In MRI, motion correction for fet al body poses a particular challenge due to the presence of local non-rigid transformations of organs caused by bending and stretching. The existing slice-to-volume (SVR) reconstruction methods provide efficient solution for the fet al brain that undergoes only rigid transformation or 4D fet al heart with rigid states correlated to cardiac phases. However, for fet al body reconstruction, rigid registration cannot resolve the issue of misregistrations due to deformable motion. This results in propagation of registration error to the reconstructed volume and subsequent degradation of features. We propose a novel approach for non-rigid motion correction in 3D volumes based on an extension of the classical SVR method with hierarchical deformable registration scheme and structure-based outlier rejection. Deformable SVR (DSVR) method allows high resolution reconstruction of the fet al trunk and the robust scheme for structure-based rejection of misregistered slices minimises the impact of registration error. The method performance is evaluated by comparison to the SVR and patch-to-volume registration methods for reconstruction of fet al trunk on a series of fet al MRI datasets from 28-30 weeks gestational age (GA) range with varying degree of motion corruption. An additional phantom study with simulated non-rigid motion is used for the assessment of consistency of DSVR reconstructed volumes.
With respect to application of deformable SVR for motion correction, the existing solutions primarily focus on registration of intra-operative slices with a pre-operative planning volume @cite_6 @cite_10 , multimodal registration (e.g., histology to MRI) @cite_4 @cite_14 @cite_13 or motion correction within a single volume @cite_7 . The majority of monomodal methods are based on rigid SVR for global alignment followed by Free Form Deformation (FFD) registration for correction of non-rigid shape changes. Recently, @cite_17 formalised deformable graph-based SVR approach validated on a 3D heart MRI dataset. However, the existing implementation is limited to in-plane deformations only. Model-based SVR methods integrating biomechanical models for physics-based regularisation were proposed in works of @cite_28 @cite_8 .
@cite_14 @cite_4 @cite_7 @cite_8 @cite_28 @cite_10 @cite_6 @cite_13 @cite_17
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Registration of histological images to three-dimensional imaging modalities is an important step in quantitative analysis of brain structure, in architectonic mapping of the brain, and in investigation of the pathology of a brain disease. Reconstruction of histology volume from serial sections is a well-established procedure, but it does not address registration of individual slices from sparse sections, which is the aim of the slice-to-volume approach. This study presents a flexible framework for intensity-based slice-to-volume nonrigid registration algorithms with a geometric transformation deformation field parametrized by various classes of spline functions: thin-plate splines (TPS), Gaussian elastic body splines (GEBS), or cubic B-splines. Algorithms are applied to cross-modality registration of histological and magnetic resonance images of the human brain. Registration performance is evaluated across a range of optimization algorithms and intensity-based cost functions. For a particular case of histological data, best results are obtained with a TPS three-dimensional (3D) warp, a new unconstrained optimization algorithm (NEWUOA), and a correlation-coefficient-based cost function. Breathing motion is one of the main sources of artifacts in MRI acquisitions that can severely impair diagnosis. In MRI with continuously moving table, the application of common motion compensation approaches such as breath holding or the synchronization of the measurement with the breathing motion can be problematic. In this study, a technique for the reduction of breathing-motion artifacts for MRI with continuously moving table is presented, which reconstructs motion-consistent volumes from data acquired during free breathing. Axial images are acquired rapidly compared to the period of the breathing motion and consistently combined using a combination of rigid and nonrigid slice-to-volume registration. This new technique is compared to a previously reported artifact reduction method for MRI with continuously moving table that is based on the same acquisition scheme. While the latter method only suppresses ghosting artifacts, the new technique is shown to additionally reduce blurring, misregistrations, and signal cancellations in the reconstructed images. Magn Reson Med 63:701–712, 2010. © 2010 Wiley-Liss, Inc. A method is proposed for automatic registration of 3D preoperative magnetic resonance images of deformable tissue to a sequence of its 2D intraoperative images. The algorithm employs a dynamic continuum mechanics model of the deformation and similarity (distance) measures such as correlation ratio, mutual information or sum of squared differences for registration. The registration is solely based on information present in the 3D preoperative and 2D intraoperative images and does not require fiducial markers, feature extraction or image segmentation. Results of experiments with a biopsy training breast phantom show that the proposed method can perform well in the presence of large deformations. This is particularly useful for clinical applications such as MR-based breast biopsy where large tissue deformations occur. Purpose: MRI-guided prostate needle biopsy requires compensation for organ motion between target planning and needle placement. Two questions are studied and answered in this paper: 1) is rigid registration sufficient in tracking the targets with an error smaller than the clinically significant size of prostate cancer and 2) what is the effect of the number of intraoperative slices on registration accuracy and speed? Methods: we propose multislice-to-volume registration algorithms for tracking the biopsy targets within the prostate. Three orthogonal plus additional transverse intraoperative slices are acquired in the approximate center of the prostate and registered with a high-resolution target planning volume. Both rigid and deformable scenarios were implemented. Both simulated and clinical MRI-guided robotic prostate biopsy data were used to assess tracking accuracy. Results: average registration errors in clinical patient data were 2.6 mm for the rigid algorithm and 2.1 mm for the deformable algorithm. Conclusion: rigid tracking appears to be promising. Three tracking slices yield significantly high registration speed with an affordable error. Deformable image registration is a fundamental problem in computer vision and medical image computing. In this paper we investigate the use of graphical models in the context of a particular type of image registration problem, known as slice-to-volume registration. We introduce a scal-able, modular and flexible formulation that can accommodate low-rank and high order terms, that simultaneously selects the plane and estimates the in-plane deformation through a single shot optimization approach. The proposed framework is instantiated into different variants seeking either a compromise between computational efficiency (soft plane selection constraints and approximate definition of the data similarity terms through pair-wise components) or exact definition of the data terms and the constraints on the plane selection. Simulated and real-data in the context of ultrasound and magnetic resonance registration (where both framework instantiations as well as different optimization strategies are considered) demonstrate the potentials of our method.