Simultaneous Analysis and Quality Assurance for Diffusion Tensor Imaging
Publication Date
April 30, 2013
Journal
PLOS ONE
Authors
Carolyn B. Lauzon, Andrew J. Asman, Michael L. Esparza, Scott S. Burns, et al
Volume
8
Issue
4
Pages
e61737
DOI
https://dx.plos.org/10.1371/journal.pone.0061737
Publisher URL
http://journals.plos.org/plosone/article?id=10.1371%2Fjournal.pone.0061737
PubMed
http://www.ncbi.nlm.nih.gov/pubmed/23637895
PubMed Central
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3640065
Europe PMC
http://europepmc.org/abstract/MED/23637895
Web of Science
000319077300015
Scopus
84876952298
Mendeley
http://www.mendeley.com/research/simultaneous-analysis-quality-assurance-diffusion-tensor-imaging-2
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Mendeley | Further Information

{"title"=>"Simultaneous Analysis and Quality Assurance for Diffusion Tensor Imaging", "type"=>"journal", "authors"=>[{"first_name"=>"Carolyn B.", "last_name"=>"Lauzon", "scopus_author_id"=>"36711923300"}, {"first_name"=>"Andrew J.", "last_name"=>"Asman", "scopus_author_id"=>"38560957900"}, {"first_name"=>"Michael L.", "last_name"=>"Esparza", "scopus_author_id"=>"56868778900"}, {"first_name"=>"Scott S.", "last_name"=>"Burns", "scopus_author_id"=>"55672026100"}, {"first_name"=>"Qiuyun", "last_name"=>"Fan", "scopus_author_id"=>"56927493300"}, {"first_name"=>"Yurui", "last_name"=>"Gao", "scopus_author_id"=>"56163925700"}, {"first_name"=>"Adam W.", "last_name"=>"Anderson", "scopus_author_id"=>"7403369918"}, {"first_name"=>"Nicole", "last_name"=>"Davis", "scopus_author_id"=>"26633439400"}, {"first_name"=>"Laurie E.", "last_name"=>"Cutting", "scopus_author_id"=>"6603345531"}, {"first_name"=>"Bennett A.", "last_name"=>"Landman", "scopus_author_id"=>"16679175200"}], "year"=>2013, "source"=>"PLoS ONE", "identifiers"=>{"scopus"=>"2-s2.0-84876952298", "pui"=>"368837415", "pmid"=>"23637895", "issn"=>"19326203", "isbn"=>"1932-6203 (Electronic)\\n1932-6203 (Linking)", "doi"=>"10.1371/journal.pone.0061737", "sgr"=>"84876952298"}, "id"=>"98f8a06d-e298-36f2-be4e-4033741fccc7", "abstract"=>"Diffusion tensor imaging (DTI) enables non-invasive, cyto-architectural mapping of in vivo tissue microarchitecture through voxel-wise mathematical modeling of multiple magnetic resonance imaging (MRI) acquisitions, each differently sensitized to water diffusion. DTI computations are fundamentally estimation processes and are sensitive to noise and artifacts. Despite widespread adoption in the neuroimaging community, maintaining consistent DTI data quality remains challenging given the propensity for patient motion, artifacts associated with fast imaging techniques, and the possibility of hardware changes/failures. Furthermore, the quantity of data acquired per voxel, the non-linear estimation process, and numerous potential use cases complicate traditional visual data inspection approaches. Currently, quality inspection of DTI data has relied on visual inspection and individual processing in DTI analysis software programs (e.g. DTIPrep, DTI-studio). However, recent advances in applied statistical methods have yielded several different metrics to assess noise level, artifact propensity, quality of tensor fit, variance of estimated measures, and bias in estimated measures. To date, these metrics have been largely studied in isolation. Herein, we select complementary metrics for integration into an automatic DTI analysis and quality assurance pipeline. The pipeline completes in 24 hours, stores statistical outputs, and produces a graphical summary quality analysis (QA) report. We assess the utility of this streamlined approach for empirical quality assessment on 608 DTI datasets from pediatric neuroimaging studies. The efficiency and accuracy of quality analysis using the proposed pipeline is compared with quality analysis based on visual inspection. The unified pipeline is found to save a statistically significant amount of time (over 70%) while improving the consistency of QA between a DTI expert and a pool of research associates. Projection of QA metrics to a low dimensional manifold reveal qualitative, but clear, QA-study associations and suggest that automated outlier/anomaly detection would be feasible.", "link"=>"http://www.mendeley.com/research/simultaneous-analysis-quality-assurance-diffusion-tensor-imaging-2", "reader_count"=>61, "reader_count_by_academic_status"=>{"Professor > Associate Professor"=>5, "Student > Doctoral Student"=>2, "Researcher"=>18, "Student > Ph. D. Student"=>22, "Student > Postgraduate"=>3, "Student > Master"=>7, "Student > Bachelor"=>3, "Professor"=>1}, "reader_count_by_user_role"=>{"Professor > Associate Professor"=>5, "Student > Doctoral Student"=>2, "Researcher"=>18, "Student > Ph. D. Student"=>22, "Student > Postgraduate"=>3, "Student > Master"=>7, "Student > Bachelor"=>3, "Professor"=>1}, "reader_count_by_subject_area"=>{"Unspecified"=>7, "Engineering"=>7, "Agricultural and Biological Sciences"=>7, "Medicine and Dentistry"=>6, "Neuroscience"=>5, "Arts and Humanities"=>1, "Physics and Astronomy"=>4, "Psychology"=>14, "Social Sciences"=>4, "Computer Science"=>5, "Immunology and Microbiology"=>1}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>7}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>6}, "Neuroscience"=>{"Neuroscience"=>5}, "Social Sciences"=>{"Social Sciences"=>4}, "Physics and Astronomy"=>{"Physics and Astronomy"=>4}, "Psychology"=>{"Psychology"=>14}, "Immunology and Microbiology"=>{"Immunology and Microbiology"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>7}, "Computer Science"=>{"Computer Science"=>5}, "Unspecified"=>{"Unspecified"=>7}, "Arts and Humanities"=>{"Arts and Humanities"=>1}}, "reader_count_by_country"=>{"Canada"=>1, "United States"=>6, "United Kingdom"=>3}, "group_count"=>2}

Scopus | Further Information

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Figshare

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  • {"files"=>["https://ndownloader.figshare.com/files/1049945"], "description"=>"<p>Information presented on each page of the QA report.</p>", "links"=>[], "tags"=>["biotechnology", "Bioengineering", "Biomedical Engineering", "neuroscience", "neuroimaging", "Computerized simulations", "signal processing", "Image processing", "Mathematical computing", "statistics", "Biostatistics", "Clinical research design", "modeling", "Statistical methods", "neurology", "radiology", "Diagnostic radiology", "Magnetic resonance imaging", "presented", "qa"], "article_id"=>694997, "categories"=>["Information And Computing Sciences", "Mathematics", "Medicine", "Engineering", "Biological Sciences"], "users"=>["Carolyn B. Lauzon", "Andrew J. Asman", "Michael L. Esparza", "Scott S. Burns", "Qiuyun Fan", "Yurui Gao", "Adam W. Anderson", "Nicole Davis", "Laurie E. Cutting", "Bennett A. Landman"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0061737.t002", "stats"=>{"downloads"=>0, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Information_presented_on_each_page_of_the_QA_report_/694997", "title"=>"Information presented on each page of the QA report.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-04-30 01:23:17"}
  • {"files"=>["https://ndownloader.figshare.com/files/1049944"], "description"=>"1<p>Spatial dimensions are reported (left/right, back/front, foot/head).</p>2<p>Number of submitted datasets, parenthesis indicate number completed by pipeline.</p>3<p>Number of gradient directions (DWI volumes).</p>4<p>All data were collected using axial slices.</p>5<p>Contains within-study variation in some parameters. FOV changed with slice number to maintain consistent voxel size.</p>6<p>Contains within-study variation in some parameters. Voxel size and FOV changed with slice number as indicated.</p>", "links"=>[], "tags"=>["biotechnology", "Bioengineering", "Biomedical Engineering", "neuroscience", "neuroimaging", "Computerized simulations", "signal processing", "Image processing", "Mathematical computing", "statistics", "Biostatistics", "Clinical research design", "modeling", "Statistical methods", "neurology", "radiology", "Diagnostic radiology", "Magnetic resonance imaging"], "article_id"=>694996, "categories"=>["Information And Computing Sciences", "Mathematics", "Medicine", "Engineering", "Biological Sciences"], "users"=>["Carolyn B. Lauzon", "Andrew J. Asman", "Michael L. Esparza", "Scott S. Burns", "Qiuyun Fan", "Yurui Gao", "Adam W. Anderson", "Nicole Davis", "Laurie E. Cutting", "Bennett A. Landman"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0061737.t001", "stats"=>{"downloads"=>0, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_DTI_Data_/694996", "title"=>"DTI Data.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-04-30 01:23:16"}
  • {"files"=>["https://ndownloader.figshare.com/files/1049939"], "description"=>"<p>All data is shown from the same subject. The first page (P.1) parses the header (gray and blue boxes), plots patient movement in rotation and translation, plots RESTORE outliers per DWI (three upper right plots), plots (color plot), shows five ‘best’ and ‘worst’ DWI slices (bottom left), and plots a histogram of with an automatically determined magnification around the ‘noise’ lobe region as described in Papadakis et. al.. (P.2) shows a model of the 25 segmented regions (right column) and plots regional distributions of MD, FA, , and <i>B</i><sub>FA</sub> (blue boxplots) adjacent to corresponding distributions from the Multi-modal dataset (black boxplots). (P.3) Page three displays mid-axial slice views for MD, FA,, and <i>B</i><sub>FA</sub> (top row, left to right), select power curves for FA (middle column), and full mid coronal, axial, and sagittal slices for the vector colormap (R = right-left, G = anterior-posterior, B = foot-head). (P.4) Page 4 shows the vector directions (while lines, not clearly discernible at figure size) overlaid on the vector colormap for a mid-axial (left column) and mid-coronal (right column) slice. Three different enlargements are shown for improved viewing.</p>", "links"=>[], "tags"=>["biotechnology", "Bioengineering", "Biomedical Engineering", "neuroscience", "neuroimaging", "Computerized simulations", "signal processing", "Image processing", "Mathematical computing", "statistics", "Biostatistics", "Clinical research design", "modeling", "Statistical methods", "neurology", "radiology", "Diagnostic radiology", "Magnetic resonance imaging", "pages", "qa"], "article_id"=>694992, "categories"=>["Information And Computing Sciences", "Mathematics", "Medicine", "Engineering", "Biological Sciences"], "users"=>["Carolyn B. Lauzon", "Andrew J. Asman", "Michael L. Esparza", "Scott S. Burns", "Qiuyun Fan", "Yurui Gao", "Adam W. Anderson", "Nicole Davis", "Laurie E. Cutting", "Bennett A. Landman"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0061737.g003", "stats"=>{"downloads"=>0, "page_views"=>12, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_four_pages_of_the_QA_report_/694992", "title"=>"The four pages of the QA report.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-04-30 01:23:12"}
  • {"files"=>["https://ndownloader.figshare.com/files/1049949"], "description"=>"1<p>Q1. Are the rubric responses significantly different for Report vs Images?</p>2<p>Q2. Are the rubric errors significantly different for Report vs Images?</p>3<p>Q3. Are the rubric errors significantly different for Raw vs Zero mean distribution?</p>4<p>Q4. Are the rubric errors significantly different for Images vs Zero mean distribution?</p>‡<p>Evaluation responses were assigned values 0 through 5, with 0 being ‘Unacceptable’ and 5 being ‘Unusually Excellent’.</p>", "links"=>[], "tags"=>["biotechnology", "Bioengineering", "Biomedical Engineering", "neuroscience", "neuroimaging", "Computerized simulations", "signal processing", "Image processing", "Mathematical computing", "statistics", "Biostatistics", "Clinical research design", "modeling", "Statistical methods", "neurology", "radiology", "Diagnostic radiology", "Magnetic resonance imaging", "non-parametric"], "article_id"=>695001, "categories"=>["Information And Computing Sciences", "Mathematics", "Medicine", "Engineering", "Biological Sciences"], "users"=>["Carolyn B. Lauzon", "Andrew J. Asman", "Michael L. Esparza", "Scott S. Burns", "Qiuyun Fan", "Yurui Gao", "Adam W. Anderson", "Nicole Davis", "Laurie E. Cutting", "Bennett A. Landman"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0061737.t006", "stats"=>{"downloads"=>0, "page_views"=>15, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Rubric_Evaluation_Using_Non_parametric_Hypothesis_Testing_/695001", "title"=>"Rubric Evaluation Using Non-parametric Hypothesis Testing.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-04-30 01:23:21"}
  • {"files"=>["https://ndownloader.figshare.com/files/1049933"], "description"=>"<p>The DWIs were randomly chosen by a computer but are from the same gradient for each subject. DWIs and b<sub>o</sub> are shown at different intensity scales for viewing purposes. The FA color scale ranges from [blue-red] and colors map to FA values [0 1]. Inter-subject registration was not performed and the axial slices are from approximate matching locations. The FA map of subject 2 contains an anomalous bright region in the middle-right mid-brain/hemisphere (patient right is image right). This bright region is not clearly traced to DWI artifacts, but upon comparison to a re-scan of the same individual is associated with flow artifacts rather than pathology.</p>", "links"=>[], "tags"=>["biotechnology", "Bioengineering", "Biomedical Engineering", "neuroscience", "neuroimaging", "Computerized simulations", "signal processing", "Image processing", "Mathematical computing", "statistics", "Biostatistics", "Clinical research design", "modeling", "Statistical methods", "neurology", "radiology", "Diagnostic radiology", "Magnetic resonance imaging", "subjects"], "article_id"=>694986, "categories"=>["Information And Computing Sciences", "Mathematics", "Medicine", "Engineering", "Biological Sciences"], "users"=>["Carolyn B. Lauzon", "Andrew J. Asman", "Michael L. Esparza", "Scott S. Burns", "Qiuyun Fan", "Yurui Gao", "Adam W. Anderson", "Nicole Davis", "Laurie E. Cutting", "Bennett A. Landman"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0061737.g001", "stats"=>{"downloads"=>1, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Example_input_and_output_data_for_two_subjects_from_Study_II_/694986", "title"=>"Example input and output data for two subjects from Study II.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-04-30 01:23:06"}
  • {"files"=>["https://ndownloader.figshare.com/files/1049947"], "description"=>"<p>Rubric Questions.</p>", "links"=>[], "tags"=>["biotechnology", "Bioengineering", "Biomedical Engineering", "neuroscience", "neuroimaging", "Computerized simulations", "signal processing", "Image processing", "Mathematical computing", "statistics", "Biostatistics", "Clinical research design", "modeling", "Statistical methods", "neurology", "radiology", "Diagnostic radiology", "Magnetic resonance imaging"], "article_id"=>694999, "categories"=>["Information And Computing Sciences", "Mathematics", "Medicine", "Engineering", "Biological Sciences"], "users"=>["Carolyn B. Lauzon", "Andrew J. Asman", "Michael L. Esparza", "Scott S. Burns", "Qiuyun Fan", "Yurui Gao", "Adam W. Anderson", "Nicole Davis", "Laurie E. Cutting", "Bennett A. Landman"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0061737.t004", "stats"=>{"downloads"=>1, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Rubric_Questions_/694999", "title"=>"Rubric Questions.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-04-30 01:23:19"}
  • {"files"=>["https://ndownloader.figshare.com/files/1049948"], "description"=>"1<p>Mean reported as mean +/- standard deviation. Are the rubric responses significantly different for Report vs Images?</p>2<p>Inter-rater variability is calculated as defined in Methods text.</p>3<p>Mean calculated on magnitude of the errors.</p>‡<p>Evaluation responses were assigned values 0 through 5, with 0 being ‘Unacceptable’ and 5 being ‘Unusually Excellent’.</p>", "links"=>[], "tags"=>["biotechnology", "Bioengineering", "Biomedical Engineering", "neuroscience", "neuroimaging", "Computerized simulations", "signal processing", "Image processing", "Mathematical computing", "statistics", "Biostatistics", "Clinical research design", "modeling", "Statistical methods", "neurology", "radiology", "Diagnostic radiology", "Magnetic resonance imaging", "metrics", "rubric"], "article_id"=>695000, "categories"=>["Information And Computing Sciences", "Mathematics", "Medicine", "Engineering", "Biological Sciences"], "users"=>["Carolyn B. Lauzon", "Andrew J. Asman", "Michael L. Esparza", "Scott S. Burns", "Qiuyun Fan", "Yurui Gao", "Adam W. Anderson", "Nicole Davis", "Laurie E. Cutting", "Bennett A. Landman"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0061737.t005", "stats"=>{"downloads"=>0, "page_views"=>13, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Summary_Metrics_for_Rubric_Evaluations_/695000", "title"=>"Summary Metrics for Rubric Evaluations.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-04-30 01:23:20"}
  • {"files"=>["https://ndownloader.figshare.com/files/1049946"], "description"=>"<p>Stored Pipeline Outputs.</p>", "links"=>[], "tags"=>["biotechnology", "Bioengineering", "Biomedical Engineering", "neuroscience", "neuroimaging", "Computerized simulations", "signal processing", "Image processing", "Mathematical computing", "statistics", "Biostatistics", "Clinical research design", "modeling", "Statistical methods", "neurology", "radiology", "Diagnostic radiology", "Magnetic resonance imaging", "pipeline"], "article_id"=>694998, "categories"=>["Information And Computing Sciences", "Mathematics", "Medicine", "Engineering", "Biological Sciences"], "users"=>["Carolyn B. Lauzon", "Andrew J. Asman", "Michael L. Esparza", "Scott S. Burns", "Qiuyun Fan", "Yurui Gao", "Adam W. Anderson", "Nicole Davis", "Laurie E. Cutting", "Bennett A. Landman"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0061737.t003", "stats"=>{"downloads"=>0, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Stored_Pipeline_Outputs_/694998", "title"=>"Stored Pipeline Outputs.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-04-30 01:23:18"}
  • {"files"=>["https://ndownloader.figshare.com/files/1049943"], "description"=>"<p>Each of the 567 DTI datasets was characterized by a 112 element vector of stored outputs from the pipeline. PCA analysis was performed on the resulting data. (A) DTI dataset locations in the first two dimensions of the PCA analysis. Data is symbolized by study Roman numeral (<a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0061737#pone-0061737-t001\" target=\"_blank\"><b>Table 1</b></a>). Single arrow points to a data quality outlier from study I; subject 3 in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0061737#pone-0061737-g004\" target=\"_blank\">Figure 4</a> and in (B). A double headed arrow points to a cluster representing an isolated protocol sub-group from study VI. (B) FA maps from similar sagittal slice locations in two subjects from Study I. Subject 4 is the indicated outlier in (A) and subject 5 was selected from the center of the study I cluster seen in (A).</p>", "links"=>[], "tags"=>["biotechnology", "Bioengineering", "Biomedical Engineering", "neuroscience", "neuroimaging", "Computerized simulations", "signal processing", "Image processing", "Mathematical computing", "statistics", "Biostatistics", "Clinical research design", "modeling", "Statistical methods", "neurology", "radiology", "Diagnostic radiology", "Magnetic resonance imaging", "metrics", "stored", "pipeline", "evaluated"], "article_id"=>694995, "categories"=>["Information And Computing Sciences", "Mathematics", "Medicine", "Engineering", "Biological Sciences"], "users"=>["Carolyn B. Lauzon", "Andrew J. Asman", "Michael L. Esparza", "Scott S. Burns", "Qiuyun Fan", "Yurui Gao", "Adam W. Anderson", "Nicole Davis", "Laurie E. Cutting", "Bennett A. Landman"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0061737.g005", "stats"=>{"downloads"=>3, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Statistical_metrics_stored_by_pipeline_evaluated_using_PCA_/694995", "title"=>"Statistical metrics stored by pipeline evaluated using PCA.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-04-30 01:23:15"}
  • {"files"=>["https://ndownloader.figshare.com/files/1049942"], "description"=>"<p>Each piece is selected from a different page of the report. Subject 1 and subject 2 are from Study II and are the same subjects in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0061737#pone-0061737-g001\" target=\"_blank\">Figure 1</a>. Subject 3 is from Study I and subject 4 is from Study IX. (A) Colorplot of. Within each colormap, one column represents an entire normalized diffusion weighted volume, and each row represents the same axial slice. High correspond to poor data with 0.2 being definitively bad. (B) MD distributions for four segmented regions, left cerebellar gray matter (GM), right cerebellar GM, left cerebellar white matter (WM), and right cerebellar WM. Blue boxplots indicate the distribution of each subject’s MD value while the black box-plot is the reference data from the multi-modal study. (C) Power curves for theoretical sample sizes of <i>n</i> = 5 (black), <i>n</i> = 15 (red), and <i>n</i> = 30 (blue). Curves include bias estimates and pertain to voxels in the cerebral WM.</p>", "links"=>[], "tags"=>["biotechnology", "Bioengineering", "Biomedical Engineering", "neuroscience", "neuroimaging", "Computerized simulations", "signal processing", "Image processing", "Mathematical computing", "statistics", "Biostatistics", "Clinical research design", "modeling", "Statistical methods", "neurology", "radiology", "Diagnostic radiology", "Magnetic resonance imaging", "pieces", "qa"], "article_id"=>694994, "categories"=>["Information And Computing Sciences", "Mathematics", "Medicine", "Engineering", "Biological Sciences"], "users"=>["Carolyn B. Lauzon", "Andrew J. Asman", "Michael L. Esparza", "Scott S. Burns", "Qiuyun Fan", "Yurui Gao", "Adam W. Anderson", "Nicole Davis", "Laurie E. Cutting", "Bennett A. Landman"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0061737.g004", "stats"=>{"downloads"=>0, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Comparing_three_pieces_of_the_QA_report_for_four_subjects_/694994", "title"=>"Comparing three pieces of the QA report for four subjects.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-04-30 01:23:14"}

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Relative Metric

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