Study of deep learning architectures on automatic analysis for cardiac parametric mapping
Objective Neural network with end-to-end prediction ability could accelerate medical imaging processing.Currently,there is no study to compare the difference in automatically analyzing cardiac parametric mapping between different neural networks.Therefore,this study aims to investigate the capability of various neural networks for automated cardiac parametric mapping analysis and to compare their performance.Methods This study collected MOLLI T1 images,SASHA T1 images,and T2-prep bSSFP T2 images from 155 healthy volunteers and built four end-to-end neural networks with AlexNet,GoogLeNet,ResNet and DenseNet structures.We trained these neural networks to directly predict global[left ventricle(LV)myocardium and LV blood]and regional(ventricular septal and AHA segment)T1 and T2 values from corresponding maps.The strategies of training,validation,and testing for four networks were kept consistent.We compared their prediction performance.Results All four neural networks could successfully predict T1 and T2 for both global and region LV myocardium and LV blood with different accuracy,achieving automatic analysis.The performance of four networks also sensitivities to mapping sequence.DenseNet had the minimal residual error among all approaches.The prediction error of T1 for MOLLI and SASHA T1 maps was 17 ms±60 ms,and about 2 ms for T2 maps.The performance of GoogLeNet was superior to AlexNet and ResNet.Conclusions This study demonstrated that neural networks could automatically analyze cardiac parametric maps.The performance was dependent on the structure of neural networks.This study would promote the development of automatic techniques for cardiovascular magnetic resonance and provide an automatic tool for clinical practice.
deep learningcardiovascular magnetic resonanceautomatic analysisparametric mapmyocardial T1 and T2 analysis
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