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This study proposes to create a synthetic CT (sCT) from Zero-Time Echo MRI (ZTE) using deep learning for MRI-only radiotherapy planning and verification. ZTE and CT were collected prospectively from 17 patients undergoing external beam radiotherapy of the head and neck. A new contribution to the Unet architecture was made with the addition of deep residual units and attention gates. The attention deep residual Unet (ADR-Unet) approach was validated quantitatively and qualitatively with comparison to Unet++. Leave-one-out cross validation was performed. The results showed the superiority of ADR-Unet (MAE=75.54±11.4HU) compared to Unet++ (MAE=80.51 ± 8.48HU) and several state-of-the art approaches. The contrast-to-noise ratios (CNR) of the generated DRRs were computed. CNR of lateral right DRRs (CNR_ADR-Unet =44.47 ± 6.23db, CNR_Unet++ =44.33 ± 5.65db) were close to CNR of the planning CT based DRR (44.33 ± 5.65db). The same tendency was observed for anterior posterior DRRs. Future work will focus on the evaluation of sCT for dose calculation.Clinical Relevance- The purpose of this work is to implement MR-only radiotherapy. This could eliminate the need for CT scans by using MRI for both target delineation and dose calculation, significantly reducing additional radiation exposure to patients, minimizing image registration uncertainties associated with CT/MRI fusion, reducing patient visits and imaging procedures costs.