MRI and PET Coregistration—A Cross Validation of Statistical Parametric Mapping and Automated Image Registration

Kiebel, Stefan J.; Ashburner, John; Poline, Jean‐Baptiste; Friston, Karl J.

doi:10.1006/nimg.1997.0265

Cited by 119 publications

(71 citation statements)

References 9 publications

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“…Evaluation of image registration method is most often done via simulations, generating the data artificially and comparing the recovered results with the known true transformation [20]- [22]. More realistic but less widely applicable 'gold standard' approach is to use some independent and sufficiently accurate method to determine the true deformation, such as using special markers for validation which are not used for registration [23]- [25].…”

Section: Related Work On Image Registration Accuracy Evaluationmentioning

confidence: 99%

“…From the confidence interval (16) and properties of minimum we get an inequality for the true value of based on observable quantities (20) We approximate quadratically around (21) an estimate of the Hessian is available for free as a by-product of the BFGS optimization procedure. This yields (22) from which we can get an equivalent covariance matrix that a normally distributed would have for (22) to hold as an equality (23) where is the inverse cumulative distribution function. The value of can be precomputed, for example for and we get .…”

Section: E Fast Registration Accuracy Estimation (Frae)mentioning

confidence: 99%

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Bootstrap Resampling for Image Registration Uncertainty Estimation Without Ground Truth

Kybic

2010

IEEE Trans. on Image Process.

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Abstract-We address the problem of estimating the uncertainty of pixel based image registration algorithms, given just the two images to be registered, for cases when no ground truth data is available. Our novel method uses bootstrap resampling. It is very general, applicable to almost any registration method based on minimizing a pixel-based similarity criterion; we demonstrate it using the SSD, SAD, correlation, and mutual information criteria. We show experimentally that the bootstrap method provides better estimates of the registration accuracy than the state-of-the-art Cramér-Rao bound method. Additionally, we evaluate also a fast registration accuracy estimation (FRAE) method which is based on quadratic sensitivity analysis ideas and has a negligible computational overhead. FRAE mostly works better than the Cramér-Rao bound method but is outperformed by the bootstrap method.

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Section: Related Work On Image Registration Accuracy Evaluationmentioning

confidence: 99%

Section: E Fast Registration Accuracy Estimation (Frae)mentioning

confidence: 99%

Bootstrap Resampling for Image Registration Uncertainty Estimation Without Ground Truth

Kybic

2010

IEEE Trans. on Image Process.

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“…However, they allow for multiresolution registration, meaning that the user could define the level of complexity of the transformation through a specified number of degrees of freedom. Most common transformation models are splines [29], cosine basis [30], tetrahedral mesh [31,32], multi-affine [33][34][35] and free (one vector per voxel) [36][37][38].…”

Section: Registration Algorithm For Healthy Subjectmentioning

confidence: 99%

Glioma Dynamics and Computational Models: A Review of Segmentation, Registration, and In Silico Growth Algorithms and their Clinical Applications

Angelini¹,

Clatz²,

Mandonnet³

et al. 2007

CMIR

103

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Tracking gliomas dynamics on MRI has became more and more important for therapeutic management. Powerful computational tools have been recently developed in this context enabling in silico growth on a virtual brain that can be matched with real 3D segmented evolution through registration between atlases and patient brain MRI data. In this paper, we provide an extensive review of existing algorithms for the three computational tasks involved in patient-specific tumor modeling: image segmentation, image registration, and in silico growth modelling (with special emphasis on the proliferation-diffusion model). Accuracy and limits of the reviewed algorithms are systematically discussed. Finally applications of these methods for both clinical practice and fundamental research are also discussed.

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“…Several reports described the registration of MRI and PET for the cat brain, 8 the rat brain, 9 a brain phantom, 10 and radiotherapy planning. 11 There is no report on mutual i nformation image registration of µPET and MRI for the study of photodynamic therapy. We acquired both PET and MR images from mice with tumors and performed over 40 registration experiments.…”

Section: Introductionmentioning

confidence: 99%

Registration of micro-PET and high-resolution MR images of mice for monitoring photodynamic therapy

Fei¹,

Muzic

Lee

et al. 2004

SPIE Proceedings

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We are investigating imaging techniques to study the rapid biochemical and physiological response of tumors to photodynamic therapy (PDT). Positron emission tomography (PET) can provide physiological and functional images of cancers. While MRI can provide high resolution anatomical images and generate serial, noninvasive, in vivo observations of morphological changes. In this study, we investigate image registration methods to combine MRI and micro-PET (µPET) images for improved tumor monitoring. We acquired high resolution MR and PET 18 Ffluorodeoxyglucose (FDG) images from mice with RIF-1 tumors. We used rigid body registration with three translations and three angular variables. We used normalized mutual information as the similarity measure. To assess the quality of registration, we performed slice by slice review of both image volumes, manually segmented feature organs such as the left and right kidneys and the bladder in each slice, and computed the distance between corresponding centroids of the organs. We also used visual inspection techniques such as color overlay displays. Over 40 volume registration experiments were performed with MR and µPET images acquired from three C3H mice. The color overlays showed that the MR images and the PET images matched well. The distance between corresponding centroids of organs was 1.5 ± 0.4 mm which is about 2 pixels of µPET. In conclusion, registration of high resolution MR and µPET images of mice may be useful to combine anatomical and functional information that could be used for the potential application in photodynamic therapy.

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MRI and PET Coregistration—A Cross Validation of Statistical Parametric Mapping and Automated Image Registration

Cited by 119 publications

References 9 publications

Bootstrap Resampling for Image Registration Uncertainty Estimation Without Ground Truth

Bootstrap Resampling for Image Registration Uncertainty Estimation Without Ground Truth

Glioma Dynamics and Computational Models: A Review of Segmentation, Registration, and In Silico Growth Algorithms and their Clinical Applications

Registration of micro-PET and high-resolution MR images of mice for monitoring photodynamic therapy

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