Dual-camera laparoscopic imaging with super-resolution reconstruction for intraoperative hyperspectral image guidance

Ling Ma; Kelden Pruitt; Baowei Fei

doi:10.1117/12.3006573

29 March 2024 Dual-camera laparoscopic imaging with super-resolution reconstruction for intraoperative hyperspectral image guidance

Ling Ma, Kelden Pruitt, Baowei Fei

Author Affiliations +

Proceedings Volume 12928, Medical Imaging 2024: Image-Guided Procedures, Robotic Interventions, and Modeling; 129280I (2024) https://doi.org/10.1117/12.3006573
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Abstract

Laparoscopic and robotic surgery, as one type of minimally invasive surgery (MIS), has gained popularity due to the improved surgeon ergonomics, instrument precision, operative time, and postoperative recovery. Hyperspectral imaging (HSI) is an emerging medical imaging modality, which has proved useful for intraoperative image guidance. Snapshot hyperspectral cameras are ideal for intraoperative laparoscopic imaging because of their compact size and light weight, but low spatial resolution can be a limitation. In this work, we developed a dual-camera laparoscopic imaging system that consists of a high-resolution color camera and a snapshot hyperspectral camera, and we employed super-resolution reconstruction to fuse the images from both cameras to generate high-resolution hyperspectral images. The experimental results show that our method can significantly improve the resolution of hyperspectral images without compromising the image quality or spectral signatures. The proposed super-resolution reconstruction method is promising to promote the employment of high-speed hyperspectral imaging in laparoscopic surgery.

Conference Presentation

Citation Download Citation

Ling Ma, Kelden Pruitt, and Baowei Fei "Dual-camera laparoscopic imaging with super-resolution reconstruction for intraoperative hyperspectral image guidance", Proc. SPIE 12928, Medical Imaging 2024: Image-Guided Procedures, Robotic Interventions, and Modeling, 129280I (29 March 2024); https://doi.org/10.1117/12.3006573

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