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Related Concept Videos

Magnetic Resonance Imaging01:24

Magnetic Resonance Imaging

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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Diastolic function assessment with four-dimensional flow cardiovascular magnetic resonance using automatic deep

Federica Viola1, Mariana Bustamante2, Ann Bolger3

  • 1Division of Diagnostics and Specialist Medicine, Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden; Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden.

Journal of Cardiovascular Magnetic Resonance : Official Journal of the Society for Cardiovascular Magnetic Resonance
|March 31, 2024
PubMed
Summary

This study compared automated and semi-automated 4D Flow CMR methods for assessing the E/A ratio in diastolic dysfunction. The deep learning-based automated method showed strong association with echocardiography and the fastest results.

Keywords:
4D Flow CMRDeep learningDiastolic functionEA ratio

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Area of Science:

  • Cardiovascular Imaging
  • Medical Technology
  • Diastolic Function Assessment

Background:

  • Diastolic left ventricular (LV) dysfunction significantly impacts heart failure symptoms and prognosis.
  • The E/A ratio, derived from transmitral flow velocities, is crucial for grading diastolic dysfunction, especially in patients with reduced LV systolic function.
  • Doppler echocardiography is standard for diastolic function assessment, but cardiovascular magnetic resonance (CMR) is less established.

Purpose of the Study:

  • To compare automated and semi-automated 4D Flow CMR techniques for assessing the E/A ratio against conventional echocardiography.
  • To evaluate the performance of a deep learning-based automated method versus semi-automated approaches for 4D Flow CMR E/A ratio calculation.
  • To determine the feasibility of using 4D Flow CMR for diastolic function assessment in patients with cardiovascular disease.

Main Methods:

  • Ninety-seven subjects with chronic ischemic heart disease underwent both echocardiography and CMR.
  • Three 4D Flow CMR methods were used: two semi-automated (MVvel, MVflow) measuring inflow at the mitral valve plane, and one fully automated (LVvel) using deep learning for LV segmentation.
  • E/A ratios were computed using all three 4D Flow methods and compared to echocardiography-derived E/A ratios.

Main Results:

  • All three 4D Flow CMR methods (MVvel, MVflow, LVvel) demonstrated strong associations with the echocardiography-derived E/A ratio (R² = 0.60, 0.58, 0.72).
  • The automated LVvel method showed the strongest correlation (R² = 0.72) and fastest runtime (∼40 seconds).
  • Semi-automated methods (MVvel, MVflow) showed very strong association with the LVvel method, suggesting their utility as alternatives.

Conclusions:

  • The E/A ratio assessed by 4D Flow CMR, particularly the automated deep learning method, strongly correlates with Doppler echocardiography.
  • These 4D Flow CMR methods can expand the utility of CMR in assessing diastolic dysfunction in cardiovascular disease patients.
  • While absolute velocities may be underestimated, the E/A ratio derived from 4D Flow CMR is reliable, with the automated approach offering the best performance.