当前位置： SCI文献检索 > MEDICAL IMAGE ANALYSIS期刊下所有文献 > Recovering from missing data in population imaging - Cardiac MR image imputation via conditional generative adversarial nets.

Recovering from missing data in population imaging - Cardiac MR image imputation via conditional generative adversarial nets.

Abstract：

:Accurate ventricular volume measurements are the primary indicators of normal/abnor- mal cardiac function and are dependent on the Cardiac Magnetic Resonance (CMR) volumes being complete. However, missing or unusable slices owing to the presence of image artefacts such as respiratory or motion ghosting, aliasing, ringing and signal loss in CMR sequences, significantly hinder accuracy of anatomical and functional cardiac quantification, and recovering from those is insufficiently addressed in population imaging. In this work, we propose a new robust approach, coined Image Imputation Generative Adversarial Network (I2-GAN), to learn key features of cardiac short axis (SAX) slices near missing information, and use them as conditional variables to infer missing slices in the query volumes. In I2-GAN, the slices are first mapped to latent vectors with position features through a regression net. The latent vector corresponding to the desired position is then projected onto the slice manifold, conditioned on intensity features through a generator net. The generator comprises residual blocks with normalisation layers that are modulated with auxiliary slice information, enabling propagation of fine details through the network. In addition, a multi-scale discriminator was implemented, along with a discriminator-based feature matching loss, to further enhance performance and encourage the synthesis of visually realistic slices. Experimental results show that our method achieves significant improvements over the state-of-the-art, in missing slice imputation for CMR, with an average SSIM of 0.872. Linear regression analysis yields good agreement between reference and imputed CMR images for all cardiac measurements, with correlation coefficients of 0.991 for left ventricular volume, 0.977 for left ventricular mass and 0.961 for right ventricular volume.

journal_name

Med Image Anal

journal_title

Medical image analysis

authors

Xia Y,Zhang L,Ravikumar N,Attar R,Piechnik SK,Neubauer S,Petersen SE,Frangi AF

doi

10.1016/j.media.2020.101812

subject

Has Abstract

pub_date

2021-01-01 00:00:00

pages

101812

eissn

1361-8415

issn

1361-8423

pii

S1361-8415(20)30176-6

journal_volume

pub_type

杂志文章

在线工具

Recovering from missing data in population imaging - Cardiac MR image imputation via conditional generative adversarial nets.