Statistical normalization techniques for magnetic resonance imaging
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A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
No Canadian affiliation. An affiliation-only frame — the usual design — would never have seen this work. It is one of the works that make the case for inverting the frame.
Machine scores (provisional)
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
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- Teacher spread
- 0.339 · how far apart the two teachers sit on this one work
- Validation status
score_only:v0-immature-baseline· verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it
Abstract
While computed tomography and other imaging techniques are measured in absolute units with physical meaning, magnetic resonance images are expressed in arbitrary units that are difficult to interpret and differ between study visits and subjects. Much work in the image processing literature on intensity normalization has focused on histogram matching and other histogram mapping techniques, with little emphasis on normalizing images to have biologically interpretable units. Furthermore, there are no formalized principles or goals for the crucial comparability of image intensities within and across subjects. To address this, we propose a set of criteria necessary for the normalization of images. We further propose simple and robust biologically motivated normalization techniques for multisequence brain imaging that have the same interpretation across acquisitions and satisfy the proposed criteria. We compare the performance of different normalization methods in thousands of images of patients with Alzheimer's disease, hundreds of patients with multiple sclerosis, and hundreds of healthy subjects obtained in several different studies at dozens of imaging centers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
The record
- Venue
- NeuroImage Clinical
- Topic
- Medical Image Segmentation Techniques
- Field
- Computer Science
- Canadian institutions
- —
- Funders
- Commonwealth Scientific and Industrial Research OrganisationNational Institute on AgingNational Institute of Biomedical Imaging and BioengineeringUniversity of California, Los AngelesCanadian Institutes of Health ResearchNational Institutes of HealthServierEisaiNational Institute of Neurological Disorders and StrokeNorthern California Institute for Research and EducationPfizerBiogenBioClinicaSynarcAlzheimer's Disease Neuroimaging InitiativeMeso Scale DiagnosticsMedpaceBristol-Myers SquibbEli Lilly and CompanyNational Institute of Mental HealthNovartis Pharmaceuticals CorporationF. Hoffmann-La RocheAlzheimer's Drug Discovery FoundationFoundation for the National Institutes of Health
- Keywords
- Normalization (sociology)Spatial normalizationArtificial intelligenceHistogramComputer scienceMagnetic resonance imagingPattern recognition (psychology)Functional magnetic resonance imagingHistogram matchingNeuroimagingImage processingComputer visionMedicineImage (mathematics)PsychologyRadiologyVoxelNeuroscience
- Has abstract in OpenAlex
- yes