X-ray Artifact Correction via Dynamic Beam Filtering
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Solution Overview
Problem
X-ray imaging systems face challenges in addressing artifacts caused by high radiopacity objects, such as dental implants, which result in phenomena like photon starvation, photon scattering, and beam hardening, leading to suboptimal reconstructed 3D images.
Innovation Solution
The method involves identifying high radiopacity objects within the imaging field, using priori information to adjust x-ray beam dimensions and intensity, and dynamically filtering the x-ray beam to mitigate artifacts, thereby improving image quality by reducing radiation exposure and enhancing image clarity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional x-ray imaging is used to image high radiopacity objects, then the objects can be visualized, but artifacts such as photon starvation, photon scattering, and beam hardening are introduced leading to suboptimal reconstructed 3D images
Solution Approach 1:
The system performs preliminary identification of high radiopacity objects in the imaging field and obtains a priori information about their location and properties before the actual imaging process. This allows the system to pre-adjust x-ray beam parameters and apply dynamic filtering to prevent artifact formation rather than correcting them after reconstruction
Solution Approach 2:
The system applies different processing strategies to different regions of the imaging field. High radiopacity objects and their surrounding regions receive specialized handling with dynamic beam filtering and localized parameter adjustments, while other regions undergo standard imaging procedures. This region-specific approach optimizes image quality where artifacts are most likely to occur without unnecessarily complicating the overall system
2Measurement precision
If a priori information is used to adjust x-ray beam parameters and dynamically filter the beam, then artifacts are reduced and image quality is improved, but the system complexity increases
Solution Approach 1:
The system introduces an intermediary processing layer that sits between the standard imaging pipeline and the final reconstruction. This intermediary component handles the identification of high radiopacity objects, retrieves a priori information, and applies appropriate beam adjustments and filtering. By isolating the complexity in this dedicated intermediary module, the rest of the imaging system can remain relatively simple while still achieving artifact reduction
Solution Approach 2:
The system dynamically changes x-ray beam parameters such as intensity, energy spectrum, and filtration based on the identified objects and their locations. These parameter adjustments are made in real-time during the imaging process to optimize beam penetration and reduce artifacts. The ability to modify beam parameters on-the-fly allows the system to adapt to different imaging scenarios without requiring completely different hardware configurations
3Loss of energy
If dynamic filtering and beam parameter optimization are applied, then radiation exposure can be reduced while maintaining image quality, but the imaging time and processing complexity increase
Solution Approach 1:
The system applies dynamic filtering and parameter optimization selectively rather than uniformly across the entire imaging field. Full filtering and parameter adjustment are applied only to regions containing or adjacent to high radiopacity objects, while other regions receive standard or reduced filtering. This partial application of the complex processing reduces overall imaging time and computational load while still achieving radiation dose reduction and artifact mitigation where most needed
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
This approach effectively reduces artifacts in 3D reconstructed images by optimizing x-ray beam parameters based on identified objects, leading to improved image quality and reduced radiation exposure, while allowing for precise localization and correction of artifacts.
Implementation Method 1
obtaining a first plurality of x-ray projection images of a patient
Implementation Method 2
A collimator is operable to restrict dimensions of the x-ray beam to achieve a field of view (FOV) in a subsequent 3D reconstruction
Implementation Method 3
The reconstruction processor reconstructs a three dimensional (3D) volume from the plurality of projection images
Data Source
AI summary
A system and method of x-ray imaging includes obtaining a plurality of x-ray projection images of a patient. At least one object in the plurality of x-ray projection images is identified. A priori information of the identified at least one object is obtained. A three dimensional volume is reconstructed from the plurality of x-ray projection images. The a priori information is used to refine the acquisition of x-ray projection images or presentation of the three dimensional volume.