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Observer assessment of multi-pinhole SPECT geometries for prostate cancer imaging, a simulation study

机译:观察者评估用于前列腺癌成像的多针孔SPECT几何形状的模拟研究

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SPECT imaging using In-111 ProstaScint is an FDA-approved method for diagnosing prostate cancer metastases within the pelvis. However, conventional medium-energy parallel-hole (MEPAR) collimators produce poor image quality and we are investigating the use of multipinhole (MPH) imaging as an alternative. This paper presents a method for evaluating MPH designs that makes use of sampling-sensitive (SS) mathematical model observers for tumor detection-localization tasks. Key to our approach is the redefinition of a normal (or background) reference image that is used with scanning model observers. We used this approach to compare different MPH configurations for the task of small-tumor detection in the prostate and surrounding lymph nodes. Four configurations used 10, 20, 30, and 60 pinholes evenly spaced over a complete circular orbit. A fixed-count acquisition protocol was assumed. Spherical tumors were placed within a digital anthropomorphic phantom having a realistic Prostascint biodistribution. Imaging data sets were generated with an analytical projector and reconstructed volumes were obtained with the OSEM algorithm. The MPH configurations were compared in a localization ROC (LROC) study with 2D pelvic images and both human and model observers. Regular and SS versions of the scanning channelized nonprewhitening (CNPW) and visual-search (VS) model observers were applied. The SS models demonstrated the highest correlations with the average human-observer results.
机译:使用In-111 ProstaScint进行SPECT成像是FDA批准的诊断骨盆内前列腺癌转移的方法。但是,传统的中能平行孔(MEPAR)准直仪会产生较差的图像质量,我们正在研究使用多针孔(MPH)成像作为替代方案。本文提出了一种评估MPH设计的方法,该方法利用采样敏感(SS)数学模型观察器来执行肿瘤检测定位任务。我们方法的关键是重新定义与扫描模型观察者一起使用的正常(或背景)参考图像。我们使用这种方法来比较不同的MPH配置,以检测前列腺和周围淋巴结中的小肿瘤。四种配置使用了10个,20个,30个和60个针孔,这些针孔在完整的圆形轨道上均匀分布。假定使用固定计数的采集协议。将球形肿瘤放置在具有逼真的Prostascint生物分布的数字拟人模型内。使用分析投影仪生成成像数据集,并使用OSEM算法获得重建的体积。在本地ROC(LROC)研究中将MPH配置与2D骨盆图像以及人类和模型观察者进行了比较。常规和SS版本的扫描通道化非预增白(CNPW)和视觉搜索(VS)模型观察者得到了应用。 SS模型显示出与人类观察者平均结果的最高相关性。

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