Partially Conditioned Generative Adversarial Networks

Generative models are undoubtedly a hot topic in Artificial Intelligence, among which the most common type is Generative Adversarial Networks (GANs). These architectures let one synthesise artificial datasets by implicitly modelling the underlying probability distribution of a real-world training dataset. With the introduction of Conditional GANs and their variants, these methods were extended to generating samples conditioned on ancillary information available for each sample within the dataset. From a practical standpoint, however, one might desire to generate data conditioned on partial information. That is, only a subset of the ancillary conditioning variables might be of interest when synthesising data. In this work, we argue that standard Conditional GANs are not suitable for such a task and propose a new Adversarial Network architecture and training strategy to deal with the ensuing problems. Experiments illustrating the value of the proposed approach in digit and face image synthesis under partial conditioning information are presented, showing that the proposed method can effectively outperform the standard approach under these circumstances.Comment: 10 pages, 9 figure

Patient-specific quality assurance strategies for synthetic computed tomography in magnetic resonance-only radiotherapy of the abdomen

BACKGROUND AND PURPOSE The superior tissue contrast of magnetic resonance (MR) compared to computed tomography (CT) led to an increasing interest towards MR-only radiotherapy. For the latter, the dose calculation should be performed on a synthetic CT (sCT). Patient-specific quality assurance (PSQA) methods have not been established yet and this study aimed to assess several software-based solutions. MATERIALS AND METHODS A retrospective study was performed on 20 patients treated at an MR-Linac, which were selected to evenly cover four subcategories: (i) standard, (ii) air pockets, (iii) lung and (iv) implant cases. The neural network (NN) CycleGAN was adopted to generate a reference sCT, which was then compared to four PSQA methods: (A) water override of body, (B) five tissue classes with bulk densities, (C) sCT generated by a separate NN (pix2pix) and (D) deformed CT. RESULTS The evaluation of the dose endpoints demonstrated that while all methods A-D provided statistically equivalent results (p = 0.05) within the 2% level for the standard cases (i), only the methods C-D guaranteed the same result over the whole cohort. The bulk densities override was shown to be a valuable method in absence of lung tissue within the beam path. CONCLUSION The observations of this study suggested that the use of an additional sCT generated by a separate NN was an appropriate tool to perform PSQA of a sCT in an MR-only workflow at an MR-Linac. The time and dose endpoints requirements were respected, namely within 10 min and 2%

Synthetic computed tomography for low-field magnetic resonance-guided radiotherapy in the abdomen

Background and purpose The requirement of computed tomography (CT) for radiotherapy planning may be bypassed by synthetic CT (sCT) generated from magnetic resonance (MR), which has recently led to the clinical introduction of MR-only radiotherapy for specific sites. Further developments are required for abdominal sCT, mostly due to the presence of mobile air pockets affecting the dose calculation. In this study we aimed to overcome this limitation for abdominal sCT at a low field (0.35 T) hybrid MR-Linac. Materials and methods A retrospective analysis was conducted enrolling 168 patients corresponding to 215 MR-CT pairs. After the exclusion criteria, 152 volumetric images were used to train the cycle-consistent generative adversarial network (CycleGAN) and 34 to test the sCT. Image similarity metrics and dose recalculation analysis were performed. Results The generated sCT faithfully reproduced the original CT and the location of the air pockets agreed with the MR scan. The dose calculation did not require manual bulk density overrides and the mean deviations of the dose-volume histogram dosimetric points were within 1 % of the CT, without any outlier above 2 %. The mean gamma passing rates were above 99 % for the 2 %/ 2 mm analysis and no cases below 95 % were observed. Conclusions This study presented the implementation of CycleGAN to perform sCT generation in the abdominal region for a low field hybrid MR-Linac. The sCT was shown to correctly allocate the electron density for the mobile air pockets and the dosimetric analysis demonstrated the potential for future implementation of MR-only radiotherapy in the abdomen