Absolute flow velocity quantification method for laser speckle blood flow imaging and related device
By employing physical enhancement preprocessing and domain adversarial regression networks, this study addresses the limitation of traditional laser speckle blood flow imaging technology in providing full-field absolute flow velocity measurement during cardiac surgery. It achieves high-precision blood flow monitoring, adapts to complex physiological environments, and provides a new method for quantitative assessment of absolute flow velocity in cardiac surgery.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- TECHNICAL INST OF PHYSICS & CHEMISTRY - CHINESE ACAD OF SCI
- Filing Date
- 2026-03-13
- Publication Date
- 2026-06-05
AI Technical Summary
Traditional laser speckle flow imaging technology cannot provide absolute flow velocity measurement across the entire field of view in cardiac surgery, and deep learning models have insufficient generalization ability in complex physiological backgrounds, resulting in unstable measurement results.
By employing physical enhancement preprocessing and domain adversarial regression networks, a dual-channel spatiotemporal tensor is constructed. Domain-invariant features independent of tissue background are extracted through the domain adversarial regression network. Combined with a few-sample transfer learning strategy, a leapfrog quantitative measurement from relative blood flow index to absolute flow velocity is achieved.
It achieves full-field, non-contact, and high-precision blood flow monitoring, overcomes the insufficient quantitative capabilities of traditional methods, adapts to complex physiological environments, and provides a new means for quantitative assessment of absolute flow velocity in cardiac surgery.
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