Evaluating the usefulness of the direct density reconstruction algorithm for intensity modulated and passively scattered proton therapy: Validation using an anthropomorphic phantom

Yasui, Kohichiroh; Muramatsu, Rie; Kamomae, Takeshi; Toshito, T.; Kawabata, Fumitaka; Hayashi, Naoki

doi:10.1016/j.ejmp.2021.11.008

“…Images with low variability are also important for particle therapy. In particle therapy, CT images are used for range estimation [26,27] . DLR has been shown to reduce SD, and this method can be applied to particle therapy by evaluating the accuracy of calculating the stopping power.…”

Section: Discussionmentioning

confidence: 99%

Deep learning-based reconstruction: validation for treatment planning CT

Yasui

¹

,

Saito

²

,

Ito

³

et al. 2023

Preprint

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Deep learning-based CT image reconstruction (DLR) is a state-of-the-art method for obtaining CT images. This study aimed to evaluate the usefulness of DLR in radiotherapy. Data were acquired using a large-bore CT system and an electron density phantom for radiotherapy. We compared the CT values, image noise, and CT value-to-electron density conversion table of DLR and hybrid iterative reconstruction (H-IR) for various doses. Further, we evaluated three DLR reconstruction strength patterns (Mild, Standard, and Strong). The variations of CT values of DLR and H-IR were large at low doses, and the difference in average CT values was insignificant with less than 10 HU at doses of 100 mAs and above. DLR showed less change in CT values and smaller image noise relative to H-IR. The noise-reduction effect was particularly large in the low-dose region. The difference in image noise between DLR Mild and Standard/Strong was large, suggesting the usefulness of reconstruction intensities higher than Mild. DLR showed stable CT values and low image noise for various materials, even at low doses; particularly for Standard or Strong, the reduction in image noise was significant. These findings indicate the usefulness of DLR in treatment planning using large-bore CT systems.

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“…Images with low variability are also important for particle therapy. In particle therapy, CT images are used for range estimation 26 , 27 . DLR has been shown to reduce SD, and this method can be applied to particle therapy by evaluating the accuracy of calculating the stopping power.…”

Section: Discussionmentioning

confidence: 99%

Validation of deep learning-based CT image reconstruction for treatment planning

Yasui,

Saito,

Ito

et al. 2023

Sci Rep

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0

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Deep learning-based CT image reconstruction (DLR) is a state-of-the-art method for obtaining CT images. This study aimed to evaluate the usefulness of DLR in radiotherapy. Data were acquired using a large-bore CT system and an electron density phantom for radiotherapy. We compared the CT values, image noise, and CT value-to-electron density conversion table of DLR and hybrid iterative reconstruction (H-IR) for various doses. Further, we evaluated three DLR reconstruction strength patterns (Mild, Standard, and Strong). The variations of CT values of DLR and H-IR were large at low doses, and the difference in average CT values was insignificant with less than 10 HU at doses of 100 mAs and above. DLR showed less change in CT values and smaller image noise relative to H-IR. The noise-reduction effect was particularly large in the low-dose region. The difference in image noise between DLR Mild and Standard/Strong was large, suggesting the usefulness of reconstruction intensities higher than Mild. DLR showed stable CT values and low image noise for various materials, even at low doses; particularly for Standard or Strong, the reduction in image noise was significant. These findings indicate the usefulness of DLR in treatment planning using large-bore CT systems.

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“…In this study, a fourth method (d) was investigated [10] , [11] , [12] , [13] for patients. Instead of conversion at the Treatment Planning System (TPS) level, it can already be performed directly on the acquired CT by generating a density image through a specific imaging filter or kernel ( Fig.…”

Section: Introductionmentioning

confidence: 99%

“…However, it must be cautiously performed when combined with dual–energy CT [11] . It may also offer more robust results for proton dose calculations [12] . Finally, it simplifies using small field–of–view (FOV), kV–optimized images for contouring and large FOV MD images for dose calculations.…”

Section: Introductionmentioning

confidence: 99%