Tomography Motion Compensation via Projection Surface Segmentation
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Solution Overview
Problem
Existing tomography systems struggle to accurately compensate for non-rigid movements, such as deformations caused by breathing, which lead to inconsistent image reconstruction during the computation of volume models.
Innovation Solution
The method divides the projection surface into partial surfaces, each with its own correction vector, allowing for independent displacement and compensation of deformations, using algebraic reconstruction techniques and optimization criteria like entropy minimization to improve image quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single correction vector is used for the entire projection surface, then the device complexity is reduced, but the manufacturing precision and ability to compensate for non-rigid movements deteriorates
Solution Approach 1:
The projection surface is divided into multiple partial surfaces, and each partial surface is assigned its own correction vector. This segmentation allows independent correction of different regions of the projection surface, enabling precise compensation for non-rigid movements while maintaining manageable system complexity through modular correction approach.
2Manufacturing precision
If the projection surface is divided into multiple partial surfaces with independent correction vectors, then the motion compensation precision is improved, but the device complexity increases
Solution Approach 1:
The projection surface is divided into multiple partial surfaces, and each partial surface is assigned its own correction vector. This segmentation allows independent correction of different regions of the projection surface, enabling precise compensation for non-rigid movements while maintaining manageable system complexity through modular correction approach.
3Measurement precision
If correction vectors are optimized iteratively using optimization criteria, then the image quality and sharpness are improved, but the computation time and productivity are reduced
Solution Approach 1:
An optimization criterion based on image entropy is employed to iteratively adjust the correction vectors. The entropy of the reconstructed volume model is calculated, and correction vectors are updated to minimize this entropy, creating a feedback loop that continuously improves image quality and sharpness while achieving accurate motion compensation.
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 compensates for non-rigid movements, reducing imaging distortions and improving the sharpness of volume models by iteratively optimizing correction vectors, resulting in clearer and more accurate representations of body structures.
Implementation Method 1
radiation, for example X-rays, is projected through the body volume, that is to say for example the patient, onto radiation sensors of a detector in the form of beam bundles
Implementation Method 2
Each radiation sensor generates a pixel value
Data Source
AI summary
The embodiments relate to a method for producing a digital volume model of a body volume by a sensor device, which sensor device includes a plurality of radiation sensors, of which each produces a pixel value in a projection. In order to produce the volume model, a plurality of projections from different projection angles (a) are produced and the volume model is computed from sensor positions of the radiation sensors and pixel values of the radiation sensors. For at least one projection angle (a), the sensor positions are corrected by a respective correction vector for rigid motion compensation. The problem addressed is that of also compensating the non-rigid motion of the body volume (i.e., the deformation) in the computation of the volume model. This problem is solved in that, in order to correct the sensor positions, the projection surface provided by the totality of the radiation sensors is divided into a plurality of sub-surfaces and a separate correction vector is determined for each of the sub-surfaces independently of each other.