Effective deep-learning brain MRI super resolution using simulated training data.

Background: In the field of medical imaging, high-resolution (HR) magnetic resonance imaging (MRI) is essential for accurate disease diagnosis and analysis. However, HR imaging is prone to artifacts and is not universally available. Consequently, low-resolution (LR) MRI images are typically acquired.

Brain MR image simulation for deep learning based medical image analysis networks.

Background And Objective: As large sets of annotated MRI data are needed for training and validating deep learning based medical image analysis algorithms, the lack of sufficient annotated data is a critical problem. A possible solution is the generation of artificial data by means of physics-based simulations. Existing brain simulation data is limited in terms of anatomical models, tissue classes, fixed tissue characteristics, MR sequences and overall realism.

Multi-modal brain tumor segmentation via conditional synthesis with Fourier domain adaptation.

Accurate brain tumor segmentation is critical for diagnosis and treatment planning, whereby multi-modal magnetic resonance imaging (MRI) is typically used for analysis. However, obtaining all required sequences and expertly labeled data for training is challenging and can result in decreased quality of segmentation models developed through automated algorithms. In this work, we examine the possibility of employing a conditional generative adversarial network (GAN) approach for synthesizing multi-modal images to train deep learning-based neural networks aimed at high-grade glioma (HGG) segmentation.

MRI based automated diagnosis of Alzheimer's: Fusing 3D wavelet-features with clinical data.

This paper presents a novel algorithm for classification of patients with Alzheimer's disease (AD) and Mild Cognitive Impairment (MCI) from the healthy controls (HC) using structural MRI. Feature extraction is based on discrete 3D wavelet transform followed by PCA for transforming the feature space into linearly uncorrelated variables. Linear SVM is used for classification purposes with clinical dementia rating used as the target vector.