| 研究課題/領域番号 |
22K15818
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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52040:放射線科学関連
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| 研究機関 | 群馬大学 |
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研究代表者 |
Varnava Maria 群馬大学, 重粒子線医学推進機構, 助教 (40913108)
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| 研究期間 (年度) |
2022-04-01 – 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 |
4,420千円 (直接経費: 3,400千円、間接経費: 1,020千円)
2024年度: 1,170千円 (直接経費: 900千円、間接経費: 270千円)
2023年度: 2,340千円 (直接経費: 1,800千円、間接経費: 540千円)
2022年度: 910千円 (直接経費: 700千円、間接経費: 210千円)
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| キーワード | tumor tracking / pancreatic cancer / deep learning / surface image guided / carbon ion radiotherapy |
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| 研究開始時の研究の概要 |
Current tracking techniques for pancreatic cancer in carbon ion radiotherapy are invasive and require additional irradiation, or have poor robustness. This research will focus on the development of an accurate real-time markerless tracking system, which will be based on surface image guided radiation therapy and deep learning. The tracking system will detect the 3D surface of the patient using a camera and predict the target location at any time during treatment.
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| 研究実績の概要 |
Pancreatic cancer is one of the most lethal cancers. One in three deaths in pancreatic cancer patients is related to tumor progression. Therefore, treatment methods that improve local tumor control may lead to better overall survival. The purpose for this year was to focus on completing a prediction model for the location of the target based on the surface of the patient, which is the basis for the research. The following steps were taken:
・Use combinations of recurrent and convolutional neural networks to create prediction models for the location of the tumor.
・Data collection/preprocessing/mining to match the nature of the network tested each time accordingly when necessary.
・Check network performance.
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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 progress was already slightly delayed from last year.
Also delayed in obtaining satisfactory performance from the model. Different approaches were explored that required the collection and preparation of more data, which took time.
The model is still under development, because of long calculation times and difficulties in solving technical difficulties during implementation.
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| 今後の研究の推進方策 |
Complete the prediction model and continue as initially planned.
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