Detection of Fat-Water Inversions in MRI Data With Deep Learning Methods

Sammanfattning: Magnetic resonance imaging (MRI) is a widely used medical imaging technique for examinations of the body. However, artifacts are a common problem, that must be handled for reliable diagnoses and to avoid drawing inaccurate conclusions about the contextual insights. Magnetic resonance (MR) images acquired with a Dixon-sequence enables two channels with separate fat and water content. Fat-water inversions, also called swaps, are one common artifact with this method where voxels from the two channels are swapped, producing incorrect data. This thesis investigates the possibility to use deep learning methods for an automatic detection of swaps in MR volumes. The data used in this thesis are MR volumes from UK Biobank, processed by AMRA Medical. Segmentation masks of complicated swaps are created by operators who manually annotate the swap, but only if the regions affect subsequent measurements. The segmentation masks are therefore not fully reliable, and additional synthesized swaps were created. Two different deep learning approaches were investigated, a reconstruction-based method and a segmentation-based method. The reconstruction-based networks were trained to reconstruct a volume as similar as possible to the input volume without any swaps. When testing the network on a volume with a swap, the location of the swap can be estimated from the reconstructed volume with postprocessing methods. Autoencoders is an example of a reconstruction-based network. The segmentation-based models were trained to segment a swap directly from the input volume, thus using volumes with swaps both during training and testing. The segmentation-based networks were inspired by a U-Net. The performance of the models from both approaches was evaluated on data with real and synthetic swaps with the metrics: Dice coefficient, precision, and recall. The result shows that the reconstruction-based models are not suitable for swap detection. Difficulties in finding the right architecture for the models resulted in bad reconstructions, giving unreliable predictions. Further investigations in different post-processing methods, architectures, and hyperparameters might improve swap detection. The segmentation-based models are robust with reliable detections independent of the size of the swaps, despite being trained on data with synthesized swaps. The results from the models look very promising, and can probably be used as an automated method for swap detection with some further fine-tuning of the parameters.