Clinical significance of creative 3D-image fusion across multimodalities [PET+CT+MR] based on characteristic coregistration

Peng, Matthew Jian-Qiao; Ju, Xiangyang; Khambay, Balvinder; Ayoub, Ashraf; Chen, Chin-Tu; Bai, Bo

doi:10.1016/j.ejrad.2011.12.031

Cited by 9 publications

(4 citation statements)

References 13 publications

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“…To date, the software to fuse images acquired separately by PET and MRI has been widely evaluated and applied in various fields, including prostate and brain imaging [2,3]. Indeed, limitations such as movement artifacts due to changes in patient position have been overcome, offering excellent results, by using movement correction software, with the caveat that postprocessing often remains operator dependent and time consuming.…”

Section: Introductionmentioning

confidence: 99%

Pet-Mri

Tudisca

Nasoodi

Fraioli

2015

Nuclear Medicine Communications

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There is huge emphasis on noninvasive study of disease processes in the field of biomedical research. Combined PET-MRI allows simultaneous acquisition of physiologic information provided by PET images and morphologic and functional information obtained from MRI. The potential clinical and research applications of this technique in different organ systems and pathologies are promising (i.e. brain, cardiovascular, oncological, and inflammatory conditions). The purpose of this review is to illustrate the potential clinical advantages of this new technology, based on the authors' experience.

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Section: Introductionmentioning

confidence: 99%

Pet-Mri

Tudisca

Nasoodi

Fraioli

2015

Nuclear Medicine Communications

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“…When dentists design schemes, they want to consider the morphology of the soft tissue and the hard tissue together. CT image and visible spectrum image fusion models [5] can multiply various factors and help the dentists achieve the optimal implant scheme.…”

Section: Introductionmentioning

confidence: 99%

Registration of the Cone Beam CT and Blue-Ray Scanned Dental Model Based on the Improved ICP Algorithm

Mei

et al. 2014

International Journal of Biomedical Imaging

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Multimodality image registration and fusion has complementary significance for guiding dental implant surgery. As the needs of the different resolution image registration, we develop an improved Iterative Closest Point (ICP) algorithm that focuses on the registration of Cone Beam Computed Tomography (CT) image and high-resolution Blue-light scanner image. The proposed algorithm includes two major phases, coarse and precise registration. Firstly, for reducing the matching interference of human subjective factors, we extract feature points based on curvature characteristics and use the improved three point's translational transformation method to realize coarse registration. Then, the feature point set and reference point set, obtained by the initial registered transformation, are processed in the precise registration step. Even with the unsatisfactory initial values, this two steps registration method can guarantee the global convergence and the convergence precision. Experimental results demonstrate that the method has successfully realized the registration of the Cone Beam CT dental model and the blue-ray scanner model with higher accuracy. So the method could provide researching foundation for the relevant software development in terms of the registration of multi-modality medical data.

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“…The registration of multiple studies performed over multiple time intervals is critical in the longitudinal assessment of disease progression and treatment. Registration is important for various medical image analyses, including motion estimation (Buerger et al 2011), cross modality image fusion (Peng et al 2012), segmentation (van der Lijn et al 2012), morphometric study (Lepore et al 2008), treatment verification (Hua et al 2010) and deformation estimation during image guided surgery (Markelj et al 2012).…”

Section: Introductionmentioning

confidence: 99%

The adaptive FEM elastic model for medical image registration

Zhang

Wang

et al. 2013

Phys. Med. Biol.

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This paper proposes an adaptive mesh refinement strategy for the finite element method (FEM) based elastic registration model. The signature matrix for mesh refinement takes into account the regional intensity variance and the local deformation displacement. The regional intensity variance reflects detailed information for improving registration accuracy and the deformation displacement fine-tunes the mesh refinement for a more efficient algorithm. The gradient flows of two different similarity metrics, the sum of the squared difference and the spatially encoded mutual information for the mono-modal and multi-modal registrations, are used to derive external forces to drive the model to the equilibrium state. We compared our approach to three other models: (1) the conventional multi-resolution FEM registration algorithm; (2) the FEM elastic method that uses variation information for mesh refinement; and (3) the robust block matching based registration. Comparisons among different methods in a dataset with 20 CT image pairs upon artificial deformation demonstrate that our registration method achieved significant improvement in accuracies. Experimental results in another dataset of 40 real medical image pairs for both mono-modal and multi-modal registrations also show that our model outperforms the other three models in its accuracy.

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Clinical significance of creative 3D-image fusion across multimodalities [PET+CT+MR] based on characteristic coregistration

Cited by 9 publications

References 13 publications

Pet-Mri

Pet-Mri

Registration of the Cone Beam CT and Blue-Ray Scanned Dental Model Based on the Improved ICP Algorithm

The adaptive FEM elastic model for medical image registration

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