| アイテムタイプ |
学術雑誌論文 / Journal Article(1) |
| 公開日 |
2026-03-31 |
| タイトル |
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タイトル |
Machine-learning-based source localization for an intraoperative forceps-type positron emission counter |
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言語 |
en |
| 言語 |
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言語 |
eng |
| 資源タイプ |
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資源タイプ識別子 |
http://purl.org/coar/resource_type/c_6501 |
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資源タイプ |
journal article |
| 著者 |
Ohashi Ryotaro
Takyu Sodai
Ito Shigeki
Takahashi Miwako
Yamaya Taiga
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| 抄録 |
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内容記述タイプ |
Abstract |
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内容記述 |
Objective.Intraoperative identification of metastatic lymph nodes in esophageal cancer surgery could reduce unnecessary dissections. A forceps-type positron emission counter (PEC)---a compact coincidence detector designed to intraoperatively quantify18F-FDG uptake in individual lymph nodes through standard laparoscopic trocars---requires the radioactive source to be centered within its field of view for accurate quantification, yet current hardware provides no positional feedback.Approach.A position-sensitive detector was designed by segmenting the conventional monolithic scintillator into a 2x2 crystal array. A pair of such detectors provides 16 coincidence count values, which serve as input to a machine-learning model that outputs the three-dimensional center of gravity (CoG) of the source with an intrinsic uncertainty indicator. Training data were generated by Monte Carlo simulations using a sensitivity-map superposition method with random source distributions varying in size, shape, position, and activity concentration.Main results.The CoG was estimated with errors of approximately 0.34--0.42~mm per axis (Euclidean mean absolute error (MAE) 0.74~mm). In simulation, repositioning based on the estimated CoG reduced measurement variability (percent standard deviation) from 52% to 15%. A prototype experiment achieved Euclidean MAE of 1.33~mm at 100 coincidence counts.Significance.These results demonstrate that machine-learning-based source localization has substantial potential to enhance the quantitative accuracy and reliability of forceps-type PEC systems for intraoperative lymph node assessment. |
| 書誌情報 |
Physics in Medicine and Biology
発行日 2026-03
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