# Fast Estimation of Haemoglobin Concentration in Tissue Via Wavelet Decomposition

## TL;DR

This paper introduces a rapid, GPU-accelerated method for estimating tissue haemoglobin concentration from RGB images using wavelet decomposition, enabling real-time tissue perfusion assessment during minimally invasive surgery.

## Contribution

It presents a novel wavelet-based approach combined with fast regularisation techniques for multispectral imaging using standard RGB cameras, suitable for real-time surgical applications.

## Key findings

- Achieves approximately 15Hz frame rate on GPU
- Validates method on animal and human laparoscopic data
- Demonstrates accurate tissue oxygenation estimation

## Abstract

Tissue oxygenation and perfusion can be an indicator for organ viability during minimally invasive surgery, for example allowing real-time assessment of tissue perfusion and oxygen saturation. Multispectral imaging is an optical modality that can inspect tissue perfusion in wide field images without contact. In this paper, we present a novel, fast method for using RGB images for MSI, which while limiting the spectral resolution of the modality allows normal laparoscopic systems to be used. We exploit the discrete Haar decomposition to separate individual video frames into low pass and directional coefficients and we utilise a different multispectral estimation technique on each. The increase in speed is achieved by using fast Tikhonov regularisation on the directional coefficients and more accurate Bayesian estimation on the low pass component. The pipeline is implemented using a graphics processing unit (GPU) architecture and achieves a frame rate of approximately 15Hz. We validate the method on animal models and on human data captured using a da Vinci stereo laparoscope.