基于深度学习的方法缩短肺移植术后患者通气 SPECT 的采集时间

IF 1.7 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Radiological Physics and Technology Pub Date : 2024-10-23 DOI:10.1007/s12194-024-00853-3
Masahiro Nakashima, Ryohei Fukui, Seiichiro Sugimoto, Toshihiro Iguchi
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引用次数: 0

摘要

我们的目的是评估使用卷积神经网络(CNN)短暂采集的肺通气单光子发射计算机断层扫描(SPECT)图像对肺移植术后患者慢性肺异位功能障碍(CLAD)的图像质量和诊断性能,并探索短采集时间的可行性。我们回顾性地确定了 93 名连续接受通气 SPECT/计算机断层扫描(CT)的肺移植受者。我们使用 CNN 来区分全时间采集的图像和短时间采集的图像。图像质量通过结构相似性指数(SSIM)损失和归一化均方误差(NMSE)进行评估。评估了全时 SPECT 图像和预测 SPECT 图像的功能容积/形态容积(F/M)比之间的相关性。使用Bland-Altman图评估F/M比率的差异,并使用曲线下面积(AUC)比较诊断性能。使用 MSE 得出的学习曲线在 100 个历时内收敛。NMSE 明显较低(P
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Deep learning-based approach for acquisition time reduction in ventilation SPECT in patients after lung transplantation.

We aimed to evaluate the image quality and diagnostic performance of chronic lung allograft dysfunction (CLAD) with lung ventilation single-photon emission computed tomography (SPECT) images acquired briefly using a convolutional neural network (CNN) in patients after lung transplantation and to explore the feasibility of short acquisition times. We retrospectively identified 93 consecutive lung-transplant recipients who underwent ventilation SPECT/computed tomography (CT). We employed a CNN to distinguish the images acquired in full time from those acquired in a short time. The image quality was evaluated using the structural similarity index (SSIM) loss and normalized mean square error (NMSE). The correlation between functional volume/morphological volume (F/M) ratios of full-time SPECT images and predicted SPECT images was evaluated. Differences in the F/M ratio were evaluated using Bland-Altman plots, and the diagnostic performance was compared using the area under the curve (AUC). The learning curve, obtained using MSE, converged within 100 epochs. The NMSE was significantly lower (P < 0.001) and the SSIM was significantly higher (P < 0.001) for the CNN-predicted SPECT images compared to the short-time SPECT images. The F/M ratio of full-time SPECT images and predicted SPECT images showed a significant correlation (r = 0.955, P < 0.0001). The Bland-Altman plot revealed a bias of -7.90% in the F/M ratio. The AUC values were 0.942 for full-time SPECT images, 0.934 for predicted SPECT images and 0.872 for short-time SPECT images. Our findings suggest that a deep-learning-based approach can significantly curtail the acquisition time of ventilation SPECT, while preserving the image quality and diagnostic accuracy for CLAD.

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来源期刊
Radiological Physics and Technology
Radiological Physics and Technology RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING-
CiteScore
3.00
自引率
12.50%
发文量
40
期刊介绍: The purpose of the journal Radiological Physics and Technology is to provide a forum for sharing new knowledge related to research and development in radiological science and technology, including medical physics and radiological technology in diagnostic radiology, nuclear medicine, and radiation therapy among many other radiological disciplines, as well as to contribute to progress and improvement in medical practice and patient health care.
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Acknowledgment. Evaluation of calculation accuracy and computation time in a commercial treatment planning system for accelerator-based boron neutron capture therapy. Development of deep learning-based novel auto-segmentation for the prostatic urethra on planning CT images for prostate cancer radiotherapy. Effect of deep learning reconstruction on the assessment of pancreatic cystic lesions using computed tomography. Assessment of accuracy and repeatability of quantitative parameter mapping in MRI.
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