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使用数字Biograph Vision对前列腺癌患者以缩短扫描持续时间采集的[镓]镓-PSMA PET/CT图像进行评估。

Evaluation of [Ga]Ga-PSMA PET/CT images acquired with a reduced scan time duration in prostate cancer patients using the digital biograph vision.

作者信息

Weber Manuel, Jentzen Walter, Hofferber Regina, Herrmann Ken, Fendler Wolfgang Peter, Conti Maurizio, Wetter Axel, Kersting David, Rischpler Christoph, Fragoso Costa Pedro

机构信息

Department of Nuclear Medicine, University of Duisburg-Essen and German Cancer Consortium (DKTK)-University Hospital Essen, Hufelandstrasse 55, 45122, Essen, Germany.

Siemens Medical Solutions USA, INC, Knoxville, TN, USA.

出版信息

EJNMMI Res. 2021 Feb 28;11(1):21. doi: 10.1186/s13550-021-00765-y.

Abstract

AIM

[Ga]Ga-PSMA-11 PET/CT allows for a superior detection of prostate cancer tissue, especially in the context of a low tumor burden. Digital PET/CT bears the potential of reducing scan time duration/administered tracer activity due to, for instance, its higher sensitivity and improved time coincidence resolution. It might thereby expand [Ga]Ga-PSMA-11 PET/CT that is currently limited by Ge/Ga-generator yield. Our aim was to clinically evaluate the influence of a reduced scan time duration in combination with different image reconstruction algorithms on the diagnostic performance.

METHODS

Twenty prostate cancer patients (11 for biochemical recurrence, 5 for initial staging, 4 for metastatic disease) sequentially underwent [Ga]Ga-PSMA-11 PET/CT on a digital Siemens Biograph Vision. PET data were collected in continuous-bed-motion mode with a mean scan time duration of 16.7 min (reference acquisition protocol) and 4.6 min (reduced acquisition protocol). Four iterative reconstruction algorithms were applied using a time-of-flight (TOF) approach alone or combined with point-spread-function (PSF) correction, each with 2 or 4 iterations. To evaluate the diagnostic performance, the following metrics were chosen: (a) per-region detectability, (b) the tumor maximum and peak standardized uptake values (SUVmax and SUVpeak), and (c) image noise using the liver's activity distribution.

RESULTS

Overall, 98% of regions (91% of affected regions) were correctly classified in the reduced acquisition protocol independent of the image reconstruction algorithm. Two nodal lesions (each ≤ 4 mm) were not identified (leading to downstaging in 1/20 cases). Mean absolute percentage deviation of SUVmax (SUVpeak) was approximately 9% (6%) for each reconstruction algorithm. The mean image noise increased from 13 to 21% (4 iterations) and from 10 to 15% (2 iterations) for PSF + TOF and TOF images.

CONCLUSIONS

High agreement at 3.5-fold reduction of scan time in terms of per-region detection (98% of regions) and image quantification (mean deviation ≤ 10%) was demonstrated; however, small lesions can be missed in about 10% of patients leading to downstaging (T1N0M0 instead of T1N1M0) in 5% of patients. Our results suggest that a reduction of scan time duration or administered [Ga]Ga-PSMA-11 activities can be considered in metastatic patients, where missing small lesions would not impact patient management. Limitations include the small and heterogeneous sample size and the lack of follow-up.

摘要

目的

[镓]镓 - PSMA - 11 PET/CT能够更优地检测前列腺癌组织,尤其是在肿瘤负荷较低的情况下。数字PET/CT由于其更高的灵敏度和改进的时间符合分辨率,具有减少扫描时间/注射示踪剂活度的潜力。这可能会扩展目前受锗/镓发生器产量限制的[镓]镓 - PSMA - 11 PET/CT。我们的目的是临床评估缩短扫描时间并结合不同图像重建算法对诊断性能的影响。

方法

20例前列腺癌患者(11例为生化复发,5例为初始分期,4例为转移性疾病)在数字西门子Biograph Vision上依次接受[镓]镓 - PSMA - 11 PET/CT检查。PET数据以连续床位移动模式采集,平均扫描时间为16.7分钟(参考采集方案)和4.6分钟(缩短采集方案)。应用了四种迭代重建算法,单独使用飞行时间(TOF)方法或与点扩散函数(PSF)校正相结合,每种算法进行2次或4次迭代。为评估诊断性能,选择了以下指标:(a)每个区域的可检测性,(b)肿瘤最大和峰值标准化摄取值(SUVmax和SUVpeak),以及(c)使用肝脏活性分布评估图像噪声。

结果

总体而言,在缩短采集方案中,98%的区域(91%的受影响区域)被正确分类,与图像重建算法无关。两个淋巴结病变(每个≤4毫米)未被识别(导致20例中有1例分期降低)。每种重建算法的SUVmax(SUVpeak)平均绝对百分比偏差约为9%(6%)。对于PSF + TOF和TOF图像,平均图像噪声在4次迭代时从13%增加到21%,在2次迭代时从10%增加到15%。

结论

在扫描时间缩短3.5倍的情况下,在每个区域检测(98%的区域)和图像定量(平均偏差≤10%)方面显示出高度一致性;然而,约10%的患者可能会漏诊小病变,导致5%的患者分期降低(从T1N1M0降至T1N0M0)。我们的结果表明,对于转移性患者,可以考虑缩短扫描时间或减少[镓]镓 - PSMA - 11的注射活度,因为漏诊小病变不会影响患者管理。局限性包括样本量小且异质性大以及缺乏随访。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4e77/7914332/fda26004e8d9/13550_2021_765_Fig1_HTML.jpg

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