Temporal Huber Regularization for DCE-MRI

Hanhela, Matti; Kettunen, Mikko I.; Gröhn, Olli; Vauhkonen, Marko; Kolehmainen, Ville

doi:10.1007/s10851-020-00985-2

Cited by 5 publications

(13 citation statements)

References 48 publications

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“…For both the simulated and experimental test case, we use a segment length of 34, i.e., each image frame is computed from 34 spokes of GA data. This was found to be an optimal segment length for a similar dynamic simulation in [ 28 ].…”

Section: Resultsmentioning

confidence: 96%

“…We created 2800 ground truth images based on the signal templates by multiplying the signal of each pixel in the original image with the signal template of the corresponding region and adding the result of the multiplication to the original value of the pixel. 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%

“…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 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%

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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: Resultsmentioning

confidence: 96%

Section: Test Case Settingmentioning

confidence: 99%

Section: Test Case Settingmentioning

confidence: 99%

Section: Test Case Settingmentioning

confidence: 99%

“…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%

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Data-Driven Regularization Parameter Selection in Dynamic MRI

et al. 2021

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Parameter Selection in Dynamic Contrast-Enhanced Magnetic Resonance Tomography

Niinimäki

Hanhela

Kolehmainen

2020

Mathematical and Numerical Approaches for Multi-Wave Inverse Problems

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Pharmacokinetic Analysis of Enhancement-Constrained Acceleration (ECA) reconstruction-based high temporal resolution breast DCE-MRI

et al. 2023

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The high spatial and temporal resolution of dynamic contrast-enhanced MRI (DCE-MRI) can improve the diagnostic accuracy of breast cancer screening in patients who have dense breasts or are at high risk of breast cancer. However, the spatiotemporal resolution of DCE-MRI is limited by technical issues in clinical practice. Our earlier work demonstrated the use of image reconstruction with enhancement-constrained acceleration (ECA) to increase temporal resolution. ECA exploits the correlation in k-space between successive image acquisitions. Because of this correlation, and due to the very sparse enhancement at early times after contrast media injection, we can reconstruct images from highly under-sampled k-space data. Our previous results showed that ECA reconstruction at 0.25 seconds per image (4 Hz) can estimate bolus arrival time (BAT) and initial enhancement slope (iSlope) more accurately than a standard inverse fast Fourier transform (IFFT) when k-space data is sampled following a Cartesian based sampling trajectory with adequate signal-to-noise ratio (SNR). In this follow-up study, we investigated the effect of different Cartesian based sampling trajectories, SNRs and acceleration rates on the performance of ECA reconstruction in estimating contrast media kinetics in lesions (BAT, iSlope and Ktrans) and in arteries (Peak signal intensity of first pass, time to peak, and BAT). We further validated ECA reconstruction with a flow phantom experiment. Our results show that ECA reconstruction of k-space data acquired with ‘Under-sampling with Repeated Advancing Phase’ (UnWRAP) trajectories with an acceleration factor of 14, and temporal resolution of 0.5 s/image and high SNR (SNR ≥ 30 dB, noise standard deviation (std) < 3%) ensures minor errors (5% or 1 s error) in lesion kinetics. Medium SNR (SNR ≥ 20 dB, noise std ≤ 10%) was needed to accurately measure arterial enhancement kinetics. Our results also suggest that accelerated temporal resolution with ECA with 0.5 s/image is practical.

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Temporal Huber Regularization for DCE-MRI

Cited by 5 publications

References 48 publications

Data-Driven Regularization Parameter Selection in Dynamic MRI

Data-Driven Regularization Parameter Selection in Dynamic MRI

Parameter Selection in Dynamic Contrast-Enhanced Magnetic Resonance Tomography

Pharmacokinetic Analysis of Enhancement-Constrained Acceleration (ECA) reconstruction-based high temporal resolution breast DCE-MRI

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