Basal Strain Estimation in Transesophageal Echocardiography (TEE) using Deep Learning based Unsupervised Deformable Image Registration

As of today, perioperative monitoring in the operating room is based on vital signs and clinical observations by the anesthesiologist. This, however, does not offer a complete monitoring of left ventricular function throughout the intervention. We hypothesize that functional monitoring of the heart can be performed automatically based on transesophageal echocardiographic (TEE) images. The main goal of this work is therefore to compute the non-linear deformation between subsequent images in a TEE sequence of the left ventricle and estimate basal longitudinal strain to assess regional myocardial function using a deep learning approach. An unsupervised approach based on a convolutional neural network (CNN) was implemented. The output of the CNN network is a dense vector field that describes the non-linear deformation required to maximize the similarity between two images. Recordings from 42 consecutive complete TEE exams from the Echocardiography Unit were anonymized and used for training. Recordings from 5 consecutive TEE exams performed during heart surgery, also anonymized, were used as test set. For the test set patients, the basal strain was manually annotated in EchoPac by an expert echocardiographer. For this work, 19 heart cycles were randomly selected from the test set and checked to ensure visibility of the points to be tracked. Overall when estimating strain there was a mean difference of 7.25%(±4.56%) in the strain values when compared to expert annotations. We have proven that point tracking is working as expected in most images, where the myocardium is well depicted. However, dropouts, noise generated by implants or air bubbles after surgery, confuse the tracker and drifting occurs.