TY - JOUR
T1 - Comparison of Synthetic Computed Tomography Generation Methods, Incorporating Male and Female Anatomical Differences, for Magnetic Resonance Imaging-Only Definitive Pelvic Radiotherapy
AU - O’Connor, Laura M.
AU - Choi, Jae H.
AU - Dowling, Jason A.
AU - Warren-Forward, Helen
AU - Martin, Jarad
AU - Greer, Peter B.
N1 - Funding Information:
LMO, PG, JD and JM received research grant funding through the Calvary Mater Newcastle Hospital (Margaret Mitchell Research grant 16-12) for the development of atlas-based algorithms. JHC and PG received research grant funding through the Australian National Health and Medical Research Fund (The Australian MRI-Linac Program) for the development of deep learning algorithms.
Publisher Copyright:
Copyright © 2022 O’Connor, Choi, Dowling, Warren-Forward, Martin and Greer.
PY - 2022/2/8
Y1 - 2022/2/8
N2 - Purpose: There are several means of synthetic computed tomography (sCT) generation for magnetic resonance imaging (MRI)-only planning; however, much of the research omits large pelvic treatment regions and female anatomical specific methods. This research aimed to apply four of the most popular methods of sCT creation to facilitate MRI-only radiotherapy treatment planning for male and female anorectal and gynecological neoplasms. sCT methods were validated against conventional computed tomography (CT), with regard to Hounsfield unit (HU) estimation and plan dosimetry. Methods and Materials: Paired MRI and CT scans of 40 patients were used for sCT generation and validation. Bulk density assignment, tissue class density assignment, hybrid atlas, and deep learning sCT generation methods were applied to all 40 patients. Dosimetric accuracy was assessed by dose difference at reference point, dose volume histogram (DVH) parameters, and 3D gamma dose comparison. HU estimation was assessed by mean error and mean absolute error in HU value between each sCT and CT. Results: The median percentage dose difference between the CT and sCT was <1.0% for all sCT methods. The deep learning method resulted in the lowest median percentage dose difference to CT at −0.03% (IQR 0.13, −0.31) and bulk density assignment resulted in the greatest difference at −0.73% (IQR −0.10, −1.01). The mean 3D gamma dose agreement at 3%/2 mm among all sCT methods was 99.8%. The highest agreement at 1%/1 mm was 97.3% for the deep learning method and the lowest was 93.6% for the bulk density method. Deep learning and hybrid atlas techniques gave the lowest difference to CT in mean error and mean absolute error in HU estimation. Conclusions: All methods of sCT generation used in this study resulted in similarly high dosimetric agreement for MRI-only planning of male and female cancer pelvic regions. The choice of the sCT generation technique can be guided by department resources available and image guidance considerations, with minimal impact on dosimetric accuracy.
AB - Purpose: There are several means of synthetic computed tomography (sCT) generation for magnetic resonance imaging (MRI)-only planning; however, much of the research omits large pelvic treatment regions and female anatomical specific methods. This research aimed to apply four of the most popular methods of sCT creation to facilitate MRI-only radiotherapy treatment planning for male and female anorectal and gynecological neoplasms. sCT methods were validated against conventional computed tomography (CT), with regard to Hounsfield unit (HU) estimation and plan dosimetry. Methods and Materials: Paired MRI and CT scans of 40 patients were used for sCT generation and validation. Bulk density assignment, tissue class density assignment, hybrid atlas, and deep learning sCT generation methods were applied to all 40 patients. Dosimetric accuracy was assessed by dose difference at reference point, dose volume histogram (DVH) parameters, and 3D gamma dose comparison. HU estimation was assessed by mean error and mean absolute error in HU value between each sCT and CT. Results: The median percentage dose difference between the CT and sCT was <1.0% for all sCT methods. The deep learning method resulted in the lowest median percentage dose difference to CT at −0.03% (IQR 0.13, −0.31) and bulk density assignment resulted in the greatest difference at −0.73% (IQR −0.10, −1.01). The mean 3D gamma dose agreement at 3%/2 mm among all sCT methods was 99.8%. The highest agreement at 1%/1 mm was 97.3% for the deep learning method and the lowest was 93.6% for the bulk density method. Deep learning and hybrid atlas techniques gave the lowest difference to CT in mean error and mean absolute error in HU estimation. Conclusions: All methods of sCT generation used in this study resulted in similarly high dosimetric agreement for MRI-only planning of male and female cancer pelvic regions. The choice of the sCT generation technique can be guided by department resources available and image guidance considerations, with minimal impact on dosimetric accuracy.
KW - anal canal neoplasms
KW - cervix neoplasms
KW - computer-assisted radiotherapy planning
KW - endometrium neoplasms
KW - image-guided radiotherapy
KW - MRI radiotherapy planning
KW - rectum neoplasms
KW - synthetic CT
UR - http://www.scopus.com/inward/record.url?scp=85125057955&partnerID=8YFLogxK
U2 - 10.3389/fonc.2022.822687
DO - 10.3389/fonc.2022.822687
M3 - Article
AN - SCOPUS:85125057955
SN - 2234-943X
VL - 12
SP - 1
EP - 10
JO - Frontiers in Oncology
JF - Frontiers in Oncology
M1 - 822687
ER -
