Quantitative 23 Na-MRI of the intervertebral disk at 3 T.

Monitoring the tissue sodium content (TSC) in the intervertebral disk geometry noninvasively by MRI is a sensitive measure to estimate changes in the proteoglycan content of the intervertebral disk, which is a biomarker of degenerative disk disease (DDD) and of lumbar back pain (LBP). However, application of quantitative sodium concentration measurements in 23 Na-MRI is highly challenging due to the lower in vivo concentrations and smaller gyromagnetic ratio, ultimately yielding much smaller signal relative to 1 H-MRI. Moreover, imaging the intervertebral disk geometry imposes higher demands, mainly because the necessary RF volume coils produce highly inhomogeneous transmit field patterns. For an accurate absolute quantification of TSC in the intervertebral disks, the B1 field variations have to be mitigated. In this study, we report for the first time quantitative sodium concentration in the intervertebral disks at clinical field strengths (3 T) by deploying 23 Na-MRI in healthy human subjects. The sodium B1 maps were calculated by using the double-angle method and a double-tuned (1 H/23 Na) transceive chest coil, and the individual effects of the variation in the B1 field patterns in tissue sodium quantification were calculated. Phantom measurements were conducted to evaluate the quality of the Na-weighted images and B1 mapping. Depending on the disk position, the sodium concentration was calculated as 161.6 mmol/L-347 mmol/L, and the mean sodium concentration of the intervertebral disks varies between 254.6 ± 54 mmol/L and 290.1 ± 39 mmol/L. A smoothing effect of the B1 correction on the sodium concentration maps was observed, such that the standard deviation of the mean sodium concentration was significantly reduced with B1 mitigation. The results of this work provide an improved integration of quantitative 23 Na-MRI into clinical studies in intervertebral disks such as degenerative disk disease and establish alternative scoring schemes to existing morphological scoring such as the Pfirrmann score.

Two-dimensional video-based analysis of human gait using pose estimation.

Human gait analysis is often conducted in clinical and basic research, but many common approaches (e.g., three-dimensional motion capture, wearables) are expensive, immobile, data-limited, and require expertise. Recent advances in video-based pose estimation suggest potential for gait analysis using two-dimensional video collected from readily accessible devices (e.g., smartphones). To date, several studies have extracted features of human gait using markerless pose estimation. However, we currently lack evaluation of video-based approaches using a dataset of human gait for a wide range of gait parameters on a stride-by-stride basis and a workflow for performing gait analysis from video. Here, we compared spatiotemporal and sagittal kinematic gait parameters measured with OpenPose (open-source video-based human pose estimation) against simultaneously recorded three-dimensional motion capture from overground walking of healthy adults. When assessing all individual steps in the walking bouts, we observed mean absolute errors between motion capture and OpenPose of 0.02 s for temporal gait parameters (i.e., step time, stance time, swing time and double support time) and 0.049 m for step lengths. Accuracy improved when spatiotemporal gait parameters were calculated as individual participant mean values: mean absolute error was 0.01 s for temporal gait parameters and 0.018 m for step lengths. The greatest difference in gait speed between motion capture and OpenPose was less than 0.10 m s-1. Mean absolute error of sagittal plane hip, knee and ankle angles between motion capture and OpenPose were 4.0°, 5.6° and 7.4°. Our analysis workflow is freely available, involves minimal user input, and does not require prior gait analysis expertise. Finally, we offer suggestions and considerations for future applications of pose estimation for human gait analysis.

Fully automatic classification of automated breast ultrasound (ABUS) imaging according to BI-RADS using a deep convolutional neural network.

PURPOSE: The aim of this study was to develop and test a post-processing technique for detection and classification of lesions according to the BI-RADS atlas in automated breast ultrasound (ABUS) based on deep convolutional neural networks (dCNNs). METHODS AND MATERIALS: In this retrospective study, 645 ABUS datasets from 113 patients were included; 55 patients had lesions classified as high malignancy probability. Lesions were categorized in BI-RADS 2 (no suspicion of malignancy), BI-RADS 3 (probability of malignancy < 3%), and BI-RADS 4/5 (probability of malignancy > 3%). A deep convolutional neural network was trained after data augmentation with images of lesions and normal breast tissue, and a sliding-window approach for lesion detection was implemented. The algorithm was applied to a test dataset containing 128 images and performance was compared with readings of 2 experienced radiologists. RESULTS: Results of calculations performed on single images showed accuracy of 79.7% and AUC of 0.91 [95% CI: 0.85-0.96] in categorization according to BI-RADS. Moderate agreement between dCNN and ground truth has been achieved (κ: 0.57 [95% CI: 0.50-0.64]) what is comparable with human readers. Analysis of whole dataset improved categorization accuracy to 90.9% and AUC of 0.91 [95% CI: 0.77-1.00], while achieving almost perfect agreement with ground truth (κ: 0.82 [95% CI: 0.69-0.95]), performing on par with human readers. Furthermore, the object localization technique allowed the detection of lesion position slice-wise. CONCLUSIONS: Our results show that a dCNN can be trained to detect and distinguish lesions in ABUS according to the BI-RADS classification with similar accuracy as experienced radiologists. KEY POINTS: • A deep convolutional neural network (dCNN) was trained for classification of ABUS lesions according to the BI-RADS atlas. • A sliding-window approach allows accurate automatic detection and classification of lesions in ABUS examinations.

23 Na-T1 quantification with saturation recovery TrueFISP and variable flip angle GRE at 3T: A phantom study.

PURPOSE: The aim of the current study was to compare the reproducibility of sodium (23 Na)-T1 estimation using a centric-reordered saturation recovery (SR) true fast imaging with steady-state precession (TrueFISP) and a variable flip angle (VFA) spoiled gradient echo (GRE). Additionally, we evaluated the effect of spatial averaging on 23 Na-T1 estimation by the two methods. METHODS: Measurements were performed in the phantom, consisting of 10 dm3 volume rectangular polyethylene container filled with distilled water solution of 0.6% NaCl + 0.004% CuSO4 , using a dual-tunable 23 Na/1 H coil at 3 Tesla. 23 Na images were acquired for FOV = 384 × 384 mm2 and voxel size = 6 × 6 × 6 mm3 using: (1) TrueFISP: TR/TE = 900/1.5 ms, flip angle = 90°, bandwidth = 450 Hz/px, and (2) GRE: TR/TE = 30/1.5 ms, bandwidth = 350 Hz/px. 23 Na-T1 weightings were obtained with nonselective saturation prepulses delayed from the center of the k-space acquisition by 25/40/60/130/280 ms (SR-TrueFISP) and by applying different nominal flip angles: 10°/30°/50°/70°/90° (VFA-GRE). Both sequences were acquired twice, applying 20 and 30 spatial averages. The resulting images were B1 -corrected with a double-angle GRE method. RESULTS: Image acquisition varied from 5:41 to 9:37 for TrueFISP and from 12:48 to 19:12 min for GRE using 20 and 30 spatial averages, respectively. Higher averaging increased the acquisition time by 53% and mean SNR at scan < 10%, without an effect on 23 Na-T1 estimations with both methods (SR-Truefisp |Δ| = 1.58 ms, VFA-GRE |Δ| = 0.53 ms; for SNR P < .001). Overall, mean ± SD of 23 Na-T1 was found as 51 ± 3 ms with SR-TrueFISP and 53 ± 2 ms with VFA-GRE. CONCLUSION: Both SR-TrueFISP and VFA-GRE provided similar 23 Na-T1 estimates based on the phantom measurements with isotropic resolution.

Quantification of sodium T1 in abdominal tissues at 3 T.

INTRODUCTION: Although relevant for assessment of sodium in multiple endocrine pathways, 23Na-T1 quantification is challenging due to technical limitations (SAR, B1 inhomogeneity) or influence of tissue's local molecular dynamics. Hereby, we propose T1 quantification of 23Na-MRI signal acquired over the abdomen using a centric-reordered saturation-recovery (SR) true fast imaging with steady state precession (TrueFISP) sequence. MATERIALS AND METHODS: Measurements were performed at 3T using a dual-tunable 23Na/1H coil in 7 healthy volunteers (TR/TE = 858-928/1.57 ms; flip angle = 90°; bandwidth = 450 Hz/px; voxel size = 5 × 5 × 10 mm3). Variable T1-weighting was achieved applying non-selective saturation pre-pulses delayed from the centre of the k-space acquisition by 25, 40, 60, 120 and 250 ms. T1-curve fitting was performed slice-wise, separately for average intensity values from the manually segmented areas of the renal parenchyma and spinal canal, over the increasing SR times- assuming monoexponential signal pattern. RESULTS: Mean ± standard deviation of 23Na-T1 was found as 29 ± 10 ms and 35 ± 8 ms for the renal parenchyma and the spinal canal, respectively. DISCUSSION: 23Na-T1 quantification using a SR-TrueFISP is feasible in clinical settings, in the images constrained by clinically applicable acquisition time of reduced spatial resolution or averages.

Effects of short-term continuous positive airway pressure withdrawal on cerebral vascular reactivity measured by blood oxygen level-dependent magnetic resonance imaging in obstructive sleep apnoea: a randomised controlled trial.

Impaired cerebral vascular reactivity (CVR) increases long-term stroke risk. Obstructive sleep apnoea (OSA) is associated with peripheral vascular dysfunction and vascular events. The aim of this trial was to evaluate the effect of continuous positive airway pressure (CPAP) withdrawal on CVR.41 OSA patients (88% male, mean age 57±10 years) were randomised to either subtherapeutic or continuation of therapeutic CPAP. At baseline and after 2 weeks, patients underwent a sleep study and magnetic resonance imaging (MRI). CVR was estimated by quantifying the blood oxygen level-dependent (BOLD) MRI response to breathing stimuli.OSA did recur in the subtherapeutic CPAP group (mean treatment effect apnoea-hypopnoea index +38.0 events·h-1, 95% CI 24.2-52.0; p<0.001) but remained controlled in the therapeutic group. Although there was a significant increase in blood pressure upon CPAP withdrawal (mean treatment effect +9.37 mmHg, 95% CI 1.36-17.39; p=0.023), there was no significant effect of CPAP withdrawal on CVR assessed via BOLD MRI under either hyperoxic or hypercapnic conditions.Short-term CPAP withdrawal did not result in statistically significant changes in CVR as assessed by functional MRI, despite the recurrence of OSA. We thus conclude that, unlike peripheral endothelial function, CVR is not affected by short-term CPAP withdrawal.

Diffusion tensor imaging of the abdominal organs: Influence of oriented intravoxel flow compartments.

Water flow in partially oriented intravoxel compartments mimics an anisotropic fast-diffusion regime, which contributes to the signal attenuation in diffusion-weighted images. In the abdominal organs, this flow may reflect physiological fluid movements (eg, tubular urine flow in kidneys, or bile flow through the liver) and have a clinical relevance. This study investigated the influence of anisotropic intravoxel water flow on diffusion tensor imaging (DTI) of the abdominal organs. Diffusion-weighted images were acquired in five healthy volunteers using an EPI sequence with diffusion preparation (TR/TE: 1000 ms/71 ms; b-values: 0, 10, 20, 40, 70, 120, 250, 450, 700, 1000 s/mm2 ; 12 noncollinear diffusion-encoding directions). DTI of liver and kidneys was performed assuming (i) monoexponential decay of the diffusion-weighted signal, and (ii) accounting for potential anisotropy of the fast-diffusion compartments using a tensorial generalization of the IVIM model. Additionally, potential dependency of the metrics of the tensors from the anatomical location was evaluated. Significant differences in the metrics of the diffusion tensor (DT) were found in both liver and kidneys when comparing the two models. In both organs, the trace and the fractional anisotropy of the DT were significantly higher in the monoexponential model than when accounting for perfusion. The comparison of areas of the liver proximal to the hilum with distal regions and of renal cortex with the medulla also proved a location dependency of the size of the fast-diffusion compartments. Pseudo-diffusion correction in DTI enables the assessment of the solid parenchyma regardless of the organ perfusion or other pseudo-diffusive fluid movements. This may have a clinical relevance in the assessment of parenchymal pathologies (eg, liver fibrosis). The fast pseudo-diffusion components present a detectable anisotropy, which may reflect the hepatic microcirculation or other sources of mesoscopic fluid movement in the abdominal organs.

Automatic classification of ultrasound breast lesions using a deep convolutional neural network mimicking human decision-making.

OBJECTIVES: To evaluate a deep convolutional neural network (dCNN) for detection, highlighting, and classification of ultrasound (US) breast lesions mimicking human decision-making according to the Breast Imaging Reporting and Data System (BI-RADS). METHODS AND MATERIALS: One thousand nineteen breast ultrasound images from 582 patients (age 56.3 ± 11.5 years) were linked to the corresponding radiological report. Lesions were categorized into the following classes: no tissue, normal breast tissue, BI-RADS 2 (cysts, lymph nodes), BI-RADS 3 (non-cystic mass), and BI-RADS 4-5 (suspicious). To test the accuracy of the dCNN, one internal dataset (101 images) and one external test dataset (43 images) were evaluated by the dCNN and two independent readers. Radiological reports, histopathological results, and follow-up examinations served as reference. The performances of the dCNN and the humans were quantified in terms of classification accuracies and receiver operating characteristic (ROC) curves. RESULTS: In the internal test dataset, the classification accuracy of the dCNN differentiating BI-RADS 2 from BI-RADS 3-5 lesions was 87.1% (external 93.0%) compared with that of human readers with 79.2 ± 1.9% (external 95.3 ± 2.3%). For the classification of BI-RADS 2-3 versus BI-RADS 4-5, the dCNN reached a classification accuracy of 93.1% (external 95.3%), whereas the classification accuracy of humans yielded 91.6 ± 5.4% (external 94.1 ± 1.2%). The AUC on the internal dataset was 83.8 (external 96.7) for the dCNN and 84.6 ± 2.3 (external 90.9 ± 2.9) for the humans. CONCLUSION: dCNNs may be used to mimic human decision-making in the evaluation of single US images of breast lesion according to the BI-RADS catalog. The technique reaches high accuracies and may serve for standardization of highly observer-dependent US assessment. KEY POINTS: • Deep convolutional neural networks could be used to classify US breast lesions. • The implemented dCNN with its sliding window approach reaches high accuracies in the classification of US breast lesions. • Deep convolutional neural networks may serve for standardization in US BI-RADS classification.

Impact of hypertension on cerebral microvascular structure in CPAP-treated obstructive sleep apnoea patients: a diffusion magnetic resonance imaging study.

PURPOSE: Obstructive sleep apnoea (OSA) is a highly prevalent sleep-related breathing disorder associated with hypertension, impaired peripheral vascular function and an increased risk of stroke. Evidence suggests that abnormalities of the cerebral microcirculation, such as capillary rarefication, may be present in these patients. We evaluated whether the presence of hypertension may affect the cerebral capillary architecture and function assessed by Intravoxel Incoherent Motion (IVIM) magnetic resonance imaging (MRI) in patients with continuous positive airway pressure (CPAP)-treated OSA. METHODS: Forty-one patients (88% male, mean age 57 ± 10 years) with moderate-to-severe OSA were selected and divided into two groups (normotensive vs. hypertensive). All hypertensive OSA patients were adherent with their antihypertensive medication. Cerebral microvascular structure was assessed in grey (GM) and white matter (WM) using an echo-planar diffusion imaging sequence with 14 different b values. A step-wise IVIM analysis algorithm was applied to compute true diffusion (D), perfusion fraction (f) and pseudo-diffusion (D*) values. Group comparisons were performed with the Wilcoxon-Mann-Whitney-Test. Regression analysis was adjusted for age. RESULTS: Diffusion- and perfusion-related indexes in middle-aged OSA normotensive patients were quantified in both tissue types (D [10-3 mm2/s]: GM = 0.83 ± 0.03; WM = 0.72 ± 0.03; f (%) GM = 0.09 ± 0.01; WM = 0.06 ± 0.01; D* [10-3 mm2/s]: GM = 7.72 ± 0.89; WM = 7.38 ± 0.98). In the examined tissue types, hypertension did not result in changes on the estimated MRI IVIM index values. CONCLUSION: Based on IVIM analysis, cerebral microvascular structure and function showed no difference between hypertensive and normotensive patients with moderate-to-severe OSA treated with CPAP. Treatment adherence with antihypertensive drug regime and, in turn, controlled hypertension seems not to affect microvascular structure and perfusion of the brain. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT02493673.

Dependency of the blood oxygen level dependent-response to hyperoxic challenges on the order of gas administration in intracranial malignancies.

PURPOSE: Literature reports contradicting results on the response of brain tumors to vascular stimuli measured in T2*-weighted MRI. Here, we analyzed the potential dependency of the MRI-response to (hypercapnic) hyperoxia on the order of the gas administration. METHODS: T2* values were quantified at 3 Tesla in eight consenting patients at rest and during inhalation of hyperoxic/hypercapnic gas mixtures. Patients were randomly divided into two groups undergoing different gas administration protocols (group A: medical air-pure oxygen-carbogen; group B: medical air-carbogen-pure oxygen). Mann-Whitney U test and Wilcoxon signed rank test have been used to proof differences in T2* regarding respiratory challenge or different groups, respectively. RESULTS: T2* values at rest for gray and white matter were 50.3 ± 2.6 ms and 46.1 ± 2.0 ms, respectively, and slightly increased during challenge. In tumor areas, T2* at rest were: necrosis = 74.1 ± 10.1 ms; edema = 60.3 ± 17.6 ms; contrast-enhancing lesions = 48.6 ± 20.7 ms; and solid T2-hyperintense lesions = 45.0 ± 3.0 ms. Contrast-enhancing lesions strongly responded to oxygen (+ 20.7%) regardless on the gas protocol (p = 0.482). However, the response to carbogen significantly depended on the order of gas administration (group A, + 18.6%; group B, - 6.4%, p = 0.042). In edemas, a different trend between group was found when breathing oxygen (group A, - 9.9%; group B, + 19.5%, p = 0.057). CONCLUSION: Preliminary results show a dependency of the T2* response of contrast-enhancing brain tumor lesions on the order of the gas administration. The gas administration protocol is an important factor in the interpretation of the T2*-response in areas of abnormal vascular growth.

Investigation of the pulsatility of cerebrospinal fluid using cardiac-gated Intravoxel Incoherent Motion imaging.

The quantitative and non-invasive monitoring of cerebrospinal fluid (CSF) dynamics and composition may have high clinical relevance in the management of CSF disorders. In this study, we propose the use of the Intravoxel Incoherent Motion (IVIM) MRI for obtaining simultaneous measurements of CSF self-diffusion and fluid circulation. The rationale for this study was that turbulent fluid and mesoscopic fluid fluctuations can be modeled in a first approximation as a fast diffusion process. In this case, we expect that the fast fluid circulation and slower molecular diffusion dynamics can be quantified, assuming a bi-exponential attenuation pattern of the diffusion-weighted signal in MRI. IVIM indexes of fast and slow diffusion measured at different sites of the CSF system were systematically evaluated depending on both the phase of the heart cycle and the direction of the diffusion-encoding. The IVIM measurements were compared to dynamic measurements of fluid circulation performed by phase-contrast MRI. Concerning the dependence on the diffusion/flow-encoding direction, similar patterns were found both in the fraction of fast diffusion, f, and in the fluid velocity. Generally, we observed a moderate to high correlation between the fraction of fast diffusion and the maximum fluid velocity along the high-flow directions. Exploratory data analysis detected similarities in the dependency of the fraction of fast diffusion and of the velocity from the phase of the cardiac cycle. However, no significant differences were found between parameters measured during different phases of the cardiac cycle. Our results suggest that the fraction of fast diffusion may reflect CSF circulation. The bi-exponential IVIM model potentially allows us to disentangle the two diffusion components of the CSF dynamics by providing measurements of fluid cellularity (via the slow-diffusion coefficient) and circulation (via the fraction of fast-diffusion index).

Automated pixel-wise brain tissue segmentation of diffusion-weighted images via machine learning.

The diffusion-weighted (DW) MR signal sampled over a wide range of b-values potentially allows for tissue differentiation in terms of cellularity, microstructure, perfusion, and T2 relaxivity. This study aimed to implement a machine learning algorithm for automatic brain tissue segmentation from DW-MRI datasets, and to determine the optimal sub-set of features for accurate segmentation. DWI was performed at 3 T in eight healthy volunteers using 15 b-values and 20 diffusion-encoding directions. The pixel-wise signal attenuation, as well as the trace and fractional anisotropy (FA) of the diffusion tensor, were used as features to train a support vector machine classifier for gray matter, white matter, and cerebrospinal fluid classes. The datasets of two volunteers were used for validation. For each subject, tissue classification was also performed on 3D T1 -weighted data sets with a probabilistic framework. Confusion matrices were generated for quantitative assessment of image classification accuracy in comparison with the reference method. DWI-based tissue segmentation resulted in an accuracy of 82.1% on the validation dataset and of 82.2% on the training dataset, excluding relevant model over-fitting. A mean Dice coefficient (DSC) of 0.79 ± 0.08 was found. About 50% of the classification performance was attributable to five features (i.e. signal measured at b-values of 5/10/500/1200 s/mm2 and the FA). This reduced set of features led to almost identical performances for the validation (82.2%) and the training (81.4%) datasets (DSC = 0.79 ± 0.08). Machine learning techniques applied to DWI data allow for accurate brain tissue segmentation based on both morphological and functional information.

Cardiac-gated intravoxel incoherent motion diffusion-weighted magnetic resonance imaging for the investigation of intracranial cerebrospinal fluid dynamics in the lateral ventricle: a feasibility study.

PURPOSE: Intravoxel incoherent motion (IVIM) in diffusion-weighted magnetic resonance imaging (DW-MRI) attributes the signal attenuation to the molecular diffusion and to a faster pseudo-diffusion. Purpose of the study was to demonstrate the feasibility of IVIM for the investigation of intracranial cerebrospinal fluid (CSF) dynamics. METHODS: Cardiac-gated DW-MRI images with fifteen b-values (0-1300s/mm2) along three orthogonal directions (mediolateral (ML), anteroposterior (AP), and craniocaudal (CC)) were acquired during maximum systole and diastole in 10 healthy volunteers (6 males, mean age 36 ± 15 years). A pixel-wise bi-exponential fitting with an iterative nonparametric algorithm was carried out to calculate the following parameters: diffusion coefficient (D), fast diffusion coefficient (D*), and fraction of fast diffusion (f). Region of interest measurements were performed in both lateral ventricles. Comparison of IVIM parameters was performed among two cardiac cycle acquisitions and among the diffusion-encoding directions using a paired Student's t test. RESULTS: f significantly (p < 0.05) depended on the diffusion-encoding direction and on the cardiac cycle (diastole AP 0.30 ± 0.13, ML 0.22 ± 0.12, CC 0.26 ± 0.17; systole AP 0.45 ± 0.17, ML 0.34 ± 0.15, CC 0.40 ± 0.21). Neither a cardiac cycle nor a direction dependency was found among mean D values (which is in line with the expected intraventricular isotropic diffusion) and D* values (p > 0.05 each). CONCLUSION: The fraction of fast diffusion from IVIM is feasible to detect a direction-dependent and cardiac-dependent pulsatile CSF flow within the lateral ventricles allowing for quantitative monitoring of CSF dynamics. This technique might provide opportunities to further investigate the pathophysiology of various neurological disorders involving altered CSF dynamics.

The IVIM signal in the healthy cerebral gray matter: A play of spherical and non-spherical components.

The intra-voxel incoherent motion (IVIM) model assumes that blood flowing in isotropically distributed capillary segments induces a phase dispersion of the MR signal, which increases the signal attenuation in diffusion-weighted images. However, in most tissue types the capillary network has an anisotropic micro-architecture. In this study, we investigated the possibility to indirectly infer the anisotropy of the capillary network in the healthy cerebral gray matter by evaluating the dependence of the IVIM signal from the direction of the diffusion-encoding. Perfusion-related indices and self-diffusion were modelled as symmetric rank 2 tensors. The geometry of the tensors was quantified pixel-wise by decomposing the tensor in sphere-like, plane-like, and line-like components. Additionally, trace and fractional anisotropy of the tensors were computed. While the self-diffusion tensor is dominated by a spherical geometry with a residual contribution of the non-spherical components, both, fraction of perfusion and pseudo-diffusion, present a substantial (in the order of 30%) contribution of planar and linear components to the tensor metrics. This study shows that the IVIM perfusion estimates in the cerebral gray matter present a detectable deviation from the spherical model. These non-spherical components may reflect the direction-dependent morphology of the microcirculation. Therefore, the tensor generalization of the IVIM model may provide a tool for the non-invasive monitoring of cerebral capillary micro-architecture during development, aging or in pathologies.

Probing neuronal activation by functional quantitative susceptibility mapping under a visual paradigm: A group level comparison with BOLD fMRI and PET.

Dynamic changes of brain-tissue magnetic susceptibility provide the basis for functional MR imaging (fMRI) via T2*-weighted signal-intensity modulations. Promising initial work on a detection of neuronal activity via quantitative susceptibility mapping (fQSM) has been published but consistently reported on ill-understood positive and negative activation patterns (Balla et al., 2014; Chen and Calhoun, 2015a). We set out to (i) demonstrate that fQSM can exploit established fMRI data acquisition and processing methods and to (ii) better describe aspects of the apparent activation patterns using fMRI and PET as standards of reference. Under a standardized visual-stimulation paradigm PET and 3-T gradient-echo EPI-based fQSM, fMRI data from 9 healthy volunteers were acquired and analyzed by means of Independent Component Analysis (ICA) at subject level and, for the first time, at group level. Numbers of activated (z-score>2.0) voxels were counted and their mean z-scores calculated in volumes of interest (occipital lobe (Nocc_lobe), segmented occipital gray-matter (NGM_occ_lobe), large veins (Nveins)), and in occipital-lobe voxels commonly activated in fQSM and fMRI component maps. Common but not entirely congruent regions of apparent activation were found in the occipital lobe in z-score maps from all modalities, fQSM, fMRI and PET, with distinct BOLD-negatively correlated regions in fQSM data. At subject-level, Nocc_lobe, NGM_occ_lobe and their mean z-scores were significantly smaller in fQSM than in fMRI, but their ratio, NGM_occ_lobe/Nocc_lobe, was comparable. Nveins did not statistically differ and the ratio Nveins/NGM_occ_lobe as well as the mean z-scores were higher for fQSM than for fMRI. In veins and immediate vicinity, z-score maps derived from both phase and fQSM-data showed positive and negative lobes resembling dipole shapes in simulated field and phase maps with no correlate in fMRI or PET data. Our results show that standard fMRI tools can directly be used for fQSM processing, and suggest that fQSM may have the potential to detect gray-matter activation distant from large veins, to improve detection of veins with stimulus-induced venous oxygen saturation (SvO2) variations, and to better localize areas of activation. However, our results seem to clearly expose issues that phenomenologically resemble an incomplete dipolar inversion and that need to be subject to further investigation.

Intravoxel incoherent motion analysis of abdominal organs: computation of reference parameters in a large cohort of C57Bl/6 mice and correlation to microvessel density.

OBJECTIVE: Diffusion-weighted magnetic resonance imaging (DW-MRI) combined with intravoxel incoherent motion (IVIM) analysis may be applied for assessment of organ lesions, diffuse parenchymal pathologies, and therapy monitoring. The aim of this study was to determine IVIM reference parameters of abdominal organs for translational research in a large cohort of C57Bl/6 laboratory mice. MATERIALS AND METHODS: Anesthetized mice (n = 29) were measured in a 4.7 T small-animal MR scanner with a diffusion-weighted echo-planar imaging sequence at the [Formula: see text]-values 0, 13, 24, 55, 107, 260, 514, 767, 1020 s/mm(2). IVIM analysis was conducted on the liver, spleen, renal medulla and cortex, pancreas, and small bowel with computation of the true tissue diffusion coefficient [Formula: see text], the perfusion fraction [Formula: see text], and the pseudodiffusion coefficient [Formula: see text]. Microvessel density (MVD) was assessed by immunohistochemistry (IHC) against panendothelial cell antigen CD31. RESULTS: Mean values of the different organs [[Formula: see text] (10(-3) mm(2)/s); [Formula: see text] (%); [Formula: see text] (10(-3) mm(2)/s); MVD (MV/mm(2))]: liver 1.15 ± 0.14; 14.77 ± 6.15; 50.28 ± 33.21, 2008.48 ± 419.43, spleen 0.55 ± 0.12; 9.89 ± 5.69; 24.46 ± 17.31; n.d., renal medulla 1.50 ± 0.20; 14.63 ± 4.07; 35.50 ± 18.01; 1231.88 ± 290.61, renal cortex 1.34 ± 0.18; 10.83 ± 3.70; 16.74 ± 6.74; 810.09 ± 193.50, pancreas 1.23 ± 0.22; 20.12 ± 7.46; 29.35 ± 17.82, 591.15 ± 86.25 and small bowel 1.06 ± 0.13; 16.48 ± 3.63; 15.31 ± 7.00; 420.50 ± 168.42. Unlike [Formula: see text] and [Formula: see text], [Formula: see text] correlates significantly with MVD (r = 0.90, p = 0.037). CONCLUSION: This systematic evaluation of murine abdominal organs with IVIM and MVD analysis allowed to establish reference parameters for future DW-MRI translational research studies on small-animal disease models.

Effect of respiratory hyperoxic challenge on magnetic susceptibility in human brain assessed by quantitative susceptibility mapping (QSM).

The purpose of this study was to measure the regional change of magnetic susceptibility in human brain upon inhalation of 100% oxygen by MRI quantitative susceptibility mapping (QSM). Fourteen healthy volunteers were scanned in a 3 T MR scanner with a 3D multi-gradient-echo sequence while breathing medical air (normoxia) and pure oxygen (hyperoxia). QSM images and R2* maps were calculated. Mean susceptibility differences versus white matter were measured in regions of interest covering veins, gray matter (GM), and cerebrospinal fluid (CSF) under both conditions. Hyperoxia resulted in a strong susceptibility decrease in large veins (-154.4 ± 65.9 ppb, p < 10(-6)), in a smaller reduction in GM (-1.3 ± 1 ppb, p < 0.001), and in a susceptibility increase in ventricular CSF (3.8 ± 1.8 ppb, p < 10(-5)). The susceptibility decrease in veins implied an increase of venous oxygen saturation (SvO2) by 10.1 ± 4.0%. Compared with QSM, R2* was more seriously affected by long-distance effects not related to local tissue oxygenation and increased in cerebral frontal regions (3 ± 2 s(-1), p < 0.0004) due to paramagnetic molecular oxygen in cavities. The results highlight the potential of QSM to yield region-specific quantitative oxygenation information, and, thus, for applications such as oxygen-therapy monitoring or identification of hypoxic tumor tissue during radiotherapy planning.

The Contribution of Verbal Working Memory to Deaf Children's Oral and Written Production.

This study investigated the contribution of verbal working memory to the oral and written story production of deaf children. Participants were 29 severely to profoundly deaf children aged 8-13 years and 29 hearing controls, matched for grade level. The children narrated a picture story orally and in writing and performed a reading comprehension test, the Wechsler Intelligence Scale for Children-Fourth Edition forward digit span task, and a reading span task. Oral and written stories were analyzed at the microstructural (i.e., clause) and macrostructural (discourse) levels. Hearing children's stories scored higher than deaf children's at both levels. Verbal working memory skills contributed to deaf children's oral and written production over and above age and reading comprehension skills. Verbal rehearsal skills (forward digit span) contributed significantly to deaf children's ability to organize oral and written stories at the microstructural level; they also accounted for unique variance at the macrostructural level in writing. Written story production appeared to involve greater verbal working memory resources than oral story production.