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Precision Imaging of Prostate Cancer
Published in Ayman El-Baz, Gyan Pareek, Jasjit S. Suri, Prostate Cancer Imaging, 2018
The prostate is an easily deformable organ, hence, the gland deforms during and after prostatectomy. Additionally, prostate MRI is often performed by using an endorectal coil, which further deforms the gland. Specimen formalin fixation and paraffin embedding also induce variable tissue shrinkage. Deformable image registration provides a high degree of flexibility for registration of histologic images with in vivo/ex vivo MR images, and can assist in more accurate evaluation of MRI findings. Boundary landmarks and internal landmarks of the same prostate have been used in a deformable registration algorithm. Mazaheri et al. describe a semiautomatic method by using a free-form deformation (FFD) algorithm based on B-splines [125]. This method enabled successful registration of anatomical prostate MR images to pathologic slices. Jacobs et al. [126] proposed a method for the registration and warping of MR images to histologic sections. This method consists of a modified surface-based registration algorithm followed by an automated warping approach using nonlinear thin plate splines to compensate for the distortions between the datasets.
Neuromusculoskeletal modelling and simulation of tissue load in the lower extremities
Published in Youlian Hong, Roger Bartlett, Routledge Handbook of Biomechanics and Human Movement Science, 2008
David G. Lloyd, Thor F. Besier, Christopher R. Winby, Thomas S. Buchanan
Another approach to create subject-specific models is to deform and scale an existing model to match a new data set. Free form deformation morphing techniques are capable of introducing different curvature to bones and muscles and have been used to individualise musculoskeletal models based on a CT or MRI data set (Fernandez et al., 2004). These techniques make it possible to quickly generate accurate, subject-specific models of the musculoskeletal system. However, one still needs to model how muscles generate force.
Data Registration
Published in S. Sitharama Iyengar, Richard R. Brooks, Distributed Sensor Networks, 2016
Richard R. Brooks, Jacob Lamb, Lynne L. Grewe, Juan Deng
For example, in [Kim 10] they use the technique of free-form deformation modeling using B-splines. The main processes involve the two step algorithm discussed previously in ensemble image registration [Orchard 10] but, added to it is the deformation component for non-rigid objects which is performed through a manipulation of a mesh of control points using B-splines. Now, the optimization function includes a cost metric associated with the mean elastic energy of B-spline deformed images.
Lung image registration by featured surface matching method
Published in Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 2022
Shu-Te Su, Ming-Chih Ho, Jia-Yush Yen, Yung-Yaw Chen
Image deformation techniques use the displacement identified by surface matching to deform the preoperative volume model to align the deformed model with the intraoperative surface model. Free-form deformation, such as B-splines (Lange et al. 2003), is a commonly used image deformation method. Extended B-splines (Berendsen et al. 2014) uses a penalty term to optimise the surface matching result and ensure accurate registration. The Finite Element method (FE) (Cash et al. 2005; Haouchine et al. 2013) uses the displacement of surface points as a boundary condition to deform the preoperative volume model. The internal energy and external energy are calculated to achieve equilibrium, and a deformed volume model is produced. Optical flow (Castillo et al. 2009b) determines the displacement by calculating the velocity field. A large image can be formed.
A feature-based affine registration method for capturing background lung tissue deformation for ground glass nodule tracking
Published in Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 2022
Yehuda K. Ben-Zikri, María Helguera, David Fetzer, David A. Shrier, Stephen. R. Aylward, Deepak Chittajallu, Marc Niethammer, Nathan D. Cahill, Cristian A. Linte
Although rigid registration is thought to appropriately handle the global deformation of the solid portion of the lesion and maintain nodule volume, it may not correctly capture the behaviour of the non-solid portion or the lung tissue, which may be best portrayed by deformable registration, given the inherent soft tissue characteristics of the lung. Zheng et al. Zheng et al. (2007) leveraged a previous study that involved breast MR images Tanner et al. (2000) to develop a framework that allowed different transformations to the lung and to the nodule, while coupling the segmentation and registration simultaneously into a single optimisation problem. Their method modelled the lung tissue as non-rigid and the nodule as a rigid structure; the deformable registration followed the B-spline free-form deformation (FFD) implementation, while the rigid transformation imposed on the nodule preserved nodule volume and shape to avoid artificial changes.
Three-dimensional human head modelling: a systematic review
Published in Theoretical Issues in Ergonomics Science, 2018
Various studies have been carried out using either two or three 2D images taken in different planes at the same time to develop 3D models. These techniques use detection of key reference anatomical landmark and helps in measurement of key anthropometric data useful for development of 3D models (Hammond et al. 2004). Such studies use techniques similar to Free Form Deformation (FFD) (Mochimaru, Kouchi, and Dohi 2000) and use of localised deformation (DeCarlo, Metaxas, and Stone 1998) to develop 3D model based on acquired data and by using a generic 3D human body model template. Similar technique was proposed by Tang and Huang (1996) based on a template matching algorithm and use of geometric transforms on the generic head model. This can help in generating of 3D models with hair and proper textural details. Li et al. (2013) developed 3D head model with the hair style details and also soft tissue details like wrinkles. But it is very difficult to acquire an accurate 3D model as most of the data of the shadowed region like the area beneath chin, area behind ear is lost. Human face is not completely symmetrical, hence there are some errors introduced while trying to use one front image and one side image. This technique takes very less time for obtaining the data, but it takes a large amount of time for processing the data to obtain 3D model.