PET Motion Correction via Raw-Data Segmentation
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Solution Overview
Problem
Positron emission tomography (PET) images are often blurred due to non-rigid motion, such as respiration or cardiac motion, during scanning, leading to motion artifacts that existing motion correction methods may exacerbate by increasing scan and reconstruction times, making them unsuitable for routine use and dynamic imaging applications.
Innovation Solution
A non-rigid motion correction method is applied directly to original imaging data in the raw-data domain, using time-of-flight information to divide lines of response into sections and perform rigid transformations, approximating the effective line of response to correct for motion without the need for image reconstruction, thereby reducing processing time and computational resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If existing motion correction methods are applied to PET imaging, then motion artifacts are reduced, but scan time and reconstruction time increase significantly
Solution Approach 1:
The patent segments the imaging process into motion correction and image reconstruction, performing motion correction on raw data before reconstruction. This segmentation allows motion correction to be applied without requiring full reconstruction, thereby reducing overall processing time while maintaining image quality.
Solution Approach 2:
The patent applies motion correction as a preliminary step before image reconstruction. By correcting motion artifacts in the raw data domain before reconstruction, the system avoids the need for time-consuming post-reconstruction correction methods, thus reducing total processing time while improving image quality.
2Manufacturing precision
If motion correction is applied to PET imaging, then motion blurring is reduced, but computational resources and processing time increase
Solution Approach 1:
The patent separates motion correction from image reconstruction, applying motion correction to raw data in a separate preprocessing step. This segmentation enables efficient computation by avoiding the need to reprocess entire reconstructed images, thus maintaining high processing speed while improving image quality.
Solution Approach 2:
The patent replaces traditional mechanical/image-based motion correction approaches with a data-domain correction method. By operating on raw data rather than reconstructed images, the system reduces computational complexity and processing time while achieving the same motion correction effect.
3Manufacturing precision
If conventional motion correction methods are used, then motion artifacts are corrected, but the methods are not suitable for dynamic imaging applications
Solution Approach 1:
The patent implements a dynamic motion correction framework that can handle time-varying motion patterns. By applying motion correction to raw data and integrating it with dynamic imaging protocols, the system maintains adaptability for dynamic imaging applications while achieving accurate motion correction.
Solution Approach 2:
The patent creates a universal motion correction framework that works with both static and dynamic imaging protocols. The same raw data correction approach can be applied to various imaging scenarios, making the system versatile for different application types including dynamic imaging.
Data Source
AI summary
An imaging method may include obtaining original imaging data of an object in a raw-data domain including original time of flight (TOF) information. The method may also include gating the original imaging data into a plurality of data sets in the raw-data domain. The method may also include determining a plurality of motion vector fields based on the plurality of data sets. The method may also include generating corrected imaging data in the raw-data domain by performing motion correction on at least one of the plurality of data sets based on the original TOF information and at least one corresponding MVF of the plurality of MVFs. The method may also include generating one or more target images of the object by performing, based on the corrected imaging data, image reconstruction.