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Purpose: Measure the changes in relative lung water density (rLWD), lung volume, and total lung water content as a function of time after supine body positioning. Methods: An efficient ultrashort-TE pulse sequence with a yarnball k-space trajectory was used to measure water density-weighted lung images for 25 min following supine body positioning (free breathing, 74-s acquisitions, 3D images at functional residual capacity, 18 time points) in 9 healthy volunteers. Global and regional (10 chest-to-back positions) rLWD, lung volume, and total lung water volume were measured in all subjects at all time points. Volume changes were validated with a nitrogen washout study in 3 participants. Results: Global rLWD increased significantly (p=0.001) from 31.8±5.5% to 34.8±6.8%, while lung volumes decreased significantly (p<0.001) from 2390±620mL to 2130±630mL over the same 25-min interval. Total lung water volume decreased slightly from730±125mL to 706±126mL (p=0.028). There was a significant chest-to-back gradient in rLWD (20.7±4.6% to 39.9±6.1%) at all time points with absolute increases of 1.8±1.2% at the chest and 5.4±1.9% at the back. Nitrogen washout studies yielded a similar reduction in lung volume (12.5±0.9%) and time course following supine positioning. Conclusion: Lung volumes during tidal breathing decrease significantly over tens of minutes following supine body positioning, with corresponding increases in lung water density (9.2±4.4% relative increase). The total volume of lung water is slightly reduced over this interval (3.3±4.0% relative change). Evaluation of rLWD should take time after supine positioning, and more generally, all sources of lung volume changes should be taken into consideration to avoid significant bias. [ABSTRACT FROM AUTHOR]

Objective: To identify structural and neurochemical properties that underlie functional connectivity impairments of the primary motor cortex (PMC) and how these relate to clinical findings in amyotrophic lateral sclerosis (ALS).Methods: 52 patients with ALS and 52 healthy controls, matched for age and sex, were enrolled from 5 centres across Canada for the Canadian ALS Neuroimaging Consortium study. Resting-state functional MRI, diffusion tensor imaging and magnetic resonance spectroscopy data were acquired. Functional connectivity maps, diffusion metrics and neurometabolite ratios were obtained from the analyses of the acquired multimodal data. A clinical assessment of foot tapping (frequency) was performed to examine upper motor neuron function in all participants.Results: Compared with healthy controls, the primary motor cortex in ALS showed reduced functional connectivity with sensory (T=5.21), frontal (T=3.70), temporal (T=3.80), putaminal (T=4.03) and adjacent motor (T=4.60) regions. In the primary motor cortex, N-acetyl aspartate (NAA, a neuronal marker) ratios and diffusion metrics (mean, axial and radial diffusivity, fractional anisotropy (FA)) were altered. Within the ALS cohort, foot tapping frequency correlated with NAA (r=0.347) and white matter FA (r=0.537). NAA levels showed associations with disturbed functional connectivity of the motor cortex.Conclusion: In vivo neurochemistry may represent an effective imaging marker of impaired motor cortex functional connectivity in ALS. [ABSTRACT FROM AUTHOR]

Magnetic Resonance Imaging (MRI) is critical in diagnosing post-operative complications following implant surgery and imaging anatomy adjacent to implants. Increasing field strengths and use of gradient-echo sequences have highlighted difficulties from susceptibility artefacts in scan data. Artefacts manifest around metal implants, including those made from titanium alloys, making detection of complications (e.g. bleeding, infection) difficult and hindering imaging of surrounding structures such as the brain or inner ear. Existing research focusses on post-processing and unorthodox scan sequences to better capture data around these devices. This study proposes a complementary up-stream design approach using lightweight structures produced via additive manufacturing (AM). Strategic implant mass reduction presents a potential tool in managing artefacts. Uniform specimens of Ti-6Al-4V structures, including lattices, were produced using the AM process, selective laser melting, with various unit cell designs and relative densities (3.1%–96.7%). Samples, submerged in water, were imaged in a 3T MRI system using clinically relevant sequences. Artefacts were quantified by image analysis revealing a strong linear relationship (R R2 = 0.99) between severity and relative sample density. Likewise, distortion due to slice selection errors showed a squared relationship (R R2 = 0.92) with sample density. Unique artefact features were identified surrounding honeycomb samples suggesting a complex relationship exists for larger unit cells. To demonstrate clinical utility, a honeycomb design was applied to a representative cranioplasty. Analysis revealed 10% artefact reduction compared to traditional solid material illustrating the feasibility of this approach. This study provides a basis to strategically design implants to reduce MRI artefacts and improve post-operative diagnosis capability. MRI susceptibility artefacts surrounding metal implants present a clinical challenge for the diagnosis of post-operative complications relating to the implant itself or underlying anatomy. In this study for the first time we demonstrate that additive manufacturing may be exploited to create lattice structures that predictably reduce MRI image artefact severity surrounding titanium alloy implants. Specifically, a direct correlation of artefact severity, both total signal loss and distortion, with the relative material density of these functionalised materials has been demonstrated within clinically relevant MRI sequences. This approach opens the door for strategic implant design, utilising this structurally functionalised material, that may improve post-operative patient outcomes and compliments existing efforts in this area which focus on data acquisition and post-processing methods. Image, graphical abstract [ABSTRACT FROM AUTHOR]

Background: Texture analysis (TA) is an image-analysis technique that detects complex intervoxel statistical patterns. 3D TA has shown potential in detecting cerebral degeneration not perceptible to the human eye in many neurological disorders. The reliability of this method's application in a multicenter study is unknown.Purpose: To assess the intrasite and intersite reliability of a novel 3D TA method from data acquired systematically from the Canadian ALS Neuroimaging Consortium (CALSNIC).Study Type: Prospective multicenter data with harmonized MR sequence parameters acquired from five sites.Population: Six healthy subjects.Field Strength: 3T 3D-MPRAGE and 3D-SPGR T1 -weighted MRI of the brain.Assessment: Voxel-based 3D TA was performed on the whole brain to produce texture maps.Statistical Tests: Intra- and intersite reliability of texture features was assessed using a two-way mixed-effects model for intraclass correlation coefficients (ICC). ICCs were calculated in a region-of-interest (ROI) analysis of predetermined anatomically relevant areas. A voxelwise approach was used to assess the whole brain.Results: In the ROI analyses, intrasite reliability was excellent (ICC > 0.75) across most regions and texture features (autocorrelation [autoc], contrast [contr], energy [energ]). Intersite reliability was excellent for most regions with autoc, ranging from fair to excellent for contr, and ICCs ranging from poor to good (<0.40-0.75) for energ. Voxelwise analyses revealed a large range in ICC across the brain for both intrasite and intersite ICCs (0.0-0.90), with higher reliability in the cortical gray matter compared with deeper subcortical structures.Data Conclusion: Overall, the reliability of 3D TA was highly dependent on texture feature, region studied, and method of analysis (ROI or voxelwise). Intrasite reproducibility was good to excellent, and better than intersite. ROI-based analyses present higher reliability in comparison with voxelwise analyses. Autoc has overall excellent reliability. These factors might be considered when designing future 3D TA studies.Level Of Evidence: 2 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2020;51:1200-1209. [ABSTRACT FROM AUTHOR]

Deep tissue injury (DTI) is a severe form of pressure ulcer that originates at the bone-muscle interface. It results from mechanical damage and ischemic injury due to unrelieved pressure. Currently, there are no established clinical methods to detect the formation of DTI. Moreover, despite the many recommended methods for preventing pressure ulcers, none so far has significantly reduced the incidence of DTI. The goal of this study was to assess the effectiveness of a new electrical stimulation-based intervention, termed intermittent electrical stimulation (IES), in ameliorating the factors leading to DTI in individuals with compromised mobility and sensation. Specifically, we sought to determine whether IES-induced contractions in the gluteal muscles can 1) reduce pressure in tissue surrounding bony prominences susceptible to the development of DTI and 2) increase oxygenation in deep tissue. Experiments were conducted in individuals with spinal cord injury, and two paradigms of IES were utilized to induce contractions in the gluteus maximus muscles of the seated participants. Changes in surface pressure around the ischial tuberosities were assessed using a pressure-sensing mattress, and changes in deep tissue oxygenation were indirectly assessed using T2*-weighted magnetic resonance imaging (MRI) techniques. Both IES paradigms significantly reduced pressure around the bony prominences in the buttocks by an average of 10-26% (P < 0.05). Furthermore, both IES paradigms induced significant increases in T2* signal intensity (SI), indicating significant increases in tissue oxygenation, which were sustained for the duration of each 10-min trial (P < 0.05). Maximal increases in SI ranged from 2-3.3% (arbitrary units). Direct measurements of oxygenation in adult rats revealed that IES produces up to a 100% increase in tissue oxygenation. The results suggest that IES directly targets factors contributing to the development of DTI in people with reduced mobility and sensation and may therefore be an effective method for the prevention of deep pressure ulcers. [ABSTRACT FROM AUTHOR]

Amyotrophic Lateral Sclerosis (ALS) is a complex neurodegenerative disorder characterized by motor neuron degeneration. Significant research has begun to establish brain magnetic resonance imaging (MRI) as a potential biomarker to diagnose and monitor the state of the disease. Deep learning has emerged as a prominent class of machine learning algorithms in computer vision and has shown successful applications in various medical image analysis tasks. However, deep learning methods applied to neuroimaging have not achieved superior performance in classifying ALS patients from healthy controls due to insignificant structural changes correlated with pathological features. Thus, a critical challenge in deep models is to identify discriminative features from limited training data. To address this challenge, this study introduces a framework called S F 2 F o r m e r , which leverages the power of the vision transformer architecture to distinguish ALS subjects from the control group by exploiting the long-range relationships among image features. Additionally, spatial and frequency domain information is combined to enhance the network's performance, as MRI scans are initially captured in the frequency domain and then converted to the spatial domain. The proposed framework is trained using a series of consecutive coronal slices and utilizes pre-trained weights from ImageNet through transfer learning. Finally, a majority voting scheme is employed on the coronal slices of each subject to generate the final classification decision. The proposed architecture is extensively evaluated with multi-modal neuroimaging data (i.e., T1-weighted, R2*, FLAIR) using two well-organized versions of the Canadian ALS Neuroimaging Consortium (CALSNIC) multi-center datasets. The experimental results demonstrate the superiority of the proposed strategy in terms of classification accuracy compared to several popular deep learning-based techniques. • We propose a novel vision transformer model to classify ALS from healthy controls. • We analyze two independent and extensive datasets of 120 and 232 MRI scans. • We leverage multi-center and multi-modal neuroimaging data (T1W, R2*, and FLAIR). • The proposed method demonstrates state-of-the-art classification accuracy. [ABSTRACT FROM AUTHOR]

Abstract: We examined age-related changes in the cerebral white matter. Structural magnetic resonance images (MRIs) and diffusion tensor images (DTIs) were acquired from 69 healthy subjects aged 22–84years. Quantitative DTI tractography was performed for nine different white matter tracts to determine tract volume, fractional anisotropy (FA), mean diffusivity (MD), axial, and radial diffusivities. We used automated and manual segmentation to determine volumes of gray matter (GM), white mater (WM), cerebrospinal fluid (CSF), and intracranial space. The results showed significant effects of aging on WM, GM, CSF volumes, and selective effects of aging on structural integrity of different white matter tracts. WM of the prefrontal region was the most vulnerable to aging, while temporal lobe connections, cingulum, and parieto-occipital commissural connections showed relative preservation with age. This study was cross-sectional, and therefore, additional longitudinal studies are needed to confirm our findings. [ABSTRACT FROM AUTHOR]

Abstract: Volumetric changes in the amygdala and hippocampus are relevant to many disorders, but their close proximity makes it difficult to separate these structures by magnetic resonance imaging, leading many volumetric protocols to exclude problematic slices from analysis, or to analyze the amygdalo–hippocampal complex conjointly. The hippocampus tail is also often excluded, because of the difficulty in separating it from the thalamus. We have developed a reliable protocol for volumetric analysis and 3-D reconstruction of the amygdala and hippocampus (as a whole and in its anatomical parts). Twenty volunteers from clinical and healthy populations were recruited. T1-weighted images were acquired at 1.5 Tesla with native spatial resolution of 1.5 mm×1.0 mm×1.0 mm. Volumetric analyses were performed blind to diagnosis, using the interactive software package DISPLAY. Inter-rater (intrarater) intraclass correlations for the method were: 0.95 (0.88) for hippocampus tail, 0.83 (0.93) for hippocampus body, 0.95 (0.92) for hippocampus head, 0.96 (0.86) for total hippocampus and 0.86 (0.94) for amygdala. Volumes (mean±S.D.) corrected for intracranial volume for this mixed group were for the hippocampal tail: 0.325±0.087 cm3; hippocampal body: 0.662±0.120 cm3; hippocampal head: 1.23±0.174 cm3; total hippocampus: 2.218±0.217 cm3, and amygdala: 0.808±0.185 cm3. In conclusion, the study demonstrates that the amygdala and hippocampal parts can be quantified reliably. [Copyright &y& Elsevier]