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Background <p>Head 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.</p> Methods <p>Hoffman 3D brain phantom was filled with 20 and 3&#xa0;MBq of [<sup>18</sup>F]fluoro-2-deoxy-D-glucose (FDG) to simulate high- and low-radioactivity conditions corresponding to brain FDG PET and amyloid PET acquisitions, respectively. Two separate phantom measurements were performed accordingly. Motion simulation was conducted using a custom-designed motion generator, incorporating 15° and 30° rotations about the z-axis, 3° and 6° rotations about the x-axis, and 5&#xa0;mm and 10&#xa0;mm translations along the z-axis in the PET image coordinates. The data-driven MC was applied with frame durations of 1, 2, 5, 10, and 20&#xa0;s for motion estimation. The estimated motions were compared with the motions measured using an external optical tracker system. %contrast and gray matter coefficient of variation (CV%) were calculated from the motion-corrected PET images.</p> Results <p>The 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.</p> Conclusion <p>The 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.</p>

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Quantitative validation of data-driven motion correction for brain PET using phantom with motion generator system

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月16日

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  • Yuto Kamitaka,
  • Muneyuki Sakata,
  • Keiichi Oda,
  • Akie Katsuki,
  • Hirofumi Kawakami,
  • Kei Wagatsuma,
  • Masato Kobayashi,
  • Kenji Ishii

摘要

Background

Head 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.

Methods

Hoffman 3D brain phantom was filled with 20 and 3 MBq of [18F]fluoro-2-deoxy-D-glucose (FDG) to simulate high- and low-radioactivity conditions corresponding to brain FDG PET and amyloid PET acquisitions, respectively. Two separate phantom measurements were performed accordingly. Motion simulation was conducted using a custom-designed motion generator, incorporating 15° and 30° rotations about the z-axis, 3° and 6° rotations about the x-axis, and 5 mm and 10 mm translations along the z-axis in the PET image coordinates. The data-driven MC was applied with frame durations of 1, 2, 5, 10, and 20 s for motion estimation. The estimated motions were compared with the motions measured using an external optical tracker system. %contrast and gray matter coefficient of variation (CV%) were calculated from the motion-corrected PET images.

Results

The 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.

Conclusion

The 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.