| 研究課題/領域番号 |
19K20685
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| 研究種目 |
若手研究
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| 配分区分 | 基金 |
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| 審査区分 |
小区分90110:生体医工学関連
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| 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) |
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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)
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| 研究期間 (年度) |
2019-04-01 – 2025-03-31
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| 研究課題ステータス |
交付 (2023年度)
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| 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)
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| 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 |
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| 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.
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| 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.
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| 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.
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| 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.
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