Title: Direct Estimation of Regional Wall Thicknesses via Residual Recurrent Neural Network,
Abstract: Accurate estimation of regional wall thicknesses (RWT) of left ventricular
(LV) myocardium from cardiac MR sequences is of significant importance for
identification and diagnosis of cardiac disease. Existing RWT estimation still
relies on segmentation of LV myocardium, which requires strong prior
information and user interaction. No work has been devoted into direct
estimation of RWT from cardiac MR images due to the diverse shapes and
structures for various subjects and cardiac diseases, as well as the complex
regional deformation of LV myocardium during the systole and diastole phases of
the cardiac cycle. In this paper, we present a newly proposed Residual
Recurrent Neural Network (ResRNN) that fully leverages the spatial and temporal
dynamics of LV myocardium to achieve accurate frame-wise RWT estimation. Our
ResRNN comprises two paths: 1) a feed forward convolution neural network (CNN)
for effective and robust CNN embedding learning of various cardiac images and
preliminary estimation of RWT from each frame itself independently, and 2) a
recurrent neural network (RNN) for further improving the estimation by modeling
spatial and temporal dynamics of LV myocardium. For the RNN path, we design for
cardiac sequences a Circle-RNN to eliminate the effect of null hidden input for
the first time-step. Our ResRNN is capable of obtaining accurate estimation of
cardiac RWT with Mean Absolute Error of 1.44mm (less than 1-pixel error) when
validated on cardiac MR sequences of 145 subjects, evidencing its great
potential in clinical cardiac function assessment.