Predicting post-contrast information from contrast agent free cardiac MRI using machine learning: Challenges and methods.
Predicting post-contrast information from contrast agent free cardiac MRI using machine learning: Challenges and methods.
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DOI：
10.3389/fcvm.2022.894503
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发表时间：
2022
影响因子：
3.6
通讯作者：
Petersen, Steffen E.
中科院分区：
文献类型：
--
作者：
Abdulkareem, Musa;Kenawy, Asmaa A.;Rauseo, Elisa;Lee, Aaron M.;Sojoudi, Alireza;Amir-Khalili, Alborz;Lekadir, Karim;Young, Alistair A.;Barnes, Michael R.;Barckow, Philipp;Khanji, Mohammed Y.;Aung, Nay;Petersen, Steffen E.
目前，使用心脏磁共振（CMR）准确可视化和量化心肌梗死（MI）的存在、位置和程度需要使用造影剂。在这项研究中，我们的目标是调查和分析前和后对比CMR图像的目标是预测后对比信息使用前对比信息。我们提出方法并确定挑战。研究人群包括272项回顾性选择的CMR研究，诊断为MI（n = 108）和健康对照（n = 164）。我们描述了一个用于预处理该数据集以进行分析的管道。经过数据特征工程，722电影短轴（SAX）图像和分割掩模对用于实验。这分别构成了训练集、验证集和测试集的506、108和108对。我们使用深度学习（DL）分割（UNet）和分类（ResNet50）模型来发现疤痕的范围和位置，并在缺血病例和健康病例之间进行分类（即，无局部心肌瘢痕的病例）。然后，我们捕捉复杂的数据模式，代表微妙的信号和功能变化的电影SAX图像由于MI使用光流，心肌面积的变化率，和放射组学数据。我们应用该数据集探索两种监督学习方法，即支持向量机（SVM）和决策树（DT）方法，以开发用于将造影前电影SAX图像分类为MI或健康病例的预测模型。总体而言，对于UNet分割模型，基于测试集（n = 108）的平均Dice评分的性能对于内膜为0.75（±0.20），对于心外膜为0.51（±0.21），对于瘢痕为0.20（±0.17）。对于分类任务，SVM模型的准确度、F1和精确度得分分别为0.68、0.69和0.64，DT模型的准确度、F1和精确度得分分别为0.62、0.63和0.72。我们已经提出了一些有前途的方法，包括DL，SVM和DT方法，试图准确地预测非对比度图像的对比度信息。虽然我们的初步结果是适度的这一具有挑战性的任务，这一领域的研究仍然提出了几个开放的问题。
Currently, administering contrast agents is necessary for accurately visualizing and quantifying presence, location, and extent of myocardial infarction (MI) with cardiac magnetic resonance (CMR). In this study, our objective is to investigate and analyze pre- and post-contrast CMR images with the goal of predicting post-contrast information using pre-contrast information only. We propose methods and identify challenges. The study population consists of 272 retrospectively selected CMR studies with diagnoses of MI (n = 108) and healthy controls (n = 164). We describe a pipeline for pre-processing this dataset for analysis. After data feature engineering, 722 cine short-axis (SAX) images and segmentation mask pairs were used for experimentation. This constitutes 506, 108, and 108 pairs for the training, validation, and testing sets, respectively. We use deep learning (DL) segmentation (UNet) and classification (ResNet50) models to discover the extent and location of the scar and classify between the ischemic cases and healthy cases (i.e., cases with no regional myocardial scar) from the pre-contrast cine SAX image frames, respectively. We then capture complex data patterns that represent subtle signal and functional changes in the cine SAX images due to MI using optical flow, rate of change of myocardial area, and radiomics data. We apply this dataset to explore two supervised learning methods, namely, the support vector machines (SVM) and the decision tree (DT) methods, to develop predictive models for classifying pre-contrast cine SAX images as being a case of MI or healthy. Overall, for the UNet segmentation model, the performance based on the mean Dice score for the test set (n = 108) is 0.75 (±0.20) for the endocardium, 0.51 (±0.21) for the epicardium and 0.20 (±0.17) for the scar. For the classification task, the accuracy, F1 and precision scores of 0.68, 0.69, and 0.64, respectively, were achieved with the SVM model, and of 0.62, 0.63, and 0.72, respectively, with the DT model. We have presented some promising approaches involving DL, SVM, and DT methods in an attempt to accurately predict contrast information from non-contrast images. While our initial results are modest for this challenging task, this area of research still poses several open problems.
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影响因子：
3.3
作者：
Larroza, Andres;Materka, Andrzej;Moratal, David
通讯作者：
Moratal, David
影响因子：
5.1
作者：
CHU, A;SEHGAL, CM;GREENLEAF, JF
通讯作者：
GREENLEAF, JF
DOI：
10.1016/j.jacbts.2016.11.010
发表时间：
2017-06
期刊：
JACC. Basic to translational science
影响因子：
--
作者：
Johnson KW;Shameer K;Glicksberg BS;Readhead B;Sengupta PP;Björkegren JLM;Kovacic JC;Dudley JT
通讯作者：
Dudley JT
影响因子：
7.7
作者：
Avard, Elham;Shiri, Isaac;Zaidi, Habib
通讯作者：
Zaidi, Habib
影响因子：
3.3
作者：
Baessler, Bettina;Mannil, Manoj;Manka, Robert
通讯作者：
Manka, Robert