Parameter Selection in Dynamic Contrast-Enhanced Magnetic Resonance Tomography

Niinimäki, Kati; Hanhela, Matti; Kolehmainen, Ville

doi:10.1007/978-3-030-48634-1_6

Cited by 3 publications

(7 citation statements)

References 25 publications

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“…The three template signals were based on an experimental DCE-MRI measurement, which is described briefly in Section 3.2 and also in [ 28 , 29 ], from which the three different regions of interest (ROIs) were identified. The same simulated test case has been used previously in [ 17 , 28 ].…”

Section: Test Case Settingmentioning

confidence: 99%

“…This was done to weigh the ROIs with small support (vascular, tumor) appropriately in the error metric. The error metric was selected independent of the parameter selection, and the same metric was used in [ 17 , 28 ]. The error metric is described in more detail in [ 28 ].…”

Section: Test Case Settingmentioning

confidence: 99%

“…The idea in the proposed method is a 2D extension of the S-curve method, which was originally proposed for the selection of regularization parameter in sparsity promoting regularization in [ 13 ] and later applied to parameter selection in static x-ray tomography in [ 14 , 15 , 16 ]. The S-curve method was also applied to spatial TV regularization parameter selection in DCE-MRI in [ 17 ].…”

Section: Introductionmentioning

confidence: 99%

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Data-Driven Regularization Parameter Selection in Dynamic MRI

et al. 2021

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In dynamic MRI, sufficient temporal resolution can often only be obtained using imaging protocols which produce undersampled data for each image in the time series. This has led to the popularity of compressed sensing (CS) based reconstructions. One problem in CS approaches is determining the regularization parameters, which control the balance between data fidelity and regularization. We propose a data-driven approach for the total variation regularization parameter selection, where reconstructions yield expected sparsity levels in the regularization domains. The expected sparsity levels are obtained from the measurement data for temporal regularization and from a reference image for spatial regularization. Two formulations are proposed. Simultaneous search for a parameter pair yielding expected sparsity in both domains (S-surface), and a sequential parameter selection using the S-curve method (Sequential S-curve). The approaches are evaluated using simulated and experimental DCE-MRI. In the simulated test case, both methods produce a parameter pair and reconstruction that is close to the root mean square error (RMSE) optimal pair and reconstruction. In the experimental test case, the methods produce almost equal parameter selection, and the reconstructions are of high perceived quality. Both methods lead to a highly feasible selection of the regularization parameters in both test cases while the sequential method is computationally more efficient.

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Section: Test Case Settingmentioning

confidence: 99%

Section: Test Case Settingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Data-Driven Regularization Parameter Selection in Dynamic MRI

et al. 2021

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“…1 shows the signal templates for each of the different tissue regions. The same simulated test case has been used previously in [12,22].…”

Section: Simulated Test Casementioning

confidence: 99%

“…The idea in the proposed method is basically a 2D extension of the S-curve method, which was originally proposed for the selection of regularization parameter in sparsity promoting regularization in [8] and later applied to parameter selection in (static) x-ray tomography in [9][10][11]. The S-curve method was also applied to spatial TV regularization parameter selection in DCE-MRI in [12].…”

Section: Introductionmentioning

confidence: 99%

Data-driven regularization parameter selection in dynamic MRI

Hanhela¹,

Gröhn²,

Kettunen³

et al. 2020

Preprint

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In dynamic MRI, sufficient time resolution can often only be obtained using imaging protocols which produce undersampled data for each image in the time series. This has led to the popularity of compressed sensing (CS) based image reconstruction approaches. One of the main problems in the CS approaches is determining the regularization parameters, which control the balance between the data fidelity term and the spatial and temporal regularization terms.Methods: A data-driven approach is proposed for the regularization parameter selection such that the reconstructions yield expected sparsity levels in the regularization domains. The expected sparsity levels are obtained from the measurement data for the temporal regularization and from a reference image for the spatial regularization. Two formulations are proposed. The first is a 2D search for a parameter pair which produces expected sparsity in both the temporal and spatial regularization domains. In the second approach, the sparsity-based parameter selection is split to two 1D searches using the S-curve method. The approaches are evaluated using simulated and experimental DCE-MRI data.Results: In the simulated test case, both methods produce a parameter pair that is close to the optimal pair, and the reconstruction error is also close to minimum.In the experimental test case, the methods produce almost similar parameter selection, and the reconstructions are of high perceived quality. Conclusion:Both approaches lead to a highly feasible selection of the temporal and spatial regularization parameters in both the simulated and experimental test cases while the second method is computationally more efficient.

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Interactive Change Point Detection using optimisation approach and Bayesian statistics applied to real world applications

Gedda¹,

Beilina

Tan

2021

Preprint

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Change point detection becomes more and more important as datasets increase in size, where unsupervised detection algorithms can help users process data. To detect change points, a number of unsupervised algorithms have been developed which are based on different principles. One approach is to define an optimisation problem and minimise a cost function along with a penalty function. In the optimisation approach, the choice of the cost function affects the predictions made by the algorithm. In extension to the existing studies, a new type of cost function using Tikhonov regularisation is introduced. Another approach uses Bayesian statistics to calculate the posterior probability distribution of a specific point being a change point. It uses a priori knowledge on the distance between consecutive change points and a likelihood function with information about the segments. The optimisation and Bayesian approaches for offline change point detection are studied and applied to simulated datasets as well as a real world multi-phase dataset. The approaches have previously been studied separately and a novelty lies in comparing the predictions made by the two approaches in a specific setting, consisting of simulated datasets and a real world example. The study has found that the performance of the change point detection algorithms are affected by the features in the data.

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Parameter Selection in Dynamic Contrast-Enhanced Magnetic Resonance Tomography

Cited by 3 publications

References 25 publications

Data-Driven Regularization Parameter Selection in Dynamic MRI

Data-Driven Regularization Parameter Selection in Dynamic MRI

Data-driven regularization parameter selection in dynamic MRI

Interactive Change Point Detection using optimisation approach and Bayesian statistics applied to real world applications

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