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ABSTACT: OBJECTIVES: Myocardial blood flow (MBF) imaging is used in patients with suspected cardiac sarcoidosis, and also in stress/rest studies. The accuracy of MBF is dependent on imaging parameters such as new reconstruction methodologies. In this work, we aim to assess the impact of a novel PET reconstruction algorithm (Bayesian-penalized likelihood-BPL) on the values determined from the calculation of [13N]-NH3 MBF values. METHODS: Data from 21 patients undergoing rest MBF evaluation [13N]-NH3 as part of sarcoidosis imaging were retrospectively analyzed. Each scan was reconstructed with a range of BPL coefficients (1-500), and standard clinical FBP and OSEM reconstructions. MBF values were calculated via an automated software routine for all datasets. RESULTS: Reconstruction of [13N]-NH3 dynamic data using the BPL, OSEM, or FBP reconstruction showed no quantitative differences for the calculation of territorial or global MBF (P = .97). Image noise was lower using OSEM or BPL reconstructions than FBP and noise from BPL reached levels seen in OSEM images between B = 300 and B = 400. Intrasubject differences between all reconstructions over all patients in respect of all cardiac territories showed a maximum coefficient of variation of 9.74%. CONCLUSION: Quantitation of MBF via kinetic modeling of cardiac rest MBF by [13N]-NH3 is minimally affected by the use of a BPL reconstruction technique, with BPL images presenting with less noise.

Original publication

DOI

10.1007/s12350-016-0554-8

Type

Journal article

Journal

J Nucl Cardiol

Publication Date

02/2017

Volume

24

Pages

282 - 290

Keywords

N-13 ammonia, PET, physics of imaging, Algorithms, Bayes Theorem, Blood Flow Velocity, Coronary Artery Disease, Coronary Circulation, Data Interpretation, Statistical, Female, Humans, Image Enhancement, Image Interpretation, Computer-Assisted, Likelihood Functions, Male, Middle Aged, Myocardial Perfusion Imaging, Nitrogen Radioisotopes, Positron Emission Tomography Computed Tomography, Radiopharmaceuticals, Reproducibility of Results, Sensitivity and Specificity