In current clinical practice, paired computed tomography (CT) providing electron density information for dose calculation and magnetic resonance imaging (MRI) providing molecular information for GTV delineation are acquired during radiation therapy planning of head cancer. Aimed to reduce repeatedly scanning procedures, we developed a patch-based deep learning approach to generate synthetic CT from paired MRI of the same patient. In this approach, 2D slices of MRI and CT would be divided into several overlap patches and sent to cycle-consistent generative adversarial network (CycleGAN) for training with a combination of multiple loss functions. For comparison, we also applied CycleGAN and pix2pix model using whole 2D slices as input. With IRB approval, a total number of 2542 paired MRI and CT images were collected in the experiment. Mean absolute error (MAE) and peak signal to noise ratio (PSNR) were used as evaluation metrics. The result showed that our proposed model performed best on both whole brain areas. We also provided the difference map between synthetic and real CT to give a visual evaluation of our proposed model.
To meet the special demands in China and the particular needs for the radiotherapy department, a MOSAIQ Integration Platform CHN (MIP) based on the workflow of radiation therapy (RT) has been developed, as a supplement system to the Elekta MOSAIQ. The MIP adopts C/S (client-server) structure mode, and its database is based on the Treatment Planning System (TPS) and MOSAIQ SQL Server 2008, running on the hospital local network. Five network servers, as a core hardware, supply data storage and network service based on the cloud services. The core software, using C# programming language, is developed based on Microsoft Visual Studio Platform. The MIP server could offer network service, including entry, query, statistics and print information for about 200 workstations at the same time. The MIP was implemented in the past one and a half years, and some practical patient-oriented functions were developed. And now the MIP is almost covering the whole workflow of radiation therapy. There are 15 function modules, such as: Notice, Appointment, Billing, Document Management (application/execution), System Management, and so on. By June of 2016, recorded data in the MIP are as following: 13546 patients, 13533 plan application, 15475 RT records, 14656 RT summaries, 567048 billing records and 506612 workload records, etc. The MIP based on the RT workflow has been successfully developed and clinically implemented with real-time performance, data security, stable operation. And it is demonstrated to be user-friendly and is proven to significantly improve the efficiency of the department. It is a key to facilitate the information sharing and department management. More functions can be added or modified for further enhancement its potentials in research and clinical practice.