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Related Concept Videos

Aneurysm II: Clinical Manifestations and Diagnostic Studies01:21

Aneurysm II: Clinical Manifestations and Diagnostic Studies

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Thoracic, aortic arch and abdominal aneurysms are significant vascular conditions that can present with various clinical manifestations and lead to serious complications. Understanding these manifestations and the appropriate diagnostic studies is essential for effective management and treatment.Thoracic Aortic AneurysmsThoracic aortic aneurysms often remain asymptomatic until they reach a size that impinges on adjacent structures. They typically cause deep, diffuse chest pain that radiates to...
64

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Related Experiment Video

Updated: Nov 6, 2025

Manufacturing Abdominal Aorta Hydrogel Tissue-Mimicking Phantoms for Ultrasound Elastography Validation
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Improved identification of abdominal aortic aneurysm using the Kernelized Expectation Maximization algorithm.

Daniel Deidda1, Mercy I Akerele2,3, Robert G Aykroyd4

  • 1National Physical Laboratory, Teddington, UK.

Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences
|May 10, 2021
PubMed
Summary
This summary is machine-generated.

New imaging methods, hybrid kernelized expectation maximization (HKEM) and kernelized expectation maximization (KEM), improve the accuracy of abdominal aortic aneurysm diagnosis by up to 22%. These advanced techniques enhance lesion uptake quantification, aiding in predicting aneurysm growth and rupture risk.

Keywords:
PETPET/CTaortic aneurysmkernel method

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Area of Science:

  • Medical Imaging
  • Nuclear Medicine
  • Cardiovascular Imaging

Background:

  • Abdominal aortic aneurysm (AAA) rupture risk is typically assessed by diameter, but growth is unpredictable.
  • 18F]-NaF Positron Emission Tomography (PET) shows promise for AAA monitoring by measuring calcified lesion uptake.
  • Low spatial resolution in PET imaging can limit diagnostic accuracy.

Purpose of the Study:

  • To investigate the effectiveness of anatomically guided kernelized expectation maximization (KEM) and hybrid KEM (HKEM) for AAA diagnosis.
  • To statistically evaluate the improvements offered by KEM and HKEM over standard methods.
  • To assess the potential for improved quantification of small lesions in clinical applications.

Main Methods:

  • Reconstruction of 61 AAA patient and 11 control patient datasets using OSEM, HKEM, and KEM algorithms.
  • Analysis of reconstructed images using the target-to-blood-pool ratio.
  • Application of statistical tests to determine the significance of observed improvements.

Main Results:

  • All tested algorithms demonstrated comparable diagnostic power.
  • HKEM and KEM significantly improved the recovery of lesion uptake compared to OSEM.
  • Diagnostic accuracy was enhanced by up to 22% using HKEM and KEM.

Conclusions:

  • HKEM and KEM offer significant improvements in PET-based AAA diagnosis and growth prediction.
  • These advanced reconstruction methods enhance the accuracy of lesion quantification.
  • Potential for similar improvements in quantifying small lesions in other clinical areas, such as oncology.