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Published on: August 3, 2018
Data-driven voluntary body motion detection and non-rigid event-by-event correction for static and dynamic PET
Yihuan Lu1,2, Jean-Dominique Gallezot1, Mika Naganawa1
1Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, United States of America.
Body motion (BM) in positron emission tomography (PET) compromises quantitative accuracy. A new data-driven algorithm, centroid of distribution (COD), effectively detects and corrects for BM, improving image quality and tracer uptake measurements.
Area of Science:
- Medical Imaging
- Nuclear Medicine
- Biophysics
Background:
- Positron Emission Tomography (PET) enables absolute in vivo radiotracer quantitation.
- Voluntary body motion (BM) significantly degrades PET image quality, leading to inaccurate tracer uptake measurements and parameter estimations.
- Current body motion correction (BMC) methods, such as frame-based image registration (FIR) and external device tracking, have limitations in clinical practicality and intra-frame motion detection.
Purpose of the Study:
- To introduce a novel, data-driven algorithm, centroid of distribution (COD), for detecting body motion (BM) in PET imaging.
- To evaluate the efficacy of the COD-based BMC approach in improving the accuracy of quantitative PET measurements.
- To compare the performance of the COD-based BMC method against conventional FIR approaches.
Main Methods:
- A data-driven algorithm, centroid of distribution (COD), was developed to detect BM by analyzing abrupt changes in the lateral direction of a COD trace derived from time-of-flight (TOF) bins.
- Body motion was estimated using non-rigid image registrations after detection.
- Correction was performed through list-mode reconstruction.
- The COD-based BMC approach was validated in a monkey study and compared to FIR in human and dog studies using multiple tracers.
Main Results:
- The proposed COD algorithm successfully detected body motion (BM) events during PET scans.
- The COD-based BMC approach demonstrated superior correction results compared to conventional FIR methods.
- The method showed improved accuracy in quantitative measurements and image resolution.
Conclusions:
- The centroid of distribution (COD) algorithm provides an effective and practical solution for detecting and correcting body motion (BM) in PET imaging.
- This novel approach enhances the reliability of quantitative radiotracer uptake measurements, crucial for clinical applications.
- The COD-based BMC method offers a significant advancement over existing techniques, improving the diagnostic value of PET scans.
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