Assessing multiple MRI sequences in deep learning-based synthetic CT generation for MR-only radiation therapy of head and neck cancers

Abstract

Purpose

This study investigated the effect of multiple magnetic resonance (MR) sequences on the quality of deep-learning-based synthetic computed tomography (sCT) generation in the head and neck region.

Materials and methods

12 MR series (T1pre-, T1post-contrast, T2 each with 4 Dixon images) were collected from 26 patients with head and neck cancers. 14 unique deep-learning models using the U-Net framework were trained using multiple MRs as inputs to generate sCTs. Mean absolute error (MAE), Dice Similarity Coefficient (DSC), as well as Gamma pass rates were used to compare sCTs to the actual CT across the different multi-channel MR-sCT models.

Results

Using all available MR series yielded sCTs with the lowest pixel-wise error (MAE = 80.5 ± 9.9 HU), but increasing channels also increased artificial tissue which led to poorer auto-contouring and lower dosimetric accuracy. Models with T2 protocols generally resulted in poorer quality sCTs. Pre-contrast T1 with all Dixon images was the best multi-channel MR-sCT model, consistently ranking high for all sCT quality measurements (average DSC across all structures = 80.0 % ± 13.6 %, global Gamma Pass Rate = 97.9 % ± 1.7 % at 2 %/2mm dose criterion and 20 % of max dose threshold).

Conclusions

Deep-learning networks using all Dixon images from a pre-contrast T1 sequence as multi-channel inputs produced the most clinically viable sCTs. Our proposed method may enable MR-only radiotherapy planning in a clinical setting for head and neck cancers.