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Petibon, Yoann

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Petibon

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Yoann

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Petibon, Yoann
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    In Vivo Quantification of Mitochondrial Membrane Potential
    (Springer Science and Business Media LLC, 2020-07-08) Alpert, Nathaniel; Pelletier-Galarneau, Matthieu; Petibon, Yoann; Normandin, Marc; El Fakhri, Georges
    Momcilovic et al1 report mitochondrial metabolism differences amongst various mouse lung cancer subtypes, as measured by positron emission tomography (PET) and a voltage-sensitive tracer. They describe their experiments as measurements of mitochondrial membrane potential, ΔΨm, and suggest that they might be used as a non-invasive biomarker to guide the delivery of complex I inhibitors in cancer. Contrary to their claims, Momcilovic et al did not measure membrane potential in an absolute sense, instead relying on an empirical endpoint, namely the percent dose per gram of the tracer in tumor to that in heart, which only partially depends on ΔΨm. Despite the biomedical significance of their findings, their work represents critical methodological misunderstandings and omissions about the underlying basis for application of voltage sensing tracers which could ultimately hinder the successful clinical translation of the technique.
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    MR-based motion correction for cardiac PET parametric imaging: a simulation study
    (Springer International Publishing, 2018) Guo, Rong; Petibon, Yoann; Ma, Yixin; El Fakhri, Georges; Ying, Kui; Ouyang, Jinsong
    Background: Both cardiac and respiratory motions bias the kinetic parameters measured by dynamic PET. The aim of this study was to perform a realistic positron emission tomography-magnetic resonance (PET-MR) simulation study using 4D XCAT to evaluate the impact of MR-based motion correction on the estimation of PET myocardial kinetic parameters using PET-MR. Dynamic activity distributions were obtained based on a one-tissue compartment model with realistic kinetic parameters and an arterial input function. Realistic proton density/T1/T2 values were also defined for the MRI simulation. Two types of motion patterns, cardiac motion only (CM) and both cardiac and respiratory motions (CRM), were generated. PET sinograms were obtained by the projection of the activity distributions. PET image for each time frame was obtained using static (ST), gated (GA), non-motion-corrected (NMC), and motion-corrected (MC) methods. Voxel-wise unweighted least squares fitting of the dynamic PET data was then performed to obtain K1 values for each study. For each study, the mean and standard deviation of K1 values were computed for four regions of interest in the myocardium across 25 noise realizations. Results: Both cardiac and respiratory motions introduce blurring in the PET parametric images if the motion is not corrected. Conventional cardiac gating is limited by high noise level on parametric images. Dual cardiac and respiratory gating further increases the noise level. In contrast to GA, the MR-based MC method reduces motion blurring in parametric images without increasing noise level. It also improves the myocardial defect delineation as compared to NMC method. Finally, the MR-based MC method yields lower bias and variance in K1 values than NMC and GA, respectively. The reductions of K1 bias by MR-based MC are 7.7, 5.1, 15.7, and 29.9% in four selected 0.18-mL myocardial regions of interest, respectively, as compared to NMC for CRM. MR-based MC yields 85.9, 75.3, 71.8, and 95.2% less K1 standard deviation in the four regions, respectively, as compared to GA for CRM. Conclusions: This simulation study suggests that the MR-based motion-correction method using PET-MR greatly reduces motion blurring on parametric images and yields less K1 bias without increasing noise level.