DEMIST:基于深度学习的心肌灌注 SPECT 检测任务特定去噪方法

IF 4.6 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING IEEE Transactions on Radiation and Plasma Medical Sciences Pub Date : 2024-03-25 DOI:10.1109/TRPMS.2024.3379215
Md Ashequr Rahman;Zitong Yu;Richard Laforest;Craig K. Abbey;Barry A. Siegel;Abhinav K. Jha
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引用次数: 0

摘要

心肌灌注成像(MPI)单光子发射计算机断层扫描(SPECT)图像是以较低的辐射剂量和/或采集时间获得的,与低剂量图像相比,处理后的图像能提高观察者在检测灌注缺陷的临床任务中的表现。为了满足这一需求,我们基于模型-观察者理论的概念和对人类视觉系统的理解,提出了一种基于深度学习的检测任务特定方法,用于对 MPI SPECT 图像进行去噪处理(DEMIST)。该方法在进行去噪的同时,旨在保留影响观察者在检测任务中表现的特征。我们通过一项回顾性研究,使用在两台扫描仪上进行 MPI 研究的患者的匿名临床数据($N\\,=$ 338),对 DEMIST 检测灌注缺陷的任务进行了客观评估。评估是在 6.25%、12.5% 和 25% 的低剂量水平下进行的,使用的是拟人化通道化 Hotelling 观察器。使用接收器工作特性曲线下面积(AUC)对性能进行量化。与相应的低剂量图像和使用常用的基于任务识别的深度学习去噪方法去噪的图像相比,使用 DEMIST 去噪的图像的 AUC 明显更高。基于患者性别和缺陷类型的分层分析也观察到了类似的结果。此外,DEMIST 还提高了低剂量图像的视觉保真度,并使用均方根误差和结构相似性指数进行量化。数学分析显示,DEMIST 保留了有助于检测任务的特征,同时改善了噪声特性,从而提高了观察者的表现。这些结果为进一步临床评估 DEMIST 在 MPI SPECT 中对低计数图像进行去噪提供了有力证据。
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DEMIST: A Deep-Learning-Based Detection-Task-Specific Denoising Approach for Myocardial Perfusion SPECT
There is an important need for methods to process myocardial perfusion imaging (MPI) single-photon emission computed tomography (SPECT) images acquired at lower-radiation dose and/or acquisition time such that the processed images improve observer performance on the clinical task of detecting perfusion defects compared to low-dose images. To address this need, we build upon concepts from model-observer theory and our understanding of the human visual system to propose a detection task-specific deep-learning-based approach for denoising MPI SPECT images (DEMIST). The approach, while performing denoising, is designed to preserve features that influence observer performance on detection tasks. We objectively evaluated DEMIST on the task of detecting perfusion defects using a retrospective study with anonymized clinical data in patients who underwent MPI studies across two scanners ( $N\,\,=$ 338). The evaluation was performed at low-dose levels of 6.25%, 12.5%, and 25% and using an anthropomorphic channelized Hotelling observer. Performance was quantified using area under the receiver operating characteristics curve (AUC). Images denoised with DEMIST yielded significantly higher AUC compared to corresponding low-dose images and images denoised with a commonly used task-agnostic deep learning-based denoising method. Similar results were observed with stratified analysis based on patient sex and defect type. Additionally, DEMIST improved visual fidelity of the low-dose images as quantified using root mean squared error and structural similarity index metric. A mathematical analysis revealed that DEMIST preserved features that assist in detection tasks while improving the noise properties, resulting in improved observer performance. The results provide strong evidence for further clinical evaluation of DEMIST to denoise low-count images in MPI SPECT.
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来源期刊
IEEE Transactions on Radiation and Plasma Medical Sciences
IEEE Transactions on Radiation and Plasma Medical Sciences RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING-
CiteScore
8.00
自引率
18.20%
发文量
109
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