Managing patients with hydrocephalus and cerebrospinal fluid disorders requires repeated head imaging. In adults, this is typically done with computed tomography (CT) or less commonly magnetic resonance imaging (MRI). However, CT poses cumulative radiation risks and MRI is costly. Transcranial ultrasound is a radiation-free, relatively inexpensive, and optionally point-of-care alternative. The initial use of this modality has involved measuring gross brain ventricle size by manual annotation. In this work, we explore the use of deep learning to automate the segmentation of brain right ventricle from transcranial ultrasound images. We found that the vanilla U-Net architecture encountered difficulties in accurately identifying the right ventricle, which can be attributed to challenges such as limited resolution, artifacts, and noise inherent in ultrasound images. We further explore the use of coordinate convolution to augment the U-Net model, which allows us to take advantage of the established acquisition protocol. This enhancement yielded a statistically significant improvement in performance, as measured by the Dice similarity coefficient. This study presents, for the first time, the potential capabilities of deep learning in automating hydrocephalus assessment from ultrasound imaging.
Linear registration to a standard space is a crucial early step in the processing of magnetic resonance images (MRIs) of the human brain. Thus an accurate registration is essential for subsequent image processing steps, as well as downstream analyses. Registration failures are not uncommon due to poor image quality, irregular head shapes, and bad initialization. Traditional quality assurance (QA) for registration requires a substantial manual assessment of the registration results. In this paper, we propose an automatic quality assurance method for the rigid registration of brain MRIs. Without using any manual annotations in the model training, our proposed QA method achieved 99.1% sensitivity and 86.7% specificity in a pilot study on 537 T1-weighted scans acquired from multiple imaging centers.
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.
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.
Image quality control (IQC) can be used in automated magnetic resonance (MR) image analysis to exclude erroneous results caused by poorly acquired or artifact-laden images. Existing IQC methods for MR imaging generally require human effort to craft meaningful features or label large datasets for supervised training. The involvement of human labor can be burdensome and biased, as labeling MR images based on their quality is a subjective task. In this paper, we propose an automatic IQC method that evaluates the extent of artifacts in MR images without supervision. In particular, we design an artifact encoding network that learns representations of artifacts based on contrastive learning. We then use a normalizing flow to estimate the density of learned representations for unsupervised classification. Our experiments on large-scale multi-cohort MR datasets show that the proposed method accurately detects images with high levels of artifacts, which can inform downstream analysis tasks about potentially flawed data.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.