利用深度学习验证低计数骨闪烁成像的图像质量改进。

IF 1.7 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Radiological Physics and Technology Pub Date : 2024-03-01 Epub Date: 2024-02-10 DOI:10.1007/s12194-023-00776-5
Taisuke Murata, Takuma Hashimoto, Masahisa Onoguchi, Takayuki Shibutani, Takashi Iimori, Koichi Sawada, Tetsuro Umezawa, Yoshitada Masuda, Takashi Uno
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引用次数: 0

摘要

利用深度学习提高低计数骨闪烁成像的图像质量,并评估其临床适用性。本研究共纳入 600 例患者(训练,500 例;验证,50 例;评估,50 例)。低计数原始图像(75%、50%、25%、10% 和 5% 计数)是使用泊松重采样从参考图像(100% 计数)生成的。输出(DL 滤波)图像是在使用 U-Net 将参考图像作为教师数据进行训练后获得的。生成的高斯滤波图像用于对比。计算与参考图像的峰值信噪比(PSNR)和结构相似度(SSIM),以确定图像质量。使用 BONENAVI 分析计算人工神经网络(ANN)值、骨扫描指数(BSI)和热点数量(Hs),以评估诊断性能。计算了骨转移检测的准确性和曲线下面积(AUC)。在所有计数百分比中,DL 滤波图像的 PSNR 和 SSIM 最高。无论是否存在骨转移,DL 滤波图像的 BONENAVI 分析值没有显著差异。在没有骨转移的患者中,原始图像和高斯滤波图像的BONENAVI分析值在≦25%计数时有显著差异。在有骨转移的患者中,原始图像和高斯滤波图像的BSI和Hs在计数≦10%时有显著差异,而ANN值则没有。在所有计数百分比中,DL 滤波图像的骨转移检测准确率最高;AUC 没有显著差异。深度学习方法提高了低计数骨闪烁成像的图像质量和骨转移瘤检测的准确性,表明该方法适用于临床。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Verification of image quality improvement of low-count bone scintigraphy using deep learning.

To improve image quality for low-count bone scintigraphy using deep learning and evaluate their clinical applicability. Six hundred patients (training, 500; validation, 50; evaluation, 50) were included in this study. Low-count original images (75%, 50%, 25%, 10%, and 5% counts) were generated from reference images (100% counts) using Poisson resampling. Output (DL-filtered) images were obtained after training with U-Net using reference images as teacher data. Gaussian-filtered images were generated for comparison. Peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) to the reference image were calculated to determine image quality. Artificial neural network (ANN) value, bone scan index (BSI), and number of hotspots (Hs) were computed using BONENAVI analysis to assess diagnostic performance. Accuracy of bone metastasis detection and area under the curve (AUC) were calculated. PSNR and SSIM for DL-filtered images were highest in all count percentages. BONENAVI analysis values for DL-filtered images did not differ significantly, regardless of the presence or absence of bone metastases. BONENAVI analysis values for original and Gaussian-filtered images differed significantly at ≦25% counts in patients without bone metastases. In patients with bone metastases, BSI and Hs for original and Gaussian-filtered images differed significantly at ≦10% counts, whereas ANN values did not. The accuracy of bone metastasis detection was highest for DL-filtered images in all count percentages; the AUC did not differ significantly. The deep learning method improved image quality and bone metastasis detection accuracy for low-count bone scintigraphy, suggesting its clinical applicability.

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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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