Fast spoiled gradient-recalled MR imaging of thoracic aortic dissection: preliminary clinical experience at 1.5 T

R M Summers1, H D Sostman, C E Spritzer

  • 1Department of Radiology, Duke University Medical Center, Durham, NC 27710, USA.

Insights

Fast spoiled gradient-recalled (FSPGR) magnetic resonance (MR) imaging shows limited accuracy for diagnosing thoracic aortic dissection (TAD). Additional imaging sequences are recommended for reliable TAD diagnosis and classification.

Area of Science:

  • Radiology
  • Cardiovascular Imaging
  • Medical Diagnostics

Background:

  • Thoracic aortic dissection (TAD) is a life-threatening condition requiring prompt diagnosis.
  • Magnetic resonance (MR) imaging is a key modality for evaluating the aorta.
  • Fast spoiled gradient-recalled (FSPGR) sequences offer rapid image acquisition.

Purpose of the Study:

  • To evaluate the diagnostic performance of fast spoiled gradient-recalled (FSPGR) magnetic resonance (MR) imaging for thoracic aortic dissection (TAD).
  • To assess the sensitivity and specificity of FSPGR imaging in identifying TAD.
  • To determine the reliability of FSPGR in classifying the type of aortic dissection.

Main Methods:

  • Twenty-eight patients with suspected TAD underwent MR imaging, including FSPGR and cine or cardiac-gated spin-echo sequences.
  • FSPGR images were interpreted by three readers for TAD presence/absence.
  • Receiver operating characteristic (ROC) analysis was performed to assess diagnostic performance.

Main Results:

  • At 90% specificity, FSPGR sensitivity for TAD ranged from 52% to 90% across readers.
  • Pulsatility artifacts and mural thrombus caused false readings.
  • The correct dissection type was identified in only 65% of true-positive cases.

Conclusions:

  • FSPGR MR imaging has a limited role in screening for and rapid evaluation of TAD, especially in unstable patients.
  • Diagnostic accuracy is reader-dependent and susceptible to artifacts.
  • Additional MR imaging sequences are necessary for accurate TAD diagnosis and classification.

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