Motion estimation and correction for simultaneous PET/MR using SIRF and CIL

SIRF is a powerful PET/MR image reconstruction research tool for processing data and developing new algorithms. In this research, new developments to SIRF are presented, with focus on motion estimation and correction. SIRF's recent inclusion of the adjoint of the resampling operator allows gradient propagation through resampling, enabling the MCIR technique. Another enhancement enabled registering and resampling of complex images, suitable for MRI. Furthermore, SIRF's integration with the optimization library CIL enables the use of novel algorithms. Finally, SPM is now supported, in addition to NiftyReg, for registration. Results of MR and PET MCIR reconstructions are presented, using FISTA and PDHG, respectively. These demonstrate the advantages of incorporating motion correction and variational and structural priors. This article is part of the theme issue 'Synergistic tomographic image reconstruction: part 2'

NiftySim: A GPU-based nonlinear finite element package for simulation of soft tissue biomechanics

Purpose NiftySim, an open-source finite element toolkit, has been designed to allow incorporation of high-performance soft tissue simulation capabilities into biomedical applications. The toolkit provides the option of execution on fast graphics processing unit (GPU) hardware, numerous constitutive models and solid-element options, membrane and shell elements, and contact modelling facilities, in a simple to use library. Methods The toolkit is founded on the total Lagrangian explicit dynamics (TLEDs) algorithm, which has been shown to be efficient and accurate for simulation of soft tissues. The base code is written in C ++++ , and GPU execution is achieved using the nVidia CUDA framework. In most cases, interaction with the underlying solvers can be achieved through a single Simulator class, which may be embedded directly in third-party applications such as, surgical guidance systems. Advanced capabilities such as contact modelling and nonlinear constitutive models are also provided, as are more experimental technologies like reduced order modelling. A consistent description of the underlying solution algorithm, its implementation with a focus on GPU execution, and examples of the toolkit’s usage in biomedical applications are provided. Results Efficient mapping of the TLED algorithm to parallel hardware results in very high computational performance, far exceeding that available in commercial packages. Conclusion The NiftySim toolkit provides high-performance soft tissue simulation capabilities using GPU technology for biomechanical simulation research applications in medical image computing, surgical simulation, and surgical guidance applications

Finite element input mesh & data files

This compressed folder contains the patient-specific FE models that consists of the all necessary input files to run the simulations of the in-silico oncoplasic breast surgery, as well as the corresponding finite element meshes.<br>All files can be opened using a conventional text editor, while the finite element meshes can be viewed using Gmsh (http://gmsh.info/).<br><br

Pre- & Post-operative 3dMD Surface Scans

This folder contains the optical surface scans of the patients (with texture) in upright position, using the laser 3dMD system installed at the Royal Free Hospital.<div>All image files can be opened up using the free-software Paraview (www.paraview.org).</div

Finite element code (FEB3) and dependencies

"FEB3.tar.gz" contains the sources code of the finite element methodology used to simulate Oncoplastic Breast Surgery. The compressed folder contains a "README.md" that outlines the steps required in order to compile the above libraries (blitz, GSL, etc.).<br><br><br

Motion estimation and correction for simultaneous PET/MR using SIRF and CIL

SIRF is a powerful PET/MR image reconstruction research tool for processing data and developing new algorithms. In this research, new developments to SIRF are presented, with focus on motion estimation and correction. SIRF's recent inclusion of the adjoint of the resampling operator allows gradient propagation through resampling, enabling the MCIR technique. Another enhancement enabled registering and resampling of complex images, suitable for MRI. Furthermore, SIRF's integration with the optimization library CIL enables the use of novel algorithms. Finally, SPM is now supported, in addition to NiftyReg, for registration. Results of MR and PET MCIR reconstructions are presented, using FISTA and PDHG, respectively. These demonstrate the advantages of incorporating motion correction and variational and structural priors. This article is part of the theme issue 'Synergistic tomographic image reconstruction: part 2'.</p

Comparison of simulated breast shape (with texture) before and 3 months after surgery.

<p>Baseline biomechanical model (wireframe) with fitted upright surface scan that visualises the skin and oreola texture before surgery (left column). In view of the recorded surgical plan (cf. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0159766#pone.0159766.g004" target="_blank">Fig 4</a>), the surgical simulation tool predicted the breast shape following BCT (right column). The breast deformations resulted from the in-silico analysis are then projected onto the original surface scan to visualise the textured post-surgical breast shape.</p

Validation results of the in-silico model and the surface optical scans.

<p>Validation results of the in-silico model and the surface optical scans.</p

Flowchart of the multiscale FE solver.

<p>The red-dash line frames the mechano-biological wound healing, angiogenesis and wound contraction numerical methodology (where WH/C is wound healing/contraction).</p