Phase-Restoring Multidimensional Image Shifting for FD-OCT Motion Correction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods in Fourier-domain optical coherence tomography (FD-OCT) fail to accurately restore phase components when images are translationally shifted over arbitrary real-valued displacements, leading to motion-induced phase errors due to bulk motion of samples, such as heartbeat, breathing, and environmental vibrations.
Innovation Solution
A phase-restoring subpixel motion correction (PRSMC) method that shifts multidimensional images over arbitrary real-valued displacements by correcting axial and lateral displacements in the spectral and spatial frequency domains, respectively, using Doppler shifts and normalized cross-correlation, to restore physically accurate phase components.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If image registration techniques are used to correct bulk motion in FD-OCT, then temporal stability of phase components is improved, but motion-induced phase errors persist when translating images over arbitrary real-valued displacements
Solution Approach 1:
The patent transforms the image correction problem from spatial domain to frequency domain by applying Fourier transforms. In the frequency domain, translational shifts manifest as phase changes that can be accurately compensated by adjusting frequency parameters, enabling precise phase restoration for arbitrary real-valued displacements without the errors inherent in spatial domain interpolation methods
Solution Approach 2:
The patent introduces frequency domain representation as an intermediary between the original spatial domain images and the final corrected images. By converting images to frequency domain, applying phase correction factors, and then transforming back, the method achieves accurate phase restoration that directly addresses bulk motion without introducing additional phase errors
2Ease of operation
If conventional image shifting methods are applied to correct arbitrary real-valued displacements, then bulk motion correction is achieved, but phase components become de-correlated and physically inaccurate
Solution Approach 1:
The patent replaces conventional spatial domain image shifting operations with frequency domain phase multiplication operations. Instead of mechanically interpolating and shifting pixel values in spatial domain, the method uses Fourier transforms to convert images to frequency domain, where shifts are applied as simple phase multiplications, eliminating the phase corruption inherent in spatial interpolation methods
Solution Approach 2:
The patent changes the operational domain from spatial to frequency domain, where translational displacement parameters are handled as phase angle parameters. This parameter transformation allows arbitrary real-valued displacements to be applied without the quantization and interpolation errors that plague spatial domain methods, preserving phase component accuracy
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
The method significantly reduces motion-induced phase errors, achieving phase sensitivities close to the fundamental limit set by the system's signal-to-noise ratio (SNR) and maintaining high phase stability and sensitivity in FD-OCT imaging.
Implementation Method 1
With the sample movement estimated from averaged Doppler shifts or normalized cross-correlation
Implementation Method 2
some offer their estimation in the integer multiples of a pixel, while others, such as normalized cross-correlation (NCC) or phase-only correlation (POC) based methods, can offer subpixel real-valued estimation of the bulk motion
Data Source
AI summary
Disclosed is a method for phase-restoring translational shifting of a multidimensional image. The method comprises computing a first shifted, complex-valued image by converting a spectral signal (corresponding to the multidimensional image) to a shifted spectral signal and transforming the shifted spectral signal along a first axis. A spatial frequency component of the multidimensional image is then computed by transforming the first shifted, complex-valued image along at least one further axis perpendicular to the first axis. Thereafter, a phase-restored image is produced by converting the spatial frequency component of the multidimensional image to a shifted spatial frequency spectrum and applying an inverse transform in each second axis. Also disclosed is a system for performing the above method.