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Efficient Delay Correction for Total-Body PET Kinetic Modeling Using Pulse Timing Methods. | LitMetric

AI Article Synopsis

  • - Quantitative kinetic modeling in total-body PET relies on an input function (IDIF) derived from imaging, but the distance between the IDIF site (like the aorta) and tissue can create a delay, complicating analysis with standard joint estimation methods that are computationally intense.
  • - The study explored pulse timing methods, specifically leading edge (LE) and constant fraction discrimination, to estimate and correct for this delay efficiently through simulations and real patient data, involving 21 subjects over a dynamic PET acquisition.
  • - Results showed that the LE method with a 10% threshold closely aligned with the joint estimation approach in assessing delay and kinetic parameters, highlighting that delay correction enhances image quality and avoids significant bias when analyzing total-body PET data.*

Article Abstract

Quantitative kinetic modeling requires an input function. A noninvasive image-derived input function (IDIF) can be obtained from dynamic PET images. However, a robust IDIF location (e.g., aorta) may be far from a tissue of interest, particularly in total-body PET, introducing a time delay between the IDIF and the tissue. The standard practice of joint estimation (JE) of delay, along with model fitting, is computationally expensive. To improve the efficiency of delay correction for total-body PET parametric imaging, this study investigated the use of pulse timing methods to estimate and correct for delay. Simulation studies were performed with a range of delay values, frame lengths, and noise levels to test the tolerance of 2 pulse timing methods-leading edge (LE) and constant fraction discrimination and their thresholds. The methods were then applied to data from 21 subjects (14 healthy volunteers, 7 cancer patients) who underwent a 60-min dynamic total-body F-FDG PET acquisition. Region-of-interest kinetic analysis was performed and parametric images were generated to compare LE and JE methods of delay correction, as well as no delay correction. Simulations demonstrated that a 10% LE threshold resulted in biases and SDs at tolerable levels for all noise levels tested, with 2-s frames. Pooled region-of-interest-based results ( = 154) showed strong agreement between LE (10% threshold) and JE methods in estimating delay (Pearson 0.96, 0.001) and the kinetic parameters ( 0.96, 0.001), ( 1.00, 0.001), and ( 0.97, 0.001). When tissues with minimal delay were excluded from pooled analyses, there were reductions in (69.4%) and (4.8%) when delay correction was not performed. Similar results were obtained for parametric images; additionally, lesion contrast was improved overall with LE and JE delay correction compared with no delay correction and Patlak analysis. This study demonstrated the importance of delay correction in total-body PET. LE delay correction can be an efficient surrogate for JE, requiring a fraction of the computational time and allowing for rapid delay correction across more than 10 voxels in total-body PET datasets.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9364346PMC
http://dx.doi.org/10.2967/jnumed.121.262968DOI Listing

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