Super Resolution Convolutional Neural Networks for Increasing Spatial Resolution of ^1 H Magnetic Resonance Spectroscopic Imaging
نویسندگان
چکیده
Proton magnetic resonance spectroscopic imaging (H-MRSI) provides noninvasive information regarding metabolic activity within the tissues. One of the main problems of MRSI is low spatial resolution due to clinical scan time limitations. Advanced post-processsing algorithms, like convolutional neural networks (CNN) might help with generation of super resolution MR spectroscopic images. In this study, the application of super resolution convolutional neural networks (SRCNN) for increasing the MRSI spatial resolution is presented. FLAIR, T1 weighted and T2 weighted MR images were used in training the SRCNN scheme. The spatial resolution of MRSI images were increased by using the model trained with the anatomical MR images. The results of the proposed technique were compared with bicubic resampling in terms of peak signal to noise ratio, structure similarity index, root mean square error, relative polar edge coherence, and visual information fidelity pixel. Our results indicated that SRCNN would contribute to reconstructing higher resolution MRSI.
منابع مشابه
A Deep Model for Super-resolution Enhancement from a Single Image
This study presents a method to reconstruct a high-resolution image using a deep convolution neural network. We propose a deep model, entitled Deep Block Super Resolution (DBSR), by fusing the output features of a deep convolutional network and a shallow convolutional network. In this way, our model benefits from high frequency and low frequency features extracted from deep and shallow networks...
متن کاملEfficient and Accurate MRI Super-Resolution using a Generative Adversarial Network and 3D Multi-Level Densely Connected Network
High-resolution (HR) magnetic resonance images (MRI) provide detailed anatomical information important for clinical application and quantitative image analysis. However, HR MRI conventionally comes at the cost of longer scan time, smaller spatial coverage, and lower signal-to-noise ratio (SNR). Recent studies have shown that single image super-resolution (SISR), a technique to recover HR detail...
متن کاملAn investigation into the effect of magnetic resonance imaging (MRI) echo time spacing and number of echoes on the sensitivity and dose resolution of PAGATUG polymer-gel dosimeter
Background: There are various methods to read out responses of a polymer-gel dosimeter, among which the Magnetic Resonance Imaging (MRI) technique is the most common one. Optimizing imaging protocols can have significant effect on the sensitivity and the dose resolution of polymer gel dosimeters. This study has investigated the effects of the number of echoes (NOE) and the echo time spacing (ES...
متن کاملProvide a Deep Convolutional Neural Network Optimized with Morphological Filters to Map Trees in Urban Environments Using Aerial Imagery
Today, we cannot ignore the role of trees in the quality of human life, so that the earth is inconceivable for humans without the presence of trees. In addition to their natural role, urban trees are also very important in terms of visual beauty. Aerial imagery using unmanned platforms with very high spatial resolution is available today. Convolutional neural networks based deep learning method...
متن کاملImage Super-Resolution with Fast Approximate Convolutional Sparse Coding
We present a computationally e cient architecture for image super-resolution that achieves state-of-the-art results on images with large spatial extend. Apart from utilizing Convolutional Neural Networks, our approach leverages recent advances in fast approximate inference for sparse coding. We empirically show that upsampling methods work much better on latent representations than in the origi...
متن کامل