Optimal B-spline Mapping of Flow Imaging Data for Imposing Patient-specific Velocity Profiles in Computational Hemodynamics.

OBJECTIVE: We propose a novel method to map patient-specific blood velocity profiles obtained from imaging data such as 2D flow MRI or 3D colour Doppler ultrasound) to geometric vascular models suitable to perform CFD simulations of haemodynamics. We describe the implementation and utilisation of the method within an open-source computational hemodynamics simulation software (CRIMSON). METHODS: The proposed method establishes point-wise correspondences between the contour of a fixed geometric model and time-varying contours containing the velocity image data, from which a continuous, smooth and cyclic deformation field is calculated. Our methodology is validated using synthetic data, and demonstrated using two different in-vivo aortic velocity datasets: a healthy subject with normal tricuspid valve and a patient with bicuspid aortic valve. RESULTS: We compare our method with the state-of-the-art Schwarz-Christoffel method, in terms of preservation of velocities and execution time. Our method is as accurate as the Schwarz-Christoffel method, while being over 8 times faster. CONCLUSIONS: Our mapping method can accurately preserve either the flow rate or the velocity field through the surface, and can cope with inconsistencies in motion and contour shape. SIGNIFICANCE: The proposed method and its integration into the CRIMSON software enable a streamlined approach towards incorporating more patient-specific data in blood flow simulations

Unsupervised segmentation of MRI knees using Image Partition Forests

Nowadays many people are affected by arthritis, a condition of the joints with limited prevention measures, but with various options of treatment the most radical of which is surgical. In order for surgery to be successful, it relies on careful analysis of patient–based models generated from medical images, usually by manual segmentation. In this work we show how to automate the segmentation of a crucial and complex joint – the knee. To achieve this goal we rely on our novel way of representing a 3D voxel volume as a hierarchical structure of partitions which we have named Image Partition Forest (IPF). The IPF contains several partition layers of increasing coarseness, with partitions nested across layers in the form of adjacency graphs. On the basis of a set of properties (size, mean intensity, coordinates) of each node in the IPF we classify nodes into different features. Values indicating whether or not any particular node belongs to the femur or tibia are assigned through node filtering and node– based region growing. So far we have evaluated our method on 15 MRI knee scans. Our unsupervised segmentation compared against a hand–segmented gold standard has achieved an average Dice similarity coefficient of 0.87 for both femur and tibia, and an average symmetric surface distance of 3.77 mm for femur and 1.52 mm for tibia. The paper also discusses ways to introduce stricter morphological and spatial conditioning in the bone labelling process

Verslag van het fabrieksschema voor de bereiding van soda volgens het solvay-proces

Document(en) uit de collectie Chemische ProcestechnologieDelftChemTechApplied Science

Unsupervised segmentation of MRI knees using Image Partition Forests

Nowadays many people are affected by arthritis, a condition of the joints with limited prevention measures, but with various options of treatment the most radical of which is surgical. In order for surgery to be successful, it relies on careful analysis of patient–based models generated from medical images, usually by manual segmentation. In this work we show how to automate the segmentation of a crucial and complex joint – the knee. To achieve this goal we rely on our novel way of representing a 3D voxel volume as a hierarchical structure of partitions which we have named Image Partition Forest (IPF). The IPF contains several partition layers of increasing coarseness, with partitions nested across layers in the form of adjacency graphs. On the basis of a set of properties (size, mean intensity, coordinates) of each node in the IPF we classify nodes into different features. Values indicating whether or not any particular node belongs to the femur or tibia are assigned through node filtering and node– based region growing. So far we have evaluated our method on 15 MRI knee scans. Our unsupervised segmentation compared against a hand–segmented gold standard has achieved an average Dice similarity coefficient of 0.87 for both femur and tibia, and an average symmetric surface distance of 3.77 mm for femur and 1.52 mm for tibia. The paper also discusses ways to introduce stricter morphological and spatial conditioning in the bone labelling process

Segmentation of hip cartilage in compositional magnetic resonance imaging: A fast, accurate, reproducible, and clinically viable semi‐automated methodology

Manual segmentation is a significant obstacle in the analysis of compositional MRI for clinical decision-making and research. Our aim was to produce a fast, accurate, reproducible, and clinically viable semi-automated method for segmentation of hip MRI. We produced a semi-automated segmentation method for cartilage segmentation of hip MRI sequences consisting of a two step process: (1) fully automated hierarchical partitioning of the data volume generated using a bespoke segmentation approach applied recursively, followed by (2) user selection of the regions of interest using a region editor. This was applied to dGEMRIC scans at 3T taken from a prospective longitudinal study of individuals considered at high risk of developing osteoarthritis (SibKids) which were also manually segmented for comparison. Fourteen hips were segmented both manually and using our semi-automated method. Per hip, processing time for semi-automated and manual segmentation was 10-15 minutes, and 60-120 minutes respectively. Accuracy and Dice similarity coefficient (DSC) for the comparison of semi-automated and manual segmentations was 0.9886 and 0.8803 respectively. Intra-observer and inter-observer reproducibility of the semi-automated segmentation method gave an accuracy of 0.9997 and 0.9991, and DSC of 0.9726 and 0.9354 respectively. We have proposed a fast, accurate, reproducible, and clinically viable semi-automated method for segmentation of hip MRI sequences. This enables accurate anatomical and biochemical measurements to be obtained quickly and reproducibly. This is the first such method that shows clinical applicability, and could have large ramifications for the use of compositional MRI in research and clinically

Segmentation of hip cartilage in compositional magnetic resonance imaging: A fast, accurate, reproducible, and clinically viable semi‐automated methodology

Manual segmentation is a significant obstacle in the analysis of compositional MRI for clinical decision-making and research. Our aim was to produce a fast, accurate, reproducible, and clinically viable semi-automated method for segmentation of hip MRI. We produced a semi-automated segmentation method for cartilage segmentation of hip MRI sequences consisting of a two step process: (1) fully automated hierarchical partitioning of the data volume generated using a bespoke segmentation approach applied recursively, followed by (2) user selection of the regions of interest using a region editor. This was applied to dGEMRIC scans at 3T taken from a prospective longitudinal study of individuals considered at high risk of developing osteoarthritis (SibKids) which were also manually segmented for comparison. Fourteen hips were segmented both manually and using our semi-automated method. Per hip, processing time for semi-automated and manual segmentation was 10-15 minutes, and 60-120 minutes respectively. Accuracy and Dice similarity coefficient (DSC) for the comparison of semi-automated and manual segmentations was 0.9886 and 0.8803 respectively. Intra-observer and inter-observer reproducibility of the semi-automated segmentation method gave an accuracy of 0.9997 and 0.9991, and DSC of 0.9726 and 0.9354 respectively. We have proposed a fast, accurate, reproducible, and clinically viable semi-automated method for segmentation of hip MRI sequences. This enables accurate anatomical and biochemical measurements to be obtained quickly and reproducibly. This is the first such method that shows clinical applicability, and could have large ramifications for the use of compositional MRI in research and clinically

Anatomically realistic simulations of liver ablation by irreversible electroporation: impact of blood vessels on ablation volumes and undertreatment

Irreversible electroporation is a novel tissue ablation technique which entails delivering intense electrical pulses to target tissue, hence producing fatal defects in the cell membrane. The present study numerically analyzes the potential impact of liver blood vessels on ablation by irreversible electroporation because of their influence on the electric field distribution. An anatomically realistic computer model of the liver and its vasculature within an abdominal section was employed, and blood vessels down to 0.4 mm in diameter were considered. In this model, the electric field distribution was simulated in a large series of scenarios (N = 576) corresponding to plausible percutaneous irreversible electroporation treatments by needle electrode pairs. These modeled treatments were relatively superficial (maximum penetration depth of the electrode within the liver = 26 mm) and it was ensured that the electrodes did not penetrate the vessels nor were in contact with them. In terms of total ablation volume, the maximum deviation caused by the presence of the vessels was 6%, which could be considered negligible compared to the impact by other sources of uncertainty. Sublethal field magnitudes were noticed around vessels covering volumes of up to 228 mm3. If in this model the blood was substituted by a liquid with a low electrical conductivity (0.1 S/m), the maximum volume covered by sublethal field magnitudes was 3.7 mm3 and almost no sublethal regions were observable. We conclude that undertreatment around blood vessels may occur in current liver ablation procedures by irreversible electroporation. Infusion of isotonic low conductivity liquids into the liver vasculature could prevent this risk.This work received financial support from the Spanish Government through grants TEC2010-17285, TEC2011-27133-C02, and TEC2014-52383-C3 (TEC2014-52383-C3-2) and from the European Commission through the Marie Curie IRG grant “TAMIVIVE” 256376