Machine-learning-based prediction of regularization parameters for seismic inverse problems

Liu, Shihuan; Zhang, Jiashu

doi:10.1007/s11600-021-00569-7

Cited by 10 publications

(6 citation statements)

References 41 publications

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“…The regularization parameter selection (for λ 1 and λ 2 here) is one important issue. While results obtained with different combinations of λ 1 and λ 2 indicate that the denoising performance remains robust to a large range of parameter values (with smaller values for λ 2 achieving better balance between additional SNR enhancement and resolution loss; figure not shown due to space consideration), better parameter selection strategies, including those optimized for metrics beyond MSE and machine learning based approaches may be considered [58].…”

Section: Discussionmentioning

confidence: 99%

SNR Enhancement for Multi-TE MRSI Using Joint Low-Dimensional Model and Spatial Constraints

Wang

Lam

2022

IEEE Trans. Biomed. Eng.

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We present a novel method to enhance the SNR for multi-TE MR spectroscopic imaging (MRSI) data by integrating learned nonlinear low-dimensional model and spatial constraints. A deep complex convolutional autoencoder (DCCAE) was developed to learn a nonlinear lowdimensional representation of the high-dimensional multi-TE 1 H spectroscopy signals. The learned model significantly reduces the data dimension thus serving as an effective constraint for noise reduction. A reconstruction formulation was proposed to integrate the spatiospectral encoding model, the learned model, and a spatial constraint for an SNR-enhancing reconstruction from multi-TE data.The proposed method has been evaluated using both numerical simulations and in vivo brain MRSI experiments. The superior denoising performance of the proposed over alternative methods was demonstrated, both qualitatively and quantitatively. In vivo multi-TE data was used to assess the improved metabolite quantification reproducibility and accuracy achieved by the proposed method. We expect the proposed SNR-enhancing reconstruction to enable faster and/or higher-resolution multi-TE 1 H-MRSI of the brain, potentially useful for various clinical applications.

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Section: Discussionmentioning

confidence: 99%

SNR Enhancement for Multi-TE MRSI Using Joint Low-Dimensional Model and Spatial Constraints

Wang

Lam

2022

IEEE Trans. Biomed. Eng.

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“…to train a deep neural network (DNN) to approximate the mapping from observation data to desired (e.g. regularization) parameters [10,11]; however, these approaches do not allow for UQ of the QoI. • By leveraging VED networks, we can perform efficient UQ of the QoI.…”

Section: Overview Of Contributionsmentioning

confidence: 99%

Uncertainty quantification for goal-oriented inverse problems via variational encoder-decoder networks

Afkham,

Chung,

Chung

2024

Inverse Problems

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In this work, we describe a new approach that uses variational encoder-decoder (VED) networks for efficient uncertainty quantification for goal-oriented inverse problems. Contrary to standard inverse problems, these approaches are goal-oriented in that the goal is to estimate some quantities of interest (QoI) that are functions of the solution of an inverse problem, rather than the solution itself. Moreover, we are interested in computing uncertainty metrics associated with the QoI, thus utilizing a Bayesian approach for inverse problems that incorporates the prediction operator and techniques for exploring the posterior. This may be particularly challenging, especially for nonlinear, possibly unknown, operators and nonstandard prior assumptions. We harness recent advances in machine learning, i.e., VED networks, to describe a data-driven approach to large-scale inverse problems. This enables a real-time uncertainty quantification for the QoI. One of the advantages of our approach is that we avoid the need to solve challenging inversion problems by training a network to approximate the mapping from observations to QoI. Another main benefit is that we enable uncertainty quantification for the QoI by leveraging probability distributions in the latent and target spaces. This allows us to efficiently generate QoI samples and circumvent complicated or even unknown forward models and prediction operators. Numerical results from medical tomography reconstruction and nonlinear hydraulic tomography demonstrate the potential and broad applicability of the approach.

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“…However, for more complex forward models (for example including sparsity terms), this task could become extremely time consuming. Future experiments will explore the possibility to use automatic prediction of these paremeters [21], [22], [23], which would speed-up reconstruction process even with higher number of regularization terms.…”

Section: Supplementary Information I Data Fusion Retrieval Algorithmmentioning

confidence: 99%

Giga-voxel multidimensional fluorescence imaging combining single-pixel detection and data fusion

Soldevila,

Lenz,

Ghezzi

et al. 2021

Preprint

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Time-resolved fluorescence imaging is a key tool in biomedical applications, as it allows to non-invasively obtain functional and structural information. However, the big amount of collected data introduces challenges in both acquisition speed and processing needs. Here, we introduce a novel technique that allows to reconstruct a Giga-voxel 4D hypercube in a fast manner while only measuring 0.03% of the information. The system combines two single-pixel cameras and a conventional 2D array detector working in parallel. Data fusion techniques are introduced to combine the individual 2D & 3D projections acquired by each sensor in the final high-resolution 4D hypercube, which can be used to identify different fluorophore species by their spectral and temporal signatures.

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Machine-learning-based prediction of regularization parameters for seismic inverse problems

Cited by 10 publications

References 41 publications

SNR Enhancement for Multi-TE MRSI Using Joint Low-Dimensional Model and Spatial Constraints

SNR Enhancement for Multi-TE MRSI Using Joint Low-Dimensional Model and Spatial Constraints

Uncertainty quantification for goal-oriented inverse problems via variational encoder-decoder networks

Giga-voxel multidimensional fluorescence imaging combining single-pixel detection and data fusion

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