用深度学习提升稀疏视角心肌灌注SPECT图像质量，更好检测缺血病灶。
A detection-task-specific deep-learning method to improve the quality of sparse-view myocardial perfusion SPECT images
- 针对检测任务设计专用损失函数，保留人眼感知关键特征。
- 检测缺血病灶的AUC显著优于原始稀疏视角方案。
- 能有效恢复左心室壁结构，减少采样不足导致的伪影。
心肌灌注显像（MPI）结合单光子发射计算机断层扫描（SPECT）是诊断冠状动脉疾病的常用且经济的方法。然而，该成像过程耗时较长，易引起患者不适、运动伪影，并因SPECT与用于衰减校正的CT扫描不同步而导致诊断误差。减少投影角度可缩短扫描时间，但会降低重建图像质量。为此，我们提出一种面向检测任务的深度学习方法，用于稀疏视角下的心肌灌注SPECT图像。该方法引入观察者损失项，惩罚人眼感知通道特征的丢失，以提升灌注缺损检测性能。实验表明，在检测心肌灌注缺损任务中，所提方法的受试者工作特征曲线下面积（AUC）显著高于稀疏视角协议。此外，该方法能够有效恢复左心室壁结构，展现出克服稀疏采样伪影的能力。初步结果表明该方法具有进一步评估的价值。
原文摘要 · Abstract (English)
Myocardial perfusion imaging (MPI) with single-photon emission computed tomography (SPECT) is a widely used and cost-effective diagnostic tool for coronary artery disease. However, the lengthy scanning time in this imaging procedure can cause patient discomfort, motion artifacts, and potentially inaccurate diagnoses due to misalignment between the SPECT scans and the CT-scans which are acquired for attenuation compensation. Reducing projection angles is a potential way to shorten scanning time, but this can adversely impact the quality of the reconstructed images. To address this issue, we propose a detection-task-specific deep-learning method for sparse-view MPI SPECT images. This method integrates an observer loss term that penalizes the loss of anthropomorphic channel features with the goal of improving performance in perfusion defect-detection task. We observed that, on the task of detecting myocardial perfusion defects, the proposed method yielded an area under the receiver operating characteristic (ROC) curve (AUC) significantly larger than the sparse-view protocol. Further, the proposed method was observed to be able to restore the structure of the left ventricle wall, demonstrating ability to overcome sparse-sampling artifacts. Our preliminary results motivate further evaluations of the method.
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