Low-Dose X-Ray CT Perfusion Imaging With Joint Reconstruction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional CT perfusion imaging systems face challenges in achieving diagnostic-quality images at low doses, leading to suboptimal CT images and compromised perfusion maps due to the fixed reconstruction of CT images during optimization, which affects the signal-to-noise ratio and hinders accurate diagnosis.
Innovation Solution
A method and system that model and solve an optimization problem for joint estimation of structural CT images and perfusion maps using 3D image-gradient-based and patch-based priors, iteratively reconstructing and averaging frames to obtain average structural and functional images, while applying deconvolution terms and patch-based prior information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If low-dose X-ray CT scanning is used, then radiation dose is reduced, but signal-to-noise ratio deteriorates and image quality compromises diagnostic accuracy
Solution Approach 1:
The patent merges structural image reconstruction and perfusion map computation into a unified joint estimation framework. By combining these previously separate processes, the system simultaneously optimizes both structural quality and perfusion accuracy, allowing noise reduction in structural images without compromising perfusion map quality, thus maintaining diagnostic accuracy at low radiation doses
Solution Approach 2:
The patent introduces multiple regularization parameters (λ1, λ2, λ3) that control the trade-off between data fidelity and various prior constraints (3D gradient-based prior, patch-based prior, deconvolution term). By dynamically adjusting these parameters during iterative optimization, the system adapts to low-dose conditions while maintaining image quality and perfusion accuracy
2Device complexity
If CT images are fixed during optimization process, then reconstruction complexity is reduced, but perfusion map quality becomes suboptimal
Solution Approach 1:
The patent transforms the static fixed-image approach into a dynamic joint estimation process where both structural images and perfusion maps are simultaneously optimized. The iterative algorithm alternates between updating structural images using 3D gradient-based regularization and updating perfusion maps using patch-based regularization and deconvolution, allowing both to adapt to each other's constraints and achieve mutual optimization
Solution Approach 2:
The joint estimation framework establishes feedback loops where structural image quality influences perfusion map computation and vice versa. The regularization terms provide feedback constraints that guide the optimization: the 3D gradient-based prior feedback ensures structural coherence, while the patch-based prior feedback ensures local texture consistency in perfusion maps, creating a self-correcting optimization system
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 proposed method improves the quality of CT perfusion maps by reducing noise and enhancing structural and functional image clarity, achieving better signal-to-noise ratios and enabling accurate diagnostic imaging at lower radiation doses.
Implementation Method 1
acquire a plurality of frames from a low-dose X-ray computerized tomography (CT) perfusion scan of a subject
Data Source
AI summary
State of the art mechanisms being used for achieving diagnostic-quality images under low-dose settings for general CT imaging have the disadvantages that CT images are fixed during the optimization process to generate perfusion maps, which can lead to suboptimal CT images with respect to the perfusion maps generated, although they might appear spatially smooth or denoised. The disclosure herein generally relates to Computer Tomography (CT) scanning, and, more particularly, to a method and system for CT image reconstruction. The system performs modelling an optimization problem for joint estimation of a set of structural CT images and a perfusion map, and further solves the optimization problem for the reconstruction of the CT images of a subject.