A Bayesian approach for CT perfusion parameters estimation with imperfect measurement

Tao Sun; Roger Fulton

doi:10.1117/12.2581477

15 February 2021 A Bayesian approach for CT perfusion parameters estimation with imperfect measurement

Tao Sun, Roger Fulton

Proceedings Volume 11595, Medical Imaging 2021: Physics of Medical Imaging; 1159508 (2021) https://doi.org/10.1117/12.2581477
Event: SPIE Medical Imaging, 2021, Online Only

Abstract

Perfusion CT imaging is commonly used for the rapid assessment of patients presenting with symptoms of acute stroke. Maps of perfusion parameters such as cerebral blood volume (CBV), cerebral blood flow (CBF), and mean transit time (MTT) derived from the scan data provide crucial information for stroke diagnosis and treatment decisions. Most vendors implement singular value decomposition (SVD)-based methods on their scanners to calculate these parameters. However, SVD-based method is known to have issues of improperly handling the imperfect scan. For example, increasing the acquisition interval or decreasing the scan duration may introduce a bias in the estimated perfusion parameters. In this work, we propose a Bayesian inference algorithm, which can tolerate the imperfect scan conditions better than conventional method and is able to derive the uncertainty of a given perfusion parameter. We apply the variational technique to the inference problem, which becomes an expectation-maximization problem. The probability distribution (with Gaussian mean and variance) of each estimated parameter can be obtained. We perform evaluations in simulation studies both with full and incomplete data. The proposed method can obtain much less bias in estimation than the conventional method, and additionally providing the degree of the uncertainty in measurement.

Conference Presentation

Citation Download Citation

Tao Sun and Roger Fulton "A Bayesian approach for CT perfusion parameters estimation with imperfect measurement", Proc. SPIE 11595, Medical Imaging 2021: Physics of Medical Imaging, 1159508 (15 February 2021); https://doi.org/10.1117/12.2581477

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