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Related Experiment Video

Updated: Feb 11, 2026

Management of Respiratory Motion Artefacts in 18F-fluorodeoxyglucose Positron Emission Tomography using an Amplitude-Based Optimal Respiratory Gating Algorithm
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K-CC-MoCo: A Fast k-Space-Based Respiratory Motion Correction for Highly Accelerated First-Pass Perfusion

Elisa Moya-Sáez1,2, Rosa-María Menchón-Lara1,3, Javier Sánchez-González4

  • 1ETSI de Telecomunicación, Universidad de Valladolid, Valladolid, Spain.

Magnetic Resonance in Medicine
|February 10, 2026
PubMed
Summary
This summary is machine-generated.

K-CC-MoCo corrects respiratory motion in k-space for faster, clearer free-breathing first-pass perfusion cardiovascular MR (FPP-CMR) scans. This method enables high-quality imaging from highly accelerated acquisitions, outperforming traditional image-based correction.

Keywords:
motion correctionmyocardial first‐pass perfusionrespiratory motionrigid registration

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Area of Science:

  • Cardiovascular Magnetic Resonance Imaging
  • Medical Imaging Physics
  • Biomedical Engineering

Background:

  • First-pass perfusion cardiovascular MR (FPP-CMR) is crucial for diagnosing microcirculation and coronary artery disease.
  • Current free-breathing FPP-CMR motion correction occurs in the image domain, limiting advanced reconstruction techniques.
  • Model-based and deep learning reconstructions offer high-quality imaging from accelerated scans but are hindered by current motion correction methods.

Purpose of the Study:

  • To develop and validate a novel k-space-based motion correction method for free-breathing FPP-CMR.
  • To enable motion correction directly within k-space, bypassing initial image reconstruction.
  • To facilitate the integration of motion correction with advanced model-based and deep learning reconstruction techniques for accelerated FPP-CMR.

Main Methods:

  • Proposed K-CC-MoCo, a k-space-based rigid motion correction approach.
  • Utilized a normalized cross-correlation objective function adapted for dynamic contrast.
  • Implemented an ROI-based coil-compression to focus on the cardiac region.
  • Compared K-CC-MoCo against state-of-the-art image-based registration using digital phantoms and real acquisitions.

Main Results:

  • K-CC-MoCo demonstrated approximately 2x speed improvement over image-based methods.
  • Successfully corrected respiratory motion at high acceleration factors (up to 50x), where image-based methods failed.
  • Resulted in visibly less blurred time-averaged images, supported by quantitative metrics like SSIM.

Conclusions:

  • K-CC-MoCo significantly outperforms image-based motion correction in accelerated free-breathing FPP-CMR.
  • Enables robust respiratory motion estimation and correction directly in k-space.
  • Facilitates high-quality, highly accelerated FPP-CMR using model-based and deep learning reconstructions.