Adaptive Low-Light Medical Imaging for Motion-Aware Signal Enhancement
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Solution Overview
Problem
Medical imaging systems face challenges in visualizing low light signals, particularly fluorescence images, due to low signal levels and motion blurring, which hinder the visualization of fine details and require insufficient compensation methods.
Innovation Solution
An adaptive imaging method that assesses relative movement between the image acquisition assembly and the object, adjusts image processing levels based on this movement, and generates low light video output from a quantity of frames to enhance signal visibility and reduce artifacts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple low light video frames are combined to enhance signal, then signal-to-noise ratio improves, but motion blurring increases
Solution Approach 1:
The system dynamically adjusts the number of frames to combine based on real-time motion assessment. When motion is detected, the system reduces the number of frames combined to minimize blurring; when motion is low, it increases the number of frames to maximize signal enhancement. This dynamic adaptation resolves the contradiction between signal enhancement and motion blurring.
Solution Approach 2:
The system continuously monitors motion characteristics of the imaged object and uses this feedback to adjust the frame combination strategy. By assessing motion in real-time and adapting the processing parameters accordingly, the system maintains optimal balance between signal-to-noise ratio and motion blurring without requiring manual intervention.
2Measurement precision
If image processing level is increased to reduce noise, then image quality improves, but processing time increases
Solution Approach 1:
The system dynamically adjusts the level of image processing based on real-time assessment of motion and signal characteristics. When motion is detected or signal quality is poor, the system applies higher processing levels to enhance image quality. When conditions are stable, it reduces processing intensity to minimize time loss, thereby adapting processing resources to actual needs.
Solution Approach 2:
The system changes processing parameters such as the number of frames combined, filtering strength, and enhancement intensity based on assessed motion and signal conditions. By adjusting these parameters dynamically rather than using fixed high processing levels, the system achieves good image quality while minimizing unnecessary processing time.
3Measurement precision
If frame combination quantity is increased to enhance signal, then visualization quality improves, but responsiveness to motion decreases
Solution Approach 1:
The system dynamically adjusts the quantity of frames to combine based on real-time motion detection. When motion is detected, it reduces the frame quantity to maintain responsiveness and avoid excessive blurring. When motion is minimal, it increases the frame quantity to maximize visualization quality. This dynamic adjustment ensures the system responds appropriately to changing conditions.
Solution Approach 2:
The system uses feedback from motion assessment to adjust the frame combination quantity in real-time. This feedback mechanism ensures that the system maintains optimal balance between visualization quality and responsiveness, adapting to motion conditions without requiring predetermined fixed parameters.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Improves visualization of low light image signals by limiting motion blurring and noise, providing enhanced image quality in real-time medical imaging applications.
Implementation Method 1
the white light video is typically acquired by illuminating the tissue with full visible spectrum light and imaging the illumination light that is reflected from the tissue surface
Implementation Method 2
fluorescence video, for example, may be acquired by illuminating the tissue with excitation light and imaging the fluorescence light that is emitted by excited fluorophores located in the tissue
Data Source
AI summary
Adaptive imaging methods and systems for generating enhanced low light video of an object for medical visualization are disclosed and include acquiring, with an image acquisition assembly, a sequence of reference frames and/or a sequence of low light video frames depicting the object, assessing relative movement between the image acquisition assembly and the object based on at least a portion of the acquired sequence of reference video frames or the acquired sequence of low light video frames, adjusting a level of image processing of the low light video frames based at least in part on the relative movement between the image acquisition assembly and the object, and generating a characteristic low light video output from a quantity of the low light video frames, wherein the quantity of the low light video frames is based on the adjusted level of image processing of the low light video frames.