Improving accelerated 3D imaging in MRI-guided radiotherapy for prostate cancer using a deep learning method.
Radiat Oncol
; 18(1): 108, 2023 Jul 01.
Article
in En
| MEDLINE
| ID: mdl-37393282
ABSTRACT
PURPOSE:
This study was to improve image quality for high-speed MR imaging using a deep learning method for online adaptive radiotherapy in prostate cancer. We then evaluated its benefits on image registration.METHODS:
Sixty pairs of 1.5 T MR images acquired with an MR-linac were enrolled. The data included low-speed, high-quality (LSHQ), and high-speed low-quality (HSLQ) MR images. We proposed a CycleGAN, which is based on the data augmentation technique, to learn the mapping between the HSLQ and LSHQ images and then generate synthetic LSHQ (synLSHQ) images from the HSLQ images. Five-fold cross-validation was employed to test the CycleGAN model. The normalized mean absolute error (nMAE), peak signal-to-noise ratio (PSNR), structural similarity index measurement (SSIM), and edge keeping index (EKI) were calculated to determine image quality. The Jacobian determinant value (JDV), Dice similarity coefficient (DSC), and mean distance to agreement (MDA) were used to analyze deformable registration.RESULTS:
Compared with the LSHQ, the proposed synLSHQ achieved comparable image quality and reduced imaging time by ~ 66%. Compared with the HSLQ, the synLSHQ had better image quality with improvement of 57%, 3.4%, 26.9%, and 3.6% for nMAE, SSIM, PSNR, and EKI, respectively. Furthermore, the synLSHQ enhanced registration accuracy with a superior mean JDV (6%) and preferable DSC and MDA values compared with HSLQ.CONCLUSION:
The proposed method can generate high-quality images from high-speed scanning sequences. As a result, it shows potential to shorten the scan time while ensuring the accuracy of radiotherapy.Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Prostatic Neoplasms
/
Radiation Oncology
/
Deep Learning
Limits:
Humans
/
Male
Language:
En
Journal:
Radiat Oncol
Journal subject:
NEOPLASIAS
/
RADIOTERAPIA
Year:
2023
Document type:
Article
Affiliation country:
China