Shape constrained CNN for segmentation guided prediction of myocardial shape and pose parameters in cardiac MRI.

Semantic segmentation using convolutional neural networks (CNNs) is the state-of-the-art for many medical image segmentation tasks including myocardial segmentation in cardiac MR images. However, the predicted segmentation maps obtained from such standard CNN do not allow direct quantification of regional shape properties such as regional wall thickness. Furthermore, the CNNs lack explicit shape constraints, occasionally resulting in unrealistic segmentations. In this paper, we use a CNN to predict shape parameters of an underlying statistical shape model of the myocardium learned from a training set of images. Additionally, the cardiac pose is predicted, which allows to reconstruct the myocardial contours. The integrated shape model regularizes the predicted contours and guarantees realistic shapes. We enforce robustness of shape and pose prediction by simultaneously performing pixel-wise semantic segmentation during training and define two loss functions to impose consistency between the two predicted representations: one distance-based loss and one overlap-based loss. We evaluated the proposed method in a 5-fold cross validation on an in-house clinical dataset with 75 subjects and on the ACDC and LVQuan19 public datasets. We show that the two newly defined loss functions successfully increase the consistency between shape and pose parameters and semantic segmentation, which leads to a significant improvement of the reconstructed myocardial contours. Additionally, these loss functions drastically reduce the occurrence of unrealistic shapes in the semantic segmentation output.

Robust motion correction for cardiac T1 and ECV mapping using a T1 relaxation model approach.

T1 and ECV mapping are quantitative methods for myocardial tissue characterization using cardiac MRI, and are highly relevant for the diagnosis of diffuse myocardial diseases. Since the maps are calculated pixel-by-pixel from a set of MRI images with different T1-weighting, it is critical to assure exact spatial correspondence between these images. However, in practice, different sources of motion e.g. cardiac motion, respiratory motion or patient motion, hamper accurate T1 and ECV calculation such that retrospective motion correction is required. We propose a new robust non-rigid registration framework combining a data-driven initialization with a model-based registration approach, which uses a model for T1 relaxation to avoid direct registration of images with highly varying contrast. The registration between native T1 and enhanced T1 to obtain a motion free ECV map is also calculated using information from T1 model-fitting. The method was validated on three datasets recorded with two substantially different acquisition protocols (MOLLI (dataset 1 (n=15) and dataset 2 (n=29)) and STONE (dataset 3 (n = 210))), one in breath-hold condition and one free-breathing. The average Dice coefficient increased from 72.6 ±â€¯12.1% to 82.3 ±â€¯7.4% (P < 0.05) and mean boundary error decreased from 2.91 ±â€¯1.51mm to 1.62 ±â€¯0.80mm (P < 0.05) for motion correction in a single T1-weighted image sequence (3 datasets) while average Dice coefficient increased from 63.4 ±â€¯22.5% to 79.2 ±â€¯8.5% (P < 0.05) and mean boundary error decreased from 3.26 ±â€¯2.64mm to 1.77 ±â€¯0.86mm (P < 0.05) between native and enhanced sequences (dataset 1 and 2). Overall, the native T1 SD error decreased from 67.32 ±â€¯32.57ms to 58.11 ±â€¯21.59ms (P < 0.05), enhanced SD error from 30.15 ±â€¯25ms to 22.74 ±â€¯8.94ms (P < 0.05) and ECV SD error from 10.08 ±â€¯9.59% to 5.42 ±â€¯3.21% (P < 0.05) (dataset 1 and 2).