Image-guided filtering for improving photoacoustic tomographic image reconstruction

Navchetan Awasthi, Sandeep Kumar Kalva, Manojit Pramanik, Phaneendra K Yalavarthy

Research output: Contribution to journalArticleAcademicpeer-review

14 Citations (SciVal)


Several algorithms exist to solve the photoacoustic image reconstruction problem depending on the expected reconstructed image features. These reconstruction algorithms promote typically one feature, such as being smooth or sharp, in the output image. Combining these features using a guided filtering approach was attempted in this work, which requires an input and guiding image. This approach act as a postprocessing step to improve commonly used Tikhonov or total variational regularization method. The result obtained from linear backprojection was used as a guiding image to improve these results. Using both numerical and experimental phantom cases, it was shown that the proposed guided filtering approach was able to improve (as high as 11.23 dB) the signal-to-noise ratio of the reconstructed images with the added advantage being computationally efficient. This approach was compared with state-of-the-art basis pursuit deconvolution as well as standard denoising methods and shown to outperform them.

Original languageEnglish
Pages (from-to)1-22
Number of pages22
JournalJournal of Biomedical Optics
Issue number9
Publication statusPublished - Sep 2018
Externally publishedYes

Bibliographical note

(2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE).


  • Algorithms
  • Animals
  • Brain/diagnostic imaging
  • Female
  • Image Processing, Computer-Assisted/methods
  • Phantoms, Imaging
  • Photoacoustic Techniques/methods
  • Rats
  • Signal Processing, Computer-Assisted
  • Signal-To-Noise Ratio
  • Tomography/methods
  • basis pursuit deconvolution
  • photoacoustic imaging
  • Lanczos Tikhonov
  • guided image filtering
  • total variation
  • model-based reconstruction


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