Fully Automated Myocardial Strain Estimation from Cardiovascular MRI–tagged Images Using a Deep Learning Framework in the UK Biobank

Figure 1:. Manual myocardial landmark generation and tracking. A Placement of endocardial (green) and epicardial (blue) contours at end diastole (ED) (tag lines shown in yellow), B myocardial landmark points (red) at ED generated automatically, C tracked tag lines (yellow) at end systole (ES) with green points showing manual edits to the displacements, and D final landmarks (red) at ES.

Figure 2:. Overview of the machine learning framework for automatic myocardial strain estimation from cardiovascular MRI tagging. CNN = convolutional neural network, ED = end diastole, ES = end systole, LSTM = long short-term memory, RNN = recurrent neural network.

Figure 3a:. Measurements of radial and circumferential inter-landmark distances at (a) frame 1 (end diastole, assumed as the reference frame) and (b) frame t. Both images depict the seven circumferential rings of landmarks. Subendocardial, midwall, and subepicardial circumferential strain was calculated from the second, fourth, and sixth rings from the center, respectively. ΔRkt shows the distance for radial line k in frame t, while ΔCkt shows the distance for circumferential line k in frame t.

Figure 3b:. Measurements of radial and circumferential inter-landmark distances at (a) frame 1 (end diastole, assumed as the reference frame) and (b) frame t. Both images depict the seven circumferential rings of landmarks. Subendocardial, midwall, and subepicardial circumferential strain was calculated from the second, fourth, and sixth rings from the center, respectively. ΔRkt shows the distance for radial line k in frame t, while ΔCkt shows the distance for circumferential line k in frame t.

Figure 4a:. Example results from the localization network. (a) Intersection over union (IoU) of 53% and (b) IoU of 63% are the worst prediction results within the test set. (c, d) Typical prediction results. (e) Histogram shows the distribution of accuracy (IoU) throughout the test set.

Figure 4b:. Example results from the localization network. (a) Intersection over union (IoU) of 53% and (b) IoU of 63% are the worst prediction results within the test set. (c, d) Typical prediction results. (e) Histogram shows the distribution of accuracy (IoU) throughout the test set.

Figure 4c:. Example results from the localization network. (a) Intersection over union (IoU) of 53% and (b) IoU of 63% are the worst prediction results within the test set. (c, d) Typical prediction results. (e) Histogram shows the distribution of accuracy (IoU) throughout the test set.

Figure 4d:. Example results from the localization network. (a) Intersection over union (IoU) of 53% and (b) IoU of 63% are the worst prediction results within the test set. (c, d) Typical prediction results. (e) Histogram shows the distribution of accuracy (IoU) throughout the test set.

Figure 4e:. Example results from the localization network. (a) Intersection over union (IoU) of 53% and (b) IoU of 63% are the worst prediction results within the test set. (c, d) Typical prediction results. (e) Histogram shows the distribution of accuracy (IoU) throughout the test set.

Figure 5:. Bland-Altman plots of the strains obtained for the left ventricle at end systole. The strain values obtained from the predicted landmarks were compared with those from the ground truth landmark. The first row shows radial strains for three different short-axis slices; the second row shows average circumferential strains. Blue line denotes the mean difference; red lines denote the 95% limits of agreement (mean ± 1.96 · standard deviation [SD]).

Figure 6:. Example of ground truth compared with estimated landmarks during end diastole (ED) and end systole (ES) (top row) and strain calculation for the whole cine; circumferential strain (bottom left) and radial strain (bottom right). Vertical line marks the ES frame.