PMID- 36212472
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20221012
IS  - 2234-943X (Print)
IS  - 2234-943X (Electronic)
IS  - 2234-943X (Linking)
VI  - 12
DP  - 2022
TI  - Synthetic CT generation for MRI-guided adaptive radiotherapy in prostate cancer.
PG  - 969463
LID - 10.3389/fonc.2022.969463 [doi]
LID - 969463
AB  - Current MRI-guided adaptive radiotherapy (MRgART) workflows require 
      fraction-specific electron and/or mass density maps, which are created by 
      deformable image registration (DIR) between the simulation CT images and daily MR 
      images. Manual density overrides may also be needed where DIR-produced results 
      are inaccurate. This approach slows the adaptive radiotherapy workflow and 
      introduces additional dosimetric uncertainties, especially in the presence of the 
      magnetic field. This study investigated a method based on a conditional 
      generative adversarial network (cGAN) with a multi-planar method to generate 
      synthetic CT images from low-field MR images to improve efficiency in MRgART 
      workflows for prostate cancer. Fifty-seven male patients, who received MRI-guided 
      radiation therapy to the pelvis using the ViewRay MRIdian Linac, were selected. 
      Forty-five cases were randomly assigned to the training cohort with the remaining 
      twelve cases assigned to the validation/testing cohort. All patient datasets had 
      a semi-paired DIR-deformed CT-sim image and 0.35T MR image acquired using a true 
      fast imaging with steady-state precession (TrueFISP) sequence. Synthetic CT 
      images were compared with deformed CT images to evaluate image quality and 
      dosimetric accuracy. To evaluate the dosimetric accuracy of this method, clinical 
      plans were recalculated on synthetic CT images in the MRIdian treatment planning 
      system. Dose volume histograms for planning target volumes (PTVs) and 
      organs-at-risk (OARs) and dose distributions using gamma analyses were evaluated. 
      The mean-absolute-errors (MAEs) in CT numbers were 30.1 +/- 4.2 HU, 19.6 +/- 2.3 HU 
      and 158.5 +/- 26.0 HU for the whole pelvis, soft tissue, and bone, respectively. 
      The peak signal-to-noise ratio was 35.2 +/- 1.7 and the structural index similarity 
      measure was 0.9758 +/- 0.0035. The dosimetric difference was on average less than 
      1% for all PTV and OAR metrics. Plans showed good agreement with gamma pass rates 
      of 99% and 99.9% for 1%/1 mm and 2%/2 mm, respectively. Our study demonstrates 
      the potential of using synthetic CT images created with a multi-planar cGAN 
      method from 0.35T MRI TrueFISP images for the MRgART treatment of prostate 
      radiotherapy. Future work will validate the method in a large cohort of patients 
      and investigate the limitations of the method in the adaptive workflow.
CI  - Copyright (c) 2022 Hsu, Han, Leeman, Hu, Mak and Sudhyadhom.
FAU - Hsu, Shu-Hui
AU  - Hsu SH
AD  - Department of Radiation Oncology, Brigham and Women's Hospital and Dana-Farber 
      Cancer Institute, Boston, MA, United States.
FAU - Han, Zhaohui
AU  - Han Z
AD  - Department of Radiation Oncology, Brigham and Women's Hospital and Dana-Farber 
      Cancer Institute, Boston, MA, United States.
FAU - Leeman, Jonathan E
AU  - Leeman JE
AD  - Department of Radiation Oncology, Brigham and Women's Hospital and Dana-Farber 
      Cancer Institute, Boston, MA, United States.
FAU - Hu, Yue-Houng
AU  - Hu YH
AD  - Department of Radiation Oncology, Brigham and Women's Hospital and Dana-Farber 
      Cancer Institute, Boston, MA, United States.
FAU - Mak, Raymond H
AU  - Mak RH
AD  - Department of Radiation Oncology, Brigham and Women's Hospital and Dana-Farber 
      Cancer Institute, Boston, MA, United States.
FAU - Sudhyadhom, Atchar
AU  - Sudhyadhom A
AD  - Department of Radiation Oncology, Brigham and Women's Hospital and Dana-Farber 
      Cancer Institute, Boston, MA, United States.
LA  - eng
GR  - R21 EB026086/EB/NIBIB NIH HHS/United States
PT  - Journal Article
DEP - 20220923
PL  - Switzerland
TA  - Front Oncol
JT  - Frontiers in oncology
JID - 101568867
PMC - PMC9539763
OTO - NOTNLM
OT  - MRI-guided therapy
OT  - adaptive radiotherapy
OT  - deep learning
OT  - prostate radiotherapy
OT  - synthetic CT
COIS- JL has received research funding from ViewRay and NH TherAguix. The remaining 
      authors declare that the research was conducted in the absence of any commercial 
      or financial relationships that could be construed as a potential conflict of 
      interest.
EDAT- 2022/10/11 06:00
MHDA- 2022/10/11 06:01
PMCR- 2022/01/01
CRDT- 2022/10/10 04:12
PHST- 2022/06/15 00:00 [received]
PHST- 2022/08/26 00:00 [accepted]
PHST- 2022/10/10 04:12 [entrez]
PHST- 2022/10/11 06:00 [pubmed]
PHST- 2022/10/11 06:01 [medline]
PHST- 2022/01/01 00:00 [pmc-release]
AID - 10.3389/fonc.2022.969463 [doi]
PST - epublish
SO  - Front Oncol. 2022 Sep 23;12:969463. doi: 10.3389/fonc.2022.969463. eCollection 
      2022.