Mobility decline is a major concern for older adults. A key component of maintaining mobility with advancing age is the ability to learn and adapt to the environment. The split-belt treadmill paradigm is an experimental protocol that tests the ability to adapt to a dynamic environment. Here we examined the magnetic resonance imaging (MRI) derived structural neural correlates of individual differences in adaptation to split-belt walking for younger and older adults. We have previously shown that younger adults adopt an asymmetric walking pattern during split-belt walking, particularly in the medial-lateral (ML) direction, but older adults do not. We collected
Humans are exposed to extreme environmental stressors during spaceflight and return with alterations in brain structure and shifts in intracranial fluids. To date, no studies have evaluated the effects of spaceflight on perivascular spaces (PVSs) within the brain, which are believed to facilitate fluid drainage and brain homeostasis. Here, we examined how the number and morphology of magnetic resonance imaging (MRI)-visible PVSs are affected by spaceflight, including prior spaceflight experience. Fifteen astronauts underwent six
- NSF-PAR ID:
- 10379968
- Publisher / Repository:
- Nature Publishing Group
- Date Published:
- Journal Name:
- Scientific Reports
- Volume:
- 12
- Issue:
- 1
- ISSN:
- 2045-2322
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Background Magnetic resonance imaging (MRI) scans are known to suffer from a variety of acquisition artifacts as well as equipment‐based variations that impact image appearance and segmentation performance. It is still unclear whether a direct relationship exists between magnetic resonance (MR) image quality metrics (IQMs) (e.g., signal‐to‐noise, contrast‐to‐noise) and segmentation accuracy.
Purpose Deep learning (DL) approaches have shown significant promise for automated segmentation of brain tumors on MRI but depend on the quality of input training images. We sought to evaluate the relationship between IQMs of input training images and DL‐based brain tumor segmentation accuracy toward developing more generalizable models for multi‐institutional data.
Methods We trained a 3D DenseNet model on the BraTS 2020 cohorts for segmentation of tumor subregions enhancing tumor (ET), peritumoral edematous, and necrotic and non‐ET on MRI; with performance quantified via a 5‐fold cross‐validated Dice coefficient. MRI scans were evaluated through the open‐source quality control tool MRQy, to yield 13 IQMs per scan. The Pearson correlation coefficient was computed between whole tumor (WT) dice values and IQM measures in the training cohorts to identify quality measures most correlated with segmentation performance. Each selected IQM was used to group MRI scans as “better” quality (BQ) or “worse” quality (WQ), via relative thresholding. Segmentation performance was re‐evaluated for the DenseNet model when (i) training on BQ MRI images with validation on WQ images, as well as (ii) training on WQ images, and validation on BQ images. Trends were further validated on independent test sets derived from the BraTS 2021 training cohorts.
Results For this study, multimodal MRI scans from the BraTS 2020 training cohorts were used to train the segmentation model and validated on independent test sets derived from the BraTS 2021 cohort. Among the selected IQMs, models trained on BQ images based on inhomogeneity measurements (coefficient of variance, coefficient of joint variation, coefficient of variation of the foreground patch) and the models trained on WQ images based on noise measurement peak signal‐to‐noise ratio (SNR) yielded significantly improved tumor segmentation accuracy compared to their inverse models.
Conclusions Our results suggest that a significant correlation may exist between specific MR IQMs and DenseNet‐based brain tumor segmentation performance. The selection of MRI scans for model training based on IQMs may yield more accurate and generalizable models in unseen validation.
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Background Brain tissue hypoxia is a common consequence of traumatic brain injury (TBI) due to the rupture of blood vessels during impact and it correlates with poor outcome. The current magnetic resonance imaging (MRI) techniques are unable to provide a direct map of tissue hypoxia.
Purpose To investigate whether GdDO3NI, a nitroimidazole‐based T1MRI contrast agent allows imaging hypoxia in the injured brain after experimental TBI.
Study Type Prospective.
Animal Model TBI‐induced mice (controlled cortical impact model) were intravenously injected with either conventional T1agent (gadoteridol) or GdDO3NI at 0.3 mmol/kg dose (
n = 5 for each cohort) along with pimonidazole (60 mg/kg) at 1 hour postinjury and imaged for 3 hours following which they were euthanized.Field Strength/Sequence 7 T/T2‐weighted spin echo and T1‐weighted gradient echo.
Assessment Injured animals were imaged with T2‐weighted spin‐echo sequence to estimate the extent of the injury. The mice were then imaged precontrast and postcontrast using a T1‐weighted gradient‐echo sequence for 3 hours postcontrast. Regions of interests were drawn on the brain injury region, the contralateral brain as well as on the cheek muscle region for comparison of contrast kinetics. Brains were harvested immediately post‐imaging for immunohistochemical analysis.
Statistical Tests One‐way analysis of variance and two‐sample
t ‐tests were performed with aP < 0.05 was considered statistically significant.Results GdDO3NI retention in the injury region at 2.5–3 hours post‐injection was significantly higher compared to gadoteridol (mean retention fraction 63.95% ± 27.43% vs. 20.68% ± 7.43% for gadoteridol at 3 hours) while it rapidly cleared out of the muscle region. Pimonidazole staining confirmed the presence of hypoxia in both gadoteridol and GdDO3NI cohorts, and the later cohort showed good agreement with MRI contrast enhancement.
Data Conclusion GdDO3NI was successfully shown to visualize hypoxia in the brain post‐TBI using T1‐weighted MRI at 2.5–3 hours postcontrast.
Evidence Level 1
Technical Efficacy Stage 1
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Abstract Investigating the mechanisms by which metabolic wastes are cleared from nervous tissue is important for understanding natural function and the pathophysiology of several neurological disorders including Alzheimer’s disease. Recent evidence suggests clearance may be the function of annular spaces around cerebral blood vessels, called perivascular spaces (PVS), through which cerebrospinal fluid (CSF) is transported from the subarachnoid space into brain parenchyma to exchange with interstitial fluid (also known as the glymphatic system). In this work, an MRI-based methodology was developed to reconstruct the PVS network in whole rat brain to better elucidate both PVS uptake and clearance pathways. MR visible tracer (Gd-albumin) was infused
in vivo into the CSF-filled lateral ventricle followed byex vivo high-resolution MR imaging at 17.6 T with an image voxel volume two orders of magnitude smaller than previously reported. Imaged tracer distribution patterns were reconstructed to obtain a more complete brain PVS network. Several PVS connections were repeatedly highlighted across different animals, and new PVS connections between ventricles and different parts of the brain parenchyma were revealed suggesting a possible role for the ventricles as a source or sink for solutes in the brain. In the future, this methodology may be applied to understand changes in the PVS network with disease. -
Shape priors have been widely utilized in medical image segmentation to improve segmentation accuracy and robustness. A major way to encode such a prior shape model is to use a mesh representation, which is prone to causing self-intersection or mesh folding. Those problems require complex and expensive algorithms to mitigate. In this paper, we propose a novel shape prior directly embedded in the voxel grid space, based on gradient vector flows of a pre-segmentation. The flexible and powerful prior shape representation is ready to be extended to simultaneously segmenting multiple interacting objects with minimum separation distance constraint. The segmentation problem of multiple interacting objects with shape priors is formulated as a Markov Random Field problem, which seeks to optimize the label assignment (objects or background) for each voxel while keeping the label consistency between the neighboring voxels. The optimization problem can be efficiently solved with a single minimum s-t cut in an appropriately constructed graph. The proposed algorithm has been validated on two multi-object segmentation applications: the brain tissue segmentation in MRI images and the bladder/prostate segmentation in CT images. Both sets of experiments showed superior or competitive performance of the proposed method to the compared state-of-the-art methods.more » « less
