Joint Registration and Parameter Estimation of T1 Relaxation Times Using Variable Flip Angles

Heck, Constantin; Benning, Martin; Modersitzki, Jan

doi:10.1007/978-3-662-46224-9_38

Cited by 2 publications

(3 citation statements)

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“…Note that pre-alignment of variable flip-angle data is challenging, as intensities vary with different flip-angles. To overcome this issue, a model-based registration method similar to the ones described in Hallack et al 9 and Heck et al 14 was used for registration. Note that our modification has some mild smoothness assumptions on the parameter maps for stability.…”

Section: Resultsmentioning

confidence: 99%

“…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%

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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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Section: Resultsmentioning

confidence: 99%

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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“…Therefore, an appropriate measure of dependency such as Schatten-q-norms [24] are excellent regularizers in color image denoising. This idea can be generalized to more than three channels and is therefore useful in applications such as parameter estimation in DCE-MRI [8] or the registration of multiple images as they appear for example in serial sectioning or time series [11]. , ∇u3] ∈ R 2,3 of three color channels; illustration adapted from [14].…”

Section: The Novel Similarity Measure Sqnmentioning

confidence: 99%

A Novel Similarity Measure for Image Sequences

Brehmer

Wacker

Modersitzki

2018

Biomedical Image Registration

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Quantification of image similarity is a common problem in image processing. For pairs of two images, a variety of options is available and well-understood. However, some applications such as dynamic imaging or serial sectioning involve the analysis of image sequences and thus require a simultaneous and unbiased comparison of many images. This paper proposes a new similarity measure, that takes a global perspective and involves all images at the same time. The key idea is to look at Schatten-q-norms of a matrix assembled from normalized gradient fields of the image sequence. In particular, for q = 0, the measure is minimized if the gradient information from the image sequence has a low rank. This global perspective of the novel SqN-measure does not only allow to register sequences from dynamic imaging, e.g. DCE-MRI, but is also a new opportunity to simultaneously register serial sections, e.g. in histology. In this way, an accumulation of small, local registration errors may be avoided. First numerical experiments show very promising results for a DCE-MRI sequence of a human kidney as well as for a set of serial sections. The global structure of the data used for registration with SqN is preserved in all cases.

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Joint Registration and Parameter Estimation of T1 Relaxation Times Using Variable Flip Angles

Cited by 2 publications

References 9 publications

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

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

A Novel Similarity Measure for Image Sequences

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Joint Registration and Parameter Estimation of T1 Relaxation Times Using Variable Flip Angles

Cited by 2 publications

References 9 publications

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

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

A Novel Similarity Measure for Image Sequences

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

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