Synthesizing high-resolution magnetic resonance imaging using parallel cycle-consistent generative adversarial networks for fast magnetic resonance imaging.

PURPOSE: The common practice in acquiring the magnetic resonance (MR) images is to obtain two-dimensional (2D) slices at coarse locations while keeping the high in-plane resolution in order to ensure enough body coverage while shortening the MR scan time. The aim of this study is to propose a novel method to generate HR MR images from low-resolution MR images along the longitudinal direction. In order to address the difficulty of collecting paired low- and high-resolution MR images in clinical settings and to gain the advantage of parallel cycle consistent generative adversarial networks (CycleGANs) in synthesizing realistic medical images, we developed a parallel CycleGANs based method using a self-supervised strategy. METHODS AND MATERIALS: The proposed workflow consists of two parallely trained CycleGANs to independently predict the HR MR images in the two planes along the directions that are orthogonal to the longitudinal MR scan direction. Then, the final synthetic HR MR images are generated by fusing the two predicted images. MR images, including T1-weighted (T1), contrast enhanced T1-weighted (T1CE), T2-weighted (T2), and T2 Fluid Attenuated Inversion Recovery (FLAIR), of the multimodal brain tumor segmentation challenge 2020 (BraTS2020) dataset were processed to evaluate the proposed workflow along the cranial-caudal (CC), lateral, and anterior-posterior directions. Institutional collected MR images were also processed for evaluation of the proposed method. The performance of the proposed method was investigated via both qualitative and quantitative evaluations. Metrics of normalized mean absolute error (NMAE), peak signal-to-noise ratio (PSNR), edge keeping index (EKI), structural similarity index measurement (SSIM), information fidelity criterion (IFC), and visual information fidelity in pixel domain (VIFP) were calculated. RESULTS: It is shown that the proposed method can generate HR MR images visually indistinguishable from the ground truth in the investigations on the BraTS2020 dataset. In addition, the intensity profiles, difference images and SSIM maps can also confirm the feasibility of the proposed method for synthesizing HR MR images. Quantitative evaluations on the BraTS2020 dataset shows that the calculated metrics of synthetic HR MR images can all be enhanced for the T1, T1CE, T2, and FLAIR images. The enhancements in the numerical metrics over the low-resolution and bi-cubic interpolated MR images, as well as those genearted with a comparative deep learning method, are statistically significant. Qualitative evaluation of the synthetic HR MR images of the clinical collected dataset could also confirm the feasibility of the proposed method. CONCLUSIONS: The proposed method is feasible to synthesize HR MR images using self-supervised parallel CycleGANs, which can be expected to shorten MR acquisition time in clinical practices.

Head-and-neck organs-at-risk auto-delineation using dual pyramid networks for CBCT-guided adaptive radiotherapy.

Organ-at-risk (OAR) delineation is a key step for cone-beam CT (CBCT) based adaptive radiotherapy planning that can be a time-consuming, labor-intensive, and subject-to-variability process. We aim to develop a fully automated approach aided by synthetic MRI for rapid and accurate CBCT multi-organ contouring in head-and-neck (HN) cancer patients. MRI has superb soft-tissue contrasts, while CBCT offers bony-structure contrasts. Using the complementary information provided by MRI and CBCT is expected to enable accurate multi-organ segmentation in HN cancer patients. In our proposed method, MR images are firstly synthesized using a pre-trained cycle-consistent generative adversarial network given CBCT. The features of CBCT and synthetic MRI (sMRI) are then extracted using dual pyramid networks for final delineation of organs. CBCT images and their corresponding manual contours were used as pairs to train and test the proposed model. Quantitative metrics including Dice similarity coefficient (DSC), Hausdorff distance 95% (HD95), mean surface distance, and residual mean square distance (RMS) were used to evaluate the proposed method. The proposed method was evaluated on a cohort of 65 HN cancer patients. CBCT images were collected from those patients who received proton therapy. Overall, DSC values of 0.87 ± 0.03, 0.79 ± 0.10/0.79 ± 0.11, 0.89 ± 0.08/0.89 ± 0.07, 0.90 ± 0.08, 0.75 ± 0.06/0.77 ± 0.06, 0.86 ± 0.13, 0.66 ± 0.14, 0.78 ± 0.05/0.77 ± 0.04, 0.96 ± 0.04, 0.89 ± 0.04/0.89 ± 0.04, 0.83 ± 0.02, and 0.84 ± 0.07 for commonly used OARs for treatment planning including brain stem, left/right cochlea, left/right eye, larynx, left/right lens, mandible, optic chiasm, left/right optic nerve, oral cavity, left/right parotid, pharynx, and spinal cord, respectively, were achieved. This study provides a rapid and accurate OAR auto-delineation approach, which can be used for adaptive radiation therapy.

Dosimetric comparison of volumetric modulated arc therapy and intensity-modulated radiation therapy for pancreatic malignancies.

Volumetric-modulated arc therapy (VMAT) has been previously evaluated for several tumor sites and has been shown to provide significant dosimetric and delivery benefits when compared with intensity-modulated radiation therapy (IMRT). To date, there have been no published full reports on the benefits of VMAT use in pancreatic patients compared with IMRT. Ten patients with pancreatic malignancies treated with either IMRT or VMAT were retrospectively identified. Both a double-arc VMAT and a 7-field IMRT plan were generated for each of the 10 patients using the same defined tumor volumes, organs at risk (OAR) volumes, dose, fractionation, and optimization constraints. The planning tumor volume (PTV) maximum dose (55.8 Gy vs. 54.4 Gy), PTV mean dose (53.9 Gy vs. 52.1 Gy), and conformality index (1.11 vs. 0.99) were statistically similar between the IMRT and VMAT plans, respectively. The VMAT plans had a statistically significant reduction in monitor units compared with the IMRT plans (1109 vs. 498, p < 0.001). In addition, the doses to the liver, small bowel, and spinal cord were comparable between the IMRT and VMAT plans. However, the VMAT plans demonstrated a statistically significant reduction in the mean left kidney V(25) (9.4 Gy vs. 2.3 Gy, p = 0.018), mean right kidney V(15) (53.4 Gy vs. 45.9 Gy, p = 0.035), V(20) (32.2 Gy vs. 25.5 Gy, p = 0.016), and V(25) (21.7 Gy vs. 14.9 Gy, p = 0.001). VMAT was investigated in patients with pancreatic malignancies and compared with the current standard of IMRT. VMAT was found to have similar or improved dosimetric parameters for all endpoints considered. Specifically, VMAT provided reduced monitor units and improved bilateral kidney normal tissue dose. The clinical relevance of these benefits in the context of pancreatic cancer patients, however, is currently unclear and requires further investigation.