MRI-based treatment planning for liver stereotactic body radiotherapy: Validation of a deep Learning-based synthetic CT generation method.

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Abstract

The purpose of this work is to develop and validate a learning-based method to derive electron density from routine anatomical MRI for potential MRI-based SBRT treatment planning.
We proposed to integrate dense block into cycle generative adversarial network (GAN) to effectively capture the relationship between the CT and MRI for CT synthesis. A cohort of 21 patients with co-registered CT and MR pairs were used to evaluate our proposed method by the leave-one-out cross-validation. MAE, PSNR and NCC were used to quantify the imaging differences between the synthetic CT (sCT) and CT. The accuracy of HU values in sCT for dose calculation was evaluated by comparing the dose distribution in sCT-based and CT-based treatment planning. Clinically-relevant dose volume histogram (DVH) metrics were then extracted from the sCT-based and CT-based plans for quantitative comparison.
The MAE, PSNR and NCC of the sCT were 72.87 ± 18.16 HU, 22.65 ± 3.63  dB and 0.92 ± 0.04, respectively. No significant differences were observed in the majority of the PTV and OAR DVH metrics ( p  >  0.05). The average pass rate of γ analysis was over 99 % with 1%/1  mm acceptance criteria on the coronal plane that intersects with isocenter.
The image similarity and dosimetric agreement between sCT and original CT warrant further development of an MRI-only workflow for liver SBRT.
This work is the first deep-learning-based approach to generating abdominal sCT through dense-cycle-GAN. This method can successfully generate the small bony structures such as the rib bones and is able to predict the HU values for dose calculation with comparable accuracy to reference CT images.