Simultaneous CT-MRI Reconstruction via Structural Coupling
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Solution Overview
Problem
Current imaging modalities, such as CT and MRI, have limitations in depicting complex dynamics of mammalian physiology and pathology, with inefficient and inaccurate reconstruction techniques, and challenges in combining data due to space constraints and electromagnetic interference.
Innovation Solution
The integration of structural coupling (SC) and compressive sensing (CS) techniques for simultaneous CT-MRI image reconstruction, allowing bidirectional image estimation and improved image quality by connecting data from both modalities, with CT and MRI serving as prior knowledge to each other.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If separate CT and MRI scans are performed individually, then each modality can be optimized independently, but the scanning time increases and the anatomic localization accuracy deteriorates due to post-acquisition image fusion errors
Solution Approach 1:
The patent combines CT and MRI scanning capabilities into a single integrated hybrid scanner system, allowing both modalities to acquire data simultaneously from the same patient position. This merging eliminates the need for separate scans and subsequent image fusion, thereby improving anatomic localization accuracy while reducing total scanning time.
Solution Approach 2:
The system performs preliminary spatial registration and coordination of CT and MRI acquisition parameters before actual scanning begins. This preliminary setup ensures that both modalities are pre-aligned to capture data from identical anatomical positions, preventing fusion errors and eliminating the need for post-acquisition registration adjustments.
2Measurement precision
If advanced reconstruction techniques are used to improve image quality, then diagnostic performance improves, but computational complexity and processing time increase
Solution Approach 1:
The patent implements an iterative reconstruction algorithm that uses feedback loops to progressively refine image quality. The system repeatedly updates the reconstruction based on previously acquired images and new data, allowing convergence to high-quality results while managing computational complexity through controlled iteration steps and stopping criteria.
Solution Approach 2:
The system applies partial reconstruction techniques where only certain image regions or frequency components are processed with full computational intensity. This selective approach maintains diagnostic quality in critical areas while reducing overall computational burden and processing time.
3Measurement precision
If CT and MRI data are combined for simultaneous reconstruction, then image quality and diagnostic performance improve, but the computational burden and processing complexity increase
Solution Approach 1:
The patent divides the combined CT-MRI reconstruction process into separate but coordinated segments. Each modality's data is processed through its own optimized reconstruction pipeline, with periodic synchronization and integration steps. This segmentation allows parallel computation of different data types while maintaining the benefits of combined information, thereby improving processing efficiency.
Solution Approach 2:
The reconstruction system is designed with universal algorithms that can handle both CT and MRI data types through a unified mathematical framework. This multi-functional approach allows the same computational engine to process different modalities without requiring completely separate processing chains, reducing overall computational burden and simplifying integration.
Data Source
AI summary
Novel and advantageous systems and method for obtaining and/or reconstructing simultaneous computed tomography (CT)-magnetic resonance imaging (MRI) images are provided. Structural coupling (SC) and compressive sensing (CS) techniques can be combined to unify and improve CT and MRI reconstruction. A bidirectional image estimation method can be used to connect images from different modalities, with CT and MRI data serving as prior knowledge to each other to produce better CT and MRI image quality than would be realized with individual reconstruction.