KEYWORDS: Image segmentation, Magnetic resonance imaging, Voxels, Deep learning, Thalamus, White matter, Data modeling, Visualization, Realistic image synthesis
T1-weighted (T1w) magnetic resonance (MR) neuroimages are usually acquired with an inversion time that nulls the cerebrospinal fluid—i.e., CSFn MPRAGE images—but are rarely acquired with the white matter nulled—i.e., WMn images. Since WMn images can be useful in highlighting thalamic nuclei, we develop a method to synthesize these images from other images that are often acquired. We propose a two-part model, with a deep learning based encoder and a decoder based on an imaging equation which governs the acquisition of our T1w images. This model can be trained on a subset of the dataset where the WMn MPRAGE images are available. Our model takes image contrasts that are often acquired (e.g., CSFn MPRAGE) as input, and generates WMn MPRAGE images as output, along with two quantitative parameter maps as intermediate results. After training, our model is able to generate a synthetic WMn MPRAGE image for any given subject. Our model results have high signal-to-noise ratio and are visually almost identical to the ground truth images. Furthermore, downstream thalamic nuclei segmentation on synthetic WMn MPRAGE images are consistent with ground truth WMn MPRAGE images.
White matter lesion (WML) segmentation applied to magnetic resonance imaging (MRI) scans of people with multiple sclerosis has been an area of extensive research in recent years. As with most tasks in medical imaging, deep learning (DL) methods have proven very effective and have quickly replaced existing methods. Despite the improvement offered by these networks, there are still shortcomings with these DL approaches. In this work, we compare several DL algorithms, as well as methods for ensembling the results of those algorithms, for performing MS lesion segmentation. An ensemble approach is shown to best estimate total WML and has the highest agreement with manual delineations.
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