Using Confocal Time-of-flight Diffuse Optical Tomography," in IEEE Transactions on Pattern Analysis and Machine Intelligence, doi: 10.1109/TPAMI.2021.3075366.

[2] Amir Beck and Marc Teboulle. 2009. A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems. SIAM J. Img. Sci. 2, 1 (January 2009), 183–202. DOI:https://doi.org/10.1137/080716542