Project: Developing a Rectangular CT Geometry for Cost-Effective Walk-Through PET-CT
2024-11-01 – 2028-10-31
- Abstract
Total-Body PET enables rapid (<1 min) acquisition of high-quality images at reasonable dose levels, but its high cost hampers transition into clinical routine. Our group is developing a cost-effective TB-PET configuration based on two vertical detector panels, the Walk-Through PET (WT-PET), which scans patients in a standing position. An important challenge remains the integration of CT, as traditional source-detector rotation around the patient does not fit this unconventional geometry well. Instead, we propose an innovative rectangular CT architecture, in which rotation is replaced with sequential activation of spatially distributed (stationary) carbon nanotube sources. The sinogram obtained using such system however contains gaps, which violates the assumptions of conventional image reconstruction algorithms and leads to image artifacts. In this project, a simulation tool will be developed to study the effect of various rectangular system implementations on sinogram filling and image quality. In parallel, (iterative) reconstruction algorithms will be modified and optimized (using DL) for handling simulated sparse sinogram data. Simulation insights will be used to select the optimal design of a laboratory prototype, which will be built to verify and optimise the developed methods on real data. The integration with the WT-PET will be established and verified. Finally, energy measurements by the photon counting detectors will be used to reduce remaining image artifacts.
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Extending the walk-through PET with anatomical imaging capabilities : performance and applicability of CT-less and custom-CT approaches
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- Journal Article
- A1
- open access
Simulation study of a low-cost slide-through PET scanner based on narrow flat panels with monolithic detectors
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Deep learning-based sinogram inpainting for a sparse-view rectangular CT system integrated with a dual-flat panel PET system
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- Conference Paper
- C3
- open access
Limitations of current performance evaluation standards for dual flat-panel PET systems
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Two deep learning-based approaches to obtain attenuation maps in dual flat-panel PET