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Upsampling01:22

Upsampling

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 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: May 25, 2026

Registered Bioimaging of Nanomaterials for Diagnostic and Therapeutic Monitoring
17:16

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Published on: December 9, 2010

Adaptive sampling design for compressed sensing MRI.

Saiprasad Ravishankar1, Yoram Bresler

  • 1Department of Electrical and Computer Engineering and the Coordinated Science Laboratory, University of Illinois, Urbana-Champaign, IL 61801, USA. ravisha3@illinois.edu

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
|January 19, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces adaptive sampling for Compressed Sensing (CS) MRI, improving image reconstruction. The novel framework enhances accuracy from undersampled data, offering significant performance gains.

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Area of Science:

  • Medical Imaging
  • Signal Processing
  • Magnetic Resonance Imaging

Background:

  • Compressed Sensing (CS) leverages image sparsity for accurate Magnetic Resonance Imaging (MRI) from undersampled data.
  • Current pseudo-random sampling in CS-MRI can be suboptimal with limited data.
  • Adaptive image-patch based sparsifying dictionaries offer improved reconstruction.

Purpose of the Study:

  • To propose a novel framework for adaptive sampling schemes in highly undersampled CS-MRI.
  • To enhance the accuracy and performance of CS-MRI reconstructions.
  • To integrate adaptive sampling with adaptive patch-based reconstruction algorithms.

Main Methods:

  • Developed a general framework for adaptive sampling schemes in CS-MRI.
  • Applied the framework with a recently proposed MRI reconstruction algorithm using adaptive image-patch based sparsifying dictionaries.
  • Conducted numerical experiments to evaluate reconstruction performance.

Main Results:

  • Achieved up to 7 dB improvement in reconstruction Peak Signal-to-Noise Ratio (PSNR).
  • Demonstrated significant gains over analytical sparsifying transforms for adaptive patch-based reconstruction.
  • Validated the effectiveness of the proposed adaptive sampling scheme.

Conclusions:

  • The proposed adaptive sampling framework significantly improves CS-MRI reconstruction quality.
  • Adaptive sampling is crucial for optimizing CS-MRI performance with limited data.
  • This approach offers substantial benefits for clinical MRI applications requiring fast acquisition.