Generation of Synthetic 6-Minute MRI Scans from 2-Minute MRI Scans for Use in Head and Neck Cancer Adaptive Radiotherapy

Abstract

<h3>Purpose/Objective(s)</h3> In clinical workflows for radiation therapy (RT) using integrated MRI-linear accelerator devices, quick, low-resolution images are often acquired for on-board setup imaging to minimize treatment times. However, for patients with head and neck cancer (HNC), these quick setup images are not always sufficient for discrimination of target structures and organs at risk (OAR). In this study, we investigate a deep learning (DL) approach to synthetically generate high resolution scans from low resolution scans, which we hypothesize are not significantly different from ground-truth high resolution scans. <h3>Materials/Methods</h3> Paired T2-weighted 2-minute MRI scans (2mMRI) and 6-minute MRI scans (6mMRI) acquired during the same imaging session were collected for 39 cases (36 patients with HNC; 3 volunteers). 21 cases were used to train a 2D DL generative adversarial network that utilized 2mMRI as input and 6mMRI as output. 18 cases were used to test the performance of the model. Global intensity metrics (normalized mean squared error [NMSE], structural similarity index [SSIM], and peak signal to noise ratio [PSNR]) were calculated between synthetic 6mMRI and ground-truth 6mMRI for all test cases. Additionally, a HNC OAR DL auto-segmentation model previously trained on independent 2mMRI was used to segment the right parotid gland, left parotid gland, and mandible on synthetic 6mMRI, ground-truth 6mMRI, and ground-truth 2mMRI for all test cases. Dice similarity coefficient (DSC) values were calculated between ground-truth 2mMRI and either synthetic 6mMRI or ground-truth 6mMRI for each OAR; Wilcoxon signed rank tests were applied between DSC values of the synthetic 6mMRI and the ground-truth 6mMRI for each OAR to determine any significant differences. <h3>Results</h3> Each synthetic 6mMRI took approximately 5-10 seconds to generate. Mean (standard deviation) NMSE, SSIM, and PSNR values for test cases were 0.44 (0.06), 0.90 (0.03), and 33.35 (1.60), respectively. The DSC values comparing synthetic vs. ground-truth 6mMRI auto-segmented OARs were 0.80 (0.07) vs. 0.81 (0.11), 0.83 (0.07) vs. 0.82 (0.14), and 0.82 (0.08) vs. 0.84 (0.08) for the right parotid gland, left parotid gland, and mandible, respectively; DSC values were not significantly different (p>0.05 for all OARs). <h3>Conclusion</h3> We demonstrate DL-generated synthetic 6mMRI that have low global intensity value differences compared with ground-truth 6mMRI. Moreover, auto-segmented OARs demonstrate non-significant differences when evaluated using paired synthetic or ground-truth 6mMRI. Future analysis will include the incorporation of additional training data, utilization of bias field correction, and application of human Turing test evaluation. Our study facilitates the clinical incorporation of MR-guided HNC adaptive RT using reduced acquisition times.