Fast Fluid Registration of medical images

Bro-Nielsen, Morten; Gramkow, Claus

doi:10.1007/bfb0046964

Cited by 262 publications

(191 citation statements)

References 10 publications

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“…This results in a computationally efficient algorithm compared to other non-rigid registration procedures such as those based on linear elasticity (Christensen et al, 1997). Several teams (Bro-Nielsen and Gramkov, 1996;Cachier et al, 2003;Modersitzki, 2004;Pennec et al, 1999) have worked towards providing a theoretical framework for the demons in order to understand and potentially modify the underlying assumptions.…”

Section: An Insight Into the Demons Algorithmmentioning

confidence: 99%

“…More elaborate regularization terms can lead to advanced vectorial filters (Cachier and Ayache, 2004). In this work, we focus on the first step of this alternate minimization and refer the reader to (Bro-Nielsen and Gramkov, 1996;Cachier et al, 2003;Modersitzki, 2004) for a detailed coverage of the regularization questions.…”

Section: A Deeper Understanding Of the Alternate Optimization Of The mentioning

confidence: 99%

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Diffeomorphic demons: Efficient non-parametric image registration

et al. 2009

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We propose an efficient non-parametric diffeomorphic image registration algorithm based on Thirion's demons algorithm. In the first part of this paper, we show that Thirion's demons algorithm can be seen as an optimization procedure on the entire space of displacement fields. We provide strong theoretical roots to the different variants of Thirion's demons algorithm. This analysis predicts a theoretical advantage for the symmetric forces variant of the demons algorithm. We show on controlled experiments that this advantage is confirmed in practice and yields a faster convergence. In the second part of this paper, we adapt the optimization procedure underlying the demons algorithm to a space of diffeomorphic transformations. In contrast to many diffeomorphic registration algorithms, our solution is computationally efficient since in practice it only replaces an addition of displacement fields by a few compositions. Our experiments show that in addition to being diffeomorphic, our algorithm provides results that are similar to the ones from the demons algorithm but with transformations that are much smoother and closer to the gold standard, available in controlled experiments, in terms of Jacobians.

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Section: An Insight Into the Demons Algorithmmentioning

confidence: 99%

Section: A Deeper Understanding Of the Alternate Optimization Of The mentioning

confidence: 99%

Diffeomorphic demons: Efficient non-parametric image registration

et al. 2009

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“…det(J T ) < 0.5 as demonstrated in [3]), a new source image is generated by applying the current fusion of local transformations. Hence, the total transformation field are concatenated:…”

Section: Locally Affine Registration Methods (Larm)mentioning

confidence: 99%

An Atlas-Based Segmentation Propagation Framework Using Locally Affine Registration – Application to Automatic Whole Heart Segmentation

Zhuang¹,

Rhode

Arridge³

et al. 2008

Lecture Notes in Computer Science

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Abstract. In this paper, we present a novel registration algorithm for locally affine registrations. This method preserves the anatomical and intensity class relationships between the local regions. A regularisation procedure is used to maintain a global diffeomorphic transformation. Combined with a novel generic method for accurately inverting the final deformation field, we include our techniques within an atlas-based segmentation propagation framework. We applied our method to automatically segment the whole heart from cardiac magnetic resonance images from a cohort of 18 volunteers (acquisition resolution 2 × 2 × 2 mm). The results show that the proposed method provides a robust initialisation for the atlas-based segmentation propagation framework refined with a fluid registration. We validated our approach against other registration strategies, and demonstrated that we improved the accuracy of the whole heart segmentations (1.8 ± 0.42 mm).

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“…Most previous inter-patient registration methods focused on the brain. These works used B-splines, 4 elastic body registration, 5 fluid registration, [6][7][8][9] Markov random fields, 10 and graph cuts. 11, 12 Xia et al performed inter-patient registration on uni-modal CT data of the torso 13 using intensity-based registration.…”

Section: Introductionmentioning

confidence: 99%

Patient-specific model of a scoliotic torso for surgical planning

2013

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A method for the construction of a patient-specific model of a scoliotic torso for surgical planning via interpatient registration is presented. Magnetic Resonance Images (MRI) of a generic model are registered to surface topography (TP) and X-ray data of a test patient. A partial model is first obtained via thin-plate spline registration between TP and X-ray data of the test patient. The MRIs from the generic model are then fit into the test patient using articulated model registration between the vertebrae of the generic model's MRIs in prone position and the test patient's X-rays in standing position. A non-rigid deformation of the soft tissues is performed using a modified thin-plate spline constrained to maintain bone rigidity and to fit in the space between the vertebrae and the surface of the torso. Results show average Dice values of 0.975 ± 0.012 between the MRIs following inter-patient registration and the surface topography of the test patient, which is comparable to the average value of 0.976 ± 0.009 previously obtained following intra-patient registration. The results also show a significant improvement compared to rigid inter-patient registration. Future work includes validating the method on a larger cohort of patients and incorporating soft tissue stiffness constraints. The method developed can be used to obtain a geometric model of a patient including bone structures, soft tissues and the surface of the torso which can be incorporated in a surgical simulator in order to better predict the outcome of scoliosis surgery, even if MRI data cannot be acquired for the patient.

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Fast Fluid Registration of medical images

Cited by 262 publications

References 10 publications

Diffeomorphic demons: Efficient non-parametric image registration

Diffeomorphic demons: Efficient non-parametric image registration

An Atlas-Based Segmentation Propagation Framework Using Locally Affine Registration – Application to Automatic Whole Heart Segmentation

Patient-specific model of a scoliotic torso for surgical planning

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