Fast Algorithms for Motion Correction
FALCON is a Python-based advanced, fully-automatic PET image registration and motion correction tool designed to facilitate PET motion correction, both for head and total-body scans. The software application is built around the fast Greedy registration toolkit, which serves as its registration engine. With FALCON, users can enjoy a streamlined experience for implementing motion correction.
FALCON’s open-source framework grants researchers and clinicians free access to motion-corrected PET image quality with minimal user input. Designed to effectively tackle challenging imaging scenarios, FALCON can deliver clear, reliable results. Powered by the swift and proficient greedy registration toolkit, FALCON may seamlessly integrate into existing workflows, for highly quantitative dynamic brain, total-body, or whole-body PET imaging.
Features (FALCON 2.0 version)
- offers seamless compatibility with Python libraries for convenient, effortless integration into modern workflows
- attains seamless, uniform performance and user experience across all platforms (Linux, Windows and Mac)
- provides universal computer architecture compatibility to ensure seamless operation on x86 and M1 processors, while leveraging ARM processors for even faster performance
- supports optimized out-of-core computing, powered by Dask, for computational efficiency efficiency
- utilizes the ‘Greedy’ registration library to support fast & versatile registration between images from different tracers, organs and vendors
FALCON is developed by the QIMP lab members of the Medical University of Vienna and is part of the ENHANCE.PET initiative.
- Sundar, L. K. S., Lassen, M. L., Gutschmayer, S., Ferrara, D., Calabrò, A., Yu, J., … & Muzik, O. (2023). Fully Automated, Fast Motion Correction of Dynamic Whole-Body and Total-Body PET/CT Imaging Studies. Journal of Nuclear Medicine DOI: 10.2967/jnumed.122.265362
- Gutschmayer, S., Muzik, O., Chalampalakis, Z., Ferrara, D., Yu, J., Kluge, K., … & Kumar Shiyam Sundar, L. (2022). A scale space theory based motion correction approach for dynamic PET brain imaging studies. Frontiers in Physics, 1090 DOI: 10.3389/fphy.2022.1034783
- Venet, L., Pati, S., Feldman, M. D., Nasrallah, M. P., Yushkevich, P., & Bakas, S. (2021). Accurate and robust alignment of differently stained histologic images based on Greedy diffeomorphic registration. Applied Sciences, 11(4), 1892. DOI: 10.3390/app11041892
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