The Status and Future of PET Myocardial Blood Flow Quantification Software

Nesterov, Sergey V.; Lee, Benjamin C.; Moody, Jonathan B.; Slomka, Piotr J.; Han, Chong; Knuuti, Juhani

doi:10.17996/anc.02.01.106

Cited by 10 publications

(8 citation statements)

References 34 publications

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“…Several steps have to be performed prior to quantification of MBF: (1) dynamic PET acquisition; (2) image reconstruction of the PET data; (3) segmentation of the myocardium contour; (4) derivation of time-activity curves (TACs) of the myocardium and the left ventricle (LV); (5) quality control; and (6) compartmental analyses 7…”

Section: Methodsmentioning

confidence: 99%

How to detect and correct myocardial creep in myocardial perfusion imaging using Rubidium-82 PET?

Koenders

Dijk

Jager

et al. 2019

Journal of Nuclear Cardiology

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Reliability of myocardial blood flow (MBF) quantification in myocardial perfusion imaging (MPI) using PET can majorly be affected by the occurrence of myocardial creep when using pharmacologically induced stress. In this paper, we provide instructions on how to detect and correct for myocardial creep. For example, in each time frame of the PET images the myocardium contour and the observed activity have to be compared to check for misalignments. In addition, we provide an overview of the functionality of commonly used software packages to perform this quality control step as not all software packages currently provide this functionality. Furthermore, important clinical considerations to obtain accurate MBF measurements are given.

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Section: Methodsmentioning

confidence: 99%

How to detect and correct myocardial creep in myocardial perfusion imaging using Rubidium-82 PET?

Koenders

Dijk

Jager

et al. 2019

Journal of Nuclear Cardiology

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“…Another potential bias in the TAC generations can be caused by the uncertainty of the segmented ROIs, either directly from the dynamic PET images or indirectly from the contrast‐enhanced CT images being co‐registered with the corresponding PET images. Most of clinical softwares segment the ROI edges using algorithms such as local means analysis (PMOD), wavelet‐based algorithm (ImagenQ),and gradient operators (Corridor4DM) . However, these softwares still need more than one of the manual interventions such as LV axes definition, reorientation, or localization.…”

Section: Introductionmentioning

confidence: 99%

A robust segmentation method with triple‐factor non‐negative matrix factorization for myocardial blood flow quantification from dynamic ⁸²Rb positron emission tomography

Liu

Sun³

et al. 2019

Medical Physics

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Purpose: In this work, we proposed a triple-factor non-negative matrix factorization (TNMF) method to semiautomatically segment the regions of interest (ROIs) of the left ventricular (LV) cavity and myocardium to improve the reproducibility of myocardial blood flow (MBF) quantification from dynamic 82 Rb positron emission tomography (PET). Methods: The proposed TNMF method was evaluated using NCAT phantom simulation with three noise levels. The segmented ROIs, time-activity curves (TACs), and K 1 derived from the TNMF method were compared with the ground truth simulated. The TNMF method was further evaluated in two patients each undergone both rest and stress 82 Rb PET studies. The TNMF and manual segmentations were implemented by two different observers, and the interoperator variations of MBF and myocardial flow reserve (MFR) were compared between the two methods. Results: Our simulation results showed that the TNMF method for dynamic PET image segmentation was robust as evidenced by the high Dice similarity coefficient, regardless of high or low count level. The relative bias in K 1 estimation was less than 1%. Our patient results also showed that reasonable ROIs for the LV cavity and myocardium could be obtained precisely for patients with and without myocardial perfusion defects. The TACs derived from the TNMF method were highly correlated with those obtained with the manual method (R 2 ≥ 0.964). The interoperator variations of MBF and MFR were markedly reduced using the TNMF method. Conclusions: In conclusion, the TNMF method is highly feasible for semiautomatic segmentation of the LV cavity and myocardium, with the potential to improve the precision of MBF quantification by improving segmentation.

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“…The patient populations may differ as many of the earlier studies have been performed in young people. Also, we again 7,8 attract the attention of the readership to the fundamental fact that all absolute values in PET perfusion imaging-normal and not-result from the transformation process. The measured radioactivity concentration values turn into milliliters of blood per minute per gram of myocardial tissue (mLÁminÁg).…”

mentioning

confidence: 97%

“…The process is done with dedicated software tools. It consists of several steps, and each of the steps can and does affect the results (for the detailed analysis, see 7 ). This process is less standardized that we would all want it to be: even such primary thing as the exact definition of the LV base is not clear in the 17-AHA segmentation.…”

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confidence: 99%

82Rb-PET MPQ: Do normal values exist?

Nesterov

Knuuti

2022

Journal of Nuclear Cardiology

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Introducing their recent study, Freitag et al. 1 state that ''two questions remain unanswered for quantitative 82Rb-PET: (1) which are the normal values of sMBF and rMBF? And (2) which quantitative parameter is the most accurate to allow for a precise diagnosis of CAD (sMBF or myocardial flow reserve [MFR])?''To get an answer, they performed a study of 357 patients that underwent 82Rb-PET/CT and integrated coronary CT angiography (CCTA). Based on the finding in CCTA, the patients were classified as (a) normal (no stenosis, N = 153), (b) with non-obstructive coronary artery disease (CAD) (\ 50%, N = 99), and (c) with obstructive CAD (C 50%, N = 105). Not surprisingly, the global stress myocardial blood flow (sMBF) (and myocardial flow reserve, MFR) were higher 3.61 ± 0.71 mLÁminÁg (3.08 ± 0.84) in normal patients than in patients with obstructive CAD 3.04 ± 0.77 mLÁminÁg (2.68 ± 0.79). Yet they turned out to be not different compared to patients with nonobstructive CAD (3.43 ± 0.69 mLÁminÁg and 2.99 ± 0.82). Also, sMBF was more accurate than MFR in identifying both ischemia and severe ischemia. The authors conclude that the provided ''normal quantitative values''-3.5 mLÁgÁmin-which they called the ''optimal threshold,'' can help rule out myocardial ischemia. Also, they suggest that a resting scan could be omitted in patients with sMBF values.We will not hover over the question of favoring sMBF over MFR: the same has been shown for 15O-Reprint requests:

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The Status and Future of PET Myocardial Blood Flow Quantification Software

Cited by 10 publications

References 34 publications

How to detect and correct myocardial creep in myocardial perfusion imaging using Rubidium-82 PET?

How to detect and correct myocardial creep in myocardial perfusion imaging using Rubidium-82 PET?

A robust segmentation method with triple‐factor non‐negative matrix factorization for myocardial blood flow quantification from dynamic ⁸²Rb positron emission tomography

82Rb-PET MPQ: Do normal values exist?

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The Status and Future of PET Myocardial Blood Flow Quantification Software

Cited by 10 publications

References 34 publications

How to detect and correct myocardial creep in myocardial perfusion imaging using Rubidium-82 PET?

How to detect and correct myocardial creep in myocardial perfusion imaging using Rubidium-82 PET?

A robust segmentation method with triple‐factor non‐negative matrix factorization for myocardial blood flow quantification from dynamic 82Rb positron emission tomography

82Rb-PET MPQ: Do normal values exist?

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A robust segmentation method with triple‐factor non‐negative matrix factorization for myocardial blood flow quantification from dynamic ⁸²Rb positron emission tomography