Learning-based heart coverage estimation for short-axis cine cardiac MR images - Tarroni,G; Oktay,O; Bai,W; Schuh,A; Suzuki,H; Passerat-Palmbach,J; Glocker,B; de,Marvao A; O'Regan,D; Cook,S; Rueckert,D (Springer, 2017)
The correct acquisition of short axis (SA) cine cardiac MRimage stacks requires the imaging of the full cardiac anatomy betweenthe apex and the mitral valve plane via multiple 2D slices. While in theclinical practice the SA stacks are usually checked qualitatively to en-sure full heart coverage, visual inspection can become infeasible for largeamounts of imaging data that is routinely acquired, e.g. in populationstudies such as the UK Biobank (UKBB). Accordingly, we propose alearning-based technique for the fully-automated estimation of the heartcoverage for SA image stacks. The technique relies on the identificationof cardiac landmarks (i.e. the apex and the mitral valve sides) on twochamber view long axis images and on the comparison of the landmarks’positions to the volume covered by the SA stack. Landmark detection isperformed using a hybrid random forest approach integrating both re-gression and structured classification models. The technique was appliedon 3000 cases from the UKBB and compared to visual assessment. Theobtained results (error rate = 2.3%, sens. = 73%, spec. = 90%) indicatethat the proposed technique is able to correctly detect the vast majorityof the cases with insufficient coverage, suggesting that it could be usedas a fully-automated quality control step for CMR SA image stacks.