Aggregate Phantom for Large Field-of-View Distortion Measurement
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Solution Overview
Problem
Current imaging systems face challenges in accurately measuring geometric distortion over large 3D fields of view due to the weight and handling difficulties of traditional phantoms, and existing solutions fail to provide precise measurements across entire fields of view while accounting for material restrictions and signal uniformity variations.
Innovation Solution
An aggregate phantom composed of multiple self-contained sections with fiducial features, combined using a mathematical analysis method to distinguish between actual geometric distortion and rigid-body transformations, allowing for precise distortion measurements and compensation for signal differences between sections.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If a large phantom is used to cover the entire field of view for distortion measurements, then measurement coverage is improved, but the phantom weight exceeds 100 pounds making it difficult to handle
Solution Approach 1:
The phantom is divided into multiple separate sections that can be handled individually. Each section contains fiducial markers and can be positioned independently within the imaging system, allowing coverage of large fields of view without requiring a single heavy phantom to be moved.
2Ease of operation
If a multi-section phantom is used to reduce weight, then ease of handling is improved, but achieving tight geometric tolerances between sections becomes extremely difficult
Solution Approach 1:
Reference markers are introduced as intermediary elements that facilitate precise alignment between phantom sections. These markers serve as common reference points that enable accurate registration of multiple sections without requiring extremely tight manufacturing tolerances on the sections themselves.
Solution Approach 2:
The solution replaces mechanical alignment precision requirements with computational image analysis. Instead of relying on mechanically precise section assembly, the system uses image processing to locate fiducial markers and calculate distortion, substituting mechanical precision with algorithmic precision.
3Measurement precision
If traditional distortion measurement techniques are used, then geometric distortion is measured, but the methods do not account for rigid-body transformations between phantom sections
Solution Approach 1:
The measurement method is made dynamic by allowing for rigid-body transformations between phantom sections. The system can accommodate translations and rotations of individual sections while still accurately measuring geometric distortion, making the method adaptable to various phantom configurations and positioning scenarios.
Data Source
AI summary
An apparatus and method for imaging quality assessment of an imaging system employs an aggregate phantom and a processor for imaging analysis. The aggregate phantom includes a plurality of self-contained sections configured to be moved independently and re-assembled in the imaging system. Each section includes fiducial features of known relative location. The processor: quantitatively determines location of the fiducial features within an image of the aggregate phantom; compares the determined location within the image to the known relative location of the fiducial features to produce a distortion field; and distinguishes between actual geometric distortion of the imaging system and rigid-body transformations of sections of the aggregate phantom, in the distortion field. For extended fields-of-view, the aggregate phantom may be repositioned, and sets of images combined to determine a distortion field of the extended image. A method employing virtual features for measuring spatial uniformity of an acquired signal, is also provided.