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全身参数成像使用 Patlak 模型:减少扫描时间的可行性。

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

机构信息

Department of Medical Imaging, Henan Provincial People's Hospital and People's Hospital of Zhengzhou University, Zhengzhou, China.

UIH America Inc., Houston, Texas, USA.

出版信息

Med Phys. 2022 Jul;49(7):4529-4539. doi: 10.1002/mp.15647. Epub 2022 Apr 18.

DOI:10.1002/mp.15647
PMID:35394071
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 images designated as G 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 images designated as G were generated using the PBIF. Nonlocalmeans (NLM) denoising was applied to the generated images to get two extra groups of (NLM-designated) images: G and G . Two radiologists evaluated the overall image quality, noise, and lesion detectability of the K images from different groups. The 20-60 min scans in G 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 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 images in the G and G groups generated from 30-min scans showed diagnostic value equivalent to the 40-min scans of G . While the image quality of the 16-min scans was poor, all lesions could still be detected. No significant difference was found between K values estimated with G and G 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 images in the G and G 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 values produced with G and G in muscle and lesion regions(all p > 0.7).

CONCLUSIONS

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

摘要

目的

本研究旨在探讨在 uEXPLORER 上降低 Patlak 参数成像扫描时间的可行性。

方法

本研究共纳入 65 例患者(27 例女性,38 例男性,年龄 56.1±10.4 岁)。注射 18F 氟脱氧葡萄糖,并根据体重调整剂量(4.07MBq/kg)。使用 uEXPLORER 全身正电子发射断层扫描/计算机断层扫描(CT)扫描仪对患者进行 60 分钟的全身动态扫描。从主动脉弓获取图像推导输入函数(IDIF)。采用体素 Patlak 分析生成 K 图像,记为 G,采集时间分别为 20-60、30-60、40-60 和 44-60 分钟。通过从人群中获得的 IDIF 的平均值构建群体输入函数(PBIF),并使用 PBIF 生成 K 图像。对生成的图像应用非局部均值(NLM)去噪,得到两组(NLM 标记)图像:G 和 G。两名放射科医生评估了不同组的 K 图像的整体图像质量、噪声和病灶可探测性。选择每个患者的 20-60 分钟扫描作为金标准。如果所有病灶都能被识别并满足临床标准,则认为图像质量达到足够水平。比较不同组中肌肉和病灶的 K 值,以评估定量准确性。

结果

在所有 4 组中,长时间序列的整体图像质量、图像噪声和病灶显影均显著优于短时间序列(均 P<0.001)。30 分钟扫描生成的 G 和 G 组 K 图像具有与 40 分钟扫描的 G 相当的诊断价值。虽然 16 分钟扫描的图像质量较差,但仍能检测到所有病灶。肌肉和病灶区域中使用 G 和 G 估计的 K 值之间没有显著差异(均 P>0.5)。应用 NLM 滤波器后,四种采集时间方案的病变和肌肉区域的变异系数可分别降低(1%、15%、19%和 37%)和(110%、125%、94%和 69%)。IDIF 和 PBIF 组的降低百分比没有显著差异。20 分钟采集生成的 G 和 G 组 K 图像质量可接受。所有病灶在 16 分钟扫描的 NLM 处理图像上均能找到。肌肉和病灶区域中使用 G 和 G 生成的 K 值之间没有显著差异(均 P>0.7)。

结论

使用 NLM 滤波器进行的基于 PBIF 的 Patlak 模型生成的 20 分钟动态扫描 K 图像,与 40 分钟动态数据的 G 图像具有相似的诊断效率,使用 IDIF 或 PBIF 生成的 K 图像之间没有显著差异(P>0.5)。

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