Optimization of diagnostic accuracy, radiation dose, and patient throughput for cardiac SPECT via advanced and clinically practical cardiac-respiratory motion correction and deep learning

单光子发射计算机断层扫描(SPECT)心肌灌注成像(MPI)被广泛应用 目的：检测和评估冠状动脉疾病。该项目的目标是减少辐射剂量和/或 SPECT MPI的扫描时间提高了16倍，同时保持或提高了诊断准确性。这 将能够执行SPECT MPI，例如，辐射剂量减少4倍，扫描时间缩短4倍(~2.5 分钟)，而不是典型的协议。SPECT MPI的辐射剂量已被认为是一种重要的 问题，约占医学成像患者所有辐射暴露的25%。减量 特别针对肥胖患者(他们接受更高剂量)和更年轻的患者增加的流行率 心脏病患者(由于预期寿命较长，他们受到辐射的风险较高)。缩短扫描时间 将改善老年和虚弱心脏病患者的舒适性，同时减轻身体运动图像 并通过增加临床吞吐量来降低医疗成本。我们将减少剂量和扫描时间 通过创新的图像重建方法，这些方法只需要很少的成本或不需要额外的 患者设置步骤。我们将采用新的呼吸和心脏运动补偿来减少图像 人工产物，以及新的深度学习技术，这将用于呼吸信号估计 和高性能的去噪。我们将系统地优化这些技术，然后验证我们的 多中心临床读者研究中的算法。 SA1：为低计数研究开发临床实用的呼吸运动替代品。T1：完美数据- 驱动的呼吸替代评估；T2：在减少计数时优化数据驱动的替代评估；T3： 开发和临床验证深度传感相机，用于替代呼吸和身体运动估计； T4：将数据驱动的替代估计推广到没有CT的SPECT系统。 SA2：开发深度学习重建方法并优化诊断准确性和剂量/扫描 时间到了。T1：减少计数和标准计数的三维血流灌注图像的重建后DL去噪算法 计数研究；T2：用于4D心脏门控研究的去噪算法；T3：嵌入的4D重建 Dl去噪、心脏运动估计和校正；以及T4：两种RMC的Dl重建方法 和CMC，其中投影数据使用在SA1中导出的呼吸替代信号入库。 SA3：执行多中心临床读者研究(6名临床医生、3家机构)，以验证新的 算法，并与当前临床上可用的基于诊断性能和 评价血流灌注和室壁运动缺陷的可重复性。T1：与基线临床相比 重建，评估以下方面的附加好处：a)包括衰减和散射校正，以及b)附加 包括RMC；T2：验证DL以改善血流灌注和功能(室壁运动)任务绩效 全计数级别；以及T3：验证DL在减少计数的情况下提高任务性能。

Single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) is widely used to detect and evaluate coronary artery disease. The goal of this project is to reduce the radiation dose and/or scan time of SPECT MPI by a combined factor of 16x, while maintaining or increasing diagnostic accuracy. This would enable SPECT MPI to be performed, e.g., with 4x reduced radiation dose and 4x shorter scan time (~2.5 minutes) than typical protocols. Radiation dose in SPECT MPI has been recognized as an important issue, accounting for ~25% of all radiation exposure to patients in medical imaging. Dose reduction particularly addresses the increased prevalence of obese patients (who receive higher dose) and younger cardiac patients (whose radiation risk is higher due to longer life expectancy). Reduction in scan time would improve comfort for elderly and infirm cardiac patients, while mitigating body-motion image artifacts and reducing healthcare costs by increasing clinical throughput. We will reduce dose and scan time through innovative image reconstruction methods that involve little or no cost and require no additional patient setup steps. We will employ new respiratory and cardiac motion compensation to reduce image artifacts, as well as new deep learning techniques, which will be used for both respiratory-signal estimation and high-performance denoising. We will methodically optimize these techniques and then validate our algorithms in multicenter clinical reader studies. SA1: Develop clinically practical respiratory motion surrogates for low-count studies. T1: Perfect data- driven respiratory surrogate estimation; T2: Optimize data-driven surrogate estimation at reduced counts; T3: Develop and clinically validate depth-sensing cameras for respiratory and body-motion surrogate estimation; T4: Generalization of data-driven surrogate estimation to SPECT systems not having a CT. SA2: Develop deep-learning reconstruction methods and optimize for diagnostic accuracy and dose/scan time. T1: Post-reconstruction DL denoising algorithms for 3D perfusion images for reduced-count and standard- count studies; T2: DL denoising algorithms for 4D cardiac-gated studies; T3: 4D reconstruction with embedded DL denoising, cardiac motion estimation and correction; and T4: DL reconstruction methods with both RMC and CMC, with projection data binned using respiratory surrogate signals derived in SA1. SA3: Perform multicenter clinical reader studies (6 clinicians, 3 institutions) to validate the new algorithms and compare to current clinically-available methods based on diagnostic performance and repeatability in assessing both perfusion and wall motion defects. T1: In comparison to baseline clinical reconstruction, evaluate added benefit of: a) including attenuation and scatter correction, and b) additionally including RMC; T2: Validate DL for improvement of perfusion and function (wall motion) task performance at full-count levels; and T3: Validate DL for improvement of task performance at reduced counts.