ArticleEJNMMI physics2025
Quantitative validation of data-driven motion correction for brain PET using phantom with motion generator system.
Article in EJNMMI physics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Technical Optimization Strategies for Amyloid PET Under Challenging Acquisition Conditions: A Comprehensive Narrative Review.Diagnostics (Basel, Switzerland) · 2026Review
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8 authors.
Funding
Abstract
backgroundHead motion during brain positron emission tomography (PET) degrades image quality and quantitative accuracy. Therefore, a data-driven motion correction (MC) method utilizing ultrafast list-mode reconstruction technology has been proposed and shown to considerably improve image quality. However, reproducing accurate actual motions and motion-free images using clinical data alone remains challenging. This study aimed to quantitatively evaluate data-driven MC using a brain phantom for known tracer distributions and a custom-made motion generator system for variable known motions.
methodsHoffman 3D brain phantom was filled with 20 and 3 MBq of [
resultsThe motion generator system successfully reproduced the designed motions. Motion estimation remained stable under high-radioactivity condition but showed reduced stability under low-radioactivity condition, particularly with shorter frame durations. Under both conditions, longer frame durations led to underestimation of continuous motion. The data-driven MC improved %contrast and gray matter CV% across all conditions, with shorter frame durations providing better correction for quick or continuous motions. However, shorter frame durations increased statistical noise, especially under low-radioactivity condition.
conclusionThe data-driven MC effectively improved the quality of motion-affected PET images under both high- and low-radioactivity conditions, indicating its broad applicability. However, correction accuracy deteriorated under the lower-radioactivity condition.
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