Recovering high-quality fiber orientation distributions from a reduced number of diffusion-weighted images using a model-driven deep learning architecture.

PURPOSE: The aim of this study was to develop a model-based deep learning architecture to accurately reconstruct fiber orientation distributions (FODs) from a reduced number of diffusion-weighted images (DWIs), facilitating accurate analysis with reduced acquisition times. METHODS: Our proposed architecture, Spherical Deconvolution Network (SDNet), performed FOD reconstruction by mapping 30 DWIs to fully sampled FODs, which have been fit to 288 DWIs. SDNet included DWI-consistency blocks within the network architecture, and a fixel-classification penalty within the loss function. SDNet was trained on a subset of the Human Connectome Project, and its performance compared with FOD-Net, and multishell multitissue constrained spherical deconvolution. RESULTS: SDNet achieved the strongest results with respect to angular correlation coefficient and sum of squared errors. When the impact of the fixel-classification penalty was increased, we observed an improvement in performance metrics reliant on segmenting the FODs into the correct number of fixels. CONCLUSION: Inclusion of DWI-consistency blocks improved reconstruction performance, and the fixel-classification penalty term offered increased control over the angular separation of fixels in the reconstructed FODs.

Impact of PET Reconstruction on Amyloid-ß Quantitation in Cross-Sectional and Longitudinal Analyses.

Amyloid-ß (Aß) accumulation in Alzheimer disease (AD) is typically measured using SUV ratio and the centiloid (CL) scale. The low spatial resolution of PET images is known to degrade quantitative metrics because of the partial-volume effect. This article examines the impact of spatial resolution, as determined by the reconstruction configuration, on the Aß PET quantitation in both cross-sectional and longitudinal data. Methods: The cross-sectional study involved 89 subjects with 20-min [18F]florbetapir scans generated on an mCT (44 Aß-negative [Aß-], 45 Aß-positive [Aß+]) using 69 reconstruction configurations, which varied in number of iteration updates, point-spread function, time-of-flight, and postreconstruction smoothing. The subjects were classified as Aß- or Aß+ visually. For each reconstruction, Aß CL was calculated using CapAIBL, and the spatial resolution was calculated as full width at half maximum (FWHM) using the barrel phantom method. The change in CLs and the effect size of the difference in CLs between Aß- and Aß+ groups with FWHM were examined. The longitudinal study involved 79 subjects (46 Aß-, 33 Aß+) with three 20-min [18F]flutemetamol scans generated on an mCT. The subjects were classified as Aß- or Aß+ using a cutoff CL of 20. All scans were reconstructed using low-, medium-, and high-resolution configurations, and Aß CLs were calculated using CapAIBL. Since linear Aß accumulation was assumed over a 10-y interval, for each reconstruction configuration, Aß accumulation rate differences (ARDs) between the second and first periods were calculated for all subjects. Zero ARD was used as a consistency metric. The number of Aß accumulators was also used to compare the sensitivity of CL across reconstruction configurations. Results: In the cross-sectional study, CLs in both the Aß- and the Aß+ groups were impacted by the FWHM of the reconstruction method. Without postreconstruction smoothing, Aß- CLs increased for a FWHM of 4.5 mm or more, whereas Aß+ CLs decreased across the FWHM range. High-resolution reconstructions provided the best statistical separation between groups. In the longitudinal study, the median ARD of low-resolution reconstructed data for the Aß- group was greater than zero whereas the ARDs of higher-resolution reconstructions were not significantly different from zero, indicating more consistent rate estimates in the higher-resolution reconstructions. Higher-resolution reconstructions identified 10 additional Aß accumulators in the Aß- group, resulting in a 22% increased group size compared with the low-resolution reconstructions. Higher-resolution reconstructions reduced the average CLs of the negative group by 12 points. Conclusion: High-resolution PET reconstructions, inherently less impacted by partial-volume effect, may improve Aß PET quantitation in both cross-sectional and longitudinal data. In the cross-sectional analysis, separation of CLs between Aß- and Aß+ cohorts increased with spatial resolution. Higher-resolution reconstructions also exhibited both improved consistency and improved sensitivity in measures of Aß accumulation. These features suggest that higher-resolution reconstructions may be advantageous in early-stage AD therapies.

Inter-scanner Aß-PET harmonization using barrel phantom spatial resolution matching.

INTRODUCTION: The standardized uptake value ratio (SUVR) is used to measure amyloid beta-positron emission tomography (Aß-PET) uptake in the brainDifferences in PET scanner technologies and image reconstruction techniques can lead to variability in PET images across scanners. This poses a challenge for Aß-PET studies conducted in multiple centers. The aim of harmonization is to achieve consistent Aß-PET measurements across different scanners. In this study, we propose an Aß-PET harmonization method of matching spatial resolution, as measured via a barrel phantom, across PET scanners. Our approach was validated using paired subject data, for which patients were imaged on multiple scanners. METHODS: In this study, three different PET scanners were evaluated: the Siemens Biograph Vision 600, Siemens Biograph molecular computed tomography (mCT), and Philips Gemini TF64. A total of five, eight, and five subjects were each scanned twice with [18F]-NAV4694 across Vision-mCT, mCT-Philips, and Vision-Philips scanner pairs. The Vision and mCT scans were reconstructed using various iterations, subsets, and post-reconstruction Gaussian smoothing, whereas only one reconstruction configuration was used for the Philips scans. The full-width at half-maximum (FWHM) of each reconstruction configuration was calculated using [18F]-filled barrel phantom scans with the Society of Nuclear Medicine and Molecular Imaging (SNMMI) phantom analysis toolkit. Regional SUVRs were calculated from 72 brain regions using the automated anatomical labelling atlas 3 (AAL3) atlas for each subject and reconstruction configuration. Statistical similarity between SUVRs was assessed using paired (within subject) t-tests for each pair of reconstructions across scanners; the higher the p-value, the greater the similarity between the SUVRs. RESULTS: Vision-mCT harmonization: Vision reconstruction with FWHM = 4.10 mm and mCT reconstruction with FWHM = 4.30 mm gave the maximal statistical similarity (maximum p-value) between regional SUVRs. Philips-mCT harmonization: The FWHM of the Philips reconstruction was 8.2 mm and the mCT reconstruction with the FWHM of 9.35 mm, which gave the maximal statistical similarity between regional SUVRs. Philips-Vision harmonization: The Vision reconstruction with an FWHM of 9.1 mm gave the maximal statistical similarity between regional SUVRs when compared with the Philips reconstruction of 8.2 mm and were selected as the harmonized for each scanner pair. CONCLUSION: Based on data obtained from three sets of participants, each scanned on a pair of PET scanners, it has been verified that using reconstruction configurations that produce matched-barrel, phantom spatial resolutions results in maximally harmonized Aß-PET quantitation between scanner pairs. This finding is encouraging for the use of PET scanners in multi-center trials or updates during longitudinal studies. Highlights: Question: Does the process of matching the barrel phantom-derived spatial resolution between scanners harmonize amyloid beta-standardized uptake value ratio (Aß-SUVR) quantitation? Pertinent findings: It has been validated that reconstruction pairs with matched barrel phantom-derived spatial resolution maximize the similarity between subjects paired Aß-PET (positron emission tomography) SUVR values recorded on two scanners. Implications for patient care: Harmonization between scanners in multi-center trials and PET camera updates in longitudinal studies can be achieved using a simple and efficient phantom measurement procedure, beneficial for the validity of Aß-PET quantitation measurements.

Transfer learning for auto-segmentation of 17 organs-at-risk in the head and neck: Bridging the gap between institutional and public datasets.

BACKGROUND: Auto-segmentation of organs-at-risk (OARs) in the head and neck (HN) on computed tomography (CT) images is a time-consuming component of the radiation therapy pipeline that suffers from inter-observer variability. Deep learning (DL) has shown state-of-the-art results in CT auto-segmentation, with larger and more diverse datasets showing better segmentation performance. Institutional CT auto-segmentation datasets have been small historically (n < 50) due to the time required for manual curation of images and anatomical labels. Recently, large public CT auto-segmentation datasets (n > 1000 aggregated) have become available through online repositories such as The Cancer Imaging Archive. Transfer learning is a technique applied when training samples are scarce, but a large dataset from a closely related domain is available. PURPOSE: The purpose of this study was to investigate whether a large public dataset could be used in place of an institutional dataset (n > 500), or to augment performance via transfer learning, when building HN OAR auto-segmentation models for institutional use. METHODS: Auto-segmentation models were trained on a large public dataset (public models) and a smaller institutional dataset (institutional models). The public models were fine-tuned on the institutional dataset using transfer learning (transfer models). We assessed both public model generalizability and transfer model performance by comparison with institutional models. Additionally, the effect of institutional dataset size on both transfer and institutional models was investigated. All DL models used a high-resolution, two-stage architecture based on the popular 3D U-Net. Model performance was evaluated using five geometric measures: the dice similarity coefficient (DSC), surface DSC, 95th percentile Hausdorff distance, mean surface distance (MSD), and added path length. RESULTS: For a small subset of OARs (left/right optic nerve, spinal cord, left submandibular), the public models performed significantly better (p < 0.05) than, or showed no significant difference to, the institutional models under most of the metrics examined. For the remaining OARs, the public models were inferior to the institutional models, although performance differences were small (DSC ≤ 0.03, MSD < 0.5 mm) for seven OARs (brainstem, left/right lens, left/right parotid, mandible, right submandibular). The transfer models performed significantly better than the institutional models for seven OARs (brainstem, right lens, left/right optic nerve, left/right parotid, spinal cord) with a small margin of improvement (DSC ≤ 0.02, MSD < 0.4 mm). When numbers of institutional training samples were limited, public and transfer models outperformed the institutional models for most OARs (brainstem, left/right lens, left/right optic nerve, left/right parotid, spinal cord, and left/right submandibular). CONCLUSION: Training auto-segmentation models with public data alone was suitable for a small number of OARs. Using only public data incurred a small performance deficit for most other OARs, when compared with institutional data alone, but may be preferable over time-consuming curation of a large institutional dataset. When a large institutional dataset was available, transfer learning with models pretrained on a large public dataset provided a modest performance improvement for several OARs. When numbers of institutional samples were limited, using the public dataset alone, or as a pretrained model, was beneficial for most OARs.

GutMap: A New Interface for Analysing Regional Motility Patterns in ex vivo Mouse Gastrointestinal Preparations.

Different regions of the gastrointestinal tract have specific functions and thus distinct motility patterns. Motility is primarily regulated by the enteric nervous system (ENS), an intrinsic network of neurons located within the gut wall. Under physiological conditions, the ENS is influenced by the central nervous system (CNS). However, by using ex vivo organ bath experiments, ENS regulation of gut motility can also be studied in the absence of CNS influences. The current technique enables the characterisation of small intestinal, caecal, and colonic motility patterns using an ex vivo organ bath and video imaging protocol. This approach is combined with the novel edge detection script GutMap, available in MATLAB, that functions across Windows and Mac platforms. Dissected intestinal segments are cannulated in an organ bath containing physiological saline with a camera mounted overhead. Video recordings of gut contractions are then converted to spatiotemporal heatmaps and analysed using the GutMap software interface. Using data analysed from the heatmaps, parameters of contractile patterns (including contraction propagation frequency and velocity as well as gut diameter) at baseline and in the presence of drugs/treatments/genetic mutations can be compared. Here, we studied motility patterns of female mice at baseline and in the presence of a nitric oxide synthase inhibitor (Nω-Nitro-L-arginine; NOLA) (nitric oxide being the main inhibitory neurotransmitter of gut motility) to showcase the application of GutMap. This technique is suitable for application to a broad range of animal models of clinical disorders to understand underlying biological pathways contributing to gastrointestinal dysfunction. Key features • Enhanced video imaging analysis of gut contractility in rodents using a novel software interface. • New edge detection algorithm to accurately contour curvatures of the gastrointestinal tract. • Allows for output of high-resolution spatiotemporal heatmaps across Windows and Mac platforms. • Edge detection and analysis method makes motility measurements accessible in different gut regions including the caecum and stomach.

Train Smart Study: protocol for a randomised trial investigating the role of exercise training dose on markers of brain health in sedentary middle-aged adults.

INTRODUCTION: Regular aerobic exercise is associated with improved cognitive function, implicating it as a strategy to reduce dementia risk. This is reinforced by the association between greater cardiorespiratory fitness and larger brain volume, superior cognitive performance and lower dementia risk. However, the optimal aerobic exercise dose, namely the intensity and mode of delivery, to improve brain health and lower dementia risk has received less attention. We aim to determine the effect of different doses of aerobic exercise training on markers of brain health in sedentary middle-aged adults, hypothesising that high-intensity interval training (HIIT) will be more beneficial than moderate-intensity continuous training (MICT). METHODS AND ANALYSIS: In this two-group parallel, open-label blinded endpoint randomised trial, 70 sedentary middle-aged (45-65 years) adults will be randomly allocated to one of two 12-week aerobic exercise training interventions matched for total exercise training volume: (1) MICT (n=35) or HIIT (n=35). Participants will perform ~50 min exercise training sessions, 3 days per week, for 12 weeks. The primary outcome will be measured as between-group difference in cardiorespiratory fitness (peak oxygen uptake) change from baseline to the end of training. Secondary outcomes include between-group differences in cognitive function and ultra-high field MRI (7T) measured markers of brain health (brain blood flow, cerebrovascular function, brain volume, white matter microstructural integrity and resting state functional brain activity) changes from baseline to the end of training. ETHICS AND DISSEMINATION: The Victoria University Human Research Ethics Committee (VUHREC) has approved this study (HRE20178), and all protocol modifications will be communicated to the relevant parties (eg, VUHREC, trial registry). Findings from this study will be disseminated via peer-review publications, conference presentations, clinical communications and both mainstream and social media. TRIAL REGISTRATION NUMBER: ANZCTR12621000144819.

Hybrid adiabatic pulse with asymmetry (HAPY): An asymmetric adiabatic pulse with an application in pulsed arterial spin labeling at 7T.

PURPOSE: A new class of asymmetric adiabatic radiofrequency (RF) pulses, Hybrid Adiabatic Pulse with asYmmetry (HAPY), is designed to be used as the labeling pulse for Pulsed Arterial Spin labeling (PASL) at 7T to reduce overall specific absorption rate (SAR) while maintaining high labeling efficiency with B 0 $$ {\mathrm{B}}_0 $$ and B 1 + $$ {\mathrm{B}}_1^{+} $$ inhomogeneities. METHODS: Realistic Δ B 0 $$ \Delta {\mathrm{B}}_0 $$ and B 1 + $$ {\mathrm{B}}_1^{+} $$ distributions were extracted from multiple in vivo scans. The proposed class of asymmetric pulses was parameterized and optimized considering these conditions. Simulation and phantoms experiments were performed to compare the optimized pulses with HS-3, GOIA, and trFOCI pulses. In vivo experiments were conducted to demonstrate the application of HAPY in PICORE PASL at 7T, compared with the GOIA and trFOCI pulses. RESULTS: HAPYs with different amounts of pulse energy reduction are obtained by the proposed optimization framework. Both simulation and phantom experiments demonstrate that HAPY achieves high labeling efficiency and high selectivity along the critical side despite B 0 $$ {\mathrm{B}}_0 $$ off-resonance and low B 1 + $$ {\mathrm{B}}_1^{+} $$ amplitude. In vivo experiments reveal that HAPY is able to generate robust perfusion signal with less overall SAR or shorter pulse repetition time, compared to the GOIA and trFOCI pulses. CONCLUSION: The HAPY class of pulses, obtained via systematic optimization tailored to the application of PASL at 7T, reduces power deposition without affecting labeling efficiency, which provides a prospect of further exploiting the benefits of ultra-high field in ASL.

Mapping the association between tau-PET and Aß-amyloid-PET using deep learning.

In Alzheimer's disease, the molecular pathogenesis of the extracellular Aß-amyloid (Aß) instigation of intracellular tau accumulation is poorly understood. We employed a high-resolution PET scanner, with low detection thresholds, to examine the Aß-tau association using a convolutional neural network (CNN), and compared results to a standard voxel-wise linear analysis. The full range of Aß Centiloid values was highly predicted by the tau topography using the CNN (training R2 = 0.86, validation R2 = 0.75, testing R2 = 0.72). Linear models based on tau-SUVR identified widespread positive correlations between tau accumulation and Aß burden throughout the brain. In contrast, CNN analysis identified focal clusters in the bilateral medial temporal lobes, frontal lobes, precuneus, postcentral gyrus and middle cingulate. At low Aß levels, information from the middle cingulate, frontal lobe and precuneus regions was more predictive of Aß burden, while at high Aß levels, the medial temporal regions were more predictive of Aß burden. The data-driven CNN approach revealed new associations between tau topography and Aß burden.

Long-term structural brain changes in adult rats after mild ischaemic stroke.

Preclinical studies of remote degeneration have largely focused on brain changes over the first few days or weeks after stroke. Accumulating evidence suggests that neurodegeneration occurs in other brain regions remote to the site of infarction for months and even years following ischaemic stroke. Brain atrophy appears to be driven by both axonal degeneration and widespread brain inflammation. The evolution and duration of these changes are increasingly being described in human studies, using advanced brain imaging techniques. Here, we sought to investigate long-term structural brain changes in a model of mild focal ischaemic stroke following injection of endothlin-1 in adult Long-Evans rats (n = 14) compared with sham animals (n = 10), over a clinically relevant time-frame of 48 weeks. Serial structural and diffusion-weighted MRI data were used to assess dynamic volume and white matter trajectories. We observed dynamic regional brain volume changes over the 48 weeks, reflecting both normal changes with age in sham animals and neurodegeneration in regions connected to the infarct following ischaemia. Ipsilesional cortical volume loss peaked at 24 weeks but was less prominent at 36 and 48 weeks. We found significantly reduced fractional anisotropy in both ipsi- and contralesional motor cortex and cingulum bundle regions of infarcted rats (P < 0.05) from 4 to 36 weeks, suggesting ongoing white matter degeneration in tracts connected to but distant from the stroke. We conclude that there is evidence of significant cortical atrophy and white matter degeneration up to 48 weeks following infarct, consistent with enduring, pervasive stroke-related degeneration.

Cortico-cognition coupling in treatment resistant schizophrenia.

BACKGROUND: Brain structural alterations and cognitive dysfunction are independent predictors for poor clinical outcome in schizophrenia, and the associations between these domains remains unclear. We employed a novel, multiblock partial least squares correlation (MB-PLS-C) technique and investigated multivariate cortico-cognitive patterns in patients with treatment-resistant schizophrenia (TRS) and matched healthy controls (HC). METHOD: Forty-one TRS patients (age 38.5 ± 9.1, 30 males (M)), and 45 HC (age 40.2 ± 10.6, 29 M) underwent 3T structural MRI. Volumes of 68 brain regions and seven variables from CANTAB covering memory and executive domains were included. Univariate group differences were assessed, followed by the MB-PLS-C analyses to identify group-specific multivariate patterns of cortico-cognitive coupling. Supplementary three-group analyses, which included 23 non-affected first-degree relatives (NAR), were also conducted. RESULTS: Univariate tests demonstrated that TRS patients showed impairments in all seven cognitive tasks and volume reductions in 12 cortical regions following Bonferroni correction. The MB-PLS-C analyses revealed two significant latent variables (LVs) explaining > 90% of the sum-of-squares variance. LV1 explained 78.86% of the sum-of-squares variance, describing a shared, widespread structure-cognitive pattern relevant to both TRS patients and HCs. In contrast, LV2 (13.47% of sum-of-squares variance explained) appeared specific to TRS and comprised a differential cortico-cognitive pattern including frontal and temporal lobes as well as paired associates learning (PAL) and intra-extra dimensional set shifting (IED). Three-group analyses also identified two significant LVs, with NARs more closely resembling healthy controls than TRS patients. CONCLUSIONS: MB-PLS-C analyses identified multivariate brain structural-cognitive patterns in the latent space that may provide a TRS signature.

The "Spin-3/2 Bloch Equation": System matrix formalism of excitation, relaxation, and off-resonance effects in biological tissue.

PURPOSE: This work proposes "Spin-3/2 Bloch Equation" (SBE), a consolidated formalism for spin-3/2 dynamics in biological environments. The formalism encapsulates excitation, relaxation, and off-resonance with accessible matrix representation for a straightforward implementation with high computational efficiency. THEORY: The SBE is derived using spherical tensor operators to encapsulate the spin-3/2 dynamics in biological systems in a single system matrix, a formalism akin to the well-known Bloch Equations (BE). METHODS: Using the proposed SBE, simulations of three classical 23 Na pulse sequences were performed to demonstrate the versatility and applicability of the model, returning the evolution of the 23 Na spin system during these experiments: soft rectangular and adiabatic inversion recovery (IR) and triple-quantum filtering. IR simulations were compared with two existing spin-3/2 simulators and the adaptive BE as a first-order approximation. RESULTS: The proposed SBE is straightforward to implement and facilitates accurate and fast simulations of the underlying higher order coherence in sodium experiments of biological tissues. SBE simulations and comparison spin-3/2 simulators outperform the BE simulations as expected, with the SBE offering superior computational efficiency achieved by the single system matrix formalism. CONCLUSION: The proposed SBE enables comprehensive and accurate simulations for spin-3/2 systems in biological tissue. With a one-line call to an ordinary differential equation solver, it offers a computationally efficient and accessible method for use in 23 Na pulse sequence design.

Longitudinal hippocampal volumetric changes in mice following brain infarction.

Hippocampal atrophy is increasingly described in many neurodegenerative syndromes in humans, including stroke and vascular cognitive impairment. However, the progression of brain volume changes after stroke in rodent models is poorly characterized. We aimed to monitor hippocampal atrophy occurring in mice up to 48-weeks post-stroke. Male C57BL/6J mice were subjected to an intraluminal filament-induced middle cerebral artery occlusion (MCAO). At baseline, 3-days, and 1-, 4-, 12-, 24-, 36- and 48-weeks post-surgery, we measured sensorimotor behavior and hippocampal volumes from T2-weighted MRI scans. Hippocampal volume-both ipsilateral and contralateral-increased over the life-span of sham-operated mice. In MCAO-subjected mice, different trajectories of ipsilateral hippocampal volume change were observed dependent on whether the hippocampus contained direct infarction, with a decrease in directly infarcted tissue and an increase in non-infarcted tissue. To further investigate these volume changes, neuronal and glial cell densities were assessed in histological brain sections from the subset of MCAO mice lacking hippocampal infarction. Our findings demonstrate previously uncharacterized changes in hippocampal volume and potentially brain parenchymal cell density up to 48-weeks in both sham- and MCAO-operated mice.

Correcting for Non-stationarity in BOLD-fMRI Connectivity Analyses.

In this work fMRI BOLD datasets are shown to contain slice-dependent non-stationarities. A model containing slice-dependent, non-stationary signal power is proposed to address time-varying signal power during BOLD data acquisition. The impact of non-stationary power on functional MRI connectivity is analytically derived, establishing that pairwise connectivity estimates are scaled by a function of the time-varying signal power, with magnitude upper bound by 1, and that the variance of sample correlation is increased, thereby inducing spurious connectivity. Consequently, we make the observation that time-varying power during acquisition of BOLD timeseries has the propensity to diminish connectivity estimates. To ameliorate the impact of non-stationary signal power, a simple correction for slice-dependent non-stationarity is proposed. Our correction is analytically shown to restore both signal stationarity and, subsequently, the integrity of connectivity estimates. Theoretical results are corroborated with empirical evidence demonstrating the utility of our correction. In addition, slice-dependent non-stationary variance is experimentally determined to be optimally characterized by an inverse Gamma distribution. The resulting distribution of a voxel's signal intensity is analytically derived to be a generalized Student's-t distribution, providing support for the Gaussianity assumption typically imposed by fMRI connectivity methods.

QSMART: Quantitative susceptibility mapping artifact reduction technique.

PURPOSE: Quantitative susceptibility mapping (QSM) is a novel MR technique that allows mapping of tissue susceptibility values from MR phase images. QSM is an ill-conditioned inverse problem, and although several methods have been proposed in the field, in the presence of a wide range of susceptibility sources, streaking artifacts appear around high susceptibility regions and contaminate the whole QSM map. QSMART is a post-processing pipeline that uses two-stage parallel inversion to reduce the streaking artifacts and remove banding artifact at the cortical surface and around the vasculature. METHOD: Tissue and vein susceptibility values were separately estimated by generating a mask of vasculature driven from the magnitude data using a Frangi filter. Spatially dependent filtering was used for the background field removal step and the two susceptibility estimates were combined in the final QSM map. QSMART was compared to RESHARP/iLSQR and V-SHARP/iLSQR inversion in a numerical phantom, 7T in vivo single and multiple-orientation scans, 9.4T ex vivo mouse data, and 4.7T in vivo rat brain with induced focal ischemia. RESULTS: Spatially dependent filtering showed better suppression of phase artifacts near cortex compared to RESHARP and V-SHARP, while preserving voxels located within regions of interest without brain edge erosion. QSMART showed successful reduction of streaking artifacts as well as improved contrast between different brain tissues compared to the QSM maps obtained by RESHARP/iLSQR and V-SHARP/iLSQR. CONCLUSION: QSMART can reduce QSM artifacts to enable more robust estimation of susceptibility values in vivo and ex vivo.

A gyrification analysis approach based on Laplace Beltrami eigenfunction level sets.

An accurate measure of the complexity of patterns of cortical folding or gyrification is necessary for understanding normal brain development and neurodevelopmental disorders. Conventional gyrification indices (GIs) are calculated based on surface curvature (curvature-based GI) or an outer hull surface of the cortex (outer surface-based GI). The latter is dependent on the definition of the outer hull surface and a corresponding function between surfaces. In the present study, we propose the Laplace Beltrami-based gyrification index (LB-GI). This is a new curvature-based local GI computed using the first three Laplace Beltrami eigenfunction level sets. As with outer surface-based GI methods, this method is based on the hypothesis that gyrification stems from a flat surface during development. However, instead of quantifying gyrification with reference to corresponding points on an outer hull surface, LB-GI quantifies the gyrification at each point on the cortical surface with reference to their surrounding gyral points, overcoming several shortcomings of existing methods. The LB-GI was applied to investigate the cortical maturation profile of the human brain from preschool to early adulthood using the PING database. The results revealed more detail in patterns of cortical folding than conventional curvature-based methods, especially on frontal and posterior tips of the brain, such as the frontal pole, lateral occipital, lateral cuneus, and lingual. Negative associations of cortical folding with age were observed at cortical regions, including bilateral lingual, lateral occipital, precentral gyrus, postcentral gyrus, and superior frontal gyrus. The results also indicated positive significant associations between age and the LB-GI of bilateral insula, the medial orbitofrontal, frontal pole and rostral anterior cingulate regions. It is anticipated that the LB-GI will be advantageous in providing further insights in the understanding of brain development and degeneration in large clinical neuroimaging studies.

White matter changes following experimental pediatric traumatic brain injury: an advanced diffusion-weighted imaging investigation.

Pediatric traumatic brain injury (pTBI) is a major community health concern. Due to ongoing maturation, injury to the brain at a young age can have devastating consequences in later life. However, how pTBI affects brain development, including white matter maturation, is still poorly understood. Here, we used advanced diffusion weighted imaging (DWI) to assess chronic white matter changes after experimental pTBI. Mice at post-natal day 21 sustained a TBI using the controlled cortical impact model and magnetic resonance imaging (MRI) was performed at 6 months post-injury using a 4.7 T Bruker scanner. Four diffusion shells with 81 directions and b-values of 1000, 3000, 5000, and 7000s/mm2 were acquired and analyzed using MRtrix3 software. Advanced DWI metrics, including fiber density, fiber cross-section and a combined fiber density and cross-section measure, were investigated together with three track-weighted images (TWI): the average pathlength map, mean curvature and the track density image. These advanced metrics were compared to traditional diffusion tensor imaging (DTI) metrics which indicated that TBI injured mice had reduced fractional anisotropy and increased radial diffusivity in the white matter when compared to age-matched sham controls. Consistent with previous findings, fiber density and TWI metrics appeared to be more sensitive to white matter changes than DTI metrics, revealing widespread reductions in fiber density and TWI metrics in pTBI mice compared to sham controls. These results provide additional support for the use of advanced DWI metrics in assessing white matter degeneration following injury and highlight the chronic outcomes that can follow pTBI.

Continuous monitoring of plant sodium transport dynamics using clinical PET.

BACKGROUND: The absorption, translocation, accumulation and excretion of substances are fundamental processes in all organisms including plants, and have been successfully studied using radiotracers labelled with 11C, 13N, 14C and 22Na since 1939. Sodium is one of the most damaging ions to the growth and productivity of crops. Due to the significance of understanding sodium transport in plants, a significant number of studies have been carried out to examine sodium influx, compartmentation, and efflux using 22Na- or 24Na-labeled salts. Notably, however, most of these studies employed destructive methods, which has limited our understanding of sodium flux and distribution characteristics in real time, in live plants. Positron emission tomography (PET) has been used successfully in medical research and diagnosis for decades. Due to its ability to visualise and assess physiological and metabolic function, PET imaging has also begun to be employed in plant research. Here, we report the use of a clinical PET scanner with a 22Na tracer to examine 22Na-influx dynamics in barley plants (Hordeum vulgare L. spp. Vulgare-cultivar Bass) under variable nutrient levels, alterations in the day/night light cycle, and the presence of sodium channel inhibitors. RESULTS: 3D dynamic PET images of whole plants show readily visible 22Na translocation from roots to shoots in each examined plant, with rates influenced by both nutrient status and channel inhibition. PET images show that plants cultivated in low-nutrient media transport more 22Na than plants cultivated in high-nutrient media, and that 22Na uptake is suppressed in the presence of a cation-channel inhibitor. A distinct diurnal pattern of 22Na influx was discernible in curves displaying rates of change of relative radioactivity. Plants were found to absorb more 22Na during the light period, and anticipate the change in the light/dark cycle by adjusting the sodium influx rate downward in the dark period, an effect not previously described experimentally. CONCLUSIONS: We demonstrate the utility of clinical PET/CT scanners for real-time monitoring of the temporal dynamics of sodium transport in plants. The effects of nutrient deprivation and of ion channel inhibition on sodium influx into barley plants are shown in two proof-of-concept experiments, along with the first-ever 3D-imaging of the light and dark sodium uptake cycles in plants. This method carries significant potential for plant biology research and, in particular, in the context of genetic and treatment effects on sodium acquisition and toxicity in plants.

Predicting individual improvement in schizophrenia symptom severity at 1-year follow-up: Comparison of connectomic, structural, and clinical predictors.

In a machine learning setting, this study aims to compare the prognostic utility of connectomic, brain structural, and clinical/demographic predictors of individual change in symptom severity in individuals with schizophrenia. Symptom severity at baseline and 1-year follow-up was assessed in 30 individuals with a schizophrenia-spectrum disorder using the Brief Psychiatric Rating Scale. Structural and functional neuroimaging was acquired in all individuals at baseline. Machine learning classifiers were trained to predict whether individuals improved or worsened with respect to positive, negative, and overall symptom severity. Classifiers were trained using various combinations of predictors, including regional cortical thickness and gray matter volume, static and dynamic resting-state connectivity, and/or baseline clinical and demographic variables. Relative change in overall symptom severity between baseline and 1-year follow-up varied markedly among individuals (interquartile range: 55%). Dynamic resting-state connectivity measured within the default-mode network was the most accurate single predictor of change in positive (accuracy: 87%), negative (83%), and overall symptom severity (77%) at follow-up. Incorporating predictors based on regional cortical thickness, gray matter volume, and baseline clinical variables did not markedly improve prediction accuracy and the prognostic utility of these predictors in isolation was moderate (<70%). Worsening negative symptoms at 1-year follow-up were predicted by hyper-connectivity and hypo-dynamism within the default-mode network at baseline assessment, while hypo-connectivity and hyper-dynamism predicted worsening positive symptoms. Given the modest sample size investigated, we recommend giving precedence to the relative ranking of the predictors investigated in this study, rather than the prediction accuracy estimates.

Microstructural correlates of 23Na relaxation in human brain at 7 Tesla.

23Na provides the second strongest MR-observable signal in biological tissue and exhibits bi-exponential T2∗ relaxation in micro-environments such as the brain. There is significant interest in developing 23Na biomarkers for neurological diseases that are associated with sodium channel dysfunction such as multiple sclerosis and epilepsy. We have previously reported methods for acquisition of multi-echo sodium MRI and continuous distribution modelling of sodium relaxation properties as surrogate markers of brain microstructure. This study aimed to compare 23Na T2∗ relaxation times to more established measures of tissue microstructure derived from advanced diffusion MRI at 7 â€‹T. Six healthy volunteers were scanned using a 3D multi-echo radial ultra-short TE sequence using a dual-tuned 1H/23Na birdcage coil, and a high-resolution multi-shell, high angular resolution diffusion imaging sequence using a 32-channel 1H receive coil. 23Na T2∗ relaxation parameters [mean T2∗ (T2∗mean) and fast relaxation fraction (T2∗ff)] were calculated from a voxel-wise continuous gamma distribution signal model. White matter (restricted anisotropic diffusion) and grey matter (restricted isotropic diffusion) density were calculated from multi-shell multi-tissue constrained spherical deconvolution. Sodium parameters were compared with white and grey matter diffusion properties. Sodium T2∗mean and T2∗ff showed little variation across a range of white matter axonal fibre and grey matter densities. We conclude that sodium T2∗ relaxation parameters are not greatly influenced by relative differences in intra- and extracellular partial volumes. We suggest that care be taken when interpreting sodium relaxation changes in terms of tissue microstructure in healthy tissue.

Compressed sensing effects on quantitative analysis of undersampled human brain sodium MRI.

PURPOSE: The clinical application of sodium MRI is hampered due to relatively low image quality and associated long acquisition times. Compressed sensing (CS) aims at a reduction of measurement time, but has been found to encompass quantitative estimation bias when used in low SNR x-Nuclei imaging. This work analyses CS in quantitative human brain sodium MRI from undersampled acquisitions and provides recommendations for tissue sodium concentration (TSC) estimation. METHODS: CS reconstructions from 3D radial acquisitions of 5 healthy volunteers were investigated over varying undersampling factors (USFs) and CS penalty weights on different sparsity domains, Wavelet, Discrete Cosine Transform (DCT), and Identity. Resulting images were compared with highly sampled and undersampled NUFFT-based images and evaluated for image quality (i.e. structural similarity), image intensity bias, and its effect on TSC estimates in gray and white matter. RESULTS: Wavelet-based CS reconstructions show highest image quality with stable TSC estimates for most USFs. Up to an USF of 4, images showed good structural detail. DCT and Identity-based CS enable good image quality, however show a bias in TSC with a reduction in estimates across USFs. CONCLUSIONS: The image intensity bias is lowest in Wavelet-based reconstructions and enables an up to fourfold acquisition speed up while maintaining good structural detail. The associated acquisition time reduction can facilitate a translation of sodium MRI into clinical routine.