Multi-Source Gantry Imaging for Scalable Field-of-View CT
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Solution Overview
Problem
Conventional CT imaging systems face issues with laterally truncated data and insufficient sampling, leading to biased CT numbers and inadequate reconstruction of large fields-of-view (FOV) images, particularly in cases where the transaxial FOV does not cover the entire patient, and CBCT systems suffer from scattering noise and artifacts.
Innovation Solution
A multimodal imaging system combining low-energy (kV) and high-energy (MV) radiation sources, utilizing a rotatable gantry with selective collimation and detector readout, enables helical scanning and scatter estimation to enhance image quality and overcome FOV limitations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of moving object
If multiple image capture devices are used to increase field of view, then the field of view is improved, but the device complexity increases
Solution Approach 1:
The imaging system is divided into multiple independent image capture devices, each capturing a portion of the overall scene. These segmented devices work together to provide a comprehensive wide-area view, resolving the contradiction by achieving extended field of view through modular segmentation rather than a single complex device
Solution Approach 2:
Multiple image capture devices are merged into a coordinated system with unified calibration and processing. The individual devices are combined such that their captured images are integrated through geometric relationships and calibration data, achieving a merged wide-area view while managing system complexity through standardized integration protocols
2Area of moving object
If multiple image capture devices are used to increase field of view, then the field of view is improved, but the cost increases
Solution Approach 1:
The system employs multiple identical or similar image capture devices that can serve multiple functions: each device captures images for the overall wide-area view, can individually monitor specific zones, and provides redundancy. This multi-functionality justifies the quantity of devices by maximizing their utility across different operational requirements
Solution Approach 2:
The system achieves cost-effective wide-area imaging by changing parameters such as using standardized commercial-off-the-shelf camera modules rather than custom expensive sensors, optimizing the number of devices based on mathematical field-of-view coverage calculations, and adjusting capture frequencies dynamically to reduce processing loads and energy consumption
3Area of moving object
If multiple image capture devices are used to increase field of view, then the field of view is improved, but the processing power required increases
Solution Approach 1:
Calibration data and geometric relationships between multiple image capture devices are pre-computed and stored before operation. During runtime, the system uses these pre-established spatial models to efficiently map and integrate images from different devices, avoiding the need for complex real-time calculations and reducing processing power requirements
Solution Approach 2:
The system processes only the necessary portions of images from multiple devices based on detected events or regions of interest. Rather than processing all pixels from all devices at full resolution continuously, the system applies partial processing to relevant areas, reducing overall computational load while maintaining effective wide-area surveillance
Data Source
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AI summary
Multimodal imaging apparatus and methods include a rotatable gantry system with multiple sources of radiation comprising different energy levels (for example, kV and MV). Fast slip-ring technology and helical scans allow data from multiple sources of radiation to be combined or utilized to generate improved images and workflows, including for IGRT. Features include large field-of-view (LFOV) MV imaging, kV region-of-interest (ROI) imaging, and scalable field-of-view (SFOV) dual energy imaging.