AUTHOR=O’Connor Laura M. , Choi Jae H. , Dowling Jason A. , Warren-Forward Helen , Martin Jarad , Greer Peter B. 

TITLE=Comparison of Synthetic Computed Tomography Generation Methods, Incorporating Male and Female Anatomical Differences, for Magnetic Resonance Imaging-Only Definitive Pelvic Radiotherapy

JOURNAL=Frontiers in Oncology

VOLUME=Volume 12 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2022.822687

DOI=10.3389/fonc.2022.822687

ISSN=2234-943X

ABSTRACT=Purpose: There are several means of synthetic computed tomography (sCT) generation for magnetic resonance imaging (MRI) -only planning, however much of the research omit 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 gynaecological neoplasms. sCT methods were validated against conventional computed tomography (CT), with regards to Hounsfield unit (HU) estimation and plan dosimetry.  
Methods and Materials: Paired MRI and CT scans of forty 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 greatest difference at -0.73% (IQR -0.10, -1.01). The mean 3D gamma dose agreement at 3%/2mm amongst all sCT methods was 99.8%. The highest agreement at 1%/1mm was 97.3% for the deep learning method and 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 use in this study resulted in similarly high dosimetric agreement for MRI only planning of male and female cancers pelvic region. Choice of sCT generation technique can be guided by department resources available and image guidance considerations, with minimal impact on dosimetric accuracy.