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

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

doi:10.3389/fonc.2022.822687

Abstract

PurposeThere 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 MaterialsPaired 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.ResultsThe 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.ConclusionsAll 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.

Highlights

Magnetic resonance imaging (MRI)-based radiotherapy treatment planning is an increasingly popular concept in radiation oncology
All synthetic CT (sCT) generation methods were successfully applied to the MRI scan of each patient
The closest agreement in mean error (ME) and mean absolute error (MAE) in Hounsfield unit (HU) estimation was for the deep learning and hybrid atlas techniques for the whole body, bone, and soft tissue estimations (Table 3)

Summary

Introduction

Magnetic resonance imaging (MRI)-based radiotherapy treatment planning is an increasingly popular concept in radiation oncology. Larger pelvic treatment sites have received less attention in this area of work than prostate treatment sites, with previous larger pelvis sCT generation methods utilizing small groups of patient numbers and without consideration of the differences in male and female pelvic anatomy [4,5,6,7]. This is significant as the treatment volumes for colorectal and gynecological cancers traverse a more variable body contour and bony anatomy than prostate treatments. Anal canal, and gynecological treatments involve the treatment of larger and more variable body contour and bony anatomy than prostate treatments with differing prescription doses to the gross tumor volume, surrounding tissue deemed to be at high risk of tumor spread; the disease-positive nodes; and the surrounding local nodal volumes

Objectives

Methods

Results

Discussion

Conclusion