Group-wise motion correction of brain perfusion images

Zhang, Li; Chefd’hotel, Christophe; Bousquet, Guillaume

doi:10.1109/isbi.2010.5490115

Cited by 5 publications

(3 citation statements)

References 8 publications

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“…As an alternative to registration, use of groupwise motion correction based on an optimization of a global cost function has been suggested [ 76 ]. There are also several approaches for motion correction in fMRI data [ 77 ].…”

Section: Practical Implementationmentioning

confidence: 99%

Deconvolution-Based CT and MR Brain Perfusion Measurement: Theoretical Model Revisited and Practical Implementation Details

Fieselmann

Kowarschik

Ganguly

et al. 2011

International Journal of Biomedical Imaging

181

215

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Deconvolution-based analysis of CT and MR brain perfusion data is widely used in clinical practice and it is still a topic of ongoing research activities. In this paper, we present a comprehensive derivation and explanation of the underlying physiological model for intravascular tracer systems. We also discuss practical details that are needed to properly implement algorithms for perfusion analysis. Our description of the practical computer implementation is focused on the most frequently employed algebraic deconvolution methods based on the singular value decomposition. In particular, we further discuss the need for regularization in order to obtain physiologically reasonable results. We include an overview of relevant preprocessing steps and provide numerous references to the literature. We cover both CT and MR brain perfusion imaging in this paper because they share many common aspects. The combination of both the theoretical as well as the practical aspects of perfusion analysis explicitly emphasizes the simplifications to the underlying physiological model that are necessary in order to apply it to measured data acquired with current CT and MR scanners.

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Section: Practical Implementationmentioning

confidence: 99%

Deconvolution-Based CT and MR Brain Perfusion Measurement: Theoretical Model Revisited and Practical Implementation Details

Fieselmann

Kowarschik

Ganguly

et al. 2011

International Journal of Biomedical Imaging

181

215

View full text Add to dashboard Cite

show abstract

“…This is achieved by joint segmentation and groupwise registration of DCE-MRI. There are not many works dealing with joint segmentation and groupwise registration of cardiac perfusion MRI, although Zhang et al in [22] describe a method for the rigid registration of brain perfusion images. The cost function is derived from the total quadratic variation of image intensity.…”

Section: Introductionmentioning

confidence: 99%

Joint Segmentation and Groupwise Registration of Cardiac Perfusion Images Using Temporal Information

Mahapatra

2012

ISRN Machine Vision

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We propose a joint segmentation and groupwise registration method for cardiac perfusion images by using temporal information. The nature of perfusion images makes groupwise registration especially attractive as the temporal information from the entire image sequence can be used. Registration aims to maximize the smoothness of the intensity signal, while segmentation minimizes a pixel's dissimilarity with other pixels having the same segmentation label. The cost function is optimized in an iterative fashion using B-splines. Tests on real patient datasets show that compared to two other methods, our method shows lower registration error and higher segmentation accuracy. This is attributed to the use of temporal information for groupwise registration and mutually complementary registration and segmentation information in one framework, while other methods solve the two problems separately.

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“…There are not many works dealing with joint segmentation and groupwise registration of cardiac perfusion MRI, although Zhang et al in Ref. [19] describe a method for the rigid registration of brain perfusion images. The cost function is derived from the total quadratic variation of image intensity.…”

Section: Introductionmentioning

confidence: 99%

Joint Segmentation and Groupwise Registration of Cardiac Perfusion Images Using Temporal Information

Mahapatra

2012

J Digit Imaging

View full text Add to dashboard Cite

We propose a joint segmentation and groupwise registration method for dynamic cardiac perfusion images that uses temporal information. The nature of perfusion images makes groupwise registration especially attractive as the temporal information from the entire image sequence can be used. Registration aims to maximize the smoothness of the intensity signal while segmentation minimizes a pixel's dissimilarity with other pixels having the same segmentation label. The cost function is optimized in an iterative fashion using B-splines. Tests on real patient datasets show that compared with two other methods, our method shows lower registration error and higher segmentation accuracy. This is attributed to the use of temporal information for groupwise registration and mutual complementary registration and segmentation information in one framework while other methods solve the two problems separately.

show abstract

Group-wise motion correction of brain perfusion images

Cited by 5 publications

References 8 publications

Deconvolution-Based CT and MR Brain Perfusion Measurement: Theoretical Model Revisited and Practical Implementation Details

Deconvolution-Based CT and MR Brain Perfusion Measurement: Theoretical Model Revisited and Practical Implementation Details

Joint Segmentation and Groupwise Registration of Cardiac Perfusion Images Using Temporal Information

Joint Segmentation and Groupwise Registration of Cardiac Perfusion Images Using Temporal Information

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