Input parameterized physics informed neural networks for de noising, super-resolution, and imaging artifact mitigation in time resolved three dimensional phase-contrast magnetic resonance imaging

IF 8 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Engineering Applications of Artificial Intelligence Pub Date : 2025-03-25 DOI:10.1016/j.engappai.2025.110600

Amin Pashaei Kalajahi , Hunor Csala , Zayeed Bin Mamun , Sangeeta Yadav , Omid Amili , Amirhossein Arzani , Roshan M. D’Souza

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引用次数: 0

Abstract

Motivation:

Hemodynamic analysis is crucial for diagnosing and predicting cardiovascular diseases. However, methods relying on fluid flow simulations or blood flow imaging are complex, time-consuming, and require specialized expertise, limiting their clinical use.

Goal:

This research aims to automate the enhancement of blood flow images, providing clinicians with a fast, accurate tool for hemodynamic analysis without requiring advanced expertise.

Objectives:

A software tool based on physics-constrained neural networks was developed to enable clinicians to easily select and process regions of interest (ROIs) in time-resolved three-dimensional phase contrast magnetic resonance imaging (4D-Flow MRI) blood flow images for quick, accurate analysis.

Methods:

The Input Parameterized Physics-Informed Neural Network (IP-PINN) was introduced to improve the spatio-temporal resolution of 4D-Flow MRI. IP-PINN mitigates noise, velocity aliasing, and phase errors. A convolutional neural network processes ROI data into latent vectors, which are then used to predict velocity, pressure, and spin density via a multi-layer perceptron. The method is trained with synthetic blood flow data using an innovative loss function that addresses noise and artifacts.

Results:

IP-PINN successfully enhanced image resolution, reducing noise and artifacts when tested on synthetic 4D-Flow MRI data derived from blood flow simulations of intracranial aneurysms. For data with 20 decibels (dB) signal-to-noise ratio, results closely matched the ground truth with less than 5.5% relative error. Processing took under two minutes. The method also has the potential to reduce data acquisition time by 25%.

Conclusions:

IP-PINN could significantly enhance the clinical use of 4D-Flow MRI for personalized hemodynamic analysis in cardiovascular diseases.

Abstract Image

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输入参数化物理告知神经网络去噪，超分辨率和成像伪影缓解在时间分辨三维相对比磁共振成像

动机：血液动力学分析是诊断和预测心血管疾病的关键。然而，依靠流体流动模拟或血流成像的方法复杂、耗时且需要专业知识，限制了它们的临床应用。目的：本研究旨在实现血流图像的自动化增强，为临床医生提供一个快速、准确的血流动力学分析工具，而无需高级专业知识。目的：开发了一种基于物理约束神经网络的软件工具，使临床医生能够轻松地选择和处理时间分辨三维相位对比磁共振成像（4D-Flow MRI）血流图像中的感兴趣区域（roi），以进行快速，准确的分析。方法：引入输入参数化物理信息神经网络（IP-PINN），提高4D-Flow MRI的时空分辨率。IP-PINN可以减轻噪声、速度混叠和相位误差。卷积神经网络将ROI数据处理成潜在向量，然后通过多层感知器预测速度、压力和旋转密度。该方法使用合成血流数据进行训练，使用创新的损失函数来处理噪声和伪像。结果：在模拟颅内动脉瘤血流的合成4D-Flow MRI数据上，IP-PINN成功地提高了图像分辨率，减少了噪声和伪影。对于信噪比为20分贝（dB）的数据，结果与地面真实值接近，相对误差小于5.5%。处理过程不到两分钟。该方法还具有将数据采集时间减少25%的潜力。结论：IP-PINN可显著提高4D-Flow MRI在心血管疾病个体化血流动力学分析中的临床应用。

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

Engineering Applications of Artificial Intelligence 工程技术-工程：电子与电气

CiteScore

9.60

自引率

10.00%

发文量

505

审稿时长

68 days

期刊介绍： Artificial Intelligence (AI) is pivotal in driving the fourth industrial revolution, witnessing remarkable advancements across various machine learning methodologies. AI techniques have become indispensable tools for practicing engineers, enabling them to tackle previously insurmountable challenges. Engineering Applications of Artificial Intelligence serves as a global platform for the swift dissemination of research elucidating the practical application of AI methods across all engineering disciplines. Submitted papers are expected to present novel aspects of AI utilized in real-world engineering applications, validated using publicly available datasets to ensure the replicability of research outcomes. Join us in exploring the transformative potential of AI in engineering.