Title

Personalized Models for Injected Activity Levels in SPECT Myocardial Perfusion Imaging

UMMS Affiliation

Department of Radiology, Division of Nuclear Medicine

Publication Date

2018-12-06

Document Type

Article

Disciplines

Radiology

Abstract

We propose a patient-specific ("personalized") approach for tailoring the injected activities to individual patients in order to achieve dose reduction in SPECT-myocardial perfusion imaging (MPI). First, we develop a strategy to determine the minimum dose levels required for each patient in a large set of clinical acquisitions (857 subjects) such that the reconstructed images are sufficiently similar to that obtained at conventional clinical dose. We then apply machine learning models to predict the required dose levels on an individual basis based on a set of patient attributes which include body measurements and various clinical variables. We demonstrate the personalized dose models for two commonly used reconstruction methods in clinical SPECT-MPI: 1) conventional filtered backprojection (FBP) with post-filtering, and 2) ordered-subsets expectation-maximization (OS-EM) with corrections for attenuation, scatter and resolution, and evaluate their performance in perfusion-defect detection by using the clinical Quantitative Perfusion SPECT (QPS) software package. The results indicate that the achieved dose reduction can vary greatly among individuals from their conventional clinical dose, and that the personalized dose models can achieve further reduction on average compared to a global (non-patient specific) dose reduction approach. In particular, the average personalized dose level can be reduced to 58% and 54% of the full clinical dose, respectively, for FBP and OS-EM reconstruction, while without deteriorating the accuracy in perfusion-defect detection. Furthermore, with the average personalized dose further reduced to only 16% of full dose, OS-EM can still achieve a detection accuracy level comparable to that of FBP with full dose.

DOI of Published Version

10.1109/TMI.2018.2885319

Source

IEEE Trans Med Imaging. 2018 Dec 6. doi: 10.1109/TMI.2018.2885319. Link to article on publisher's site

Journal/Book/Conference Title

IEEE transactions on medical imaging

Related Resources

Link to Article in PubMed

PubMed ID

30530358

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