Recurrent neural networks (RNNs) enable the production and processing of time-dependent signals such as those involved in movement or working memory. Classic gradient-based algorithms for training RNNs have been available for decades, but are inconsi...
Cardiac signal contamination has long confounded the analysis of blood-oxygenation-level-dependent (BOLD) functional magnetic resonance imaging (fMRI). Cardiac pulsation results in significant BOLD signal changes, especially in and around blood vesse...
PET images often suffer poor signal-to-noise ratio (SNR). Our objective is to improve the SNR of PET images using a deep neural network (DNN) model and MRI images without requiring any higher SNR PET images in training. Our proposed DNN model consist...
Unilateral peripheral vestibular lesions not only lead to vertigo, nystagmus and imbalance, but also to a bias in the perception of verticality, which can be measured as tilt of the subjective visual vertical (SVV). Previously, this tilt has been ass...
PURPOSE: Stereotactic radiosurgery (SRS) is widely used to obliterate arteriovenous malformations (AVMs). Its performance relies on the accuracy of delineating the target AVM. Manual segmentation during a framed SRS procedure is time consuming and su...
Although distinct categories are reliably decoded from fMRI brain responses, it has proved more difficult to distinguish visually similar inputs, such as different faces. Here, we apply a recently developed deep learning system to reconstruct face im...
Fully convolutional networks have been achieving remarkable results in image semantic segmentation, while being efficient. Such efficiency results from the capability of segmenting several voxels in a single forward pass. So, there is a direct spatia...
PURPOSE: To introduce a combined machine learning (ML)- and physics-based image reconstruction framework that enables navigator-free, highly accelerated multishot echo planar imaging (msEPI) and demonstrate its application in high-resolution structur...
Detecting cerebral microbleeds (CMBs) is important in diagnosing a variety of diseases including dementia, stroke and traumatic brain injury. However, manual detection of CMBs can be time-consuming and prone to errors, whereas the current automatic a...
Adequate reliability of measurement is a precondition for investigating individual differences and age-related changes in brain structure. One approach to improve reliability is to identify and control for variables that are predictive of within-pers...
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