A New Similarity Metric for Groupwise Registration of Variable Flip Angle Sequences for Improved T 10 Estimation in DCE-MRI

Hallack, Andre; Chappell, Michael A.; Gooding, Mark J.; Schnabel, Julia A.

doi:10.1007/978-3-319-08554-8_16

Cited by 6 publications

(19 citation statements)

References 9 publications

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“…We thoroughly validate the proposed joint maximum likelihood estimator (MLE) with realistic Monte Carlo (MC) simulations and compare it with the conventional two-step approach as well as the newest state-of-the-art model-based approach of Hallack [18]. We show that substantially more accurate T 1 maps as well as motion parameters can be obtained with our proposed joint MLE.…”

Section: Introductionmentioning

confidence: 94%

“…In what follows, we will restrict the motion model of the unified ML framework to inter-image motion, that is, motion between the 3D T 1 -weighted images, as in [18]. In the discussion section (Section VI), we further elaborate on extensions of the unified ML framework in which intra-image motion is incorporated, in particular, motion between the slices of a multi-slice T 1 -weighted image.…”

Section: B Motion Modelmentioning

confidence: 99%

“…Such techniques integrate the signal model connecting the series of images (such as a T 1 relaxation model) into the registration step. State-of-the-art model-based methods have shown to outperform the conventional two-step approach in terms of accuracy, for example, in myocardial T 1 mapping [17], [18]. Unfortunately, they all come with serious limitations for precise and accurate T 1 mapping, mainly because different criteria for registration and estimation are heuristically combined [17], [18].…”

Section: Introductionmentioning

confidence: 99%

“…State-of-the-art model-based methods have shown to outperform the conventional two-step approach in terms of accuracy, for example, in myocardial T 1 mapping [17], [18]. Unfortunately, they all come with serious limitations for precise and accurate T 1 mapping, mainly because different criteria for registration and estimation are heuristically combined [17], [18]. Since they do not constitute a truly unified framework, the output of the algorithms cannot be related to the optimal value of a given global informationbased criterion.…”

Section: Introductionmentioning

confidence: 99%

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A Unified Maximum Likelihood Framework for Simultaneous Motion and $T_{1}$ Estimation in Quantitative MR $T_{1}$ Mapping

Ramos‐Llordén

Dekker

Steenkiste

et al. 2017

IEEE Trans. Med. Imaging

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In quantitative MR T mapping, the spin-lattice relaxation time T of tissues is estimated from a series of T -weighted images. As the T estimation is a voxel-wise estimation procedure, correct spatial alignment of the T -weighted images is crucial. Conventionally, the T -weighted images are first registered based on a general-purpose registration metric, after which the T map is estimated. However, as demonstrated in this paper, such a two-step approach leads to a bias in the final T map. In our work, instead of considering motion correction as a preprocessing step, we recover the motion-free T map using a unified estimation approach. In particular, we propose a unified framework where the motion parameters and the T map are simultaneously estimated with a Maximum Likelihood (ML) estimator. With our framework, the relaxation model, the motion model as well as the data statistics are jointly incorporated to provide substantially more accurate motion and T parameter estimates. Experiments with realistic Monte Carlo simulations show that the proposed unified ML framework outperforms the conventional two-step approach as well as state-of-the-art model-based approaches, in terms of both motion and T map accuracy and mean-square error. Furthermore, the proposed method was additionally validated in a controlled experiment with real T -weighted data and with two in vivo human brain T -weighted data sets, showing its applicability in real-life scenarios.

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Section: Introductionmentioning

confidence: 94%

Section: B Motion Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Unified Maximum Likelihood Framework for Simultaneous Motion and $T_{1}$ Estimation in Quantitative MR $T_{1}$ Mapping

Ramos‐Llordén

Dekker

Steenkiste

et al. 2017

IEEE Trans. Med. Imaging

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“…13 In the special case of abdominal MRI of, e.g., kidney or prostate, an additional requirement is measurement speed, as motion by breathing or heart-beat complicates the acquisition process. 9,14 A T 1 reconstruction method which fulfils these requirements is the variable flip angle method. 3,4,[10][11][12] The variable flip angle method has been shown to yield both fast and accurate results and is based on so-called spoiled gradient echo (SPGR) sequences, which are common in clinical MRI.…”

Section: Introductionmentioning

confidence: 99%

A practical guideline for T₁ reconstruction from various flip angles in MRI

Sandmann

Hodneland

Modersitzki

2016

Journal of Algorithms & Computational Technology

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A fast and robust method for T 1 estimation in MRI is the so-called variable flip angle technique. We introduce a novel family of T 1 reconstruction methods from data acquired with various flip angles and propose a family member which combines the robustness of a nonlinear-with the computational advantages of a linear reconstruction. The constructed family contains the most common approaches for T 1 estimation, namely a linear and a nonlinear approach. A general sensitivity analysis for arbitrary members of the family is established. Advantages of the optimized reconstruction are demonstrated on phantom-as well as real data, showing improvements of up to 24% as compared with the linear method. As a further means to stabilize T 1 estimation, spatial stabilization methods are compared. We demonstrate on phantom and on real data that improved results can be obtained if not only T 1 but also a second unknown M 0 in the reconstruction is stabilized.

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Liver DCE‐MRI registration based on sparse recovery of contrast agent curves

et al. 2021

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Dynamic contrast-enhanced MRI (DCE-MRI) registration is a challenging task because of the effect of remarkable intensity changes caused by contrast agent injections. Unrealistic deformation usually occurs by using traditional intensity-based algorithms. To alleviate the effect of contrast agent on registration, we proposed a DCE-MRI registration strategy and investigated the registration performance on the clinical DCE-MRI time series of liver. Method: We reconstructed the time-intensity curves of the contrast agent through sparse representation with a predefined dictionary whose columns were the time-intensity curves with high correlations with respect to a preselected contrast agent curve. After reshaping 1D-reconstructed contrast agent time-intensity curves into a 4D contrast agent time series, we aligned the original time series to the reconstructed contrast agent time series through traditional free-form deformation (FFD) registration scheme combined with a residual complexity (RC) similarity and an iterative registration strategy. This study included the DCE-MRI time series of 20 patients with liver cancer. Results: Qualitatively, the time-cut images and subtraction images of different registration methods did not obviously differ. Quantitatively, the mean (standard deviation) of temporal intensity smoothness of all the patients achieved 54. 910 (18.819), 54.609 (18.859), and 53.391 (19.031) in FFD RC, RDDR, Zhou et al.'s method and the proposed method,respectively.The mean (standard deviation) of changes in the lesion volume were 0.985 (0.041), 0.983 (0.041), 0.981 (0.046), and 0.989 (0.036) in FFD RC, RDDR, Zhou et al.'s method and the proposed method. Conclusion: Our proposed method would be an effective registration strategy for DCE-MRI time series, and its performance was comparable with that of three advanced registration methods.

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A New Similarity Metric for Groupwise Registration of Variable Flip Angle Sequences for Improved T 10 Estimation in DCE-MRI

Cited by 6 publications

References 9 publications

A Unified Maximum Likelihood Framework for Simultaneous Motion and $T_{1}$ Estimation in Quantitative MR $T_{1}$ Mapping

A Unified Maximum Likelihood Framework for Simultaneous Motion and $T_{1}$ Estimation in Quantitative MR $T_{1}$ Mapping

A practical guideline for T₁ reconstruction from various flip angles in MRI

Liver DCE‐MRI registration based on sparse recovery of contrast agent curves

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A New Similarity Metric for Groupwise Registration of Variable Flip Angle Sequences for Improved T 10 Estimation in DCE-MRI

Cited by 6 publications

References 9 publications

A Unified Maximum Likelihood Framework for Simultaneous Motion and $T_{1}$ Estimation in Quantitative MR $T_{1}$ Mapping

A Unified Maximum Likelihood Framework for Simultaneous Motion and $T_{1}$ Estimation in Quantitative MR $T_{1}$ Mapping

A practical guideline for T1 reconstruction from various flip angles in MRI

Liver DCE‐MRI registration based on sparse recovery of contrast agent curves

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Product

Resources

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A practical guideline for T₁ reconstruction from various flip angles in MRI