Abdominal synthetic CT reconstruction with intensity projection prior for MRI-only adaptive radiotherapy
Sven Olberg, Jaehee Chun, Byong Su Choi, Inkyung Park, Hyun Kim, Taeho Kim, Jin Sung Kim, Olga Green, Justin C. Park
An MRI-only adaptive radiotherapy (ART) workflow is desirable for managing
interfractional changes in anatomy, but producing synthetic CT (sCT) data
through paired data-driven deep learning (DL) for abdominal dose calculations
remains a challenge due to the highly variable presence of intestinal gas. We
present the preliminary dosimetric evaluation of our novel approach to sCT
reconstruction that is well suited to handling intestinal gas in abdominal
MRI-only ART.
We utilize a paired data DL approach enabled by the intensity projection
prior, in which well-matching training pairs are created by propagating air
from MRI to corresponding CT scans. Evaluations focus on two classes: patients
with (1) little involvement of intestinal gas, and (2) notable differences in
intestinal gas presence between corresponding scans. Comparisons between
sCT-based plans and CT-based clinical plans for both classes are made at the
first treatment fraction to highlight the dosimetric impact of the variable
presence of intestinal gas.
Class 1 patients ($n=13$) demonstrate differences in prescribed dose coverage
of the PTV of $1.3 \pm 2.1\%$ between clinical plans and sCT-based plans. Mean
DVH differences in all structures for Class 1 patients are found to be
statistically insignificant. In Class 2 ($n=20$), target coverage is $13.3 \pm
11.0\%$ higher in the clinical plans and mean DVH differences are found to be
statistically significant.
Significant deviations in calculated doses arising from the variable presence
of intestinal gas in corresponding CT and MRI scans may limit the effectiveness
of adaptive dose escalation efforts. We have proposed a paired data-driven DL
approach to sCT reconstruction for accurate dose calculations in abdominal ART
enabled by the creation of a clinically unavailable training data set with
well-matching representations of intestinal gas.