Clinic of Nuclear Medicine, Friedrich-Alexander-University Erlangen-Nürnberg (FAU), Ulmenweg 18, 91054, Erlangen, Germany.
Pattern Recognition Lab, Friedrich-Alexander-University Erlangen-Nürnberg (FAU), Erlangen, Germany.
J Nucl Cardiol. 2020 Oct;27(5):1469-1482. doi: 10.1007/s12350-018-1272-1. Epub 2018 Apr 13.
The SMARTZOOM multifocal collimator from Siemens Healthcare was developed to improve the γ-photon sensitivity in myocardial perfusion imaging without truncating the field of view. As part of the IQ-SPECT package, it may be used to reduce radiopharmaceutical dose to patients, as well as acquisition time. The aim of this study was twofold: (1) to evaluate the influence of dose reduction in semi-automated MPI scoring, with focus on different strategies for the choice of normal data (count-matched, full-count), and (2) to evaluate the effect of dose reduction afforded by Siemens' IQ-SPECT package.
50 patients underwent Tc-99m-sestamibi one-day stress/rest SPECT/CT. Multiple levels of count reduction were generated using binomial thinning. Using Corridor 4DM, summed stress score (SSS) was calculated using either count-matched or full-count normal data. Studies were classified as low-risk (SSS < 4) or intermediate/high-risk (SSS ≥ 4).
Count reduction using count-matched normal data increases false-normal rate and decreases sensitivity. With full-count normal data, count reduction increases false-hypoperfusion rate, leading to decreased specificity. Altogether, rate of reclassification was significant at roughly 67% dose and below.
Significant bias results from count level of normal data relative to actual patient data. Compared to standard LEHR, IQ-SPECT should allow for significant dose reduction.
西门子医疗的 SMARTZOOM 多焦点准直器旨在提高心肌灌注成像中 γ 光子的灵敏度，而不会截断视野。作为 IQ-SPECT 软件包的一部分，它可用于减少患者的放射性药物剂量和采集时间。本研究旨在：(1)评估半自动化 MPI 评分中剂量降低的影响，重点关注正常数据选择的不同策略（计数匹配、全计数）；(2)评估西门子 IQ-SPECT 软件包提供的剂量降低的效果。
50 例患者接受了 Tc-99m-sestamibi 一日负荷/静息 SPECT/CT。使用二项式稀疏法生成多个计数减少水平。使用 Corridor 4DM，使用计数匹配或全计数正常数据计算总和应激评分（SSS）。研究分为低危（SSS＜4）或中高危（SSS≥4）。
使用计数匹配正常数据进行计数减少会增加假正常率并降低灵敏度。使用全计数正常数据进行计数减少会增加假灌注率，从而降低特异性。总的来说，重新分类率在大约 67%剂量及以下时显著。
正常数据的计数水平与实际患者数据相比会产生显著的偏差。与标准 LEHR 相比，IQ-SPECT 应该允许显著的剂量减少。
