Online Spatial Normalization for Real-Time fMRI
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{"title"=>"Online spatial normalization for real-time fMRI", "type"=>"journal", "authors"=>[{"first_name"=>"Xiaofei", "last_name"=>"Li", "scopus_author_id"=>"55870789500"}, {"first_name"=>"Li", "last_name"=>"Yao", "scopus_author_id"=>"7201688042"}, {"first_name"=>"Qing", "last_name"=>"Ye", "scopus_author_id"=>"57201324194"}, {"first_name"=>"Xiaojie", "last_name"=>"Zhao", "scopus_author_id"=>"7407542291"}], "year"=>2014, "source"=>"PLoS ONE", "identifiers"=>{"scopus"=>"2-s2.0-84904685058", "doi"=>"10.1371/journal.pone.0103302", "sgr"=>"84904685058", "isbn"=>"1932-6203 (Electronic)\\r1932-6203 (Linking)", "pmid"=>"25050799", "issn"=>"19326203", "pui"=>"373596121"}, "id"=>"a36b601e-1215-3f79-8608-49b4532e4cb1", "abstract"=>"Real-time functional magnetic resonance imaging (rtfMRI) is a recently emerged technique that demands fast data processing within a single repetition time (TR), such as a TR of 2 seconds. Data preprocessing in rtfMRI has rarely involved spatial normalization, which can not be accomplished in a short time period. However, spatial normalization may be critical for accurate functional localization in a stereotactic space and is an essential procedure for some emerging applications of rtfMRI. In this study, we introduced an online spatial normalization method that adopts a novel affine registration (AFR) procedure based on principal axes registration (PA) and Gauss-Newton optimization (GN) using the self-adaptive β parameter, termed PA-GN(β) AFR and nonlinear registration (NLR) based on discrete cosine transform (DCT). In AFR, PA provides an appropriate initial estimate of GN to induce the rapid convergence of GN. In addition, the β parameter, which relies on the change rate of cost function, is employed to self-adaptively adjust the iteration step of GN. The accuracy and performance of PA-GN(β) AFR were confirmed using both simulation and real data and compared with the traditional AFR. The appropriate cutoff frequency of the DCT basis function in NLR was determined to balance the accuracy and calculation load of the online spatial normalization. Finally, the validity of the online spatial normalization method was further demonstrated by brain activation in the rtfMRI data.", "link"=>"http://www.mendeley.com/research/online-spatial-normalization-realtime-fmri", "reader_count"=>16, "reader_count_by_academic_status"=>{"Professor > Associate Professor"=>1, "Researcher"=>2, "Student > Ph. D. Student"=>9, "Student > Master"=>2, "Lecturer"=>1, "Professor"=>1}, "reader_count_by_user_role"=>{"Professor > Associate Professor"=>1, "Researcher"=>2, "Student > Ph. D. Student"=>9, "Student > Master"=>2, "Lecturer"=>1, "Professor"=>1}, "reader_count_by_subject_area"=>{"Unspecified"=>2, "Engineering"=>3, "Medicine and Dentistry"=>2, "Neuroscience"=>4, "Physics and Astronomy"=>1, "Psychology"=>3, "Computer Science"=>1}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>3}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>2}, "Neuroscience"=>{"Neuroscience"=>4}, "Physics and Astronomy"=>{"Physics and Astronomy"=>1}, "Psychology"=>{"Psychology"=>3}, "Computer Science"=>{"Computer Science"=>1}, "Unspecified"=>{"Unspecified"=>2}}, "reader_count_by_country"=>{"Netherlands"=>1, "United States"=>1, "Brazil"=>1, "United Kingdom"=>1}, "group_count"=>1}

Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/1605470"], "description"=>"<p>(A) AFR without improvements; (B) AFR with β parameter only; (C) AFR with PA only; (D) PA-GN(β) AFR. The mean iteration number using the traditional AFR was 14.05±1.24, while that using the PA-GN(β) AFR was 9.50±0.50¸ which is approximately two-thirds of the traditional AFR requirement. Each dot represented a subject in the right figure, and there were fourteen dots overlapping with the six dots, as shown.</p>", "links"=>[], "tags"=>["neuroscience", "neuroimaging", "Functional magnetic resonance imaging", "signal processing", "Image processing", "convergence", "iteration"], "article_id"=>1114688, "categories"=>["Biological Sciences"], "users"=>["Xiaofei Li", "Li Yao", "Qing Ye", "Xiaojie Zhao"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0103302.g003", "stats"=>{"downloads"=>0, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Simulation_convergence_condition_left_and_the_required_iteration_number_right_/1114688", "title"=>"Simulation: convergence condition (left) and the required iteration number (right).", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-07-22 03:15:30"}
  • {"files"=>["https://ndownloader.figshare.com/files/1605468"], "description"=>"<p>The source image (<b>F</b>) was the first image in the real data, which was zoomed 0.7 times in advance. The reference image (<b>G</b>) was the source image transformed using the given parameters (<a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0103302#pone-0103302-t001\" target=\"_blank\">Table 1</a>).</p>", "links"=>[], "tags"=>["neuroscience", "neuroimaging", "Functional magnetic resonance imaging", "signal processing", "Image processing", "simulation"], "article_id"=>1114686, "categories"=>["Biological Sciences"], "users"=>["Xiaofei Li", "Li Yao", "Qing Ye", "Xiaojie Zhao"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0103302.g002", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Construction_of_simulation_data_/1114686", "title"=>"Construction of simulation data.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-07-22 03:15:30"}
  • {"files"=>["https://ndownloader.figshare.com/files/1605490"], "description"=>"<p>*The MSE using offline spatial normalization with default <i>L<sub>c</sub></i> of 25 mm was 0.2624±0.0135.</p>", "links"=>[], "tags"=>["neuroscience", "neuroimaging", "Functional magnetic resonance imaging", "signal processing", "Image processing", "runtime", "spatial", "normalization", "cutoff"], "article_id"=>1114708, "categories"=>["Biological Sciences"], "users"=>["Xiaofei Li", "Li Yao", "Qing Ye", "Xiaojie Zhao"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0103302.t002", "stats"=>{"downloads"=>2, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Accurary_and_runtime_of_online_spatial_normalization_with_different_cutoff_frequencies_/1114708", "title"=>"Accurary and runtime of online spatial normalization with different cutoff frequencies.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-07-22 03:15:30"}
  • {"files"=>["https://ndownloader.figshare.com/files/1605487"], "description"=>"<p>The images were obtained from one subject’s first image in each of eight on-going runs of an rtfMRI experiment, which lasted approximately 90 mins. <b>A.</b> The same slices of source images in different runs showed that the inter-run motion accumulated, and the image in the last run was different from the image in the first run by almost one slice. <b>B.</b> The corresponding slices in the normalized images using offline spatial normalization. The normalization parameters were estimated only in the first run, which simulated the processes of the previous spatial normalization methods <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0103302#pone.0103302-Lee1\" target=\"_blank\">[25]</a>, <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0103302#pone.0103302-Gao1\" target=\"_blank\">[26]</a>, <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0103302#pone.0103302-Desmond1\" target=\"_blank\">[42]</a>. It was evident that the inter-run motion negatively affected the accuracy of the normalized images in the subsequent runs. <b>C.</b> The corresponding slices in the normalized images by online spatial normalization using the parameters estimated in each run. The inter-run motion was effectively avoided, and the normalized images were nearly the same.</p>", "links"=>[], "tags"=>["neuroscience", "neuroimaging", "Functional magnetic resonance imaging", "signal processing", "Image processing", "inter-run", "accumulation", "avoided", "spatial"], "article_id"=>1114705, "categories"=>["Biological Sciences"], "users"=>["Xiaofei Li", "Li Yao", "Qing Ye", "Xiaojie Zhao"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0103302.g006", "stats"=>{"downloads"=>1, "page_views"=>12, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_negative_effects_of_inter_run_motion_accumulation_avoided_using_online_spatial_normalization_/1114705", "title"=>"The negative effects of inter-run motion accumulation avoided using online spatial normalization.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-07-22 03:15:30"}
  • {"files"=>["https://ndownloader.figshare.com/files/1605472"], "description"=>"<p>(A) AFR without improvements; (B) AFR with the β parameter only; (C) AFR with PA only; (D) PA-GN(β) AFR. The mean iteration number using the traditional AFR was 15.75±1.58, while that using the PA-GN(β) AFR was 8.60±1.07, which is approximately 50% of the traditional AFR requirement. Each dot represented a subject in the right figure, and there were six dots overlapping with the fourteen dots, as shown.</p>", "links"=>[], "tags"=>["neuroscience", "neuroimaging", "Functional magnetic resonance imaging", "signal processing", "Image processing", "convergence", "iteration"], "article_id"=>1114690, "categories"=>["Biological Sciences"], "users"=>["Xiaofei Li", "Li Yao", "Qing Ye", "Xiaojie Zhao"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0103302.g004", "stats"=>{"downloads"=>0, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Real_data_convergence_condition_left_and_the_required_iteration_number_right_/1114690", "title"=>"Real data: convergence condition (left) and the required iteration number (right).", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-07-22 03:15:30"}
  • {"files"=>["https://ndownloader.figshare.com/files/1605466"], "description"=>"<p>The image registration includes affine registration (AFR) and nonlinear registration (NLR), which are used to estimate the optimal normalization parameters. The image transformation is then used to transform the source images to a stereotactic space using the estimated parameters; tri-linear interpolation is used in this study. The PA-GN(β) AFR method is advanced based on traditional AFR, in which the PA provides a better initial estimate for GN with the self-adaptive β parameter for the iteration step adjustment.</p>", "links"=>[], "tags"=>["neuroscience", "neuroimaging", "Functional magnetic resonance imaging", "signal processing", "Image processing", "workflow", "spatial", "normalization", "affine", "registration"], "article_id"=>1114684, "categories"=>["Biological Sciences"], "users"=>["Xiaofei Li", "Li Yao", "Qing Ye", "Xiaojie Zhao"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0103302.g001", "stats"=>{"downloads"=>0, "page_views"=>13, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_workflow_of_spatial_normalization_left_and_advanced_affine_registration_right_/1114684", "title"=>"The workflow of spatial normalization (left) and advanced affine registration (right).", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-07-22 03:15:30"}
  • {"files"=>["https://ndownloader.figshare.com/files/1605480"], "description"=>"<p>There were slight differences, such as the areas indicated in the blue circles, but no significant differences were observed between the activation patterns.</p>", "links"=>[], "tags"=>["neuroscience", "neuroimaging", "Functional magnetic resonance imaging", "signal processing", "Image processing", "activation", "maps", "offline", "spatial", "normalization"], "article_id"=>1114698, "categories"=>["Biological Sciences"], "users"=>["Xiaofei Li", "Li Yao", "Qing Ye", "Xiaojie Zhao"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0103302.g005", "stats"=>{"downloads"=>0, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_group_activation_maps_using_offline_spatial_normalization_left_and_online_spatial_normalization_right_/1114698", "title"=>"The group activation maps using offline spatial normalization (left) and online spatial normalization (right).", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-07-22 03:15:30"}
  • {"files"=>["https://ndownloader.figshare.com/files/1605489"], "description"=>"<p>Activation coverage rate and activation center distance in different brain regions.</p>", "links"=>[], "tags"=>["neuroscience", "neuroimaging", "Functional magnetic resonance imaging", "signal processing", "Image processing", "activation"], "article_id"=>1114707, "categories"=>["Biological Sciences"], "users"=>["Xiaofei Li", "Li Yao", "Qing Ye", "Xiaojie Zhao"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0103302.t003", "stats"=>{"downloads"=>2, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Activation_coverage_rate_and_activation_center_distance_in_different_brain_regions_/1114707", "title"=>"Activation coverage rate and activation center distance in different brain regions.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-07-22 03:15:30"}
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