Abstract
ObjectivesMyocardial 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. MethodsData 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. ResultsReconstruction 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%. ConclusionQuantitation 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.
Highlights
Quantitative myocardial blood flow (MBF) imaging with PET is utilized in many centers around the world in a clinical setting for investigation of the human coronary circulation.[1,2,3] The technique allows for the quantitative assessment of the distribution of flow for delineation of the extent and severity of coronary artery diseases, microvascular function, as well as other conditions such as cardiac sarcoidosis
Quantification of MBF has been routinely used as an aid in suspected cardiac sarcoidosis in order to rule out coronary artery disease or to identify resting perfusion defects suggestive of inflammationinduced tissue damage.[6]
filtered back-projection (FBP) showed a consistently higher AUC, maximum and minimum differences in AUC between FBP and Bayesian-penalized likelihood (BPL) algorithms did not correspond with any specific B value
Summary
Quantitative myocardial blood flow (MBF) imaging with PET is utilized in many centers around the world in a clinical setting for investigation of the human coronary circulation.[1,2,3] The technique allows for the quantitative assessment of the distribution of flow for delineation of the extent and severity of coronary artery diseases, microvascular function, as well as other conditions such as cardiac sarcoidosis. Many new commercial methods include data corrections such as point-spread function modeling of the entire PET field of view aimed at improving spatial resolution during image reconstruction.[9,10] One such example of this, and of interest to this work, is a new Bayesian-penalized likelihood (BPL) reconstruction algorithm developed by GE Healthcare (commercially named Q.ClearÒ). The technique involves point-spread-function modeling with noise modeling controlled through the use of a penalty term that penalizes image intensity differences between neighboring pixels. The algorithm is allowed to run to effective convergence, allowing for an improved quantitative accuracy of imaging rather than suspending the algorithm after a certain number of iterations to control the image noise
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