Brain MRI super-resolution using generative adversarial networks

In this work we propose an adversarial learning approach to generate high resolution MRI scans from low resolution images. The architecture, based on the SRGAN model, adopts 3D convolutions to exploit volumetric information. For the discriminator, the adversarial loss uses least squares in order to stabilize the training. For the generator, the loss function is a combination of a least squares adversarial loss and a content term based on mean square error and image gradients in order to improve the quality of the generated images. We explore different solutions for the up sampling phase. We present promising results that improve classical interpolation, showing the potential of the approach for 3D medical imaging super-resolution.Postprint (published version

Generation of synthetic rat brain MRI scans with a 3D enhanced alpha generative adversarial network

Translational brain research using Magnetic Resonance Imaging (MRI) is becoming increasingly popular as animal models are an essential part of scientific studies and more ultra-high-field scanners are becoming available. Some disadvantages of MRI are the availability of MRI scanners and the time required for a full scanning session. Privacy laws and the 3Rs ethics rule also make it difficult to create large datasets for training deep learning models. To overcome these challenges, an adaptation of the alpha Generative Adversarial Networks (GANs) architecture was used to test its ability to generate realistic 3D MRI scans of the rat brain in silico. As far as the authors are aware, this was the first time a GAN-based approach was used to generate synthetic MRI data of the rat brain. The generated scans were evaluated using various quantitative metrics, a Turing test, and a segmentation test. The last two tests proved the realism and applicability of the generated scans to real problems. Therefore, by using the proposed new normalisation layer and loss functions, it was possible to improve the realism of the generated rat MRI scans, and it was shown that using the generated data improved the segmentation model more than using the conventional data augmentation.FCT-ANR/NEU-OSD/0258/2012. This project was co-financed by the French public funding agency ANR (Agence Nationale pour la Recherche, APP Blanc International II 2012), the Portuguese FCT (Fundação para a Ciência e Tecnologia) and the Portuguese North Regional Operational Program (ON.2-O Novo Norte) under the National Strategic Reference Framework (QREN), through the European Regional Development Fund (FEDER), as well as the Projecto Estratégico cofunded by FCT (PEst-C/SAU/LA0026/2013) and the European Regional Development Fund COMPETE (FCOMP-01-0124-FEDER-037298). France Life Imaging is acknowledged for its support in funding the NeuroSpin platform of preclinical MRI scanners. This work of André Ferreira and Victor Alves has been supported by FCT-Fundação para a Ciência e a Tecnologia within the R&D Units Project Scope: UIDB/00319/202

AAAI Workshop on Artificial Intelligence with Biased or Scarce Data (AIBSD)

This book is a collection of the accepted papers presented at the Workshop on Artificial Intelligence with Biased or Scarce Data (AIBSD) in conjunction with the 36th AAAI Conference on Artificial Intelligence 2022. During AIBSD 2022, the attendees addressed the existing issues of data bias and scarcity in Artificial Intelligence and discussed potential solutions in real-world scenarios. A set of papers presented at AIBSD 2022 is selected for further publication and included in this book

Image Processing in MRI Guided Real-TimeAdaptive Radiotheraphy - Upsampling and Segmentation of Target Volume and Organs at Risk

Magnetic Resonance Imaging (MRI) is a useful medical imaging technique that is used for cancer treatment.The major drawback of this method is the relatively long scan time, limiting its use for real time tracking of a potentially moving target during the radiotherapy session. In this work, we aim to develop a real-time segmentation method that generates high-resolution segmentation by combining prior knowledge about the patient geometry MRI and the online low-resolution MRI image data.The intended approach is based on Generative Adversarial Networks(GAN),which generate high-resolutionsegmentation based on the low-resolution images acquired during treatment. The two GAN networks implemented in this work are - Brain MRI super-resolution using 3D generative adversarial networks(3D GAN) and Super-resolution and segmentation using a generative adversarial network: Application to neonatal brain MRI (SegSRGAN). The visual and numerical results, such as PSNR and SSIM show that the 3D GAN network has produced better SR reconstruction images compared to SegSRGAN network. Furthermore, SegSRGAN has produced promising results simultaneously for the SR reconstruction and multi-organ segmentation of Rectum, Bladder and Prostate. We conclude by implementing different GAN frameworks to develop real-time segmentation that generates high-resolution segmentation from low-resolution MRI images and could possibly, reduce the scan time