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A convolutional neural network based approach for generating full PET/CT image series from shorter scan time

研究課題

研究課題/領域番号 19K20685
研究種目

若手研究

配分区分基金
審査区分 小区分90110:生体医工学関連
研究機関千葉大学 (2023)
国立研究開発法人量子科学技術研究開発機構 (2019-2022)

研究代表者

BhusalChhatkuli Ritu  千葉大学, 子どものこころの発達教育研究センター, 特任助教 (50836591)

研究期間 (年度) 2019-04-01 – 2025-03-31
研究課題ステータス 交付 (2023年度)
配分額 *注記
4,160千円 (直接経費: 3,200千円、間接経費: 960千円)
2021年度: 390千円 (直接経費: 300千円、間接経費: 90千円)
2020年度: 910千円 (直接経費: 700千円、間接経費: 210千円)
2019年度: 2,860千円 (直接経費: 2,200千円、間接経費: 660千円)
キーワードPET/CT / CNN-LSTM / Neural Network / Prediction / dynamic images / RNN / list mode PET / neural networks / image generation / CNN / time series / prediction
研究開始時の研究の概要

In recent years, machine learning and deep learning has become a subject of interest for many researchers worldwide. Whereas, CNN have become a methodology of choice for analyzing medical images. In this research, we propose a CNN based approach for generating PET/CT image series for malignant tumors in pancreas in shorter scan time. The images are currently obtained during the delayed scan during the regular PET/CT imaging analysis.

研究実績の概要

Initial and delayed scans, also known as dual-time-point scans, are widely used in positron emission tomography/computed tomography (PET/CT) for the diagnosis and delineation of pancreatic cancer; however, their acquisition is relatively time-consuming. The purpose of this pilot study was to use neural network based method to eliminate the need of delayed PET/CT scan for diagnosis. The results obtained from our CNN-LSTM based analysis suggested that our study could obviate the need for delayed scans however validations studies are required for the clinical application of our model.

現在までの達成度 (区分)
現在までの達成度 (区分)

4: 遅れている

理由

The pilot study (Data acquisition and analysis) has been completed and the journal paper was submitted, unfortunately it could not be accepted hence we are currently correcting the manuscript and preparing for submission in other journal.

今後の研究の推進方策

Currently the manuscript is being prepared for this work and is to be submitted in a scientific journal.

報告書

(5件)
  • 2023 実施状況報告書
  • 2022 実施状況報告書
  • 2021 実施状況報告書
  • 2020 実施状況報告書
  • 2019 実施状況報告書
  • 研究成果

    (1件)

すべて 2021

すべて 学会発表 (1件)

  • [学会発表] Generation of delayed PET/CT images for pancreatic cancer using CNN-LSTM model2021

    • 著者名/発表者名
      Ritu Bhusal Chhatkuli
    • 学会等名
      Japan Society of Medical Imaging Technology
    • 関連する報告書
      2021 実施状況報告書

URL: 

公開日: 2019-04-18   更新日: 2024-12-25  

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