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Related Concept Videos

Upsampling01:22

Upsampling

744
Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
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Magnetic Resonance Imaging01:24

Magnetic Resonance Imaging

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Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...
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Related Experiment Video

Updated: Apr 27, 2026

High-resolution Functional Magnetic Resonance Imaging Methods for Human Midbrain
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MRI upsampling using feature-based nonlocal means approach.

Kourosh Jafari-Khouzani

    IEEE Transactions on Medical Imaging
    |June 22, 2014
    PubMed
    Summary

    This study introduces a novel non-local means feature-based technique to improve magnetic resonance imaging (MRI) resolution. The method enhances image quality for better quantitative analysis and faster processing in various neurological conditions.

    Area of Science:

    • Medical Imaging
    • Image Processing
    • Neuroscience

    Background:

    • Magnetic resonance imaging (MRI) spatial resolution is often limited by acquisition time and motion.
    • Anisotropic voxel sizes in MRI lead to distorted edges after standard interpolation.
    • Limitations in low-resolution (LR) MRI hinder quantitative and voxel-wise analyses.

    Purpose of the Study:

    • To develop an advanced interpolation technique for low-resolution (LR) magnetic resonance imaging (MRI) data.
    • To overcome limitations in quantitative analysis and registration of anisotropic MRI scans.
    • To improve the accuracy and speed of MRI image upsampling.

    Main Methods:

    • Proposed a non-local means feature-based interpolation technique.
    • Utilized structural information from high-resolution (HR) images with different contrasts.

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  • Employed a feature vector characterizing laminar brain structures for accurate voxel similarity estimation.

    • Main Results:

    • The new method demonstrated superior accuracy compared to conventional patch-based techniques.
    • Achieved significantly faster computation times, requiring fewer calculations.
    • Successfully applied to MRI scans from patients with brain tumors, multiple sclerosis, epilepsy, and healthy controls.

    Conclusions:

    • The proposed feature-based technique effectively interpolates LR MRI images, even with anisotropic voxel sizes.
    • Offers a more accurate and computationally efficient solution for MRI image upsampling.
    • Enables improved quantitative analysis and registration of medical imaging data, including upsampling regions of interest.