Joint Motion Estimation and Segmentation from Undersampled Cardiac MR Image

Abstract

Accelerating the acquisition of magnetic resonance imaging (MRI) is a challenging problem, and many works have been proposed to reconstruct images from undersampled k-space data. However, if the main purpose is to extract certain quantitative measures from the images, perfect reconstructions may not always be necessary as long as the images enable the means of extracting the clinically relevant measures. In this paper, we work on jointly predicting cardiac motion estimation and segmentation directly from undersampled data, which are two important steps in quantitatively assessing cardiac function and diagnosing cardiovascular diseases. In particular, a unified model consisting of both motion estimation branch and segmentation branch is learned by optimising the two tasks simultaneously. Additional corresponding fully-sampled images are incorporated into the network as a parallel sub-network to enhance and guide the learning during the training process. Experimental results using cardiac MR images from 220 subjects show that the proposed model is robust to undersampled data and is capable of predicting results that are close to that from fully-sampled ones, while bypassing the usual image reconstruction stage.

Disentangled representation learning in cardiac image analysis

“…Segmentation can also be treated as a regression task [Tan et al, 2017]. Finally, temporal information related to the cardiac motion has been used for segmentation of all cardiac phases [Qin et al, 2018, Bai et al, 2018b. Differently from the above, in this work we focus on learning meaningful spatial representations, and leveraging these for improved semi-supervised segmentation results, and performing auxiliary tasks.…”

DeepStrain: A Deep Learning Workflow for the Automated Characterization of Cardiac Mechanics

“…Deep Learning (DL) methods have demonstrated the advantage of allowing real-world data guide learning of abstract representations that can be used to accomplish pre-specified tasks, and have been shown to be more robust to image artifacts than non-learning techniques for some applications (16,17). DL segmentation methods have been proposed (18)(19)(20)(21) and implemented within strain computational pipelines (22,23), and recent studies have shown that cardiac motion estimation can also be recast as a learnable problem (24)(25)(26)(27)(28). These methods usually consist of an intensity-based loss function and a constrain term (24,27), the latter using common machine learning techniques [e.g., L2 regularization of all learnable parameters (25)] or direct regularization of the motion estimates [e.g., smoothness penalty (24), anatomy-aware (28)].…”

“…DL segmentation methods have been proposed (18)(19)(20)(21) and implemented within strain computational pipelines (22,23), and recent studies have shown that cardiac motion estimation can also be recast as a learnable problem (24)(25)(26)(27)(28). These methods usually consist of an intensity-based loss function and a constrain term (24,27), the latter using common machine learning techniques [e.g., L2 regularization of all learnable parameters (25)] or direct regularization of the motion estimates [e.g., smoothness penalty (24), anatomy-aware (28)]. However, none of these methods have considered the accuracy of myocardial strain as a design factor or have been applied to strain analysis.…”

End‐to‐end deep learning nonrigid motion‐corrected reconstruction for highly accelerated free‐breathing coronary MRA

“… is calculated based on the ground truth respiratory‐resolved images because the undersampling artefacts in the ZF bin images will influence the loss calculation. A similar strategy has been used in a previous study, which trained a network to predict 2D nonrigid cardiac motion from undersampled cardiac cine images 34 …”

“…A similar strategy has been used in a previous study, which trained a network to predict 2D nonrigid cardiac motion from undersampled cardiac cine images. 34…”