Noise suppressed, multifocus image fusion for enhanced intraoperative navigation

Area of Science:

• Medical imaging
• Image processing
• Surgical technology

Background:

• Current intraoperative imaging systems lack sharpness over large areas, hindering detection of critical structures like tissue margins or malignant cells, especially under low signal-to-noise ratios.
• Sub-optimal imaging conditions are common in real-time fluorescence intraoperative surgery, posing challenges for accurate visualization.

Purpose of the Study:

• To develop and present a noise-suppressed multifocus image fusion algorithm for enhanced intraoperative imaging.
• To improve the detection of distinct structures and tissue margins in challenging imaging scenarios.

Main Methods:

• The algorithm utilizes the Anscombe transform combined with a multi-level stationary wavelet transform and individual threshold-based shrinkage.
• It is designed to provide detailed image reconstructions from sub-optimal acquired images.
• The system can be adapted to commercial imaging systems and is integrated with a respiratory monitor triggering system.

Main Results:

• The developed algorithm provides detailed reconstructions of images acquired under sub-optimal conditions.
• It effectively suppresses noise, leading to sharper images for improved intraoperative visualization.
• The algorithm enhances the detectability of distinct structures, crucial for surgical guidance.

Conclusions:

• The noise-suppressed multifocus image fusion algorithm significantly improves intraoperative imaging quality.
• This advancement aids in the detection of critical structures, enhancing surgical precision.
• The algorithm is provided as an Osirix plugin, facilitating its adoption in clinical practice.