Evaluation of CBCT scatter correction using deep convolutional neural networks for head and neck adaptive proton therapy. (4th December 2020)
- Record Type:
- Journal Article
- Title:
- Evaluation of CBCT scatter correction using deep convolutional neural networks for head and neck adaptive proton therapy. (4th December 2020)
- Main Title:
- Evaluation of CBCT scatter correction using deep convolutional neural networks for head and neck adaptive proton therapy
- Authors:
- Lalonde, Arthur
Winey, Brian
Verburg, Joost
Paganetti, Harald
Sharp, Gregory C - Abstract:
- Abstract: Adaptive proton therapy (APT) is a promising approach for the treatment of head and neck cancers. One crucial element of APT is daily volumetric imaging of the patient in the treatment position. Such data can be acquired with cone-beam computed tomography (CBCT), although scatter artifacts make uncorrected CBCT images unsuitable for proton therapy dose calculation. The purpose of this work is to evaluate the performance of a U-shape deep convolutive neural network (U-Net) to perform projection-based scatter correction and enable fast and accurate dose calculation on CBCT images in the context of head and neck APT. CBCT projections are simulated for a cohort of 48 head and neck patients using a GPU accelerated Monte Carlo (MC) code . A U-Net is trained to reproduce MC projection-based scatter correction from raw projections. The accuracy of the scatter correction is experimentally evaluated using CT and CBCT images of an anthropomorphic head phantom. The potential of the method for head and neck APT is assessed by comparing proton therapy dose distributions calculated on scatter-free, uncorrected and scatter-corrected CBCT images. Finally, dose calculation accuracy is estimated in experimental patient images using a previously validated empirical scatter correction as reference. The mean and mean absolute HU differences between scatter-free and scatter-corrected images are -0.8 and 13.4 HU, compared to -28.6 and 69.6 HU for the uncorrected images. In the headAbstract: Adaptive proton therapy (APT) is a promising approach for the treatment of head and neck cancers. One crucial element of APT is daily volumetric imaging of the patient in the treatment position. Such data can be acquired with cone-beam computed tomography (CBCT), although scatter artifacts make uncorrected CBCT images unsuitable for proton therapy dose calculation. The purpose of this work is to evaluate the performance of a U-shape deep convolutive neural network (U-Net) to perform projection-based scatter correction and enable fast and accurate dose calculation on CBCT images in the context of head and neck APT. CBCT projections are simulated for a cohort of 48 head and neck patients using a GPU accelerated Monte Carlo (MC) code . A U-Net is trained to reproduce MC projection-based scatter correction from raw projections. The accuracy of the scatter correction is experimentally evaluated using CT and CBCT images of an anthropomorphic head phantom. The potential of the method for head and neck APT is assessed by comparing proton therapy dose distributions calculated on scatter-free, uncorrected and scatter-corrected CBCT images. Finally, dose calculation accuracy is estimated in experimental patient images using a previously validated empirical scatter correction as reference. The mean and mean absolute HU differences between scatter-free and scatter-corrected images are -0.8 and 13.4 HU, compared to -28.6 and 69.6 HU for the uncorrected images. In the head phantom, the root-mean square difference of proton ranges calculated in the reference CT and corrected CBCT is 0.73 mm. The average 2%/2 mm gamma pass rate for proton therapy plans optimized in the scatter free images and re-calculated in the scatter-corrected ones is 98.89%. In experimental CBCT patient images, a 3%/3 mm passing rate of 98.72% is achieved between the proposed method and the reference one. All CBCT projection volume could be corrected in less than 5 seconds. … (more)
- Is Part Of:
- Physics in medicine & biology. Volume 65:Number 24(2020:Dec.)
- Journal:
- Physics in medicine & biology
- Issue:
- Volume 65:Number 24(2020:Dec.)
- Issue Display:
- Volume 65, Issue 24 (2020)
- Year:
- 2020
- Volume:
- 65
- Issue:
- 24
- Issue Sort Value:
- 2020-0065-0024-0000
- Page Start:
- Page End:
- Publication Date:
- 2020-12-04
- Subjects:
- CBCT -- U-Net -- adaptive radiotherapy -- proton therapy -- scatter correction -- deep learning
Biophysics -- Periodicals
Medical physics -- Periodicals
610.153 - Journal URLs:
- http://ioppublishing.org/ ↗
http://iopscience.iop.org/0031-9155 ↗ - DOI:
- 10.1088/1361-6560/ab9fcb ↗
- Languages:
- English
- ISSNs:
- 0031-9155
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - BLDSS-3PM
British Library STI - ELD Digital store - Ingest File:
- 21982.xml