Đang tải... (xem toàn văn)
(BQ) Part 2 book Intracranial pressure & neuromonitoring XVI has contents: Comparing models of spontaneous variations, maneuvers and indexes to assess dynamic cerebral autoregulation, medical waveform format encoding rules representation of neurointensive care waveform data, room air readings of brain tissue oxygenation probes,... and other contents.
Increasing the Contrast-to-Noise Ratio of MRI Signals for Regional Assessment of Dynamic Cerebral Autoregulation José L. Jara, Nazia P. Saeed, Ronney B. Panerai, and Thompson G. Robinson Abstract Objective: To devise an appropriate measure of the quality of a magnetic resonance imaging (MRI) signal for the assessment of dynamic cerebral autoregulation, and propose simple strategies to improve its quality Materials and methods: Magnetic resonance images of 11 healthy subjects were scanned during a transient decrease in arterial blood pressure (BP) Mean signals were extracted from non-overlapping brain regions for each image An adhoc contrast-to-noise ratio (CNR) was used to evaluate the quality of these regional signals Global mean signals were obtained by averaging the set of regional signals resulting after applying a Hampel filter and discarding a proportion of the lower quality component signals Results: Significant improvements in CNR values of global mean signals were obtained, whilst maintaining significant correlation with the original ones A Hampel filter with a small moving window and a low rejection threshold combined with a selection of the 50% component signals seems a recommendable option Conclusions: This work has demonstrated the possibility of improving the quality of MRI signals acquired during transient drops in BP. This approach needs validation at a voxel level, which could help to consolidate MRI as a technological alternative to the standard techniques for the study of cerebral autoregulation J.L Jara (*) Departamento de Ingeniería Informática, Universidad de Santiago de Chile, USACH, Santiago, Chile e-mail: joseluis.jara@usach.cl N.P Saeed Ageing and Stroke Medicine, Department of Cardiovascular Sciences, University of Leicester, Leicester, UK R.B Panerai • T.G Robinson Ageing and Stroke Medicine, Department of Cardiovascular Sciences, University of Leicester, Leicester, UK NIHR Biomedical Research Unit for Cardiovascular Sciences, University of Leicester, Leicester, UK Keywords Dynamic cerebral autoregulation · Magnetic resonance imaging · Contrast-to-noise ratio · Hampel filter Introduction Using magnetic resonance imaging (MRI) as an alternative to the standard transcranial Doppler (TCD) approach to assess dynamic cerebral autoregulation (dCA) is under investigation The similarity between the time courses of the signal recorded with TCD and the MRI signal was established for healthy subjects in Saeed et al [1] A mean map of dCA efficiency for a group of healthy subjects was presented in Horsfield et al [2] More recently, the use of hemispheric TCD signals and hemispheric MRI signals to discriminate between healthy and acute ischaemic stroke populations was investigated in Panerai et al [3] However, as reported in the most recent study, many time courses were excluded from the analysis, mostly from stroke patients, by a panel of four of the authors who visually inspected the signals on a computer screen, because they presented large artefacts or they did not show the expected temporal pattern, reflecting a sudden drop in arterial blood pressure (BP) The MRI-based method of evaluating dCA efficiency proposed in Horsfield et al [2] resembles the functional MRI paradigm (fMRI) of cognitive neuroimaging studies Both approaches seek variations in the blood-oxygen-level- dependent (BOLD) contrast as a surrogate of changes in cerebral blood flow Although in fMRI, the goal is to identify an increase in the BOLD signal in response to neuronal activity, the MRI-based dCA evaluation is aimed at detecting a drop in the BOLD signal in response to a slump in BP produced by the sudden release of inflated bilateral thigh cuffs (THCs) In both cases, the change in BOLD signal is rather small and transient, starting a few seconds after the stimulus and returning to baseline level over time T Heldt (ed.), Intracranial Pressure & Neuromonitoring XVI, Acta Neurochirurgica Supplement, Vol 126, https://doi.org/10.1007/978-3-319-65798-1_32, © Springer International Publishing AG 2018 153 154 The signal of an individual voxel in an MRI image can be affected by BOLD fluctuations due to ghost images, blood flow artefacts in the vicinity of large vessels and, in particular, by spontaneous neuronal activity [4, 5] Similar to the hemispheric MRI signals of Panerai et al [3], regional indicators for the efficiency of cerebral autoregulation could be obtained from a dCA efficiency map by averaging the values of the hundreds or thousands of voxels that usually comprise the region of interest (ROI) Moreover, recognising and filtering out corrupted voxels in the ROI could yield less noisy regional signals or more accurate regional measures of dCA efficiency Two important questions arise from these observations, which are addressed in this study First, it is necessary to devise a more objective and easily comparable measure of the quality of a signal time course than visual inspection This measure must be applicable to the hundreds of thousands of voxels in an MRI image and consider that spontaneous fluctuations should be limited, whereas the variation introduced by the THC deflation should be much larger and clear Second, simple strategies must be proposed to further reduce common artefacts and improve the quality of the MRI signals at both voxel level and regional level Materials and Methods Subjects and Data Gradient-echo EPI sequences were used to scan the brains of 11 healthy subjects at a rate of 1 Hz During the initial 3 min, the subjects lay supine in the scanner with filled THCs Then, a transient decrease in BP was provoked by the sudden deflation of the THCs The series continued until 240-s images were acquired After the first run was completed, the THCs were re-inflated and the procedure was repeated twice more during a single session The protocol is detailed in Saeed et al., Horsfield et al and Panerai et al [1–3] and the 33 images considered in this study are the same as those used in Horsfield et al [2] The study was approved by the Leicestershire, Northamptonshire and Rutland Research Ethics Committee (REC 09/H0403/25) and all subjects gave written informed consent As it would be impossible to hand-inspect for improvements in the quality of the signal of all voxels in a real situation, a more manageable simplification was used: the global mean signal of an image is the result of averaging regional mean signals that result from the applications of 32 masks of non-overlapping brain regions In this way, a global mean signal can be compared before and after manipulating its 32 component signals, which can also be examined to determine the effect on them of any proposed method J.L Jara et al MRI Signal Quality Contrast-to-noise ratio (CNR) has been found to be more suitable for fMRI than more traditional measures of signal quality [6, 7], as higher CNR values yield to better identification of the actual stimulus-related fluctuations and it encapsulates all relevant quality factors into a single and intuitive parameter [8] There are different definitions and methods of estimating CNR, but all of them conceptualise this parameter as the ratio between the amplitude of BOLD fluctuations produced by the stimulus and the variability of the noise over time [7, 8] The definition used is CNR = A/σN The estimation begins with the selection of two segments of the signal: the baseline samples bi are the 10 signal values before the deflation of the THCs, and the response samples ri correspond to the 10 values between the 3 s and 12 s after the THC deflation, in which the reaction to the stimulus is expected The variability of the noise in the signal is the standard deviation of the baseline residuals: σN = sd(bi – mean(bi)), and the amplitude of the response to the stimulus is determined as the difference between the baseline value and the minimum signal value in the response segment: A = mean(bi) – min(bi) Thus, the clearer the response to the stimulus with regard to the observed noise, the higher the CNR value obtained It must be noted that negative CNR values are possible when the signal exhibits an increase during the stimulus segment Outlier Filtering The estimation of CNR involves mean and standard deviation values, which are both known to be sensitive to outliers In addition, artefacts in the form of “narrow spikes” (