| Strain metrics6,7 . | . |
|---|---|
| Basic parameters | Global longitudinal strain, global circumferential strain, global radial strain, time to maximal peak (TTPmax), average systolic strain rate, average diastolic strain rate |
| Measures of dys-synchrony | Onset delay (absolute time delay between onset of septal and lateral wall shortening) Peak delay (absolute difference between the lateral and septal wall TTPmax) Standard deviation of TTPmax of all LV segments (TTPSD) |
| Measures of discoordination | Systolic rebound stretch of the septum (total amount of stretch after initial shortening of the septum) Systolic stretch index (total amount of stretch of both the lateral and septal walls in systole) Internal stretch factor (ratio of total systolic stretch to systolic shortening of the septal and lateral walls) |
| Strain metrics6,7 . | . |
|---|---|
| Basic parameters | Global longitudinal strain, global circumferential strain, global radial strain, time to maximal peak (TTPmax), average systolic strain rate, average diastolic strain rate |
| Measures of dys-synchrony | Onset delay (absolute time delay between onset of septal and lateral wall shortening) Peak delay (absolute difference between the lateral and septal wall TTPmax) Standard deviation of TTPmax of all LV segments (TTPSD) |
| Measures of discoordination | Systolic rebound stretch of the septum (total amount of stretch after initial shortening of the septum) Systolic stretch index (total amount of stretch of both the lateral and septal walls in systole) Internal stretch factor (ratio of total systolic stretch to systolic shortening of the septal and lateral walls) |
| Radiomics (texture analysis)8–12 . | . |
|---|---|
| Histogram-based features (intensity-based spatial independent voxel statistics) | Mean, standard deviation, variance, median, interquartile range, range, maximum, minimum, 1st/10th/90th percentiles, kurtosis, entropy, skewness, mean positive pixel |
| Shape features | Sphericity, elongation, compactness, surface area, flatness |
| Second-order features (matrix-based measures of spatial distribution of voxel SI) | GLCM (table of frequencies of different voxel SI pairings in a specific direction): sum average (measure of relationship between high and low SI pairs), sum entropy (measure of randomness of SI distribution), and homogeneity (measure of similarity across the image) Grey-level run-length matrix (table of runs of a specific voxel SI in a specific direction): GLNU (measure of grey-level intensity similarity across image), RLNU (measure of run-length similarity across the image), long-run emphasis (measure of number of long-run lengths in the image), fraction (measure of the number of voxels involved in runs) Grey-level size zone matrix (table of zones of a specific voxel SI): large area emphasis (measure of number of large zones in image) |
| Higher order features | Local binary patterns (binary code texture descriptor that is generated at each voxel by converting its neighbouring voxels to either 0 or 1 based on the centre voxel) Autoregressive model (description of voxel SI as the weighted sum of surrounding voxel SIs): e.g. Teta1 Wavelet transform (transformation of voxel SI variation into frequency signals on different scales and in different directions): e.g. WavEnLL and WavEnHH |
| Radiomics (texture analysis)8–12 . | . |
|---|---|
| Histogram-based features (intensity-based spatial independent voxel statistics) | Mean, standard deviation, variance, median, interquartile range, range, maximum, minimum, 1st/10th/90th percentiles, kurtosis, entropy, skewness, mean positive pixel |
| Shape features | Sphericity, elongation, compactness, surface area, flatness |
| Second-order features (matrix-based measures of spatial distribution of voxel SI) | GLCM (table of frequencies of different voxel SI pairings in a specific direction): sum average (measure of relationship between high and low SI pairs), sum entropy (measure of randomness of SI distribution), and homogeneity (measure of similarity across the image) Grey-level run-length matrix (table of runs of a specific voxel SI in a specific direction): GLNU (measure of grey-level intensity similarity across image), RLNU (measure of run-length similarity across the image), long-run emphasis (measure of number of long-run lengths in the image), fraction (measure of the number of voxels involved in runs) Grey-level size zone matrix (table of zones of a specific voxel SI): large area emphasis (measure of number of large zones in image) |
| Higher order features | Local binary patterns (binary code texture descriptor that is generated at each voxel by converting its neighbouring voxels to either 0 or 1 based on the centre voxel) Autoregressive model (description of voxel SI as the weighted sum of surrounding voxel SIs): e.g. Teta1 Wavelet transform (transformation of voxel SI variation into frequency signals on different scales and in different directions): e.g. WavEnLL and WavEnHH |
LV, left ventricle; WavEnHH, energy of wavelet coefficients in horizontal high-frequency subband; WavEnLL, energy of wavelet coefficients in low-frequency subband.
| Strain metrics6,7 . | . |
|---|---|
| Basic parameters | Global longitudinal strain, global circumferential strain, global radial strain, time to maximal peak (TTPmax), average systolic strain rate, average diastolic strain rate |
| Measures of dys-synchrony | Onset delay (absolute time delay between onset of septal and lateral wall shortening) Peak delay (absolute difference between the lateral and septal wall TTPmax) Standard deviation of TTPmax of all LV segments (TTPSD) |
| Measures of discoordination | Systolic rebound stretch of the septum (total amount of stretch after initial shortening of the septum) Systolic stretch index (total amount of stretch of both the lateral and septal walls in systole) Internal stretch factor (ratio of total systolic stretch to systolic shortening of the septal and lateral walls) |
| Strain metrics6,7 . | . |
|---|---|
| Basic parameters | Global longitudinal strain, global circumferential strain, global radial strain, time to maximal peak (TTPmax), average systolic strain rate, average diastolic strain rate |
| Measures of dys-synchrony | Onset delay (absolute time delay between onset of septal and lateral wall shortening) Peak delay (absolute difference between the lateral and septal wall TTPmax) Standard deviation of TTPmax of all LV segments (TTPSD) |
| Measures of discoordination | Systolic rebound stretch of the septum (total amount of stretch after initial shortening of the septum) Systolic stretch index (total amount of stretch of both the lateral and septal walls in systole) Internal stretch factor (ratio of total systolic stretch to systolic shortening of the septal and lateral walls) |
| Radiomics (texture analysis)8–12 . | . |
|---|---|
| Histogram-based features (intensity-based spatial independent voxel statistics) | Mean, standard deviation, variance, median, interquartile range, range, maximum, minimum, 1st/10th/90th percentiles, kurtosis, entropy, skewness, mean positive pixel |
| Shape features | Sphericity, elongation, compactness, surface area, flatness |
| Second-order features (matrix-based measures of spatial distribution of voxel SI) | GLCM (table of frequencies of different voxel SI pairings in a specific direction): sum average (measure of relationship between high and low SI pairs), sum entropy (measure of randomness of SI distribution), and homogeneity (measure of similarity across the image) Grey-level run-length matrix (table of runs of a specific voxel SI in a specific direction): GLNU (measure of grey-level intensity similarity across image), RLNU (measure of run-length similarity across the image), long-run emphasis (measure of number of long-run lengths in the image), fraction (measure of the number of voxels involved in runs) Grey-level size zone matrix (table of zones of a specific voxel SI): large area emphasis (measure of number of large zones in image) |
| Higher order features | Local binary patterns (binary code texture descriptor that is generated at each voxel by converting its neighbouring voxels to either 0 or 1 based on the centre voxel) Autoregressive model (description of voxel SI as the weighted sum of surrounding voxel SIs): e.g. Teta1 Wavelet transform (transformation of voxel SI variation into frequency signals on different scales and in different directions): e.g. WavEnLL and WavEnHH |
| Radiomics (texture analysis)8–12 . | . |
|---|---|
| Histogram-based features (intensity-based spatial independent voxel statistics) | Mean, standard deviation, variance, median, interquartile range, range, maximum, minimum, 1st/10th/90th percentiles, kurtosis, entropy, skewness, mean positive pixel |
| Shape features | Sphericity, elongation, compactness, surface area, flatness |
| Second-order features (matrix-based measures of spatial distribution of voxel SI) | GLCM (table of frequencies of different voxel SI pairings in a specific direction): sum average (measure of relationship between high and low SI pairs), sum entropy (measure of randomness of SI distribution), and homogeneity (measure of similarity across the image) Grey-level run-length matrix (table of runs of a specific voxel SI in a specific direction): GLNU (measure of grey-level intensity similarity across image), RLNU (measure of run-length similarity across the image), long-run emphasis (measure of number of long-run lengths in the image), fraction (measure of the number of voxels involved in runs) Grey-level size zone matrix (table of zones of a specific voxel SI): large area emphasis (measure of number of large zones in image) |
| Higher order features | Local binary patterns (binary code texture descriptor that is generated at each voxel by converting its neighbouring voxels to either 0 or 1 based on the centre voxel) Autoregressive model (description of voxel SI as the weighted sum of surrounding voxel SIs): e.g. Teta1 Wavelet transform (transformation of voxel SI variation into frequency signals on different scales and in different directions): e.g. WavEnLL and WavEnHH |
LV, left ventricle; WavEnHH, energy of wavelet coefficients in horizontal high-frequency subband; WavEnLL, energy of wavelet coefficients in low-frequency subband.
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