Deep learning (DL) has led to significant improvements in medical image synthesis, enabling advanced image-toimage translation to generate synthetic images. However, DL methods face challenges such as domain shift and high demands for training data, limiting their generalizability and applicability. Historically, image synthesis was also carried out using deformable image registration (DIR), a method that warps moving images of a desired modality to match the anatomy of a fixed image. However, concerns about its speed and accuracy led to its decline in popularity. With the recent advances of DL-based DIR, we now revisit and reinvigorate this line of research. In this paper, we propose a fast and accurate synthesis method based on DIR. We use the task of synthesizing a rare magnetic resonance (MR) sequence, white matter nulled (WMn) T1-weighted (T1-w) images, to demonstrate the potential of our approach. During training, our method learns a DIR model based on the widely available MPRAGE sequence, which is a cerebrospinal fluid nulled (CSFn) T1-w inversion recovery gradient echo pulse sequence. During testing, the trained DIR model is first applied to estimate the deformation between moving and fixed CSFn images. Subsequently, this estimated deformation is applied to align the paired WMn counterpart of the moving CSFn image, yielding a synthetic WMn image for the fixed CSFn image. Our experiments demonstrate promising results for unsupervised image synthesis using DIR. These findings highlight the potential of our technique in contexts where supervised synthesis methods are constrained by limited training data.
Normal Pressure Hydrocephalus (NPH) is a brain disorder associated with ventriculomegaly. Accurate segmentation of the ventricle system into its sub-compartments from magnetic resonance images (MRIs) could help evaluate NPH patients for surgical intervention. In this paper, we modify a 3D U-net utilizing probability maps to perform accurate ventricle parcellation, even with grossly enlarged ventricles and post-surgery shunt artifacts, from MRIs. Our method achieves a mean dice similarity coefficient (DSC) on whole ventricles for healthy controls of 0.864 ± 0.047 and 0.961 ± 0.024 for NPH patients. Furthermore, with the benefit of probability maps, the proposed method provides superior performance on MRI with grossly enlarged ventricles (mean DSC value of 0.965 ± 0.027) or post-surgery shunt artifacts (mean DSC value of 0.964 ± 0.031). Results indicate that our method provides a high robust parcellation tool on the ventricular systems which is comparable to other state-of-the-art methods.
The thalamus is a subcortical gray matter structure that plays a key role in relaying sensory and motor signals within the brain. Its nuclei can atrophy or otherwise be affected by neurological disease and injuries including mild traumatic brain injury. Segmenting both the thalamus and its nuclei is challenging because of the relatively low contrast within and around the thalamus in conventional magnetic resonance (MR) images. This paper explores imaging features to determine key tissue signatures that naturally cluster, from which we can parcellate thalamic nuclei. Tissue contrasts include T1-weighted and T2-weighted images, MR diffusion measurements including FA, mean diffusivity, Knutsson coefficients that represent fiber orientation, and synthetic multi-TI images derived from FGATIR and T1-weighted images. After registration of these contrasts and isolation of the thalamus, we use the uniform manifold approximation and projection (UMAP) method for dimensionality reduction to produce a low-dimensional representation of the data within the thalamus. Manual labeling of the thalamus provides labels for our UMAP embedding from which k nearest neighbors can be used to label new unseen voxels in that same UMAP embedding. N-fold cross-validation of the method reveals comparable performance to state-of-the-art methods for thalamic parcellation.
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