Dynamic MRI interpolation in temporal direction using an unsupervised generative model

IF 5.4 2区医学 Q1 ENGINEERING, BIOMEDICAL

Computerized Medical Imaging and Graphics Pub Date : 2024-09-22 DOI:10.1016/j.compmedimag.2024.102435

Corbin Maciel , Qing Zou

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引用次数: 0

Abstract

Purpose

Cardiac cine magnetic resonance imaging (MRI) is an important tool in assessing dynamic heart function. However, this technique requires long acquisition time and long breath holds, which presents difficulties. The aim of this study is to propose an unsupervised neural network framework that can perform cardiac cine interpolation in time, so that we can increase the temporal resolution of cardiac cine without increasing acquisition time.

Methods

In this study, a subject-specific unsupervised generative neural network is designed to perform temporal interpolation for cardiac cine MRI. The network takes in a 2D latent vector in which each element corresponds to one cardiac phase in the cardiac cycle and then the network outputs the cardiac cine images which are acquired on the scanner. After the training of the generative network, we can interpolate the 2D latent vector and input the interpolated latent vector into the network and the network will output the frame-interpolated cine images. The results of the proposed cine interpolation neural network (CINN) framework are compared quantitatively and qualitatively with other state-of-the-art methods, the ground truth training cine frames, and the ground truth frames removed from the original acquisition. Signal-to-noise ratio (SNR), structural similarity index measures (SSIM), peak signal-to-noise ratio (PSNR), strain analysis, as well as the sharpness calculated using the Tenengrad algorithm were used for image quality assessment.

Results

As shown quantitatively and qualitatively, the proposed framework learns the generative task well and hence performs the temporal interpolation task well. Furthermore, both quantitative and qualitative comparison studies show the effectiveness of the proposed framework in cardiac cine interpolation in time.

Conclusion

The proposed generative model can effectively learn the generative task and perform high quality cardiac cine interpolation in time.

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利用无监督生成模型进行动态磁共振成像时间方向插值

目的心脏电影磁共振成像（MRI）是评估动态心脏功能的重要工具。然而，这项技术需要较长的采集时间和较长的屏气时间，这给研究带来了困难。本研究的目的是提出一种无监督神经网络框架，该框架可对心脏彩超进行时间插值，从而在不增加采集时间的情况下提高心脏彩超的时间分辨率。方法在本研究中，我们设计了一种针对特定对象的无监督生成神经网络，用于对心脏彩超进行时间插值。该网络接收二维潜向量，其中每个元素对应心脏周期中的一个心脏相位，然后该网络输出在扫描仪上获取的心脏显像图像。生成式网络训练完成后，我们可以对二维潜向量进行插值，然后将插值后的潜向量输入网络，网络将输出帧插值后的电影图像。我们将拟议的电影插值神经网络（CINN）框架的结果与其他最先进的方法、地面实况训练电影帧以及从原始采集中移除的地面实况帧进行了定量和定性比较。信噪比（SNR）、结构相似性指数（SSIM）、峰值信噪比（PSNR）、应变分析以及使用 Tenengrad 算法计算的清晰度都被用于图像质量评估。此外，定量和定性比较研究表明，所提出的框架在心脏实时插值中非常有效。结论所提出的生成模型可以有效地学习生成任务，并执行高质量的心脏实时插值。

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

Computerized Medical Imaging and Graphics 医学-核医学

CiteScore

10.70

自引率

3.50%

发文量

审稿时长

26 days

期刊介绍： The purpose of the journal Computerized Medical Imaging and Graphics is to act as a source for the exchange of research results concerning algorithmic advances, development, and application of digital imaging in disease detection, diagnosis, intervention, prevention, precision medicine, and population health. Included in the journal will be articles on novel computerized imaging or visualization techniques, including artificial intelligence and machine learning, augmented reality for surgical planning and guidance, big biomedical data visualization, computer-aided diagnosis, computerized-robotic surgery, image-guided therapy, imaging scanning and reconstruction, mobile and tele-imaging, radiomics, and imaging integration and modeling with other information relevant to digital health. The types of biomedical imaging include: magnetic resonance, computed tomography, ultrasound, nuclear medicine, X-ray, microwave, optical and multi-photon microscopy, video and sensory imaging, and the convergence of biomedical images with other non-imaging datasets.