Development of an individual display optimization system based on deep convolutional neural network transition learning for somatostatin receptor scintigraphy.

IF 1.7 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Radiological Physics and Technology Pub Date : 2024-03-01 Epub Date: 2024-01-02 DOI:10.1007/s12194-023-00766-7
Shun Matsumoto, Yuki Nakahara, Teppei Yonezawa, Yuto Nakamura, Masahiro Tanabe, Mayumi Higashi, Junji Shiraishi
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引用次数: 0

Abstract

Somatostatin receptor scintigraphy (SRS) is an essential examination for the diagnosis of neuroendocrine tumors (NETs). This study developed a method to individually optimize the display of whole-body SRS images using a deep convolutional neural network (DCNN) reconstructed by transfer learning of a DCNN constructed using Gallium-67 (67Ga) images. The initial DCNN was constructed using U-Net to optimize the display of 67Ga images (493 cases/986 images), and a DCNN with transposed weight coefficients was reconstructed for the optimization of whole-body SRS images (133 cases/266 images). A DCNN was constructed for each observer using reference display conditions estimated in advance. Furthermore, to eliminate information loss in the original image, a grayscale linear process is performed based on the DCNN output image to obtain the final linearly corrected DCNN (LcDCNN) image. To verify the usefulness of the proposed method, an observer study using a paired-comparison method was conducted on the original, reference, and LcDCNN images of 15 cases with 30 images. The paired comparison method showed that in most cases (29/30), the LcDCNN images were significantly superior to the original images in terms of display conditions. When comparing the LcDCNN and reference images, the number of LcDCNN and reference images that were superior to each other in the display condition was 17 and 13, respectively, and in both cases, 6 of these images showed statistically significant differences. The optimized SRS images obtained using the proposed method, while reflecting the observer's preference, were superior to the conventional manually adjusted images.

开发基于深度卷积神经网络过渡学习的个体显示优化系统,用于体生长抑素受体闪烁成像。
体生长抑素受体闪烁成像(SRS)是诊断神经内分泌肿瘤(NET)的重要检查手段。本研究开发了一种方法,通过对使用镓-67(67Ga)图像构建的深度卷积神经网络(DCNN)进行迁移学习,重建深度卷积神经网络,从而单独优化全身SRS图像的显示。最初的 DCNN 是用 U-Net 构建的,用于优化 67Ga 图像的显示(493 例/986 幅图像),而带有转置权重系数的 DCNN 是为优化全身 SRS 图像而重建的(133 例/266 幅图像)。每个观察者的 DCNN 都是利用事先估计的参考显示条件构建的。此外,为了消除原始图像中的信息损失,在 DCNN 输出图像的基础上进行了灰度线性处理,以获得最终的线性校正 DCNN(LcDCNN)图像。为了验证所提方法的实用性,我们使用成对比较法对 15 个病例的原始图像、参考图像和 LcDCNN 图像共 30 幅图像进行了观察研究。配对比较法显示,在大多数情况下(29/30),LcDCNN 图像在显示条件方面明显优于原始图像。在比较 LcDCNN 和参考图像时,在显示条件方面优于对方的 LcDCNN 和参考图像数量分别为 17 幅和 13 幅,在这两种情况下,其中 6 幅图像显示出统计学上的显著差异。使用拟议方法获得的优化 SRS 图像在反映观察者偏好的同时,还优于传统的手动调整图像。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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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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