Magnitude-based PDFF (Proton Density Fat Fraction) and R2∗ mapping with resolved water-fat ambiguity is extended to calculate field inhomogeneity (field map) using the phase images. The estimation is formulated in matrix form, resolving the field map in a least-squares sense. PDFF and R2∗ from magnitude fitting may be updated using the estimated field maps. The limits of quantification of our voxel-independent implementation were assessed. Bland-Altman was used to compare PDFF and field maps from our method against a reference complex-based method on 152 UK Biobank subjects (1.5 T Siemens). A separate acquisition (3 T Siemens) presenting field inhomogeneities was also used. The proposed field mapping was accurate beyond double the complex-based limit range. High agreement was obtained between the proposed method and the reference in UK. Robust field mapping was observed at 3 T, for inhomogeneities over 400 Hz including rapid variation across edges. Field mapping following unambiguous magnitude-based water-fat separation was demonstrated in-vivo and showed potential at 3 T.

There is emerging evidence that sampling the blood-oxygen-level-dependent (BOLD) response with high temporal resolution opens up new avenues to study the in vivo functioning of the human brain with functional magnetic resonance imaging. Because the speed of sampling and the signal level are intrinsically connected in magnetic resonance imaging via the T1 relaxation time, optimization efforts usually must make a trade-off to increase the temporal sampling rate at the cost of the signal level. We present a method, which combines a sparse event-related stimulus paradigm with subsequent data reshuffling to achieve high temporal resolution while maintaining high signal levels (HiHi). The proof-of-principle is presented by separately measuring the single-voxel time course of the BOLD response in both the primary visual and primary motor cortices with 100-ms temporal resolution.

BACKGROUND & AIMS:MRI-based proton density fat fraction (PDFF) and the ultrasound-derived controlled attenuation parameter (CAP) are non-invasive techniques for quantifying liver fat, which can be used to assess steatosis in patients with non-alcoholic fatty liver disease (NAFLD). This study compared both of these techniques to histopathological graded steatosis for the assessment of fat levels in a large pooled NAFLD cohort.METHODS:This retrospective study pooled N = 581 participants from two suspected NAFLD cohorts (mean age (SD) 56 (12.7), 60% females). Steatosis was graded according to NASH-CRN criteria. Liver fat was measured non-invasively using PDFF (with Liver MultiScan's Iterative Decomposition of water and fat with Echo Asymmetry and Least-squares estimation method, LMS-IDEAL, Perspectum, Oxford) and CAP (FibroScan, Echosens, France), and their diagnostic performances were compared.RESULTS:LMS-IDEAL and CAP detected steatosis grade ≥ 1 with AUROCs of 1.00 (95% CI, 0.99-1.0) and 0.95 (95% CI, 0.91-0.99), respectively. LMS-IDEAL was superior to CAP for detecting steatosis grade ≥ 2 with AUROCs of 0.77 (95% CI, 0.73-0.82] and 0.60 (95% CI, 0.55-0.65), respectively. Similarly, LMS-IDEAL outperformed CAP for detecting steatosis grade ≥ 3 with AUROCs of 0.81 (95% CI, 0.76-0.87) and 0.63 (95% CI, 0.56-0.70), respectively.CONCLUSION:LMS-IDEAL was able to diagnose individuals accurately across the spectrum of histological steatosis grades. CAP performed well in identifying individuals with lower levels of fat (steatosis grade ≥1); however, its diagnostic performance was inferior to LMS-IDEAL for higher levels of fat (steatosis grades ≥2 and ≥3).TRIAL REGISTRATION:ClinicalTrials.gov (NCT03551522); https://clinicaltrials.gov/ct2/show/NCT03551522. UMIN Clinical Trials Registry (UMIN000026145); https://upload.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi?recptno=R000026145.

Purpose To develop a postprocessing algorithm for multiecho chemical‐shift encoded water–fat separation that estimates proton density fat fraction (PDFF) maps over the full dynamic range (0‐100%) using multipeak fat modeling and multipoint search optimization. To assess its accuracy, reproducibility, and agreement with state‐of‐the‐art complex‐based methods, and to evaluate its robustness to artefacts in abdominal PDFF maps. Methods We introduce MAGO (MAGnitude‐Only), a magnitude‐based reconstruction that embodies multipeak liver fat spectral modeling and multipoint optimization, and which is compatible with asymmetric echo acquisitions. MAGO is assessed first for accuracy and reproducibility on publicly available phantom data. Then, MAGO is applied to N = 178 UK Biobank cases, in which its liver PDFF measures are compared using Bland‐Altman analysis with those from a version of the hybrid iterative decomposition of water and fat with echo asymmetry and least squares estimation (IDEAL) algorithm, LiverMultiScan IDEAL (LMS IDEAL, Perspectum Diagnostics Ltd, Oxford, UK). Finally, MAGO is tested on a succession of high field challenging cases for which LMS IDEAL generated artefacts in the PDFF maps. Results Phantom data showed accurate, reproducible MAGO PDFF values across manufacturers, field strengths, and acquisition protocols. Moreover, we report excellent agreement between MAGO and LMS IDEAL for 6‐echo, 1.5 tesla human acquisitions (bias = −0.02% PDFF, 95% confidence interval = ±0.13% PDFF). When tested on 12‐echo, 3 tesla cases from different manufacturers, MAGO was shown to be more robust to artefacts compared to LMS IDEAL. Conclusion MAGO resolves the water–fat ambiguity over the entire fat fraction dynamic range without compromising accuracy, therefore enabling robust PDFF estimation where phase data is inaccessible or unreliable and complex‐based and hybrid methods fail.

Liver disease has reached worryingly high levels worldwide and there is a need for better analysis to monitor progression of disease and response to therapy. Quantitative imaging such as corrected T1 and PDFF can accurately quantify levels of inflammation/fibrosis and fat. In this study we develop a method to assess regional change throughout the liver to characterise disease change. We show that this method is stable in healthy test-retest cases but is able to characterise change in disease in autoimmune hepatitis cases.

PurposeSeveral studies have demonstrated the accuracy, precision, and reproducibility of proton density fat fraction (PDFF) quantification using vendor-specific image acquisition protocols and PDFF estimation methods. The purpose of this work is to validate a confounder-corrected, cross-vendor, cross field-strength, in-house variant LMS IDEAL of the IDEAL method licensed from the University of Wisconsin, which has been developed for routine clinical use.MethodsLMS IDEAL is implemented using a combination of patented and/or published acquisition and some novel model fitting methods required to correct confounds which result from the imaging and estimation processes, including: water-fat ambiguity; T2* relaxation; multi-peak fat modelling; main field inhomogeneity; T1 and noise bias; bipolar readout gradients; and eddy currents. LMS IDEAL has been designed to use image acquisition protocols that can be installed on most MRI scanners and cloud-based image processing to provide fast, standardized clinical results. Publicly available phantom data were used to validate LMS IDEAL PDFF calculations against results from originally published IDEAL methodology. LMS PDFF and T2* measurements were also compared with an independent technique in human volunteer data (n = 179) acquired as part of the UK Biobank study.ResultsWe demonstrate excellent agreement of LMS IDEAL across vendors, field strengths, and over a wide range of PDFF and T2* values in the phantom study. The performance of LMS IDEAL was then assessed in vivo against widely accepted PDFF and T2* estimation methods (LMS Dixon and LMS T2*, respectively), demonstrating the robustness of LMS IDEAL to potential sources of error.ConclusionThe development and clinical validation of the LMS IDEAL algorithm as a chemical shift-encoded MRI method for PDFF and T2* estimation contributes towards robust, unbiased applications for quantification of hepatic steatosis and iron overload, which are key features of chronic liver disease.

Brain-computer-interfaces (BCI) provide a means of using human brain activations to control devices for communication. Until now this has only been demonstrated in primary motor and sensory brain regions, using surgical implants or non-invasive neuroimaging techniques. Here, we provide proof-of-principle for the use of higher-order brain regions involved in complex cognitive processes such as attention. Using realtime fMRI, we implemented an online ‘winner-takes-all approach’ with quadrant-specific parameter estimates, to achieve single-block classification of brain activations. These were linked to the covert allocation of attention to real-world images presented at 4-quadrant locations. Accuracies in three target regions were significantly above chance, with individual decoding accuracies reaching upto 70%. By utilising higher order mental processes, ‘cognitive BCIs’ access varied and therefore more versatile information, potentially providing a platform for communication in patients who are unable to speak or move due to brain injury.

Liver disease, especially Non-Alcoholic Fatty Liver Disease has reached high levels, and there is a need for non-invasive tests based on quantitative MRI to replace biopsy in order to better assess liver health. An automated quantitative liver segmentation approach is required to automate these tests and in this work we propose a fully convolutional framework with a novel objective function for quantitative liver segmentation. The method has (to date) been tested on quantitative T1 maps generated from the UK Biobank study. We obtained extremely encouraging results on an unseen test set with a Dice score of 0.95, and Sensitivity 0.98 and Specificity 0.99.

Calculating attenuation correction for brain PET imaging rather than using CT presents opportunities for low radiation dose applications such as pediatric imaging and serial scans to monitor disease progression. Our goal is to evaluate the iterative time-of-flight based maximum-likelihood activity and attenuation correction factors estimation (MLACF) method for clinical FDG brain PET imaging. FDG PET/CT brain studies were performed in 57 patients using the Biograph mCT (Siemens) four-ring scanner. The time-of-flight PET sinograms were acquired using the standard clinical protocol consisting of a CT scan followed by 10 min of single-bed PET acquisition. Images were reconstructed using CT-based attenuation correction (CTAC) and used as a gold standard for comparison. Two methods were compared with respect to CTAC: a calculated brain attenuation correction (CBAC) and MLACF based PET reconstruction. Plane-by-plane scaling was performed for MLACF images in order to fix the variable axial scaling observed. The noise structure of the MLACF images was different compared to those obtained using CTAC and the reconstruction required a higher number of iterations to obtain comparable image quality. To analyze the pooled data, each dataset was registered to a standard template and standard regions of interest were extracted. An SUVr analysis of the brain regions of interest showed that CBAC and MLACF were each well correlated with CTAC SUVrs. A plane-by-plane error analysis indicated that there were local differences for both CBAC and MLACF images with respect to CTAC. Mean relative error in the standard regions of interest was less than 5% for both methods and the mean absolute relative errors for both methods were similar (3.4%  ±  3.1% for CBAC and 3.5%  ±  3.1% for MLACF). However, the MLACF method recovered activity adjoining the frontal sinus regions more accurately than CBAC method. The use of plane-by-plane scaling of MLACF images was found to be a crucial step in order to obtain improved activity estimates. Presence of local errors in both MLACF and CBAC based reconstructions would require the use of a normal database for clinical assessment. However, further work is required in order to assess the clinical advantage of MLACF over CBAC based method.

1 Real-time decoding of covert attention in higher-order visual areas 2 3 4 5 6 Authors: 7 Jinendra Ekanayake, Chloe Hutton, Gerard Ridgway, Frank Scharnowski, 8 Nikolaus Weiskopf, Geraint Rees 9 10 Institutions: 11 Wellcome Trust Centre for Interventional and Surgical Sciences, University College 12 London, London, United Kingdom, 13 Wellcome Trust Centre for Neuroimaging, University College London, London, 14 United Kingdom, 15 Institute of Cognitive Neuroscience, University College London, London, United 16 Kingdom, 17 Siemens Molecular Imaging, Oxford, United Kingdom, 18 University of Oxford, Headington, United Kingdom, 19 Psychiatric University Hospital, University of Zürich, Lenggstrasse 31, 8032 Zürich, 20 Switzerland 21 7 Neuroscience Center Zürich, University of Zürich and Swiss Federal Institute of 22 Technology, Winterthurerstr. 190, 8057 Zürich, Switzerland 23 Zürich Center for Integrative Human Physiology (ZIHP), University of Zürich, 24 Winterthurerstr. 190, 8057 Zürich, Switzerland 25 Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain 26 Sciences, Leipzig, Germany 27 28 29 30 31 Corresponding Author 32 Jinendra Ekanayake j.ekanayake@ucl.ac.uk 33 34 35 36 37 Address 38 Institute of Cognitive Neuroscience, 39 12 Queen Square, WC1N 3AR 40 41

ObjectiveDopamine transporter single-photon emission computed tomography (SPECT) with I-123-FP-CIT is used widely in the diagnosis of clinically uncertain parkinsonian syndromes. In terms of the evaluation of FP-CIT SPECT, some practice guidelines state that visual interpretation alone is generally sufficient in clinical patient care, whereas other guidelines consider semiquantitative analysis of striatal dopamine transporter availability mandatory. This discrepancy might be because of a relative lack of widely available display tools for FP-CIT SPECT. In this study, we evaluate a semiquantitative slab view display optimized for visual evaluation of FP-CIT SPECT that might resolve the discrepancy.Patients and methodsThe reconstructed FP-CIT SPECT image was stereotactically normalized and scaled voxel by voxel to the mean uptake in the entire brain without striata. From the resulting distribution volume ratio image, a 12-mm-thick transversal slice (slab) through the striata was displayed with a standard colour table with predefined fixed thresholds on the distribution volume ratio. Visual scoring of the semiquantitative slab view was performed twice by four independent readers in 235 unselected patients. The specific binding ratio in the caudate and putamen was computed by fully automated semiquantitative analysis with predefined standard regions of interest in template space.ResultsIntrarater and inter-rater agreement of binary visual categorization as normal' or reduced' was excellent (mean Cohen's =0.88 and 0.83, respectively). The area under the receiver-operator characteristic curve of the specific putamen-binding ratio for differentiation between visually normal and visually reduced (majority read) was 0.96.ConclusionVisual interpretation of FP-CIT SPECT on the basis of the semiquantitative slab view display provides excellent stability within and between readers as well as very high agreement with semiquantitative analysis. This suggests that the slab view display enables reliable visual interpretation of FP-CIT SPECT in clinical routine patient care. Copyright (C) 2016 Wolters Kluwer Health, Inc. All rights reserved.

Brain amyloid burden may be quantitatively assessed from positron emission tomography imaging using standardised uptake value ratios. Using these ratios as an adjunct to visual image assessment has been shown to improve inter-reader reliability, however, the amyloid positivity threshold is dependent on the tracer and specific image regions used to calculate the uptake ratio. To address this problem, we propose a machine learning approach to amyloid status classification, which is independent of tracer and does not require a specific set of regions of interest. Our method extracts feature vectors from amyloid images, which are based on histograms of oriented three-dimensional gradients. We optimised our method on 133 18F-florbetapir brain volumes, and applied it to a separate test set of 131 volumes. Using the same parameter settings, we then applied our method to 209 11C-PiB images and 128 18F-florbetaben images. We compared our method to classification results achieved using two other methods: standardised uptake value ratios and a machine learning method based on voxel intensities. Our method resulted in the largest mean distances between the subjects and the classification boundary, suggesting that it is less likely to make low-confidence classification decisions. Moreover, our method obtained the highest classification accuracy for all three tracers, and consistently achieved above 96% accuracy.

Background: Physiological noise is one of the major confounds for fMRI. A common class of correction methods model noise from peripheral measures, such as ECGs or pneumatic belts. However, physiological noise correction has not emerged as a standard preprocessing step for fMRI data yet due to: (1) the varying data quality of physiological recordings, (2) non-standardized peripheral data formats and (3) the lack of full automatization of processing and modeling physiology, required for large-cohort studies.New methods: We introduce the PhysIO Toolbox for preprocessing of physiological recordings and model based noise correction. It implements a variety of noise models, such as RETROICOR, respiratory volume per time and heart rate variability responses (RVT/HRV). The toolbox covers all intermediate steps from flexible read-in of data formats to GLM regressor/contrast creation without any manual intervention.Results: We demonstrate the workflow of the toolbox and its functionality for datasets from different vendors, recording devices, field strengths and subject populations. Automatization of physiological noise correction and performance evaluation are reported in a group study (N=35). Comparison with existing methods: The PhysIO Toolbox reproduces physiological noise patterns and correction efficacy of previously implemented noise models. It increases modeling robustness by outperforming vendor-provided peak detection methods for physiological cycles. Finally, the toolbox offers an integrated framework with full automatization, including performance monitoring, and flexibility with respect to the input data.Conclusions: Through its platform-independent Matlab implementation, open-source distribution, and modular structure, the PhysIO Toolbox renders physiological noise correction an accessible preprocessing step for fMRI data. (C) 2016 The Authors. Published by Elsevier B.V.

18F-Florbetapir positron emission tomography (PET) can be used to image amyloid burden in the human brain. A previously developed research method has been shown to have a high test-retest reliability and good correlation between standardized uptake value ratio (SUVR) and amyloid burden at autopsy. The goal of this study was to determine how well SUVRs computed using the research method could be reproduced using an automatic quantification method, developed for clinical use.

Previous studies have demonstrated that quantification of F-18-florbetapir uptake in the brain can be used to distinguish between populations of Alzheimers disease (AD) patients and healthy controls. Typically, quantification involves the calculation of standardised uptake value ratios (SUVRs), which requires registration to a template space in which regions of interest are defined. Consequently, SUVRs could be affected by the registration method used. We examine the effect of PETbased, MR-based and joint PET-MR registration on the SUVR. To achieve this, we introduce a joint-modality image-to-template registration framework that allows for variable contributions of PET and MR data to the registration process. We extend this further by proposing a method to determine the optimum combination of PET and MR information at each voxel. Following registrations of 100 subjects from the Alzheimers Disease Neuroimaging Initiative database, we show that there is a significant separation inmeanSUVRbetween populations ofADpatients and healthy controls for all registration methods. MR-only and PET-MR based methods slightly outperformed PET-only registration, however, diagnostic power was not affected by the registration method.

Neurofeedback based on real-time functional magnetic resonance imaging (fMRI) is a new approach that allows training of voluntary control over regionally specific brain activity. However, the neural basis of successful neurofeedback learning remains poorly understood. Here, we assessed changes in effective brain connectivity associated with neurofeedback training of visual cortex activity. Using dynamic causal modeling (DCM), we found that training participants to increase visual cortex activity was associated with increased effective connectivity between the visual cortex and the superior parietal lobe. Specifically, participants who learned to control activity in their visual cortex showed increased top-down control of the superior parietal lobe over the visual cortex, and at the same time reduced bottom-up processing. These results are consistent with efficient employment of top-down visual attention and imagery, which were the cognitive strategies used by participants to increase their visual cortex activity.

Neuroimaging holds the promise that it may one day aid the clinical assessment of individual psychiatric patients. However, the vast majority of studies published so far have been based on average differences between groups, which do not permit accurate inferences at the level of the individual. We examined the potential of structural Magnetic Resonance Imaging (MRI) data for making accurate quantitative predictions about symptom progression in individuals at ultra-high risk for developing psychosis. Forty people at ultra-high risk for psychosis were scanned using structural MRI at first clinical presentation and assessed over a period of 2?years using the Positive and Negative Syndrome Scale. Using a multivariate machine learning method known as relevance vector regression (RVR), we examined the relationship between brain structure at first clinical presentation, characterized in terms of gray matter (GM) volume and cortical thickness (CT), and symptom progression at 2-year follow-up. The application of RVR to whole-brain CT MRI data allowed quantitative prediction of clinical scores with statistically significant accuracy (correlation = 0.34, p = 0.026; Mean Squared-Error = 249.63, p = 0.024). This prediction was informed by regions traditionally associated with schizophrenia, namely the right lateral and medial temporal cortex and the left insular cortex. In contrast, the application of RVR to GM volume did not allow prediction of symptom progression with statistically significant accuracy. These results provide proof-of-concept that it could be possible to use structural MRI to inform quantitative prediction of symptom progression in individuals at ultra-high risk of developing psychosis. This would enable clinicians to target those individuals at greatest need of preventative interventions thereby resulting in a more efficient use of health care resources.