Mri Feature Engineering
Mri Feature Engineering - Related Videos
06:06
Optogenetic Functional MRI
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Cited by 15 •
2016
This protocol describes the steps and data analysis required to successfully perform optogenetic functional magnetic resonance imaging (ofMRI). ofMRI is a novel technique that combines high-field fMRI readout with optogenetic stimulation, allowing for cell type-specific mapping of functional neural circuits and their dynamics across the whole living brain.
05:42
Visual Search for Features and Conjunctions
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2023
Source: Laboratory of Jonathan Flombaum—Johns Hopkins University How do people find objects in cluttered visual scenes? Think, for example, of looking for keys on a messy desk, finding the ripest-looking fruit at the grocery store, locating your car when you can’t quite remember where you parked it, or finding an old friend at an airport exit gate. Clearly, an understanding of visual perception is going to play a role in any answers, and more specifically, an understanding of visual attention...
07:21
Magnetic Resonance Derived Myocardial Strain Assessment Using Feature Tracking
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Cited by 186 •
2011
An accurate and practical method to measure parameters like strain in myocardial tissue is of great clinical value, since it has been shown, that strain is a more sensitive and earlier marker for contractile dysfunction than the frequently used parameter EF.
12:18
Magnetic Resonance Elastography Methodology for the Evaluation of Tissue Engineered Construct Growth
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Cited by 16 •
2012
The procedure demonstrates the methodology of magnetic resonance elastography for monitoring the engineered outcome of adipose and osteogenic tissue engineered constructs through noninvasive local assessment of the mechanical properties using microscopic magnetic resonance elastography (μMRE).
08:20
MRI-guided dmPFC-rTMS as a Treatment for Treatment-resistant Major Depressive Disorder
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Cited by 31 •
2015
Here we outline the procedure for MRI-guided repetitive transcranial magnetic stimulation to the dorsomedial prefrontal cortex as an experimental treatment for major depressive disorder.
FAQs
Acquisition settings can alter measured signal intensity, texture, and other image-derived values even when the underlying tissue is unchanged. As a result, features may reflect imaging conditions rather than biological differences. Accounting for this sensitivity is important when comparing subjects, evaluating engineered or regenerated tissues, or building statistical and machine-learning models intended to perform consistently.
The selected region of interest determines which tissue or anatomical area contributes to the measurements. Segmentation choices can therefore change calculated intensity, texture, shape, and spatial features, particularly when boundaries are uncertain. Consistent region definition supports reproducibility, while poorly matched regions can obscure disease-related changes or weaken associations with biological outcomes.
Signal-intensity features describe image brightness, whereas texture features characterize patterns within tissue. Shape features summarize anatomical form, and spatial relationships describe how structures are positioned relative to one another. Using these categories together can provide complementary information about tissue structure, anatomical organization, or functional characteristics rather than relying on a single image measurement.
A typical workflow begins with image preprocessing, followed by definition of the relevant region of interest and extraction of quantitative variables. The resulting features can then enter statistical or machine-learning analyses. Researchers should examine how preprocessing and segmentation decisions affect the measurements, because these upstream choices influence reproducibility and the reliability of subsequent model results.
In bioengineering, extracted features can characterize tissue structure, quantify changes associated with disease, and evaluate engineered or regenerated tissues. They can also support image-based prediction of biological outcomes. These uses connect measurable MRI patterns with questions about tissue condition and development, allowing imaging data to contribute quantitative evidence to biological or engineering investigations.
Feature selection and validation should reflect both technical and biological sources of variation. Acquisition settings, segmentation choices, and natural biological variability can influence feature values and reduce reproducibility. Evaluating these influences helps determine whether a model captures meaningful tissue or anatomical characteristics rather than unstable image properties, improving confidence in predictions and comparisons.