Dataset Information

Learning-Based Regularization for Cardiac Strain Analysis via Domain Adaptation.

ABSTRACT: Reliable motion estimation and strain analysis using 3D+ time echocardiography (4DE) for localization and characterization of myocardial injury is valuable for early detection and targeted interventions. However, motion estimation is difficult due to the low-SNR that stems from the inherent image properties of 4DE, and intelligent regularization is critical for producing reliable motion estimates. In this work, we incorporated the notion of domain adaptation into a supervised neural network regularization framework. We first propose a semi-supervised Multi-Layered Perceptron (MLP) network with biomechanical constraints for learning a latent representation that is shown to have more physiologically plausible displacements. We extended this framework to include a supervised loss term on synthetic data and showed the effects of biomechanical constraints on the network's ability for domain adaptation. We validated the semi-supervised regularization method on in vivo data with implanted sonomicrometers. Finally, we showed the ability of our semi-supervised learning regularization approach to identify infarct regions using estimated regional strain maps with good agreement to manually traced infarct regions from postmortem excised hearts.

SUBMITTER: Lu A

PROVIDER: S-EPMC8442959 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature

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Learning-Based Regularization for Cardiac Strain Analysis via Domain Adaptation.

Lu Allen A Ahn Shawn S SS Ta Kevinminh K Parajuli Nripesh N Stendahl John C JC Liu Zhao Z Boutagy Nabil E NE Jeng Geng-Shi GS Staib Lawrence H LH O'Donnell Matthew M Sinusas Albert J AJ Duncan James S JS

IEEE transactions on medical imaging 20210831 9

Reliable motion estimation and strain analysis using 3D+ time echocardiography (4DE) for localization and characterization of myocardial injury is valuable for early detection and targeted interventions. However, motion estimation is difficult due to the low-SNR that stems from the inherent image properties of 4DE, and intelligent regularization is critical for producing reliable motion estimates. In this work, we incorporated the notion of domain adaptation into a supervised neural network regu ...[more]

PMID: 33872145

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Project description:Cardiac strain imaging (CSI) plays a critical role in the detection of myocardial motion abnormalities. Displacement estimation is an important processing step to ensure the accuracy and precision of derived strain tensors. In this paper, we propose and implement Spatiotemporal Bayesian regularization (STBR) algorithms for two-dimensional (2-D) normalized cross-correlation (NCC) based multi-level block matching along with incorporation into a Lagrangian cardiac strain estimation framework. Assuming smooth temporal variation over a short span of time, the proposed STBR algorithm performs displacement estimation using at least four consecutive ultrasound radio-frequency (RF) frames by iteratively regularizing 2-D NCC matrices using information from a local spatiotemporal neighborhood in a Bayesian sense. Two STBR schemes are proposed to construct Bayesian likelihood functions termed as Spatial then Temporal Bayesian (STBR-1) and simultaneous Spatiotemporal Bayesian (STBR-2). Radial and longitudinal strain estimated from a finite-element-analysis (FEA) model of realistic canine myocardial deformation were utilized to quantify strain bias, normalized strain error and total temporal relative error (TTR). Statistical analysis with one-way analysis of variance (ANOVA) showed that all Bayesian regularization methods significantly outperform NCC with lower bias and errors (p < 0.001). However, there was no significant difference among Bayesian methods. For example, mean longitudinal TTR for NCC, SBR, STBR-1 and STBR-2 were 25.41%, 9.27%, 10.38% and 10.13% respectively An in vivo feasibility study using RF data from ten healthy mice hearts were used to compare the elastographic signal-to-noise ratio (SNR e ) calculated using stochastic analysis. STBR-2 had the highest expected SNR e both for radial and longitudinal strain. The mean expected SNR e values for accumulated radial strain for NCC, SBR, STBR-1 and STBR-2 were 5.03, 9.43, 9.42 and 10.58, respectively. Overall results suggest that STBR improves CSI in vivo.

Project description:BackgroundPhoton-counting computed tomography (CT) is an advanced imaging technique that enables multi-energy imaging from a single scan. However, the limited photon count assigned to narrow energy bins leads to increased quantum noise in the reconstructed spectral images. To address this issue, leveraging the prior information in the spectral images is essential. This study aimed to develop an efficient algorithm that enhances image reconstruction quality by reducing noise levels and preserving image details.MethodsTo improve image reconstruction quality for photon-counting CT, we propose an algorithm based on the subspace-assisted multi-prior information, including global, nonlocal, and local priors, for spectral CT reconstruction. Specifically, the algorithm first maps spectral CT images, which exhibit global low-rank characteristics, to low-dimensional eigenimages using subspace decomposition. Then, similar image patches are extracted based on the manifold structure distance from highly correlated eigenimages in both spectral and spatial domains. These patches are stacked to form a nonlocal full-channel tensor group. Subsequently, non-convex structural sparsity is applied to this tensor group through adaptive dictionary learning, exploiting nonlocal similarity. Finally, the alternating direction method of multipliers (ADMM) is applied to solve the optimization model iteratively.ResultsThe simulated walnut and real mouse data were applied to validate the effectiveness of the proposed method. In the simulation experiments, the proposed method reduced the root mean square error (RMSE) by 87.74%, 86.88%, 67.01%, 46.42%, and 13.51% compared to the respective state-of-the-art five comparison methods. The time taken for one iteration of the proposed algorithm was as low as 32.57 seconds, which was 92.07% less than framelet tensor nuclear norm [framelet tensor sparsity with block-matching method (FTNN)] method and 74.13% less than total variation regularization [tensor nonlocal similarity and local TV sparsity method (ITS_TV)] method, the other two tensor block-matching (BM)-based comparison methods. The material decomposition results in real mouse data further validated the accuracy of the proposed method for different materials.ConclusionsThe experimental results indicate that the proposed algorithm effectively reduces computational costs while improving the accuracy of image reconstruction and material decomposition, showing promising advantages over the compared method.

Dataset Information

Learning-Based Regularization for Cardiac Strain Analysis via Domain Adaptation.

Publications

Learning-Based Regularization for Cardiac Strain Analysis via Domain Adaptation.

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