Non-iterative and uncertainty-aware MRI-based liver fat estimation using an unsupervised deep learning method

IF 11.8 1区 医学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Juan P. Meneses , Cristian Tejos , Enes Makalic , Sergio Uribe
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引用次数: 0

Abstract

Liver proton density fat fraction (PDFF), the ratio between fat-only and overall proton densities, is an extensively validated biomarker associated with several diseases. In recent years, numerous deep learning-based methods for estimating PDFF have been proposed to optimize acquisition and post-processing times without sacrificing accuracy, compared to conventional methods. However, the lack of interpretability and the often poor generalizability of these DL-based models undermine the adoption of such techniques in clinical practice.
In this work, we propose an Artificial Intelligence-based Decomposition of water and fat with Echo Asymmetry and Least-squares (AI-DEAL) method, designed to estimate both proton density fat fraction (PDFF) and the associated uncertainty maps. Once trained, AI-DEAL performs a one-shot MRI water-fat separation by first calculating the nonlinear confounder variables, R2 and off-resonance field. It then employs a weighted least squares approach to compute water-only and fat-only signals, along with their corresponding covariance matrix, which are subsequently used to derive the PDFF and its associated uncertainty.
We validated our method using in vivo liver CSE-MRI, a fat-water phantom, and a numerical phantom. AI-DEAL demonstrated PDFF biases of 0.25% and −0.12% at two liver ROIs, outperforming state-of-the-art deep learning-based techniques. Although trained using in vivo data, our method exhibited PDFF biases of −3.43% in the fat-water phantom and −0.22% in the numerical phantom with no added noise. The latter bias remained approximately constant when noise was introduced. Furthermore, the estimated uncertainties showed good agreement with the observed errors and the variations within each ROI, highlighting their potential value for assessing the reliability of the resulting PDFF maps.
基于无监督深度学习方法的非迭代和不确定性感知核磁共振肝脏脂肪估计。
肝质子密度脂肪分数(PDFF),即脂肪密度与总质子密度之间的比率,是一种被广泛验证的与多种疾病相关的生物标志物。近年来,与传统方法相比,人们提出了许多基于深度学习的PDFF估计方法,以优化采集和后处理时间,同时不牺牲精度。然而,这些基于dl的模型缺乏可解释性和通常较差的通用性,破坏了这些技术在临床实践中的采用。在这项工作中,我们提出了一种基于人工智能的水和脂肪分解方法,采用回声不对称和最小二乘(AI-DEAL)方法,旨在估计质子密度脂肪分数(PDFF)和相关的不确定性图。一旦训练,AI-DEAL通过首先计算非线性混杂变量R2 *和非共振场来执行一次MRI水脂肪分离。然后,它采用加权最小二乘法来计算仅水和仅脂肪的信号,以及它们相应的协方差矩阵,这些协方差矩阵随后用于推导PDFF及其相关的不确定性。我们使用活体肝脏CSE-MRI、脂肪-水模型和数值模型验证了我们的方法。AI-DEAL在两个肝脏roi上的PDFF偏差分别为0.25%和-0.12%,优于最先进的基于深度学习的技术。尽管使用体内数据进行训练,我们的方法在没有添加噪声的情况下,在脂肪-水模型中显示出-3.43%的PDFF偏差,在数值模型中显示出-0.22%的PDFF偏差。当引入噪声时,后一种偏置保持近似恒定。此外,估计的不确定性与观察到的误差和每个ROI内的变化表现出良好的一致性,突出了它们对评估所得PDFF图的可靠性的潜在价值。
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来源期刊
Medical image analysis
Medical image analysis 工程技术-工程:生物医学
CiteScore
22.10
自引率
6.40%
发文量
309
审稿时长
6.6 months
期刊介绍: Medical Image Analysis serves as a platform for sharing new research findings in the realm of medical and biological image analysis, with a focus on applications of computer vision, virtual reality, and robotics to biomedical imaging challenges. The journal prioritizes the publication of high-quality, original papers contributing to the fundamental science of processing, analyzing, and utilizing medical and biological images. It welcomes approaches utilizing biomedical image datasets across all spatial scales, from molecular/cellular imaging to tissue/organ imaging.
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