Abstract:Changes in left ventricular (LV) aggregate cardiomyocyte orientation and deformation underlie cardiac function and dysfunction. As such, in vivo aggregate cardiomyocyte "myofiber" strain (E ff ) has mechanistic significance, but currently there exists no established technique to measure in vivo E ff . [1] The objective of this work is to describe and validate a pipeline to compute in vivo E ff from magnetic resonance imaging (MRI) data. Our pipeline integrates LV motion from multi-slice Displacement Encoded wi… Show more
“…McGowan et al 37 found a similar strain (E ff = −0.14 ± 0.01) while Wang et al 13 found a higher magnitude (E ff = −0.18 ± 0.03), using both MRI tagging imaging and ex vivo myofiber orientations. Very similar results were found in our previous work 15 in healthy swine (E ff = −0.14 [−0.16, −0.14]) using in vivo cDTI and 3D displacement encoded DENSE. Slightly lower E ff magnitudes were obtained in healthy human in vivo (E ff = −0.13 ± 0.02) using a single DENSE slice with an imposed incompressibility constraint 14 …”
Section: Discussionsupporting
confidence: 90%
“…The proposed post-processing approach to combine cDTI and DENSE is in continuity of the study done by Verzhbinsky et al 15 In this previous study, two SA slices of 3D displacement encoding DENSE were acquired immediately above and below one SA cDTI to estimate E ff . In this work instead, a stack of 2D SA and LA DENSE slices were combined with the cDTI data.…”
Section: Combination Of Cdti and Dense Acquisitionsmentioning
confidence: 93%
“…The red dashed line provides a −0.15 strain reference E ll using DENSE 4 (E ll = −0.16 ± 0.02), but were slightly lower than global longitudinal strain (GLS) measured with feature tracking 24 respectively), was found significantly lower than other reports using DENSE (E rr = 0.42 ± 0.11) in Moore et al 26 and (E rr = 0.33 ± 0.10) in Zhong et al 4 Cao et al 27 showed a poor agreement in E rr between DENSE (E rr = 0.4 ± 0.28), tagging (E rr = 0.32 ± 0.24) and three feature tracking methods (E rr = 0.47 0.26, E rr = 0.64 ± 0.33, E rr = 0.23 ± 0.9). The DENSE processing pipeline has been shown in previous in silico work 15,28 to induce significant error in computing E rr , which may account for the poor reproducibility of E rr , [29][30][31][32] and the wide range of radial strain values reported in the literature.…”
Section: Dense Acquisitionmentioning
confidence: 99%
“…From the simulated displacement field, six cine DENSE simulated images were then generated, as described in, 15 in the short-axis (SA) and long-axis (LA) views with sequence parameters matched to the in vivo balanced 3-point encoding…”
Purpose: Myofiber strain, E ff , is a mechanistically relevant metric of cardiac cell shortening and is expected to be spatially uniform in healthy populations, making it a prime candidate for the evaluation of local cardiomyocyte contractility. In this study, a new, efficient pipeline was proposed to combine microstructural cDTI and functional DENSE data in order to estimate E ff in vivo. Methods: Thirty healthy volunteers were scanned with three long-axis (LA) and three short-axis (SA) DENSE slices using 2D displacement encoding and one SA slice of cDTI. The total acquisition time was 11 minutes ± 3 minutes across volunteers. The pipeline first generates 3D SA displacements from all DENSE slices which are then combined with cDTI data to generate a cine of myofiber orientations and compute E ff. The precision of the post-processing pipeline was assessed using a computational phantom study. Transmural myofiber strain was compared to circumferential strain, E cc , in healthy volunteers using a Wilcoxon sign rank test. Results: In vivo, computed E ff was found uniform transmurally compared to E cc
“…McGowan et al 37 found a similar strain (E ff = −0.14 ± 0.01) while Wang et al 13 found a higher magnitude (E ff = −0.18 ± 0.03), using both MRI tagging imaging and ex vivo myofiber orientations. Very similar results were found in our previous work 15 in healthy swine (E ff = −0.14 [−0.16, −0.14]) using in vivo cDTI and 3D displacement encoded DENSE. Slightly lower E ff magnitudes were obtained in healthy human in vivo (E ff = −0.13 ± 0.02) using a single DENSE slice with an imposed incompressibility constraint 14 …”
Section: Discussionsupporting
confidence: 90%
“…The proposed post-processing approach to combine cDTI and DENSE is in continuity of the study done by Verzhbinsky et al 15 In this previous study, two SA slices of 3D displacement encoding DENSE were acquired immediately above and below one SA cDTI to estimate E ff . In this work instead, a stack of 2D SA and LA DENSE slices were combined with the cDTI data.…”
Section: Combination Of Cdti and Dense Acquisitionsmentioning
confidence: 93%
“…The red dashed line provides a −0.15 strain reference E ll using DENSE 4 (E ll = −0.16 ± 0.02), but were slightly lower than global longitudinal strain (GLS) measured with feature tracking 24 respectively), was found significantly lower than other reports using DENSE (E rr = 0.42 ± 0.11) in Moore et al 26 and (E rr = 0.33 ± 0.10) in Zhong et al 4 Cao et al 27 showed a poor agreement in E rr between DENSE (E rr = 0.4 ± 0.28), tagging (E rr = 0.32 ± 0.24) and three feature tracking methods (E rr = 0.47 0.26, E rr = 0.64 ± 0.33, E rr = 0.23 ± 0.9). The DENSE processing pipeline has been shown in previous in silico work 15,28 to induce significant error in computing E rr , which may account for the poor reproducibility of E rr , [29][30][31][32] and the wide range of radial strain values reported in the literature.…”
Section: Dense Acquisitionmentioning
confidence: 99%
“…From the simulated displacement field, six cine DENSE simulated images were then generated, as described in, 15 in the short-axis (SA) and long-axis (LA) views with sequence parameters matched to the in vivo balanced 3-point encoding…”
Purpose: Myofiber strain, E ff , is a mechanistically relevant metric of cardiac cell shortening and is expected to be spatially uniform in healthy populations, making it a prime candidate for the evaluation of local cardiomyocyte contractility. In this study, a new, efficient pipeline was proposed to combine microstructural cDTI and functional DENSE data in order to estimate E ff in vivo. Methods: Thirty healthy volunteers were scanned with three long-axis (LA) and three short-axis (SA) DENSE slices using 2D displacement encoding and one SA slice of cDTI. The total acquisition time was 11 minutes ± 3 minutes across volunteers. The pipeline first generates 3D SA displacements from all DENSE slices which are then combined with cDTI data to generate a cine of myofiber orientations and compute E ff. The precision of the post-processing pipeline was assessed using a computational phantom study. Transmural myofiber strain was compared to circumferential strain, E cc , in healthy volunteers using a Wilcoxon sign rank test. Results: In vivo, computed E ff was found uniform transmurally compared to E cc
“…1 ) comprised of the following steps: (a) LV segmentation, (b) identification of the anterior RV-LV insertion point, (c) phase unwrapping, and (d) displacement and strain analysis. Steps (a)–(c) utilize CNNs, and step (d) uses previously-developed and validated fully-automatic methods [ 5 , 31 ]. Fig.…”
Background
Cardiovascular magnetic resonance (CMR) cine displacement encoding with stimulated echoes (DENSE) measures heart motion by encoding myocardial displacement into the signal phase, facilitating high accuracy and reproducibility of global and segmental myocardial strain and providing benefits in clinical performance. While conventional methods for strain analysis of DENSE images are faster than those for myocardial tagging, they still require manual user assistance. The present study developed and evaluated deep learning methods for fully-automatic DENSE strain analysis.
Methods
Convolutional neural networks (CNNs) were developed and trained to (a) identify the left-ventricular (LV) epicardial and endocardial borders, (b) identify the anterior right-ventricular (RV)-LV insertion point, and (c) perform phase unwrapping. Subsequent conventional automatic steps were employed to compute strain. The networks were trained using 12,415 short-axis DENSE images from 45 healthy subjects and 19 heart disease patients and were tested using 10,510 images from 25 healthy subjects and 19 patients. Each individual CNN was evaluated, and the end-to-end fully-automatic deep learning pipeline was compared to conventional user-assisted DENSE analysis using linear correlation and Bland Altman analysis of circumferential strain.
Results
LV myocardial segmentation U-Nets achieved a DICE similarity coefficient of 0.87 ± 0.04, a Hausdorff distance of 2.7 ± 1.0 pixels, and a mean surface distance of 0.41 ± 0.29 pixels in comparison with manual LV myocardial segmentation by an expert. The anterior RV-LV insertion point was detected within 1.38 ± 0.9 pixels compared to manually annotated data. The phase-unwrapping U-Net had similar or lower mean squared error vs. ground-truth data compared to the conventional path-following method for images with typical signal-to-noise ratio (SNR) or low SNR (p < 0.05), respectively. Bland–Altman analyses showed biases of 0.00 ± 0.03 and limits of agreement of − 0.04 to 0.05 or better for deep learning-based fully-automatic global and segmental end-systolic circumferential strain vs. conventional user-assisted methods.
Conclusions
Deep learning enables fully-automatic global and segmental circumferential strain analysis of DENSE CMR providing excellent agreement with conventional user-assisted methods. Deep learning-based automatic strain analysis may facilitate greater clinical use of DENSE for the quantification of global and segmental strain in patients with cardiac disease.
Myocardial strain is increasingly used to assess left ventricular (LV) function. Incorporation of LV deformation into finite element (FE) modeling environment with subsequent strain calculation will allow analysis to reach its full potential. We describe a new kinematic model-based analysis framework (KMAF) to calculate strain from 3D cine-DENSE (displacement encoding with stimulated echoes) MRI. Methods: Cine-DENSE allows measurement of 3D myocardial displacement with high spatial accuracy. The KMAF framework uses cine cardiovascular magnetic resonance (CMR) to facilitate cine-DENSE segmentation, interpolates cine-DENSE displacement, and kinematically deforms an FE model to calculate strain. This framework was validated in an axially compressed gel phantom and applied in 10 healthy sheep and 5 sheep after myocardial infarction (MI). Results: Excellent Bland-Altman agreement of peak circumferential (E cc ) and longitudinal (E ll ) strain (mean difference = 0.021 ± 0.04 and −0.006 ± 0.03, respectively), was found between KMAF estimates and idealized FE simulation. E rr had a 2106 | WANG et Al.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
