基于Ga-67混合SPECT图像的纵隔病灶检测迭代重建策略评价。

N F Pereira, H C Gifford, P H Pretorius, T Farncombe, M Smyczynski, R Licho, P Schneider, M A King
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引用次数: 0

摘要

与使用数字幻影构建的研究相比,混合LROC研究可用于更真实地评估重建策略的影响。这是因为混合数据提供了临床成像中存在的背景可变性,以及对进行有意义测试所需的关键成像参数的控制。混合数据是通过将蒙特卡罗模拟病变添加到无病临床投影数据中获得的。由于Ga-67是一种特别具有挑战性的成像放射性核素,我们使用Ga-67混合SPECT数据来研究各种校正策略的有效性,以解释SPECT成像中的退化。我们的数据是通过GE-VG双探测器SPECT-CT相机获得的。在确定目标病变对比后,我们进行试点LROC研究,以获得不同策略的近似最优重建参数集。然后使用这些接近最优的参数来重建最终的评估研究集。本文报道的所有LROC研究结果仅采用人类观察员获得。我们使用最终的LROC研究结果来评估衰减补偿、散射补偿和探测器分辨率补偿对RBI-EM算法重构数据的影响。我们还将这些与相同数据集的FBP重建进行了比较。我们的实验表明检测精度有所提高,因为图像采集过程中固有的各种退化在重建过程中得到了补偿。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An Evaluation of Iterative Reconstruction Strategies on Mediastinal Lesion Detection Using Hybrid Ga-67 SPECT Images.

Hybrid LROC studies can be used to more realistically assess the impact of reconstruction strategies, compared to those constructed with digital phantoms. This is because hybrid data provides the background variability that is present in clinical imaging, as well as, control over critical imaging parameters, required to conduct meaningful tests. Hybrid data is obtained by adding Monte Carlo simulated lesions to disease free clinical projection data. Due to Ga-67 being a particularly challenging radionuclide for imaging, we use Ga-67 hybrid SPECT data to study the effectiveness of the various correction strategies developed to account for degradations in SPECT imaging. Our data was obtained using GE-VG dual detector SPECT-CT camera. After determining a target lesion contrast we conduct pilot LROC studies to obtain a near-optimal set of reconstruction parameters for the different strategies individually. These near-optimal parameters are then used to reconstruct the final evaluation study sets. All LROC study results reported here were obtained employing human observers only. We use final LROC study results to assess the impact of attenuation compensation, scatter compensation and detector resolution compensation on data reconstructed with the RBI-EM algorithm. We also compare these with FBP reconstructions of the same dataset. Our experiment indicates an improvement in detection accuracy, as various degradations inherent in the image acquisition process are compensated for in the reconstruction process.

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