Constructing high-quality enhanced 4D-MRI with personalized modeling for liver cancer radiotherapy

IF 2.7 3区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Physica Medica-European Journal of Medical Physics Pub Date : 2025-06-26 DOI:10.1016/j.ejmp.2025.104955

Yuhe Yao , Bo Chen , Ke Wang , Ying Cao , Lijing Zuo , Kaixuan Zhang , Xinyuan Chen , Men Kuo , Jianrong Dai

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引用次数: 0

Abstract

Background

For magnetic resonance imaging (MRI), a short acquisition time and good image quality are incompatible. Thus, reconstructing time-resolved volumetric MRI (4D-MRI) to delineate and monitor thoracic and upper abdominal tumor movements is a challenge. Existing MRI sequences have limited applicability to 4D-MRI.

Purpose

A method is proposed for reconstructing high-quality personalized enhanced 4D-MR images. Low-quality 4D-MR images are scanned followed by deep learning–based personalization to generate high-quality 4D-MR images.

Methods

High-speed multiphase 3D fast spoiled gradient recalled echo (FSPGR) sequences were utilized to generate low-quality enhanced free-breathing 4D-MR images and paired low-/high-quality breath-holding 4D-MR images for 58 liver cancer patients. Then, a personalized model guided by the paired breath-holding 4D-MR images was developed for each patient to cope with patient heterogeneity.

Results

The 4D-MR images generated by the personalized model were of much higher quality compared with the low-quality 4D-MRI images obtained by conventional scanning as demonstrated by significant improvements in the peak signal-to-noise ratio, structural similarity, normalized root mean square error, and cumulative probability of blur detection. The introduction of individualized information helped the personalized model demonstrate a statistically significant improvement compared to the general model (p < 0.001).

Conclusion

The proposed method can be used to quickly reconstruct high-quality 4D-MR images and is potentially applicable to radiotherapy for liver cancer.

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构建肝癌放疗高质量增强4D-MRI个性化建模

对于磁共振成像（MRI）来说，短的采集时间和好的图像质量是不相容的。因此，重建时间分辨体积MRI （4D-MRI）来描绘和监测胸部和上腹部肿瘤的运动是一个挑战。现有的MRI序列对4D-MRI的适用性有限。目的提出一种重建高质量个性化增强4D-MR图像的方法。扫描低质量的4D-MR图像，然后基于深度学习的个性化生成高质量的4D-MR图像。方法采用高速多相3D快速破坏梯度回忆回声（FSPGR）序列对58例肝癌患者进行低质量增强自由呼吸4D-MR图像和配对低/高质量屏气4D-MR图像的采集。然后，以配对屏气4D-MR图像为指导，为每位患者建立个性化模型，以应对患者的异质性。结果在峰值信噪比、结构相似度、归一化均方根误差、模糊检测累积概率等方面，个性化模型生成的4D-MR图像质量明显高于常规扫描获得的低质量4D-MRI图像。与一般模型相比，个性化信息的引入帮助个性化模型显示出统计学上显著的改进(p <；0.001)。结论该方法可快速重建高质量的4D-MR图像，具有应用于肝癌放疗的潜力。

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

Physica Medica-European Journal of Medical Physics 生物-生物物理

CiteScore

6.80

自引率

14.70%

发文量

493

审稿时长

78 days

期刊介绍： Physica Medica, European Journal of Medical Physics, publishing with Elsevier from 2007, provides an international forum for research and reviews on the following main topics: Medical Imaging Radiation Therapy Radiation Protection Measuring Systems and Signal Processing Education and training in Medical Physics Professional issues in Medical Physics.