Deep Learning Techniques for Low-Field MRI
D.B. Geçmen (TU Delft - Electrical Engineering, Mathematics and Computer Science)
MB Van Gijzen – Mentor (TU Delft - Numerical Analysis)
Merel de Leeuw den Bouter – Graduation committee member (TU Delft - Numerical Analysis)
Rob F. Remis – Coach (TU Delft - Signal Processing Systems)
HX Lin – Coach (TU Delft - Mathematical Physics)
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Abstract
Delft University of Technology (TU Delft), Leiden University Medical Center (LUMC), Pennsylvania State University (PSU) and Mbarara University of Science and Technology (MUST) have an ongoing collaboration to create an affordable, portable and simplified version of the magnetic resonance imaging (MRI) scan for the CURE children’s hospital to diagnose children with hydrocephalus (water on the brain). As opposed to the conventional MRI scan, the low-field MRI prototype uses permanent magnets to create a magnetic field in the order of Milliteslas (mT). A downside of the low-field MRI application is the difficulty with spatial encoding due to small variations in the strength of magnetic field. This is a major problem for image reconstruction. The purpose of this research was to implement a deep learning (DL) network to overcome two of the major bottlenecks in image reconstruction for low-field MRI. These are the lack of real measured data for DL purposes, and the signal model associated with the low-field MRI. For DL purposes we generated synthetic data and acquired measured data. Each dataset consists of samples and each sample consist of an image and the corresponding signal. Due to technical limitations the measured dataset is small, 53 samples. To partially circumvent the problem, the data set was augmented to a total of 1908 samples. In addition, we used Transfer learning, which is a powerful method that applies knowledge gained from one problem to a different but related problem. We present three image reconstruction techniques, Model I, II, and III, based on convolutional and feedforward neural networks, which take MR signal data as input and directly and quickly outputs an image. We demonstrated that DL generates high quality images using synthetic data. In addition, we showed that Model III needs less training to reconstructs good quality images compared to Models I and III, respectively. Finally, Models I and III were unsuccessfully applied to real measured data. However, this study shows that neural networks are able to find a mapping between signal and image, therefore this idea can be extended to work on real measured data.