Pushing the limits of zero-shot self-supervised super-resolution of anisotropic MR images.

Magnetic resonance images are often acquired as several 2D slices and stacked into a 3D volume, yielding a lower through-plane resolution than in-plane resolution. Many super-resolution (SR) methods have been proposed to address this, including those that use the inherent high-resolution (HR) in-plane signal as HR data to train deep neural networks. Techniques with this approach are generally both self-supervised and internally trained, so no external training data is required.

Self-Supervised Super-Resolution of 2D Pre-clinical MRI Acquisitions.

Animal models are pivotal in disease research and the advancement of therapeutic methods. The translation of results from these models to clinical applications is enhanced by employing technologies which are consistent for both humans and animals, like Magnetic Resonance Imaging (MRI), offering the advantage of longitudinal disease evaluation without compromising animal welfare. However, current animal MRI techniques predominantly employ 2D acquisitions due to constraints related to organ size, scan duration, image quality, and hardware limitations.

Serum NfL and GFAP as biomarkers of progressive neurodegeneration in TBI.

Background: We examined spatial patterns of brain atrophy after mild, moderate, and severe traumatic brain injury (TBI), the relationship between progression of brain atrophy with initial traumatic axonal injury (TAI), cognitive outcome, and with serum biomarkers of brain injury.

Methods: A total of 143 patients with TBI and 43 controls were studied cross-sectionally and longitudinally up to 5 years with multiple assessments, which included brain magnetic resonance imaging, cognitive testing, and serum biomarkers.

Results: TBI patients showed progressive volume loss regardless of injury severity over several years, and TAI was independently associated with accelerated brain atrophy.

A Pilot Study Investigating the Use of Serum Glial Fibrillary Acidic Protein to Monitor Changes in Brain White Matter Integrity After Repetitive Head Hits During a Single Collegiate Football Game.

Repetitive head hits (RHHs) in sports and military settings are increasingly recognized as a risk factor for adverse neurological outcomes, but they are not currently tracked. Blood-based biomarkers of concussion have recently been shown to increase after nonconcussive RHHs during a single sporting contest, raising the possibility that they could be used in real time to monitor the brain's early response to repeated asymptomatic head hits. To test this hypothesis, we measured GFAP in serum immediately before (T0), immediately after (T1) and 45 min (T2) after a single collegiate football game in 30 athletes.

Neuroimaging Findings in US Government Personnel and Their Family Members Involved in Anomalous Health Incidents.

Importance: US government personnel stationed internationally have reported anomalous health incidents (AHIs), with some individuals experiencing persistent debilitating symptoms.

Objective: To assess the potential presence of magnetic resonance imaging (MRI)-detectable brain lesions in participants with AHIs, with respect to a well-matched control group.

Design, Setting, And Participants: This exploratory study was conducted at the National Institutes of Health (NIH) Clinical Center and the NIH MRI Research Facility between June 2018 and November 2022.

Associations between white matter integrity and postural control in adults with traumatic brain injury.

Abnormalities of postural sway have been extensively reported in traumatic brain injury (TBI). However, the underlying neural correlates of balance disturbances in TBI remain to be elucidated. Studies in children with TBI have reported associations between the Sensory Organization Test (SOT) and measures of white matter (WM) integrity with diffusion tensor imaging (DTI) in brain areas responsible for multisensory integration.

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.

Comparison of Deformation Patterns Excited in the Human Brain In Vivo by Harmonic and Impulsive Skull Motion.

Noninvasive measurements of brain deformation in human participants in vivo are needed to develop models of brain biomechanics and understand traumatic brain injury (TBI). Tagged magnetic resonance imaging (tagged MRI) and magnetic resonance elastography (MRE) are two techniques to study human brain deformation; these techniques differ in the type of motion and difficulty of implementation. In this study, oscillatory strain fields in the human brain caused by impulsive head acceleration and measured by tagged MRI were compared quantitatively to strain fields measured by MRE during harmonic head motion at 10 and 50 Hz.

In vivo MRI is sensitive to remyelination in a nonhuman primate model of multiple sclerosis.

Remyelination is crucial to recover from inflammatory demyelination in multiple sclerosis (MS). Investigating remyelination in vivo using magnetic resonance imaging (MRI) is difficult in MS, where collecting serial short-interval scans is challenging. Using experimental autoimmune encephalomyelitis (EAE) in common marmosets, a model of MS that recapitulates focal cerebral inflammatory demyelinating lesions, we investigated whether MRI is sensitive to, and can characterize, remyelination.

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.

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.

Effects of Physical Exertion on Early Changes in Blood-Based Brain Biomarkers: Implications for the Acute Point of Care Diagnosis of Concussion.

Blood-based brain biomarkers (BBM) such as glial fibrillary acidic protein (GFAP) and ubiquitin carboxy-terminal hydrolase L1 (UCH-L1) have potential to aid in the diagnosis of concussion. Recently developed point-of-care test devices would enable BBMs to be measured in field settings such military and sport environments within minutes of a suspicious head hit. However, head hits in these environments typically occur in the setting of vigorous physical exertion, which can itself increase BBMs levels.

Simultaneous Acquisition of Diffusion Tensor and Dynamic Diffusion MRI.

Background: Dynamic diffusion magnetic resonance imaging (ddMRI) metrics can assess transient microstructural alterations in tissue diffusivity but requires additional scan time hindering its clinical application.

Purpose: To determine whether a diffusion gradient table can simultaneously acquire data to estimate dynamic and diffusion tensor imaging (DTI) metrics.

Study Type: Prospective.

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.

Segmented 3D Echo Planar Acquisition for Rapid Susceptibility-Weighted Imaging: Application to Microhemorrhage Detection in Traumatic Brain Injury.

Background: Susceptibility-weighted imaging (SWI) provides superior image contrast of cerebral microhemorrhages (CMBs). It is based on a three-dimensional (3D) gradient echo (GRE) sequence with a relatively long imaging time.

Purpose: To evaluate whether an accelerated 3D segmented echo planar imaging SWI is comparable to GRE SWI in detecting CMBs in traumatic brain injury (TBI).

Lesion size and shape in central vein sign assessment for multiple sclerosis diagnosis: An in vivo and postmortem MRI study.

Background: The "central vein sign" (CVS), a linear hypointensity on T2*-weighted imaging corresponding to a central vein/venule, is associated with multiple sclerosis (MS) lesions. The effect of lesion-size exclusion criteria on MS diagnostic accuracy has not been extensively studied.

Objective: Investigate the optimal lesion-size exclusion criteria for CVS use in MS diagnosis.

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.

Central Vein Sign Profile of Newly Developing Lesions in Multiple Sclerosis: A 3-Year Longitudinal Study.

Background And Objectives: The central vein sign (CVS), a central linear hypointensity within lesions on T2*-weighted imaging, has been established as a sensitive and specific biomarker for the diagnosis of multiple sclerosis (MS). However, the CVS has not yet been comprehensively studied in newly developing MS lesions. We aimed to identify the CVS profiles of new white matter lesions in patients with MS followed over time and investigate demographic and clinical risk factors associated with new CVS+ or CVS- lesion development.