Keywords: Magnetic Resonance Imaging, Image reconstruction, MRI contrast, Deep Learning, Unsupervised Learning, GAN
TL;DR: A GAN was trained on pelvic MR images from five different contrasts, where by design the quantitative maps (PD, T1, T2) are inherently learned, so the proposed network that can generate MR data with any contrast settings (TE and TR) set by the user.
Abstract: The contrast settings to select before acquiring magnetic resonance imaging (MRI) signal depend heavily on the subsequent tasks. As each contrast highlights different tissues, automated segmentation tools for example might be optimized for a certain contrast. While for radiotherapy, multiple scans of the same region with different contrasts can achieve a better accuracy for delineating tumours and organs at risk. Unfortunately, the optimal contrast for the subsequent automated methods might not be known during the time of signal acquisition, and performing multiple scans with different contrasts increases the total examination time and registering the sequences introduces extra work and potential errors. Building on the recent achievements of deep learning in medical applications, the presented work describes a novel approach for transferring any contrast to any other. The novel model architecture incorporates the signal equation for spin echo sequences, and hence the model inherently learns the unknown quantitative maps for proton density, $T1$ and $T2$ relaxation times ($PD$, $T1$ and $T2$, respectively). This grants the model the ability to retrospectively reconstruct spin echo sequences by changing the contrast settings Echo and Repetition Time ($TE$ and $TR$, respectively). The model learns to identify the contrast of pelvic MR images, therefore no paired data of the same anatomy from different contrasts is required for training. This means that the experiments are easily reproducible with other contrasts or other patient anatomies. Despite the contrast of the input image, the model achieves accurate results for reconstructing signal with contrasts available for evaluation. For the same anatomy, the quantitative maps are consistent for a range of contrasts of input images. Realized in practice, the proposed method would greatly simplify the modern radiotherapy pipeline. The trained model is made public together with a tool for testing the model on example images.
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Paper Type: methodological development
Primary Subject Area: Image Acquisition and Reconstruction
Secondary Subject Area: Unsupervised Learning and Representation Learning
Source Latex: zip