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Background: A previous study has demonstrated the feasibility of 3D phase-resolved functional lung (PREFUL) MRI in healthy volunteers and patients with chronic pulmonary disease. Before clinical use, the repeatability of the ventilation parameters derived from 3D PREFUL MRI must be determined. Purpose: To evaluate repeatability of 3D PREFUL and to compare with pulmonary functional lung testing (PFT). Study Type: Prospective. Population: Fifty-three healthy subjects and 13 patients with chronic obstructive pulmonary disease (COPD). Field Strength/Sequence: A prototype 3D stack-of-stars spoiled-gradient-echo sequence at 1.5 T. Assessment: Study participants underwent repeated MRI examination (median time interval between scans COPD/healthy subjects [interquartile range]: 7/0 days [6-8/0-0 days]) and one PFT carried out at the time of the baseline MRI. For 3D PREFUL, regional ventilation (RVent) and flow-volume loops were computed and rated by cross-correlation (CC). Also, ventilation time-to-peak (VTTP) was computed. Ventilation defect percentage (VDP) maps were obtained for RVent and CC. Statistical Tests: Repeatability of 3D PREFUL parameters was evaluated using Bland-Altman analysis, coefficient of variation (COV) and intraclass correlation coefficient (ICC). The relation between 3D PREFUL and PFT measures (forced expiratory volume in 1 second (FEV 1 ) and forced vital capacity (FVC) was assessed using the Pearson correlation coefficient (r). Results: In healthy subjects and COPD patients, no significant bias (all P range: 0.09-0.77) and a moderate to good repeatability of RVent, VTTP, and VDP RVent were found (COV range: 0.1%-18.2%, ICC range: 0.51-0.88). For CC and VDP CC moderate repeatability was found (COV range: 0.6%-43.6%, ICC: 0.38-0.60). CC, VDP RVent , and VDP CC showed a good correlation with FEV 1 (all jrj > 0.58, all P < 0.05) and FEV 1 /FVC ratio (all jrj > 0.62, all P < 0.05). Data Conclusion: 3D PREFUL provided a good repeatability of RVent, VTTP, and VDP RVent and moderate repeatability of CC and VDP CC in healthy volunteers and COPD patients, and correlated well with FEV 1 and FEV 1 /FVC. Level of Evidence: 2 Technical Efficacy Stage: 2

Purpose:To reduce acquisition time and improve image quality and robustness of ventilation assessment in a single breath-hold using 1 H-guided reconstruction of fluorinated gas ( 19 F) MRI. Methods: Reconstructions constraining total variation in the image domain, L1 norm in the wavelet domain, and directional total variation between 19 F and 1 H images were compared in order to accelerate 19 F ventilation imaging using retrospectively undersampled data from a healthy volunteer. Using the optimal constrained reconstruction in 8 patients with chronic obstructive pulmonary disease (16-seconds breath-hold), ventilation maps of various acceleration factors (2-fold to 13-fold) were compared with maps of the full data set using the Dice coefficient, difference in volume defect percentage and overlap percentage, as well as hyperpolarized 129 Xe gas MRI. Results: The reconstruction constraining total variation and directional total variation simultaneously performed best in the healthy volunteer (RMS error = 0.07, structural similarity index = 0.77) for a measurement time of 2 seconds. Using the same reconstruction in the patients with chronic obstructive pulmonary disease, the Dice coefficient of defect volumes was 0.86 ± 0.05, the mean difference in volume defect percentage was −1.0 ± 1.7 percentage points, and the overlap percentage was 87% ± 2% for a measurement time of 6 seconds. Between volume defect percentage of 19 F and 129 Xe, a linear correlation (r = 0.75; P = .03) was found, with 19 F volume defect percentage being significantly higher (mean difference = 11%; P = .04). Conclusion: 1 H-guided reconstruction of pulmonary 19 F gas MRI enables reduction of acquisition time while maintaining image quality and robustness of functional parameters.

Purpose To examine the time‐dependent diffusion of fluorinated (19F) gas in human lungs for determination of surface‐to‐volume ratio in comparison to results from hyperpolarized 129Xe and lung function testing in healthy volunteers and patients with chronic obstructive pulmonary disease. Methods Diffusion of fluorinated gas in the short‐time regime was measured using multiple gradient‐echo sequences with a single pair of trapezoidal gradient pulses. Pulmonary surface‐to‐volume ratio was calculated using a first‐order approximation of the time‐dependent diffusion in a study with 20 healthy volunteers and 22 patients with chronic obstructive pulmonary disease. The repeatability after 7 days as well as the correlation with hyperpolarized 129Xe diffusion MRI and lung function testing was analyzed. Results Using 19F diffusion MRI, the median surface‐to‐volume ratio is significantly decreased in chronic obstructive pulmonary disease patients (S/V = 126 cm−1 [87–144 cm−1]) compared with healthy volunteers (S/V = 164 cm−1 [160–84 cm−1], p < 0.0001). No significant difference was found between measurements within 7 days for healthy (p = 0.88, median coefficient of variation = 4.3%) and diseased subjects (p = 0.58, median coefficient of variation= 6.7%). Linear correlations were found with S/V from 129Xe diffusion MRI (r = 0.85, p = 0.001) and the forced expiratory volume in 1 second (r = 0.68, p < 0.0001). Conclusion Examination of lung microstructure using time‐dependent diffusion measurement of inhaled 19F is feasible, repeatable, and correlates with established measurements.

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The serotonin transporter (5-HTT) plays an important role in regulating serotonergic transmission via removal of serotonin (5-HT) from synaptic clefts. Alterations in 5-HTT expression and subsequent 5-HT transmission have been found to be associated with changes in behaviour, such as fearfulness or activity, in humans and other vertebrates. In humans, alterations in 5-HTT expression have been suggested to be able to lead to better learning performance, with more fearful persons being better at learning. Similar effects of the variation in the 5-HTT on fearfulness have been found in chickens, and in this study, we investigated effects on learning. Therefore, we tested 52 adult laying hens, differing in their functional 5-HTT genotype (W/W, W/D and D/D) in an operant learning paradigm in three different phases (initial learning, reversal learning and extinction) and in a tonic immobility test for fearfulness. We found that the 5-HTT polymorphism affects the initial learning performance of laying hens, with homogeneous wild-type (W/W) hens being the slowest learners, and the most fearful birds. W/W hens, showed significantly more choices to solve the initial learning task (LME, p = 0.031) and had the highest latencies in a tonic immobility test (p = 0.039), indicating the highest fearfulness. Our results provide interesting first insights into the role of 5-HTT in chickens and its sensitive interaction with the environment. We further suggest that the 5-HTT gene can be an interesting target gene for future breeding strategies as well as for further experimental studies.

Yellowstone National Park hosts over 10,000 thermal features (e.g. geysers, fumaroles, mud pots, and hot springs), yet little is known about the circulation depth of meteoric water feeding these features, the pathways that guide deep, hot fluids to the surface, or the separation depth of the steam that sources vapor-dominated systems. Previous near-surface geophysical studies have been effective in imaging shallow hydrothermal pathways in some areas of the park, but these methods are difficult to conduct over the large areas needed to characterize entire hydrothermal systems. Transient electromagnetic (TEM) soundings and 2D direct current (DC) resistivity profiles show that hydrothermal fluids at active sites have a higher electrical conductivity than the surrounding hydrothermally inactive areas. For that reason, airborne TEM should be an effective method to characterize large areas and identify hydrothermally active and inactive zones using electrical conductivity.Here we present preliminary results from an airborne transient electromagnetic (TEM) and magnetic survey acquired jointly by the U.S. Geological Survey (USGS) and the University of Wyoming (UW) in November 2016. At the time of this writing, the survey is planned to cover 2600 line-km of data at two scales: regional surveys with lines spaced 450 apart and two smaller, high-resolution surveys with line spacing of 150 m. The regional survey will cover northern Yellowstone Lake, the Norris-Mammoth corridor, and the Upper Geyser basin. The high-resolution surveys focus on the Upper Geyser Basin (including Old Faithful) and the Norris Geyser Basin. Data will be acquired with the SkyTEM 312, with a magnetic moment of 0.5 M A-m2. We will present preliminary inversions using the Aarhus Workbench software, with particular focus on the depths of vapor phase separation and the connectivity of pathways of meteoric water recharge. AbstractThe source of adequate groundwater resources to support community and industry (pastoral and mining) in the arid APY Lands of northern South Australia has been the subject of considerable concern since the establishment of cattle stations and community centres in the early 1900's. Although small, locally confined fractured rock aquifer systems have been defined, finding large sustainable sedimentary alluvial aquifers has been problematic despite numerous drilling campaigns over 60+ years. Challenges to their identification include a complex, apparently compartmentalised sedimentary (regolith) cover sequence, highly varying alluvial aquifer thicknesses, and the paucity of spatial information. The low sporadic rainfall/recharge and high average annual evaporation results in a highly variable groundwater quality adding to the complexity of resource determination.The role of geophysical data in addressing these shortcomings has been the subject of more recent investigation. Local scale exploration airborne EM data sets have highlighted the spatial complexity of the alluvial aquifers in the region. Airborne magnetic data, also acquired...

3D phase-resolved functional lung (3D-PREFUL) proton MRI enables a radiation-free and non-contrast-enhanced ventilation assessment of human lungs. However, generating high-quality images usually requires a long acquisition time. Acceleration can be achieved by undersampling k-space data, but the resulting violation of the Nyquist theorem leads to image artifacts. Deep learning (DL)-based reconstruction approaches are proposed as a solution for this dilemma. Two novel loss functions are introduced to create a deep learning based reconstruction, optimized for lung MRI. The feasibility of ventilation assessment, including ventilation defect identification, from 8x undersampled MR-images of post-COVID-19 patients, reconstructed by a neural network is demonstrated.