Automatic motion correction for breast MR imaging.

Chen, Zuo; Jiang, Aiping; Buff, B.; Mahon, Thomas G.; Wong, Terence Z.

doi:10.1148/radiology.198.3.8628891

Cited by 49 publications

(30 citation statements)

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“…To facilitate the analysis of pre-and post-contrast enhanced MRI, Zuo et al [10] proposed a registration algorithm which minimises the ratio of variance between images. However, their algorithm is based on the assumption that the breast is only undergoing rigid motion.…”

Section: Introductionmentioning

confidence: 99%

Non-rigid registration of breast MR images using mutual information

Rueckert

Hayes

Studholme

et al. 1998

Medical Image Computing and Computer-Assisted Intervention — MICCAI’98

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Abstract. We present a new approach for the non-rigid registration of contrastenhanced breast MRI using normalised mutual information. A hierarchical transformation model of the motion of the breast has been developed: The global motion of the breast is modelled using affine transformation models while the local motion of the breast is modelled using spline-based free-form deformation (FFD) models. The algorithm has been applied to the fully automated registration of 3D breast MRI. In particular, we have compared the results of the proposed nonrigid registration algorithm to those obtained using rigid and affine registration techniques. The results clearly indicate that the non-rigid registration algorithm is much better able to recover the motion and deformation of the breast than rigid or affine registration algorithms.

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Section: Introductionmentioning

confidence: 99%

Non-rigid registration of breast MR images using mutual information

Rueckert

Hayes

Studholme

et al. 1998

Medical Image Computing and Computer-Assisted Intervention — MICCAI’98

View full text Add to dashboard Cite

show abstract

“…Zuo et al [30] performed rigid registration on MR-to-MR breast images acquired in dynamic breast studies employing the approach of Woods et al [31], relying on minimization of the variance of intensity ratios. Kumar et al [32] performed nonrigid registration of two breast MR images by minimizing the sum of squared intensity differences between the images.…”

Section: Mr-to-pet Image Registrationmentioning

confidence: 99%

Computerized method for nonrigid MR-to-PET breast-image registration

Unlu

Król

Magri

et al. 2010

Computers in Biology and Medicine

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Keywords:MR-to-PET nonrigid breast-image registration FEM-based soft tissue multimodality nonrigid image registration a b s t r a c tWe have developed and tested a new simple computerized finite element method (FEM) approach to MR-to-PET nonrigid breast-image registration. The method requires five-nine fiducial skin markers (FSMs) visible in MRI and PET that need to be located in the same spots on the breast and two on the flanks during both scans. Patients need to be similarly positioned prone during MRI and PET scans. This is accomplished by means of a low gamma-ray attenuation breast coil replica used as the breast support during the PET scan. We demonstrate that, under such conditions, the observed FSM displacement vectors between MR and PET images, distributed piecewise linearly over the breast volume, produce a deformed FEM mesh that reasonably approximates nonrigid deformation of the breast tissue between the MRI and PET scans. This method, which does not require a biomechanical breast tissue model, is robust and fast. Contrary to other approaches utilizing voxel intensity-based similarity measures or surface matching, our method works for matching MR with pure molecular images (i.e. PET or SPECT only). Our method does not require a good initialization and would not be trapped by local minima during registration process. All processing including FSMs detection and matching, and mesh generation can be fully automated. We tested our method on MR and PET breast images acquired for 15 subjects. The procedure yielded good quality images with an average target registration error below 4 mm (i.e. well below PET spatial resolution of 6-7 mm). Based on the results obtained for 15 subjects studied to date, we conclude that this is a very fast and a well-performing method for MR-to-PET breast-image nonrigid registration. Therefore, it is a promising approach in clinical practice. This method can be easily applied to nonrigid registration of MRI or CT of any type of soft-tissue images to their molecular counterparts such as obtained using PET and SPECT.

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“…Rigid transformations perform translational and rotational realignments [Zuo et al, 1996], affine which perform rigid transformations as well as scaling and shearing [Jenkinson and Smith, 2001] and non-rigid transformations which compensate for local deformations. Reviews of non-rigid registration problems include [Crum et al, 2004;Lester and Arridge, 1999].…”

Section: Classifications Of Registration Problemsmentioning

confidence: 99%

Information theoretic regularization in diffuse optical tomography

Panagiotou¹,

Somayajula²,

Gibson³

et al. 2009

J. Opt. Soc. Am. A

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Diffuse optical tomography (DOT) retrieves the spatially distributed optical characteristics of a medium from external measurements. Recovering these parameters of interest involves solving a non-linear and severely ill-posed inverse problem. In this thesis we propose methods towards the regularization of DOT via the introduction of spatially unregistered, a priori information from alternative high resolution anatomical modalities, using the information theory concepts of joint entropy (JE) and mutual information (MI). Such functionals evaluate the similarity between the reconstructed optical image and the prior image, while bypassing the multi-modality barrier manifested as the incommensurate relation between the gray value representations of corresponding anatomical features in the modalities involved. By introducing structural a priori information in the image reconstruction process, we aim to improve the spatial resolution and quantitative accuracy of the solution.A further condition for the accurate incorporation of a priori information is the establishment of correct alignment between the prior image and the probed anatomy in a common coordinate system. However, limited information regarding the probed anatomy is known prior to the reconstruction process.In this work we explore the potentiality of spatially registering the prior image simultaneously with the solution of the reconstruction process.We provide a thorough explanation of the theory from an imaging perspective, accompanied by preliminary results obtained by numerical simulations as well as experimental data. In addition we compare the performance of MI and JE. Finally, we propose a method for fast joint entropy evaluation and optimization, which we later employ for the information theoretic regularization of DOT. The main areas involved in this thesis are: inverse problems, image reconstruction & regularization, diffuse optical tomography and medical image registration. Statement of intellectual contributionI, Christos Panagiotou, declare that the work presented in this thesis is my own. Where information has been derived from other sources, I confirm that this has been properly indicated in the thesis.

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Automatic motion correction for breast MR imaging.

Cited by 49 publications

References 0 publications

Non-rigid registration of breast MR images using mutual information

Non-rigid registration of breast MR images using mutual information

Computerized method for nonrigid MR-to-PET breast-image registration

Information theoretic regularization in diffuse optical tomography

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