4D-Precise: learning-based 3D motion estimation and high temporal resolution 4DCT reconstruction from treatment 2D+t X-ray projections

Background and Objective In radiotherapy treatment planning, respiration-induced motion introduces uncertainty that, if not appropriately considered, could result in dose delivery problems. 4D cone-beam computed tomography (4D-CBCT) has been developed to provide imaging guidance by reconstructing a pseudo-motion sequence of CBCT volumes through binning projection data into breathing phases. However, it suffers from artefacts and erroneously characterizes the averaged breathing motion. Furthermore, conventional 4D-CBCT can only be generated post-hoc using the full sequence of kV projections after the treatment is complete, limiting its utility. Hence, our purpose is to develop a deep-learning motion model for estimating 3D+t CT images from treatment kV projection series. Methods We propose an end-to-end learning-based 3D motion modelling and 4DCT reconstruction model named 4D-Precise, abbreviated from Probabilistic reconstruction of image sequences from CBCT kV projections. The model estimates voxel-wise motion fields and simultaneously reconstructs a 3DCT volume at any arbitrary time point of the input projections by transforming a reference CT volume. Developing a Torch-DRR module, it enables end-to-end training by computing Digitally Reconstructed Radiographs (DRRs) in PyTorch. During training, DRRs with matching projection angles to the input kVs are automatically extracted from reconstructed volumes and their structural dissimilarity to inputs is penalised. We introduced a novel loss function to regulate spatio-temporal motion field variations across the CT scan, leveraging planning 4DCT for prior motion distribution estimation. Results The model is trained patient-specifically using three kV scan series, each including over 1200 angular/temporal projections, and tested on three other scan series. Imaging data from five patients are analysed here. Also, the model is validated on a simulated paired 4DCT-DRR dataset created using the Surrogate Parametrised Respiratory Motion Modelling (SuPReMo). The results demonstrate that the reconstructed volumes by 4D-Precise closely resemble the ground-truth volumes in terms of Dice, volume similarity, mean contour distance, and Hausdorff distance, whereas 4D-Precise achieves smoother deformations and fewer negative Jacobian determinants compared to SuPReMo. Conclusions Unlike conventional 4DCT reconstruction techniques that ignore breath inter-cycle motion variations, the proposed model computes both intra-cycle and inter-cycle motions. It represents motion over an extended timeframe, covering several minutes of kV scan series

Multi-view Hybrid Graph Convolutional Network for Volume-to-mesh Reconstruction in Cardiovascular MRI

Cardiovascular magnetic resonance imaging is emerging as a crucial tool to examine cardiac morphology and function. Essential to this endeavour are anatomical 3D surface and volumetric meshes derived from CMR images, which facilitate computational anatomy studies, biomarker discovery, and in-silico simulations. However, conventional surface mesh generation methods, such as active shape models and multi-atlas segmentation, are highly time-consuming and require complex processing pipelines to generate simulation-ready 3D meshes. In response, we introduce HybridVNet, a novel architecture for direct image-to-mesh extraction seamlessly integrating standard convolutional neural networks with graph convolutions, which we prove can efficiently handle surface and volumetric meshes by encoding them as graph structures. To further enhance accuracy, we propose a multiview HybridVNet architecture which processes both long axis and short axis CMR, showing that it can increase the performance of cardiac MR mesh generation. Our model combines traditional convolutional networks with variational graph generative models, deep supervision and mesh-specific regularisation. Experiments on a comprehensive dataset from the UK Biobank confirm the potential of HybridVNet to significantly advance cardiac imaging and computational cardiology by efficiently generating high-fidelity and simulation ready meshes from CMR images

A parametric finite element solution of the generalised Bloch-Torrey equation for arbitrary domains

Nuclear magnetic resonance (NMR) has proven of enormous value in the investigation of porous media. Its use allows to study pore size distributions, tortuosity, and permeability as a function of the relaxation time, diffusivity, and flow. This information plays an important role in plenty of applications, ranging from oil industry to medical diagnosis. A complete NMR analysis involves the solution of the Bloch-Torrey (BT) equation. However, solving this equation analytically becomes intractable for all but the simplest geometries. We present an efficient numerical framework for solving the complete BT equation in arbitrarily complex domains. In addition to the standard BT equation, the generalised BT formulation takes into account the flow and relaxation terms, allowing a better representation of the phenomena under scope. The presented framework is flexible enough to deal parametrically with any order of convergence in the spatial domain. The major advantage of such approach is to allow both faster computations and sensitivity analyses over realistic geometries. Moreover, we developed a second-order implicit scheme for the temporal discretisation with similar computational demands as the existing explicit methods. This represents a huge step forward for obtaining reliable results with few iterations. Comparisons with analytical solutions and real data show the flexibility and accuracy of the proposed methodology

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

Generalised coherent point drift for group-wise multi-dimensional analysis of diffusion brain MRI data

A probabilistic framework for registering generalised point sets comprising multiple voxel-wise data features such as positions, orientations and scalar-valued quantities, is proposed. It is employed for the analysis of magnetic resonance diffusion tensor image (DTI)-derived quantities, such as fractional anisotropy (FA) and fibre orientation, across multiple subjects. A hybrid Student’s t-Watson-Gaussian mixture model-based non-rigid registration framework is formulated for the joint registration and clustering of voxel-wise DTI-derived data, acquired from multiple subjects. The proposed approach jointly estimates the non-rigid transformations necessary to register an unbiased mean template (represented as a 7-dimensional hybrid point set comprising spatial positions, fibre orientations and FA values) to white matter regions of interest (ROIs), and approximates the joint distribution of voxel spatial positions, their associated principal diffusion axes, and FA. Specific white matter ROIs, namely, the corpus callosum and cingulum, are analysed across healthy control (HC) subjects (K = 20 samples) and patients diagnosed with mild cognitive impairment (MCI) (K = 20 samples) or Alzheimer’s disease (AD) (K = 20 samples) using the proposed framework, facilitating inter-group comparisons of FA and fibre orientations. Group-wise analyses of the latter is not afforded by conventional approaches such as tract-based spatial statistics (TBSS) and voxel-based morphometry (VBM)

Creating a model of diseased artery damage and failure from healthy porcine aorta

Large quantities of diseased tissue are required in the research and development of new generations of medical devices, for example for use in physical testing. However, these are difficult to obtain. In contrast, porcine arteries are readily available as they are regarded as waste. Therefore, reliable means of creating from porcine tissue physical models of diseased human tissue that emulate well the associated mechanical changes would be valuable. To this end, we studied the effect on mechanical response of treating porcine thoracic aorta with collagenase, elastase and glutaraldehyde. The alterations in mechanical and failure properties were assessed via uniaxial tension testing. A constitutive model composed of the Gasser-Ogden-Holzapfel model, for elastic response, and a continuum damage model, for the failure, was also employed to provide a further basis for comparison (Calvo and Pena, 2006 and Gasser et al., 2006). For the concentrations used here it was found that: collagenase treated samples showed decreased fracture stress in the axial direction only; elastase treated samples showed increased fracture stress in the circumferential direction only; and glutaraldehyde samples showed no change in either direction. With respect to the proposed constitutive model, both collagenase and elastase had a strong effect on the fibre-related terms. The model more closely captured the tissue response in the circumferential direction, due to the smoother and sharper transition from damage initiation to complete failure in this direction. Finally, comparison of the results with those of tensile tests on diseased tissues suggests that these treatments indeed provide a basis for creation of physical models of diseased arteries

Controlled peel testing of a model tissue for diseased aorta

In this study, we examine the effect of collagenase, elastase and glutaraldehyde treatments on the response of porcine aorta to controlled peel testing. Specifically, the effects on the tissue׳s resistance to dissection, as quantified by critical energy release rate, are investigated. We further explore the utility of these treatments in creating model tissues whose properties emulate those of certain diseased tissues. Such model tissues would find application in, for example, development and physical testing of new endovascular devices. Controlled peel testing of fresh and treated aortic specimens was performed with a tensile testing apparatus. The resulting reaction force profiles and critical energy release rates were compared across sample classes. It was found that collagenase digestion significantly decreases resistance to peeling, elastase digestion has almost no effect, and glutaraldehyde significantly increases resistance. The implications of these findings for understanding mechanisms of disease-associated biomechanical changes, and for the creation of model tissues that emulate these changes are explored

Constitutive modeling of cartilaginous tissues: A Review

An important and longstanding field of research in orthopedic biomechanics is the elucidation and mathematical modeling of the mechanical response of cartilaginous tissues. Traditional approaches have treated such tissues as continua and have described their mechanical response in terms of macroscopic models borrowed from solid mechanics. The most important of such models are the biphasic and single-phase viscoelastic models, and the many variations thereof. These models have reached a high level of maturity and have been successful in describing a wide range of phenomena. An alternative approach that has received considerable recent interest, both in orthopedic biomechanics and in other fields, is the description of mechanical response based on consideration of a tissue’s structure—so-called microstructural modeling. Examples of microstructurally based approaches include fibril-reinforced biphasic models and homogenization approaches. A review of both macroscopic and microstructural constitutive models is given in the present work