Super-resolution multi-contrast unbiased eye atlases with deep probabilistic refinement.

Purpose: Eye morphology varies significantly across the population, especially for the orbit and optic nerve. These variations limit the feasibility and robustness of generalizing population-wise features of eye organs to an unbiased spatial reference.

Approach: To tackle these limitations, we propose a process for creating high-resolution unbiased eye atlases.

Super-Resolution in Clinically Available Spinal Cord MRIs Enables Automated Atrophy Analysis.

Background And Purpose: Measurement of the mean upper cervical cord area (MUCCA) is an important biomarker in the study of neurodegeneration. However, dedicated high-resolution scans of the cervical spinal cord are rare in standard-of-care imaging due to timing and clinical usability. Most clinical cervical spinal cord imaging is sagittally acquired in 2D with thick slices and anisotropic voxels.

Nucleus subtype classification using inter-modality learning.

Understanding the way cells communicate, co-locate, and interrelate is essential to understanding human physiology. Hematoxylin and eosin (H&E) staining is ubiquitously available both for clinical studies and research. The Colon Nucleus Identification and Classification (CoNIC) Challenge has recently innovated on robust artificial intelligence labeling of six cell types on H&E stains of the colon.

Self-Supervised Super-Resolution for Anisotropic MR Images with and Without Slice Gap.

Magnetic resonance (MR) images are often acquired as multi-slice volumes to reduce scan time and motion artifacts while improving signal-to-noise ratio. These slices often are thicker than their in-plane resolution and sometimes are acquired with gaps between slices. Such thick-slice image volumes (possibly with gaps) can impact the accuracy of volumetric analysis and 3D methods.

HACA3: A unified approach for multi-site MR image harmonization.

The lack of standardization and consistency of acquisition is a prominent issue in magnetic resonance (MR) imaging. This often causes undesired contrast variations in the acquired images due to differences in hardware and acquisition parameters. In recent years, image synthesis-based MR harmonization with disentanglement has been proposed to compensate for the undesired contrast variations.

No ultrasounds detected from fungi when dehydrated.

Many organisms (including certain plant species) can be observed to emit sounds, potentially signifying threat alerts. Sensitivity to such sounds and vibrations may also play an important role in the lives of fungi. In this work, we explore the potential of ultrasound activity in dehydrating fungi, and discover that several species of fungi do not emit sounds (detectable with conventional instrumentation) in the frequency range of 10kHz to 210kHz upon dehydration.

A deep generative prior for high-resolution isotropic MR head slices.

Generative priors for magnetic resonance (MR) images have been used in a number of medical image analysis applications. Due to the plethora of deep learning methods based on 2D medical images, it would be beneficial to have a generator trained on complete, high-resolution 2D head MR slices from multiple orientations and multiple contrasts. In this work, we trained a StyleGAN3-T model for head MR slices for T and T-weighted contrasts on public data.

ESPRESO: An algorithm to estimate the slice profile of a single magnetic resonance image.

To reduce scan time, magnetic resonance (MR) images are often acquired using 2D multi-slice protocols with thick slices that may also have gaps between them. The resulting image volumes have lower resolution in the through-plane direction than in the in-plane direction, and the through-plane resolution is in part characterized by the protocol's slice profile which acts as a through-plane point spread function (PSF). Although super-resolution (SR) has been shown to improve the visualization and down-stream processing of 2D multi-slice MR acquisitions, previous algorithms are usually unaware of the true slice profile, which may lead to sub-optimal SR performance.

Enlarged perivascular space burden associations with arterial stiffness and cognition.

Enlarged perivascular spaces (ePVS) are difficult to quantify, and their etiologies and consequences are poorly understood. Vanderbilt Memory and Aging Project participants (n = 327, 73 ± 7 years) completed 3T brain MRI to quantify ePVS volume and count, longitudinal neuropsychological assessment, and cardiac MRI to quantify aortic stiffness. Linear regressions related (1) PWV to ePVS burden and (2) ePVS burden to cross-sectional and longitudinal neuropsychological performance adjusting for key demographic and medical factors.

Bi-directional Synthesis of Pre- and Post-contrast MRI via Guided Feature Disentanglement.

Magnetic resonance imaging (MRI) with gadolinium contrast is widely used for tissue enhancement and better identification of active lesions and tumors. Recent studies have shown that gadolinium deposition can accumulate in tissues including the brain, which raises safety concerns. Prior works have tried to synthesize post-contrast T1-weighted MRIs from pre-contrast MRIs to avoid the use of gadolinium.

Efficient Quality Control with Mixed CT and CTA Datasets.

Deep learning promises the extraction of valuable information from traumatic brain injury (TBI) datasets and depends on efficient navigation when using large-scale mixed computed tomography (CT) datasets from clinical systems. To ensure a cleaner signal while training deep learning models, removal of computed tomography angiography (CTA) and scans with streaking artifacts is sensible. On massive datasets of heterogeneously sized scans, time-consuming manual quality assurance (QA) by visual inspection is still often necessary, despite the expectation of CTA annotation (artifact annotation is not expected).

Deep filter bank regression for super-resolution of anisotropic MR brain images.

In 2D multi-slice magnetic resonance (MR) acquisition, the through-plane signals are typically of lower resolution than the in-plane signals. While contemporary super-resolution (SR) methods aim to recover the underlying high-resolution volume, the estimated high-frequency information is implicit via end-to-end data-driven training rather than being explicitly stated and sought. To address this, we reframe the SR problem statement in terms of perfect reconstruction filter banks, enabling us to identify and directly estimate the missing information.

Automated Classification of Resting-State fMRI ICA Components Using a Deep Siamese Network.

Manual classification of functional resting state networks (RSNs) derived from Independent Component Analysis (ICA) decomposition can be labor intensive and requires expertise, particularly in large multi-subject analyses. Hence, a fully automatic algorithm that can reliably classify these RSNs is desirable. In this paper, we present a deep learning approach based on a Siamese Network to learn a discriminative feature representation for single-subject ICA component classification.

Joint Image and Label Self-Super-Resolution.

We propose a method to jointly super-resolve an anisotropic image volume along with its corresponding voxel labels without external training data. Our method is inspired by internally trained superresolution, or self-super-resolution (SSR) techniques that target anisotropic, low-resolution (LR) magnetic resonance (MR) images. While resulting images from such methods are quite useful, their corresponding LR labels-derived from either automatic algorithms or human raters-are no longer in correspondence with the super-resolved volume.

Federated Gradient Averaging for Multi-Site Training with Momentum-Based Optimizers.

Multi-site training methods for artificial neural networks are of particular interest to the medical machine learning community primarily due to the difficulty of data sharing between institutions. However, contemporary multi-site techniques such as weight averaging and cyclic weight transfer make theoretical sacrifices to simplify implementation. In this paper, we implement federated gradient averaging (FGA), a variant of federated learning without data transfer that is mathematically equivalent to single site training with centralized data.

Comparison of convolutional neural networks for detecting large vessel occlusion on computed tomography angiography.

Purpose: Artificial intelligence diagnosis and triage of large vessel occlusion may quicken clinical response for a subset of time-sensitive acute ischemic stroke patients, improving outcomes. Differences in architectural elements within data-driven convolutional neural network (CNN) models impact performance. Foreknowledge of effective model architectural elements for domain-specific problems can narrow the search for candidate models and inform strategic model design and adaptation to optimize performance on available data.

Generalizing deep whole-brain segmentation for post-contrast MRI with transfer learning.

Generalizability is an important problem in deep neural networks, especially with variability of data acquisition in clinical magnetic resonance imaging (MRI). Recently, the spatially localized atlas network tiles (SLANT) can effectively segment whole brain, non-contrast T1w MRI with 132 volumetric labels. Transfer learning (TL) is a commonly used domain adaptation tool to update the neural network weights for local factors, yet risks degradation of performance on the original validation/test cohorts.

Brain connections derived from diffusion MRI tractography can be highly anatomically accurate-if we know where white matter pathways start, where they end, and where they do not go.

MR Tractography, which is based on MRI measures of water diffusivity, is currently the only method available for noninvasive reconstruction of fiber pathways in the brain. However, it has several fundamental limitations that call into question its accuracy in many applications. Therefore, there has been intense interest in defining and mitigating the intrinsic limitations of the method.

LEARNING 3D WHITE MATTER MICROSTRUCTURE FROM 2D HISTOLOGY.

Histological analysis is typically the gold standard for validating measures of tissue microstructure derived from magnetic resonance imaging (MRI) contrasts. However, most histological investigations are inherently 2-dimensional (2D), due to increased field-of-view, higher in-plane resolutions, ease of acquisition, decreased costs, and a large number of available contrasts compared to 3-dimensional (3D) analysis. Because of this, it would be of great interest to be able to learn the 3D tissue microstructure from 2D histology.

Harmonizing 1.5T/3T Diffusion Weighted MRI through Development of Deep Learning Stabilized Microarchitecture Estimators.

Diffusion weighted magnetic resonance imaging (DW-MRI) is interpreted as a quantitative method that is sensitive to tissue microarchitecture at a millimeter scale. However, the sensitization is dependent on acquisition sequences (e.g.

Generalizing Deep Whole Brain Segmentation for Pediatric and Post- Contrast MRI with Augmented Transfer Learning.

Generalizability is an important problem in deep neural networks, especially in the context of the variability of data acquisition in clinical magnetic resonance imaging (MRI). Recently, the Spatially Localized Atlas Network Tiles (SLANT) approach has been shown to effectively segment whole brain non-contrast T1w MRI with 132 volumetric labels. Enhancing generalizability of SLANT would enable broader application of volumetric assessment in multi-site studies.

Extracting 2D weak labels from volume labels using multiple instance learning in CT hemorrhage detection.

Multiple instance learning (MIL) is a supervised learning methodology that aims to allow models to learn instance class labels from bag class labels, where a bag is defined to contain multiple instances. MIL is gaining traction for learning from weak labels but has not been widely applied to 3D medical imaging. MIL is well-suited to clinical CT acquisitions since (1) the highly anisotropic voxels hinder application of traditional 3D networks and (2) patch-based networks have limited ability to learn whole volume labels.