[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-134012-en":3,"doc-seo-134012-105":31,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},134012,1649267921044,"Ava Thompson","https://us-avatar.wpscdn.com/avatar/1800007509477c92dfb?_k=1786009248482753345",8,"Research & Report","Matched-Filter acquisition for BOLD fMRI - Article","Matched-filter fMRI improves BOLD (blood oxygen level dependent) sensitivity through variable-density image acquisition matched to subsequent image smoothing. Because spatial smoothing is widely used in fMRI post-processing, the strategy focuses on increasing the signal-to-noise ratio of the smoothed outputs. A theoretical SNR advantage is derived, alongside a practical 2D echo-planar acquisition implementation for common Gaussian smoothing. Concurrent magnetic-field monitoring enables reliable variable-speed trajectories, and phantom and in vivo data show ~30% SNR gains and ~35% statistical sensitivity improvements for resting-state and a preliminary task study.","NeuroImage 100 (2014) 145–160  \nContents lists available at ScienceDirect  \nNeuroImage  \njournal [homepage: www. else vier. com/locate/yn img](homepage: www. else vier. com/locate/yn img)  \n| \u003Cbr>Matched-filter acquisition for BOLD fMRI\u003Cbr>Lars Kasper a,b,⁎, Maximilian Haeberlin a, Benjamin E. Dietrich a, Simon Gross a, Christoph Barmet a,c, Bertram J. Wilm a, S. Johanna Vannesjo a, David O. Brunner a, Christian C. Ruff d,\u003Cbr>Klaas E. Stephan b,d,e, Klaas P. Pruessmann a\u003Cbr>a Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Switzerland\u003Cbr>b Translational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Switzerland c Skope Magnetic Resonance Technologies, Zurich, Switzerland\u003Cbr>d Laboratory for Social and Neural Systems Research (SNS), Department of Economics, University of Zurich, Switzerland e Wellcome Trust Center for Neuroimaging, Institute of Neurology, University College London, United Kingdom |  |  |\n| --- | --- | --- |\n| a r t i c l e i n f o |  | a b s t r a c t |\n| Article history:\u003Cbr>Accepted 10 May 2014\u003Cbr>Available online 17 May 2014 |  | We introduce matched-filter fMRI, which improves BOLD(blood oxygen level dependent)sensitivity by variabledensity image acquisition tailored to subsequent image smoothing. Image smoothing is an established postprocessing technique used in the vast majority of fMRI studies. Here we show that the signal-to-noise ratio of the resulting smoothed data can be substantially increased by acquisition weighting with a weighting function that matches the k-space filter imposed by the smoothing operation. We derive the theoretical SNR advantage of this strategy and propose a practical implementation of 2D echo-planar acquisition matched to common Gaussian smoothing. To reliably perform the involved variable-speed trajectories, concurrent magnetic field monitoring with NMR probes is used. Using this technique, phantom and in vivo measurements confirm reliable SNR improvement in the order of 30% in a “resting-state” condition and prove robust in different regimes of physiological noise. Furthermore, a preliminary task-based visual fMRI experiment equally suggests a consistent BOLD sensitivity increase in terms of statistical sensitivity (average t-value increase of about 35%). In summary, our study suggests that matched-filter acquisition is an effective means of improving BOLD SNR in studies that rely on image smoothing at the post-processing level.\u003Cbr>© 2014 Elsevier Inc. All rights reserved. |\n| Keywords: Matched-filter fMRI\u003Cbr>Image smoothing BOLD sensitivity\u003Cbr>Density-weighted EPI Magnetic field monitoring |  |  |\n\nIntroduction  \nSpatial smoothing of imaging volumes is ubiquitous in fMRI (Carp, 2012; Poldrack et al., 2008). Its routine use before statistical analysis aims at improving the sensitivity and interpretability of blood oxygen level dependent (BOLD) contrast in three ways, i.e., from the perspective of (1) signal processing, (2) statistical inference at the singlesubject level and (3) group level inference (Friston, 2007).  \nFirstly, with respect to the signal processing perspective, smoothing the data with a filter that resembles the spatially extended hemodynamic response is considered optimal to detect activation ofthis particular shape and scale, according to the matched-filter theorem (Worsley et al., 1996a, b). Secondly, regarding single-subject inference, image smoothing facilitates the application of multiple comparison correction using random field theory (Worsley et al., 1996a, b) since it ensures  \nAbbreviations: BOLD, Blood oxygen level dependent; EPI, Echo-planar imaging; EPSI, Echo-planar spectroscopic imaging; fMRI, Functional magnetic resonance imaging; FWHM, Full width at half maximum; PSF, Point spread function; SENSE, Sensitivity encoding; SFNR, Signal-to-fluctuation-noise ratio; TLS, Total least squares.  \n⁎ Corresponding author at: Institute for Biomedical Engineering, University of Zurich and ETH Z","cbCaieOKb8A1C3Kz","https://ap.wps.com/l/cbCaieOKb8A1C3Kz","pdf",3776200,4,1,16,"English","en",105,"# Introduction\n## Smoothing and BOLD sensitivity\n## Rationale from signal processing and inference\n## Noise propagation and k-space weighting\n# Theory and matched-filter acquisition concept\n## Matching acquisition weighting to smoothing filter\n## Distinguishing hemodynamic matching vs SNR optimization\n# Implementation and measurements\n## Variable-speed trajectories with magnetic-field monitoring\n## Phantom and in vivo validation\n## Resting-state and task-based results","[{\"question\":\"What is matched-filter fMRI and how does it improve BOLD sensitivity?\",\"answer\":\"Matched-filter fMRI tailors variable-density image acquisition so the acquisition weighting matches the k-space filter implied by later image smoothing, increasing the signal-to-noise ratio of the smoothed data and improving BOLD sensitivity.\"},{\"question\":\"Why is image smoothing central to this approach in fMRI studies?\",\"answer\":\"Image smoothing is a ubiquitous post-processing step in fMRI used to improve sensitivity and interpretability, and it changes both the effective point spread function and the way noise propagates into the smoothed images.\"},{\"question\":\"How are reliable variable-speed acquisition trajectories achieved in practice?\",\"answer\":\"The method uses concurrent magnetic-field monitoring with NMR probes to support dependable variable-speed trajectories during the matched acquisition.\"}]","Matched-Filter acquisition for BOLD fMRI - 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