Objective To observe the feasibility of cardiac MR tissue tracking(CMR-TT)technique for quantitatively evaluating myocardial strain of patients with myocardial amyloidosis(CA).Methods Cardiac MRI were collected from 20 patients of immunoglobulin amyloid light-chain CA(AL-CA,group A),20 cases of transthyretin CA(ATTR-CA,group B)and 20 healthy subjects(group C),and myocardial strain parameters were obtained using CMR-TT technique.Left ventricular cardiac function parameters were compared among 3 groups,so were strain parameters of each myocardial segment of left ventricle and global myocardium,including 3D longitudinal strain(LS),3D radial strain(RS)and 3D circumferential strain(CS).Results Compared with those in group C,significant differences of left ventricular cardiac function parameters were found in both group A and B(all P＜0.01),while no statistical difference was found between group A and B(all P＞0.05).Except for apical segment RS(P=0.81),strain parameters in group A and B were both lower than those in group C(all P＜0.01),while no significant difference was detected between group A and B(all P＞0.05).Conclusion CMR-TT technique could be used to quantitatively evaluate left ventricular myocardial strain of CA patients.

Objective To develop a 3D V-Net deep learning segmentation model based on multi-center data,and to evaluate its value for automatic segmentation of prostate on T2WI and apparent diffusion coefficient(ADC)maps.Methods Totally 2 894 sets of multi-parametric MRI data of 2 673 patients with clinically suspected prostate cancer from 3 medical centers within 1 month before biopsy were retrospectively collected.Finally 5 974 sets axial images were enrolled,including 3 654 sets of T2WI and 2 320 sets of ADC maps.Prostate contours were manually annotated layer by layer on axial T2WI and ADC maps,and the left-to-right,anterior-to-posterior,superior-to-inferior diameters and volume of prostate were measured and taken as reference standards.The images were divided into training set(n=4 780,including 2 907 sets of T2WI and 1 873 sets of ADC map),verification set(n=601,including 384 sets of T2WI and 217 sets of ADC map)and test set(n=593,including 363 sets of T2WI and 230 sets of ADC map)at the ratio of 8:1:1.After preprocessing and augmentation,3D V-Net was used to construct and train the segmentation model based on training and verification sets,and the segmentation performance of the model was evaluated in test set using Dice similarity coefficient(DSC),Jaccard coefficient(JACARD)and volume similarity(VS),respectively.The parameters measured with the model were compared with the reference standards,and the correlations were explored.Results Compared with the corresponding ADC maps,DSC,JACARD and VS of the model for automatic segmentation of prostate on T2WI in test set were all higher(all P＜0.001).The left-to-right,anterior-to-posterior and superior-to-inferior diameters of prostate measured with the model on both T2WI and ADC maps were all larger than the reference standards(all P＜0.001),while no significant difference of the volume was found(both P＞0.05).All parameters measured with the model on T2WI and ADC maps were positively correlated with reference standards(rs=0.794-0.985).Conclusion 3D V-Net deep learning model could automatically segment prostate on T2WI and ADC maps with high accuracy,and its efficiency based on T2WI was better than that based on ADC maps.