Handheld Multispectral Soft Tissue Imaging Motion Artifact Correction
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Solution Overview
Problem
Existing multispectral soft tissue imaging technologies, such as Laser Speckle Imaging (LSI) and Laser Doppler Imaging (LDI), are prone to motion artifacts due to target and device movement, leading to inaccurate blood flow and perfusion quantification in clinical settings.
Innovation Solution
Implementing a motion amplitude indication and monitoring strategy using hardware sensors like accelerometers and gyroscopes, combined with software analysis such as image registration and optics flow, to stabilize images during minimal motion periods and correct motion artifacts through mathematical correlation and baseline normalization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional laser-based perfusion technologies are used to measure blood flow and perfusion, then the imaging can be performed, but the measurement results are disrupted by motion artifacts causing inaccuracy
Solution Approach 1:
A reference marker is introduced as an intermediary element to separate and compensate for device motion from target tissue motion. The reference marker provides a stable reference point that allows the system to distinguish between actual tissue perfusion changes and artifacts caused by device movement or patient motion, thereby improving measurement precision while maintaining the ability to detect real physiological changes.
Solution Approach 2:
The system continuously monitors motion parameters and uses this feedback to dynamically adjust the image processing and perfusion calculation. By incorporating real-time motion information into the measurement process, the system compensates for motion artifacts as they occur, maintaining accurate blood flow quantification despite the presence of motion.
2Shape
If image stabilization is applied to handle translational camera movement, then aligned visible images can be displayed, but motion noise at subpixel level remains causing blurry images and inaccurate calculation
Solution Approach 1:
The patent replaces mechanical image stabilization with a computational approach that operates at the pixel level. Instead of physically stabilizing the image through hardware, the system uses algorithms to detect and correct motion artifacts in the digital image data, achieving subpixel-level precision that mechanical methods cannot attain.
Solution Approach 2:
The system changes the parameter of image processing from traditional pixel-level alignment to subpixel-level registration. By operating at a finer resolution and incorporating motion compensation into the perfusion calculation algorithm itself, the system eliminates the blurriness and inaccuracy that persist in conventional image stabilization methods.
3Measurement precision
If the imaging target is kept stationery to avoid motion artifacts, then accurate quantification can be achieved, but this is difficult or impossible in many clinical situations
Solution Approach 1:
The system performs self-correction by automatically detecting and compensating for motion artifacts without requiring external intervention. The motion monitoring and compensation algorithms operate autonomously, adjusting the measurements based on detected motion patterns, thereby maintaining accuracy while allowing the target to remain stationary-free and improving clinical usability.
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
Enhances the accuracy of blood flow and perfusion mapping by reducing motion artifacts, allowing for reliable clinical assessments in handheld devices.
Implementation Method 1
hardware sensors such as accelerometers, gyroscopes
Implementation Method 2
hardware sensors such as accelerometers, gyroscopes
Implementation Method 3
software analysis such as image registration
Implementation Method 4
software analysis such as optics flow
Implementation Method 5
Laser Speckle Imaging (LSI) is a valuable technique for real time mapping and assessment of blood flow and perfusion
Implementation Method 6
Laser Speckle Imaging (LSI) is a valuable technique for real time mapping and assessment of blood flow and perfusion
Implementation Method 7
Laser Doppler Imaging (LDI)
Data Source
AI summary
Various motion artifact reduction and correction methods are provided within multispectral soft tissue imaging architecture. A motion amplitude indication and monitoring strategy is useful before reducing motion and this can be achieved through hardware sensors such as accelerometers, gyroscopes, or software analysis such as image registration, optics flow. Large and medium level motion such as device shaking, and target breathing can be resolved through image stabilization and snapshot approach by only using a time interval where motion is minimum. Small and micro motion such as device vibration can be resolved through motion correction by identifying the mathematical correlation between velocity magnitude and signal to noise ratio (SNR) or baseline normalization by using a standard reflection marker. Sources of the motion need to be taken into consideration such as intrinsic motion from the target or extrinsic motion from the device. The present inventive concept can apply to broadband, narrowband, fluorescence, autofluorescence, Laser Speckle Imaging (LSI), Laser Doppler Imaging (LDI), tissue oxygenation imaging, and other variation of soft tissue imaging modalities. The imaging software includes temporally and spatially synchronized acquisition of motion amplitude, multiple imaging channels, image processing based on physics principle and mathematical equations of each imaging modality, motion reduction and correction, image fusion-based visualization and report. The system is designed to be an addon to smart phones, tablets, and other mobile devices as portable equipment.