Compressed sensing of human breast optical coherence 3-D image volume data using predictive coding

There are clinical needs for optical coherence tomography (OCT) of large areas within a short period of time, such as imaging resected breast tissue for the evaluation of cancer. We report on the use of denoising predictive coding (DN-PC), a novel compressed sensing (CS) algorithm for reconstruction of OCT volumes of human normal breast and breast cancer tissue. The DN-PC algorithm has been rewritten to allow for computational parallelization and efficient memory transfer, resulting in a net reduction of computation time by a factor of 20. We compress image volumes at decreasing A-line sampling rates to evaluate a relation between reconstruction behavior and image features of breast tissue.

Automated summary

This paper presents a novel compressed sensing algorithm, DN-PC, for OCT volume reconstruction of human breast tissue. The DN-PC algorithm is rewritten to enable computational parallelization and efficient memory transfer, resulting in a significant reduction in computation time. The study compresses image volumes at decreasing A-line sampling rates to evaluate the relationship between reconstruction behavior and image features of breast tissue.