Iterative Reconstruction Framework for Low Dose X-ray CT
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Solution Overview
Problem
Conventional low dose X-ray computed tomography (CT) methods are inefficient in reconstructing high-quality images due to noise inclusion and prolonged computation times, making it difficult to examine the internal human body effectively.
Innovation Solution
Applying an analytic principle with a differentiated backprojector as a preconditioner to transform low dose X-ray CT data, utilizing a Hilbert transform operator to redefine the optimization problem and remove noise, thereby increasing reconstruction speed and quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional iterative reconstruction method is used, then noise is removed to obtain high-quality image, but computation time increases and reconstruction time becomes too long
Solution Approach 1:
The patent applies a differentiated backprojector as a preconditioner before the main iterative reconstruction process. This preliminary action transforms the optimization problem into a form that converges faster, reducing the number of iterations needed while maintaining image quality.
Solution Approach 2:
The patent changes the mathematical formulation of the reconstruction problem by introducing a preconditioned optimization approach. This parameter change in the optimization strategy transforms the original slow-converging problem into a faster-converging one, achieving both high image quality and reduced reconstruction time.
2Object-affected harmful factors
If low dose X-ray CT is used, then radiation dose is reduced, but noise increases making image examination difficult
Solution Approach 1:
The patent converts the harmful noise in low-dose CT images into a manageable factor by formulating an optimization problem that explicitly models the noise characteristics. The differentiated backprojector preconditioner helps separate signal from noise more effectively, transforming the noise problem into a solvable optimization challenge that yields high-quality images from low-dose data.
Solution Approach 2:
The patent introduces an intermediary optimization framework that acts as a mediator between the noisy low-dose projection data and the final reconstructed image. This intermediary process, enhanced by the preconditioner, systematically removes noise while preserving diagnostic image quality.
3Measurement precision
If projector and backprojector are repeated in conventional method, then optimization problem is solved, but computational complexity increases
Solution Approach 1:
The differentiated backprojector is applied as a preconditioner before the main iterative process, transforming the optimization problem into a computationally more efficient form. This preliminary transformation reduces the computational burden of subsequent iterations while maintaining reconstruction accuracy.
Solution Approach 2:
The patent changes the mathematical parameters of the optimization problem by introducing the preconditioned formulation. This parameter change transforms the original high-complexity problem into a lower-complexity equivalent that converges faster and requires fewer computational resources.
Data Source
AI summary
Disclosed is a method of reconstructing an image. The method of reconstructing an image includes receiving low dose X-ray computed tomography (CT) data, applying an analytic principle to an optimization approach for low dose imaging to transform the low dose X-ray CT data, and removing a noise included in the low dose X-ray CT data to reconstruct a high-quality image.