基于新型变压器模型的放射治疗中伪 PET/CT 融合图像的合成。

IF 3.2 2区 医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Medical physics Pub Date : 2024-11-21 DOI:10.1002/mp.17512
Hongfei Sun, Liting Chen, Jie Li, Zhi Yang, Jiarui Zhu, Zhongfei Wang, Ge Ren, Jing Cai, Lina Zhao
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引用次数: 0

摘要

背景:PET/CT 和计划 CT 是食道癌和鼻咽癌放疗中常用的医学影像。然而,重复扫描会使患者承受额外的辐射剂量,还会产生配准误差。目的:提出一种新的 Transformer 模型,以获得用于食管癌和鼻咽癌放疗的伪 PET/CT 融合图像:方法:回顾性选取129例食管癌和141例鼻咽癌的数据进行训练、验证和测试。将 PET 和 CT 图像作为输入。基于具有 "聚焦-分散 "关注机制和多一致性损失约束的 Transformer 模型,可以有效捕捉两幅图像中的特征信息。最终合成出具有增强肿瘤区域成像的伪 PET/CT 融合图像。在测试阶段,对伪 PET/CT 融合图像的准确性进行了解剖学和剂量学验证,并选择了两个前瞻性病例进行进一步的剂量验证:结果:在解剖学验证方面,使用小波融合算法获得的 PET/CT 融合图像经临床医生校正后作为基本真实图像。根据提出的模型得到的伪融合图像与地面实况图像之间的评价指标,包括峰值信噪比、结构相似性指数、平均绝对误差和归一化均方根误差,用均值(标准偏差)表示。它们分别为 37.82 (1.57)、95.23 (2.60)、29.70 (2.49) 和 9.48 (0.32)。这些数值优于最先进的深度学习比较模型。在剂量测定验证方面,基于 3%/2 mm 伽马分析,伪融合图像(PET/CT 重量比为 2:8)与规划 CT 图像之间的全局和肿瘤区域平均通过率分别为 97.2% 和 95.5%。这些数字结果优于其他权重比的伪 PET/CT 融合图像:根据提出的模型获得的这种伪 PET/CT 融合图像有望成为食管癌和鼻咽癌放射治疗的一种新模式。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Synthesis of pseudo-PET/CT fusion images in radiotherapy based on a new transformer model

Background

PET/CT and planning CT are commonly used medical images in radiotherapy for esophageal and nasopharyngeal cancer. However, repeated scans will expose patients to additional radiation doses and also introduce registration errors. This multimodal treatment approach is expected to be further improved.

Purpose

A new Transformer model is proposed to obtain pseudo-PET/CT fusion images for esophageal and nasopharyngeal cancer radiotherapy.

Methods

The data of 129 cases of esophageal cancer and 141 cases of nasopharyngeal cancer were retrospectively selected for training, validation, and testing. PET and CT images are used as input. Based on the Transformer model with a “focus-disperse” attention mechanism and multi-consistency loss constraints, the feature information in two images is effectively captured. This ultimately results in the synthesis of pseudo-PET/CT fusion images with enhanced tumor region imaging. During the testing phase, the accuracy of pseudo-PET/CT fusion images was verified in anatomy and dosimetry, and two prospective cases were selected for further dose verification.

Results

In terms of anatomical verification, the PET/CT fusion image obtained using the wavelet fusion algorithm was used as the ground truth image after correction by clinicians. The evaluation metrics, including peak signal-to-noise ratio, structural similarity index, mean absolute error, and normalized root mean square error, between the pseudo-fused images obtained based on the proposed model and ground truth, are represented by means (standard deviation). They are 37.82 (1.57), 95.23 (2.60), 29.70 (2.49), and 9.48 (0.32), respectively. These numerical values outperform those of the state-of-the-art deep learning comparative models. In terms of dosimetry validation, based on a 3%/2 mm gamma analysis, the average passing rates of global and tumor regions between the pseudo-fused images (with a PET/CT weight ratio of 2:8) and the planning CT images are 97.2% and 95.5%, respectively. These numerical outcomes are superior to those of pseudo-PET/CT fusion images with other weight ratios.

Conclusions

This pseudo-PET/CT fusion images obtained based on the proposed model hold promise as a new modality in the radiotherapy for esophageal and nasopharyngeal cancer.

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来源期刊
Medical physics
Medical physics 医学-核医学
CiteScore
6.80
自引率
15.80%
发文量
660
审稿时长
1.7 months
期刊介绍: Medical Physics publishes original, high impact physics, imaging science, and engineering research that advances patient diagnosis and therapy through contributions in 1) Basic science developments with high potential for clinical translation 2) Clinical applications of cutting edge engineering and physics innovations 3) Broadly applicable and innovative clinical physics developments Medical Physics is a journal of global scope and reach. By publishing in Medical Physics your research will reach an international, multidisciplinary audience including practicing medical physicists as well as physics- and engineering based translational scientists. We work closely with authors of promising articles to improve their quality.
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