Image Reconstruction via Rank Minimization for Artifact Reduction
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Solution Overview
Problem
Current medical imaging techniques, such as cone beam computed tomography (CBCT) and cardiac CT imaging, face challenges with high radiation doses and sensitivity to motion artifacts, particularly in time-resolved imaging applications like breast imaging and neuro-interventional procedures, where multiple acquisitions increase radiation exposure and motion contamination.
Innovation Solution
The method involves reconstructing images using rank minimization techniques to create an augmented image matrix that accounts for data consistency, allowing for the separation of consistent and inconsistent data subsets, thereby reducing artifacts and minimizing radiation dose by using a single short scan acquisition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple CBCT acquisitions are performed to obtain time-resolved images, then diagnostic information quality is improved, but radiation dose increases and motion artifacts increase
Solution Approach 1:
The patent combines multiple CBCT acquisition datasets into a single integrated reconstruction process. By merging the data from what would traditionally be separate acquisitions into one unified reconstruction algorithm, the system achieves time-resolved imaging with a single scan, thereby reducing radiation dose while maintaining diagnostic quality.
Solution Approach 2:
The patent applies preliminary data sorting and consistency classification before the actual image reconstruction. By pre-organizing the acquired data into consistent subsets and identifying inconsistent data points beforehand, the reconstruction process can efficiently generate high-quality images without requiring multiple acquisitions, thus reducing radiation exposure.
2Measurement precision
If multiple CBCT acquisitions are performed to obtain time-resolved images, then diagnostic information quality is improved, but motion artifacts increase
Solution Approach 1:
The patent segments the acquired data into consistent subsets based on data consistency analysis. By dividing the data into subsets that are internally consistent and separately reconstructing images from each subset, the system eliminates motion artifacts that would otherwise contaminate the images, while still providing time-resolved diagnostic information.
Solution Approach 2:
The patent extracts and removes inconsistent data points from the acquisition dataset before reconstruction. By identifying and taking out data that exhibits motion-related inconsistencies, the remaining consistent data can be used to generate high-quality images free from motion artifacts, preserving diagnostic information integrity.
3Object-affected harmful factors
If a single short scan acquisition is used, then radiation dose is reduced and motion artifacts are decreased, but image quality may be compromised
Solution Approach 1:
The patent changes the reconstruction parameters and algorithms to accommodate single-scan data. By modifying the reconstruction approach to use consistency-based sorting and subset reconstruction, the system maintains high image quality from a single acquisition, overcoming the traditional limitation that single scans produce inferior images compared to multiple acquisitions.
4Loss of time
If traditional multi-acquisition methods are used, then time-resolved imaging is achieved, but device complexity and processing time increase
Solution Approach 1:
The patent replaces the mechanical approach of performing multiple physical acquisitions with a computational approach. Instead of mechanically repeating the scan multiple times, the system uses sophisticated algorithms to process a single acquisition dataset, thereby reducing both imaging time and the complexity associated with coordinating multiple acquisitions.
Data Source
AI summary
Described here is a system and method for image reconstruction that can automatically and iteratively produce multiple images from one set of acquired data, in which each of these multiple images corresponds to a subset of the acquired data that is self-consistent, but inconsistent with other subsets of the acquired data. The image reconstruction includes iteratively minimizing the rank of an image matrix whose columns each correspond to a different image, and in which one column corresponds to a user-provided prior image of the subject. The rank minimization is constrained subject to a consistency condition that enforces consistency between the forward projection of each column in the image matrix and a respective subset of the acquired data that contains data that is consistent with data in the subset, but inconsistent with data not in the subset.


