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Machine learning-based adaptive scanning overcomes the resolution-speed tradeoff in optical coherence tomography (OCT). This novel approach significantly boosts frame rates by up to 40% without sacrificing image quality.

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Area of Science:

  • Biomedical Optics
  • Medical Imaging
  • Machine Learning in Medicine

Background:

  • Conventional optical coherence tomography (OCT) faces a fundamental tradeoff between image resolution and frame rate.
  • High-resolution OCT imaging is often limited by slow acquisition speeds, hindering real-time applications.

Purpose of the Study:

  • To develop and evaluate machine learning (ML)-based adaptive scanning methods to overcome the OCT frame rate/resolution tradeoff.
  • To enhance OCT scanning speed and generalizability for improved clinical and surgical applications.

Main Methods:

  • Proposed two ML-based adaptive scanning pipelines utilizing ConvLSTM and temporal attention unit (TAU) models for scene dynamics prediction.
  • Integrated ML models with a kinodynamic path planner based on the clustered traveling salesperson problem.
  • Validated techniques using deterministic phantoms and real-time surgical tool tracking experiments.

Main Results:

  • Achieved mean frame rate speed-ups of up to 40% compared to conventional raster scanning and probabilistic adaptive scanning, without compromising image quality.
  • Demonstrated improved generalizability across diverse scenes, reducing the need for manual system parameter tuning.
  • Real-time surgical tool tracking showed an average speed-up factor of over 3.2× compared to conventional methods.

Conclusions:

  • ML-based adaptive scanning effectively resolves the OCT frame rate/resolution limitation.
  • The proposed methods offer enhanced speed, generalizability, and robustness for OCT imaging in various settings, including surgery.