Photon Arrival-Time Image Deconvolution for FLIM Resolution
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Solution Overview
Problem
Existing methods for processing photon arrival-time data in fluorescence lifetime imaging microscopy (FLIM) result in blurry images due to noise and spatial blur, and existing deconvolution techniques either fail to improve spatial resolution or lose arrival-time information, making them unsatisfactory for FLIM analysis.
Innovation Solution
A data processing apparatus and method using an iterative algorithm with an update function that depends on a point-spread function and a previous estimate of the ground-truth to deconvolute photon arrival-time data, preserving arrival-time information while improving spatial resolution and signal-to-noise ratio.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deconvolution is applied on arrival-time data, then temporal resolution is improved, but spatial resolution is not improved
Solution Approach 1:
The patent extends conventional 1D temporal deconvolution into 2D spatiotemporal deconvolution by incorporating spatial coordinates with temporal information. The deconvolution algorithm simultaneously processes both spatial and temporal dimensions, allowing improvement of spatial resolution while preserving temporal resolution through a unified multidimensional approach.
2Manufacturing precision
If multi-image deconvolution is applied, then spatial resolution is improved, but arrival-time information is lost
Solution Approach 1:
The patent merges spatial and temporal information into a unified spatiotemporal dataset before deconvolution. By combining multiple images with their corresponding arrival-time data into a single multidimensional structure, the method performs deconvolution on the integrated data, thereby improving spatial resolution while preserving arrival-time information that would be lost in separate processing.
Solution Approach 2:
The patent adds the temporal dimension to the spatial image data, transforming conventional 2D spatial images into 3D spatiotemporal data structures. This dimensional extension allows the deconvolution algorithm to simultaneously optimize spatial resolution and preserve temporal characteristics, preventing information loss.
3Manufacturing precision
If multi-image deconvolution is applied, then spatial resolution is improved, but computational efficiency deteriorates
Solution Approach 1:
The patent combines multiple deconvolution operations into a single unified spatiotemporal deconvolution process. Instead of performing separate deconvolution operations on each image (which would be computationally expensive), the method integrates all images and their arrival-time data into one processing framework, significantly improving computational efficiency while maintaining spatial resolution improvement.
4Measurement precision
If deconvolution is applied on arrival-time data, then temporal resolution is improved, but spatial blur remains
Solution Approach 1:
The patent transitions from 1D temporal processing to 2D spatiotemporal processing by incorporating spatial coordinates into the deconvolution algorithm. This dimensional extension allows the method to simultaneously address both temporal resolution and spatial blur, removing spatial blur while preserving the improved temporal resolution achieved through temporal deconvolution.
Data Source
AI summary
A data processing apparatus for processing a digital input image is configured to receive the digital input image. The digital input image includes input photon arrival-time data at input image locations. The data processing apparatus is further configured to compute a digital output image based on the digital input image by deconvolution. The digital output image includes output photon arrival-time data at output image locations. The output photon arrival-time data represent an estimate of an unblurred ground-truth of the input photon arrival-time data. The data processing apparatus is further configured to compute the deconvolution by an iterative algorithm using an update function. The update function depends on a point-spread function, a previous estimate of the ground-truth, and the input photon arrival-time data.