ArticleMedical physics2021
Optimizing the frame duration for data-driven rigid motion estimation in brain PET imaging.
Article in Medical physics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 12 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
12 citing papers in PubMed.
- Time-resolved PET imaging using a joint dynamic reconstruction and motion estimation framework (DREME-PET).Medical physics · 2026Article
- Optimization of brain PET markerless head motion correction in a large human cohort.EJNMMI physics · 2026Article
- The impact of data driven motion correction for clinical brain PET/MRI radiotracers.EJNMMI physics · 2026Article
- PET Head Motion Estimation Using Supervised Deep Learning With Attention.IEEE transactions on medical imaging · 2026Article
- Quantitative validation of data-driven motion correction for brain PET using phantom with motion generator system.EJNMMI physics · 2025Article
- High-resolution motion compensation for brain PET imaging using real-time electromagnetic motion tracking.Medical physics · 2025Article
- Effects of List-Mode-Based Intraframe Motion Correction in Dynamic Brain PET Imaging.IEEE transactions on radiation and plasma medical sciences · 2024Article
- Motion-correction strategies for enhancing whole-body PET imaging.Frontiers in nuclear medicine · 2024Article
- Evaluation of Data-Driven Rigid Motion Correction in Clinical Brain PET Imaging.Journal of nuclear medicine : official publication, Society of Nuclear Medicine · 2022Article
- Article
- Evaluating different methods of MR-based motion correction in simultaneous PET/MR using a head phantom moved by a robotic system.EJNMMI physics · 2022Article
- A solution to PET brain motion artefact.Journal of neurology · 2021Article
Corrections and comments
- Erratum issued
Authors and funding
9 authors.
Funding
Abstract
purposeData-driven rigid motion estimation for PET brain imaging is usually performed using data frames sampled at low temporal resolution to reduce the overall computation time and to provide adequate signal-to-noise ratio in the frames. In recent work it has been demonstrated that list-mode reconstructions of ultrashort frames are sufficient for motion estimation and can be performed very quickly. In this work we take the approach of using image-based registration of reconstructions of very short frames for data-driven motion estimation, and optimize a number of reconstruction and registration parameters (frame duration, MLEM iterations, image pixel size, post-smoothing filter, reference image creation, and registration metric) to ensure accurate registrations while maximizing temporal resolution and minimizing total computation time.
methodsData from
resultsFor FDG and FBB tracers using 4 × 10
conclusionsIt is shown that very short frames (≤1 sec) can be used to provide accurate and quick data-driven rigid motion estimates for use in an event-by-event motion corrected reconstruction.
Indexed as
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.