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Technical Approach for Infrared Tracking for Soft Tissue Navigation with a Holographic Head-Mounted Display and Preclinical Validation
Published on: September 2, 2025
598
Enhanced Optical Head Tracking for Cranial Radiation Therapy: Supporting Surface Registration by Cutaneous Structures
Tobias Wissel1, Patrick Stüber1, Benjamin Wagner1
1Institute for Robotics and Cognitive Systems, University of Lübeck, Lübeck, Germany; Graduate School for Computing in Medicine and Life Science, University of Lübeck, Lübeck, Germany.
Summary
This study introduces a novel method using near-infrared (NIR) light to predict forehead tissue thickness, significantly improving surface registration accuracy in cranial radiation therapy by reducing errors by a factor of seven.
Area of Science:
- Medical Physics
- Biomedical Imaging
- Radiation Oncology
Background:
- Surface registration in cranial radiation therapy is crucial for accurate tumor targeting.
- Spatial ambiguities can arise during registration, potentially leading to treatment inaccuracies.
- Near-infrared (NIR) light offers a non-invasive method for surface data acquisition.
Purpose of the Study:
- To enhance surface registration accuracy in cranial radiation therapy using structural information.
- To minimize spatial ambiguities by predicting soft-tissue thickness variations from NIR light backscatter.
Main Methods:
- A pilot study involved recording NIR surface scans using laser triangulation from 30 volunteers.
- Soft-tissue thickness was established as ground truth using MRI segmentation.
- Gaussian processes were trained to predict tissue thickness from NIR backscatter, incorporating incident angle and neighborhood information.
Main Results:
- Tissue thickness prediction achieved mean errors below 0.2 mm, independent of skin type.
- The average registration error improved from 3.4 mm to 0.48 mm (a seven-fold reduction).
- 98.9% of misalignments exceeding 1 mm were reduced to below 1 mm.
Conclusions:
- Tissue-enhanced matching significantly outperforms purely spatial registration, improving tracking robustness.
- Predicting tissue thickness from NIR backscatter effectively minimizes spatial ambiguities in surface registration.
- This approach provides valuable support for surface registration, preventing misalignment of tumor targets.
