Native-resolution myocardial principal Eulerian strain mapping using convolutional neural networks and Tagged Magnetic Resonance Imaging

Abstract

Background: Assessment of regional myocardial function at native pixel-level resolution can play a crucial role in recognizing the early signs of the decline in regional myocardial function. Extensive data processing in existing techniques limits the effective resolution and accuracy of the generated strain maps. The purpose of this study is to compute myocardial principal strain maps ε_p1 and ε_p2 from tagged MRI (tMRI) at the native image resolution using deep-learning local patch convolutional neural network (CNN) models (DeepStrain).

Methods: For network training, validation, and testing, realistic tMRI datasets were generated and consisted of 53,606 cine images simulating the heart, the liver, blood pool, and backgrounds, including ranges of shapes, positions, motion patterns, noise, and strain. In addition, 102 in-vivo image datasets from three healthy subjects, and three Pulmonary Arterial Hypertension patients, were acquired and used to assess the network's in-vivo performance. Four convolutional neural networks were trained for mapping input tagging patterns to corresponding ground-truth principal strains using different cost functions. Strain maps using harmonic phase analysis (HARP) were obtained with various spectral filtering settings for comparison. CNN and HARP strain maps were compared at the pixel level versus the ground-truth and versus the least-loss in-vivo maps using Pearson correlation coefficients (R) and the median error and Inter-Quartile Range (IQR) histograms.

Results: CNN-based local patch DeepStrain maps at a phantom resolution of 1.1mm × 1.1 mm and in-vivo resolution of 2.1mm × 1.6 mm were artifact-free with multiple fold improvement with ε_p1 ground-truth median error of 0.009(0.007) vs. 0.32(0.385) using HARP and ε_p2 ground-truth error of 0.016(0.021) vs. 0.181(0.08) using HARP. CNN-based strain maps showed substantially higher agreement with the ground-truth maps with correlation coefficients R > 0.91 for ε_p1 and ε_p2 compared to R < 0.21 and R < 0.82 for HARP-generated maps, respectively.

Conclusion: CNN-generated Eulerian strain mapping permits artifact-free visualization of myocardial function at the native image resolution.

Keywords: Convolutional neural network; Deep learning; DeepStrain; Harmonic phase; Myocardial strain mapping; Tagging MRI.

Figure 1:

Simulated left ventricle and liver shapes, the pool of values of the geometric and functional parameters, and their simulated tMRI sequence and image quality parameters.

Figure 2:

The steps of creating a simulated tMRI cine dataset. First, a background image is randomly picked and rotated. Liver and left ventricle are created using randomized geometrical and deformation properties. Second, the corresponding ground-truth principal stain maps ϵ_p1 and ϵ_p2 are generated. Third, multiplicative speckle noise is added, and the cine anatomical image is created. Fourth, cine tagged patterns and white noise are superimposed based on the formerly prescribed deformation pattern.

Figure 3:

Simulated dataset example; (a) Tagging MR cine images including horizontally and vertically tagged images on the left ROIs showing the simulated left ventricle and liver at end-diastole and end-systole time-points. (b) The ground-truth principal strains ϵ_p1 and ϵ_p2 at end-systole, on the left, followed by the calculated principal strain maps using HARP with different filter sizes (8×8), (16×16), and (24×24). (c) The estimated principal strain maps obtained using CNN using four different loss functions, MSE, MAE, and weighted MSE; WMSE₁ and WMSE₂, with standard deviations 0.1 and 0.05, respectively. * WMSE₂ is the method with the least testing error and highest correlation. A more comprehensive animated version of this figure can be found in the supplementary clip supplV01_Phantom.mp4.

Figure 4:

Normal adult tMRI example. (a) Vertically and horizontally tagged end-diastole and end-systole images as well as ROIs around the left ventricle and the liver. (b) Principal strain maps obtained using HARP with different filter sizes (8×8), (16×16), and (24×24). (c) Principal strain maps obtained using CNNs, separately utilizing the four different loss functions, MSE, MAE, and weighted MSE, WMSE₁ and WMSE₂ with standard deviations 0.1 and 0.05, respectively. Notice in CNN methods the clear sharp RV and LV boundaries and the absence of the artifacts that keep worsening with larger HARP filter sizes. * WMSE₂ is the method with the least testing error and highest correlation. A more comprehensive animated version of this figure can be found in the supplementary clip supplV02_Healthy.mp4.

Figure 5:

PAH patient example. (a) Vertically and horizontally tagged end-diastole and end-systole images as well as a zoomed-in ROIs including the right and left ventricles. (b) Calculated principal strain maps using HARP with different filter sizes (8×8), (16×16), and (24×24) demonstrating the strain underestimation at small filter sizes versus the maps with severe artifacts as filter size increases. (c) principal strains ϵ_p1 and ϵ_p2 using the proposed CNN method with four different loss functions, MSE, MAE, and weighted MSE; WMSE₁ and WMSE₂ with standard deviations 0.1 and 0.05, respectively. The red arrow in (a) points to the anterior insertion point at which CNN method successfully demonstrates the lack of strain, which is common in PAH. *WMSE₂ is the method with the least testing error and highest correlation. A more comprehensive animated version of this figure can be found in the supplementary clip supplV03_PAH.mp4.

Figure 6:

Phantom dataset principal strain maps, calculated using HARP₁₆ and CNN-WMSE₂, compared to the ground-truth maps. (a) Selected tMRI cine frames from end-diastole to end-systole. (b) The corresponding ground-truth principal strain maps ϵ_p1 and ϵ_p2. (c) and (d) show ϵ_p1 and ϵ_p2 using HARP₁₆ and CNN-WMSE₂, respectively. The full cine can be viewed in the supplementary media

Figure 7:

In-vivo principal strain maps calculated using HARP₁₆ and CNN-WMSE₂ for a healthy adult subject. Selected tMRI frames within a single cardiac cycle from systole to after end-systole are shown in (a). (b) and (c) show the corresponding ϵ_p1 and ϵ_p2 using HARP₁₆ and CNN-WMSE₂, respectively. The full cine can be viewed in the supplementary media

Figure 8:

In-vivo principal strain maps calculated using HARP₁₆ and CNN-WMSE₂ for a PAH adult patient. (a) The tMRI cine at selected frames. (b) and (c) The corresponding ϵ_p1 and ϵ_p2 maps using HARP₁₆ and CNN-WMSE₂, respectively. The full cine can be viewed in the supplementary media

Figure 9:

Bar-graphs showing the median and interquartile range (IQR) of the median error in the HARP and CNN strain maps ϵ_p1(red bars) and ϵ_p2 (blue bars). Each error bar represents a strain range of 0.05. Columns (a) and (b) are for the simulated data, where column (a) shows the error in the various methods compared to the ground-truth and column (b) shows the error in the same methods compared to CNN-WMSE₂. Column (c) shows the difference between the strain estimated by the different methods compared to that estimated using CNN-WMSE₂ for the in-vivo dataset. The top three rows compare the HARP-based methods, whereas the bottom three rows correspond to the CNN-based methods, in which the vertical axes ranged from 0 to 0.2 instead of 0.5 to magnify the differences.

Figure 10:

Scatter-plots and correlation coefficients of the pixel-to-pixel strain maps ϵ_p1(red) and ϵ_p2 (blue) calculated using various HARP and CNN based methods versus ground-truth and best-performing CNN method. Columns (a), (b), (d), and (e) are for simulated data points while columns (c) and (f) display in-vivo data points. Columns (a-c) compare the HARP-based methods to the ground-truth (a) and to CNN-WMSE₂ in (b) and (c), whereas columns (d-f) correspond to the CNN-based methods. Columns (d-f) compare the CNN-based methods to the ground-truth (d) and to CNN-WMSE₂ in (e) and (f). Columns (a) and (d) plot the simulated data estimated strain against the ground-truth. Columns (b) and (e) plot the phantom results using various methods against the CNN-WMSE₂. Columns (c) and (f) plot the in-vivo results using CNN-MSE, and CNN-WMSE₁ versus CNN-WMSE₂.