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Fluorescence lifetime (FLT) imaging has been shown to distinguish tumors from normal tissue with high accuracy. However, the practical utility of FLT imaging is hindered by slow acquisition speeds and depth-dependent inaccuracies. To address these challenges, we introduce FLT-SLAM, a novel algorithm that combines rapid FLT imaging with simultaneous localization and mapping (SLAM) for real-time 3D surface reconstruction and depth-corrected FLT estimation. Using a stereo laparoscope, our approach extracts real-time depth information to improve accuracy, while achieving acquisition speeds exceeding 5 Hz. FLT maps are overlaid onto large-scale 3D surface models generated by SLAM, improving visualization and spatial awareness. We validate FLT-SLAM through phantom and ex-vivo tissue measurements, and show that it reduces FLT estimation errors by nearly 20 \(\%\) , thereby demonstrating its potential to enhance real-time, depth-corrected FLT imaging for surgical applications.

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Real-Time SLAM-Based Correction and 3D Visualization for Fluorescence Lifetime Imaging

  • 会议论文

  • 发表日期：

    2026年

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  • Murali Krishnamoorthy,
  • Haoyin Zhou,
  • Katherine Frazee,
  • Rahul Pal,
  • Jayender Jagadeesan,
  • Anand T. N. Kumar

摘要

Fluorescence lifetime (FLT) imaging has been shown to distinguish tumors from normal tissue with high accuracy. However, the practical utility of FLT imaging is hindered by slow acquisition speeds and depth-dependent inaccuracies. To address these challenges, we introduce FLT-SLAM, a novel algorithm that combines rapid FLT imaging with simultaneous localization and mapping (SLAM) for real-time 3D surface reconstruction and depth-corrected FLT estimation. Using a stereo laparoscope, our approach extracts real-time depth information to improve accuracy, while achieving acquisition speeds exceeding 5 Hz. FLT maps are overlaid onto large-scale 3D surface models generated by SLAM, improving visualization and spatial awareness. We validate FLT-SLAM through phantom and ex-vivo tissue measurements, and show that it reduces FLT estimation errors by nearly 20 \(\%\) , thereby demonstrating its potential to enhance real-time, depth-corrected FLT imaging for surgical applications.