Recovering Dense Tissue Multispectral Signal from in vivo RGB Images

TL;DR

This paper presents a real-time algorithm that reconstructs dense multispectral tissue signals from standard RGB images, enabling enhanced intra-operative imaging without specialized hardware.

Contribution

The authors introduce a novel super-spectral-resolution method that recovers pixel-level multispectral data from RGB images in real-time, facilitating practical intra-operative multispectral imaging.

Findings

Achieves ~11 FPS on GPU for 24 spectral bands

Successfully applied to in vivo animal tissue data

Validated with unseen in vivo experiments

Abstract

Hyperspectral/multispectral imaging (HSI/MSI) contains rich information clinical applications, such as 1) narrow band imaging for vascular visualisation; 2) oxygen saturation for intraoperative perfusion monitoring and clinical decision making [1]; 3) tissue classification and identification of pathology [2]. The current systems which provide pixel-level HSI/MSI signal can be generally divided into two types: spatial scanning and spectral scanning. However, the trade-off between spatial/spectral resolution, the acquisition time, and the hardware complexity hampers implementation in real-world applications, especially intra-operatively. Acquiring high resolution images in real-time is important for HSI/MSI in intra-operative imaging, to alleviate the side effect caused by breathing, heartbeat, and other sources of motion. Therefore, we developed an algorithm to recover a pixel-level MSI stack using only the captured snapshot RGB images from a normal camera. We refer to this technique as "super-spectral-resolution". The proposed method enables recovery of pixel-level-dense MSI signals with 24 spectral bands at ~11 frames per second (FPS) on a GPU. Multispectral data captured from porcine bowel and sheep/rabbit uteri in vivo has been used for training, and the algorithm has been validated using unseen in vivo animal experiments.