Total‐body parametric imaging using the Patlak model: Feasibility of reduced scan time

Abstract

Abstract Purpose This study explored the feasibility of reducing the scan time of Patlak parametric imaging on the uEXPLORER. Methods A total of 65 patients (27 females and 38 males, age 56.1 ± 10.4) were recruited in this study. 18F fluorodeoxyglucose was injected, and its dose was adjusted by body weight (4.07 MBq/kg). Total‐body dynamic scanning was performed on the uEXPLORER total‐body Positron emission tomography/computed tomography (CT) scanner with a total scan time of 60 min from the injection. The image derived input function (IDIF) was obtained from the aortic arch. The voxelwise Patlak analysis was applied to generate the K i images designated as G IDIF with different acquisition times (20–60, 30–60, 40‐60, and 44–60 min). The population‐based input function (PBIF) was constructed from the mean value of the IDIF from the population, and K i images designated as G PBIF were generated using the PBIF. Nonlocalmeans (NLM) denoising was applied to the generated images to get two extra groups of (NLM‐designated) images: G IDIF+NLM and G PBIF+NLM . Two radiologists evaluated the overall image quality, noise, and lesion detectability of the K i images from different groups. The 20–60 min scans in G IDIF were selected as the gold standard for each patient. We determined that image quality is at sufficient level if all the lesions can be recognized and meet the clinical criteria. K i values in muscle and lesion were compared across different groups to evaluate the quantitative accuracy. Results The overall image quality, image noise, and lesion conspicuity were significantly better in long time series than short time series in all four groups (all p < 0.001). The K i images in the G IDIF and G PBIF groups generated from 30‐min scans showed diagnostic value equivalent to the 40‐min scans of G IDIF . While the image quality of the 16‐min scans was poor, all lesions could still be detected. No significant difference was found between K i values estimated with G IDIF and G PBIF in muscle and lesion regions (all p > 0.5). After applying the NLM filter, the coefficient of variation could be reduced on the order of (1%, 15%, 19%, and 37%) and (110%, 125%, 94%, and 69%) with four acquisition time schemes for lesion and muscle. The reduction percentage did not have a substantial difference in IDIF and PBIF group. The K i images in the G IDIF+NLM and G PBIF+NLM groups generated from the 20‐min acquisitions showed acceptable quality. All lesions could be found on the NLM processed images of the 16‐min scans. No significant difference was found between K i values produced with G IDIF+NLM and G PBIF+NLM in muscle and lesion regions(all p > 0.7). Conclusions The K i images generated by the PBIF‐based Patlak model using a 20‐min dynamic scan with the NLM filter achieved a similar diagnostic efficiency to images with G IDIF from 40‐min dynamic data, and there is no significant difference between K i images generated using IDIF or PBIF ( p > 0.5).

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