16 Jul 21. Featured Paper
Improved identification of abdominal aortic aneurysm using the Kernelized Expectation Maximization algorithm.
Link to paper on Philosophical Transactions of the Royal Society A: Mathematical, Physical & Engineering Sciences
Abdominal aortic aneurysm (AAA) monitoring & risk of rupture is currently assumed to be correlated with the aneurysm diameter.
Aneurysm growth, however, has been demonstrated to be unpredictable.
The PET low spatial resolution, however, can affect the accuracy of the diagnosis.
Advanced edge-preserving reconstruction algorithms can overcome this issue.
The kernel method has been demonstrated to provide noise suppression while retaining emission & edge information.
Nevertheless, these findings were obtained using simulations, phantoms & a limited amount of patient data.
In this study, the authors aim to investigate the usefulness of the anatomically guided kernelized expectation maximization (KEM) & the hybrid KEM (HKEM) methods & to judge the statistical significance of the related improvements.
Sixty-one datasets of patients with AAA & 11 from control patients were reconstructed with ordered subsets expectation maximization (OSEM), HKEM & KEM & the analysis was carried out using the target-to-blood-pool ratio, & a series of statistical tests.
The results show that all algorithms have similar diagnostic power, but HKEM & KEM can significantly recover uptake of lesions & improve the accuracy of the diagnosis by up to 22% compared to OSEM.
The same improvements are likely to be obtained in clinical applications based on the quantification of small lesions, like for example cancer.
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Featured paper: Improved identification of abdominal aortic aneurysm using the Kernelized Expectation Maximization algorithm.