  Wide-field imaging Mueller polarimetry is a revolutionary, label-free, and
non-invasive modality for computer-aided intervention: in neurosurgery it aims
to provide visual feedback of white matter fibre bundle orientation from
derived parameters. Conventionally, robust polarimetric parameters are
estimated after averaging multiple measurements of intensity for each pair of
probing and detected polarised light. Long multi-shot averaging, however, is
not compatible with real-time in-vivo imaging, and the current performance of
polarimetric data processing hinders the translation to clinical practice. A
learning-based denoising framework is tailored for fast, single-shot, noisy
acquisitions of polarimetric intensities. Also, performance-optimised image
processing tools are devised for the derivation of clinically relevant
parameters. The combination recovers accurate polarimetric parameters from fast
acquisitions with near-real-time performance, under the assumption of
pseudo-Gaussian polarimetric acquisition noise. The denoising framework is
trained, validated, and tested on experimental data comprising tumour-free and
diseased human brain samples in different conditions. Accuracy and image
quality indices showed significant improvements on testing data for a fast
single-pass denoising versus the state-of-the-art and high polarimetric image
quality standards. The computational time is reported for the end-to-end
processing. The end-to-end image processing achieved real-time performance for
a localised field of view. The denoised polarimetric intensities produced
visibly clear directional patterns of neuronal fibre tracts in line with
reference polarimetric image quality standards; directional disruption was kept
in case of neoplastic lesions. The presented advances pave the way towards
feasible oncological neurosurgical translations of novel, label free,
interventional feedback.
