Total‐body parametric imaging using the Patlak model: Feasibility of reduced scan time
Authors: Wu Yaping , Feng Tao , Shen Yu , Fu Fangfang , Meng Nan , Li Xiaochen , Xu Tianyi , Sun Tao , Gu Fengyun , Wu Qi , Zhou Yun , Han Hui , Bai Yan , Wang Meiyun 
Published: April 2022

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 Ki images designated as GIDIF 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 Ki images designated as GPBIF were generated using the PBIF. Nonlocalmeans (NLM) denoising was applied to the generated images to get two extra groups of (NLM‐designated) images: GIDIF+NLM and GPBIF+NLM. Two radiologists evaluated the overall image quality, noise, and lesion detectability of the Ki images from different groups. The 20–60 min scans in GIDIF 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. Ki 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 Ki images in the GIDIF and GPBIF groups generated from 30‐min scans showed diagnostic value equivalent to the 40‐min scans of GIDIF. While the image quality of the 16‐min scans was poor, all lesions could still be detected. No significant difference was found between Ki values estimated with GIDIF and GPBIF 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 Ki images in the GIDIF+NLM and GPBIF+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 Ki values produced with GIDIF+NLM and GPBIF+NLM in muscle and lesion regions(all p > 0.7).

Conclusions

The Ki 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 GIDIF from 40‐min dynamic data, and there is no significant difference between Ki images generated using IDIF or PBIF (p > 0.5).