Brain symmetry plane detection based on fractal analysis

Macromolecular crowding agent dependent extracellular matrix deposition and growth factor retention in human corneal fibroblast cultures

The major obstacle in the commercialisation and clinical translation of tissue engineered medicines is the required for the development of implantable tissue surrogates prolonged in vitro culture. Macromolecular crowding (MMC) enhances and accelerates extracellular matrix (ECM) deposition, thus offering an opportunity to bridge the gap between research and development in tissue engineered substitutes. However, the optimal MMC agent is still elusive. Herein, we first assessed the biophysical properties of the most widely used MMC agents [κλ carrageenan (κλ CR), λ carrageenan (λ CR) and Ficoll™ cocktail (FC)] and then assessed their effect in basic cell function, ECM deposition and growth factor retention in human corneal fibroblast (hCF) cultures. Dynamic light scattering analysis revealed that both CR macromolecules had significantly lower and higher zeta potential and hydrodynamic radius, respectively, than the FC. None of the MMC agents affected hCF morphology and all induced similar hCF viability, proliferation and metabolic activity. Electrophoresis and immunofluorescence analyses made apparent that at day 10 (longest time point assessed), the FC brought about the highest fibronectin and collagen types I, III, IV, V and VI deposition. Deposited ECM pattern analysis showed that at day 10, the FC induced the lowest lacunarity and normalised end points and the highest fractal dimension and % high density matrix. Further immunofluorescence analysis revealed no significant differences between the groups in vimentin, aldehyde dehydrogenase 3 family member A1, keratocan, paired box protein 6 and α-smooth muscle actin. Importantly, at day 10, the FC resulted in the highest growth factor retention (20 molecules). Our data clearly illustrate a MMC agent dependent cell response, with the FC having the highest positive effect in hCF cultures.

Fractals in Neuroimaging

Several natural phenomena can be described by studying their statistical scaling patterns, hence leading to simple geometrical interpretation. In this regard, fractal geometry is a powerful tool to describe the irregular or fragmented shape of natural features, using spatial or time-domain statistical scaling laws (power-law behavior) to characterize real-world physical systems. This chapter presents some works on the usefulness of fractal features, mainly the fractal dimension and the related Hurst exponent, in the characterization and identification of pathologies and radiological features in neuroimaging, mainly, magnetic resonance imaging.

Fractal Dimension-Based Infection Detection in Chest X-ray Images

The current ongoing trend of dimension detection of medical images is one of the challenging areas which facilitates several improvements in accurate measuring of clinical imaging based on fractal dimension detection methodologies. For medical diagnosis of any infection, detection of dimension is one of the major challenges due to the fractal shape of the medical object. Significantly improved outcome indicates that the performance of fractal dimension detection techniques is better than that of other state-of-the-art methods to extract diagnostically significant information from clinical image. Among the fractal dimension detection methodologies, fractal geometry has developed an efficient tool in medical image investigation. In this paper, a novel methodology of fractal dimension detection of medical images is proposed based on the concept of box counting technique to evaluate the fractal dimension. The proposed method has been evaluated and compared to other state-of-the-art approaches, and the results of the proposed algorithm graphically justify the mathematical derivation of the box counting approach in terms of Hurst exponent.

A new approach to symmetric registration of longitudinal structural MRI of the human brain

BACKGROUND

This paper presents the Automatic Temporal Registration Algorithm (ATRA) for symmetric rigid-body registration of longitudinal T1-weighted three-dimensional MRI scans of the human brain. This is a fundamental processing step in computational neuroimaging.

NEW METHOD

The notion of leave-one-out consistent (LOOC) landmarks with respect to a supervised landmark detection algorithm is introduced. An automatic algorithm is presented for identification of LOOC landmarks on MRI scans. Multiple sets of LOOC landmarks are identified on each volume and a Generalized Orthogonal Procrustes Analysis of the landmarks is used to find a rigid-body transformation of each volume into a common space where the volumes are aligned precisely.

RESULTS

Qualitative and quantitative evaluations of ATRA registration accuracy were performed using 2012 volumes from 503 subjects (4 longitudinal volumes/subject), and on a further 120 volumes acquired from 3 normal subjects (40 longitudinal volumes/subject). Since the ground truth registrations are unknown, we devised a novel method for showing that ATRA's registration accuracy is at least better than 0.5 mm translation or 0.5° rotation.

COMPARISON WITH EXISTING METHOD(S)

In comparison with existing methods, ATRA does not require any image preprocessing (e.g., skull-stripping or intensity normalization) and can handle conditions where rigid-body motion assumptions are not true (e.g., movement in eyes, jaw, neck) and brain tissue loss over time in neurodegenerative diseases. In a systematic comparison with the FSL FLIRT algorithm, ATRA provided faster and more accurate registrations.

CONCLUSIONS

The algorithm is symmetric, in the sense that any permutation of the input volumes does not change the transformation matrices, and unbiased, in that all volumes undergo exactly one interpolation operation, which precisely aligns them in a common space. There is no interpolation bias and no reference volume. All volumes are treated exactly the same. The algorithm is fast and highly accurate.

Improving the prediction of environmental fate of engineered nanomaterials by fractal modelling

A critical analysis of the available engineered nanomaterials (ENMs) environmental fate modelling approaches indicates that existing tools do not satisfactorily account for the complexities of nanoscale phenomena. Fractal modelling (FM) can complement existing kinetic fate models by including more accurate interpretations of shape and structure, density and collision efficiency parameters to better describe homo- and heteroaggregation. Pathways to including hierarchical symmetry concepts and a route to establishing a structural classification of nanomaterials based on FM are proposed.

Automatic extraction of the midsagittal surface from brain MR Images using the Kullback-Leibler measure

The midsagittal surface separates the two hemispheres of the cerebrum. This surface is often typified as a geometrical plane: the midsagittal plane. However, in subjects with a considerable amount of naturally occurring brain torque, the midsagittal surface deviates to a large extent from a plane. In the present study, an automated method to extract the midsagittal surface is proposed, evaluated on a large dataset, and compared to a conventional midsagittal plane representation. The midsagittal plane was extracted from MR images with a technique based on the Kullback-Leibler measure. This plane was used to initialize a surface, that was deformed to represent the midsagittal surface. One hundred subjects were selected from the SMART-MR study: fifty subjects with brain torque and fifty random subjects. Manual delineations of the midsagittal surface were used for evaluation. The extracted midsagittal planes and surfaces were compared to the manual delineations by assessing the absolute volume of misclassified cerebrum tissue. The midsagittal surface resulted in significantly better separations of the hemispheres. In the randomly selected subjects, the error reduced from 2.71 ± 1.05 ml to 2.20 ± 0.66 ml and in subjects with brain torque from 4.85±2.79 ml to 2.23±0.77 ml, with improvements up to 16.6 ml in individual subjects with marked brain torque.

Aneurysm permeability following coil embolization: packing density and coil distribution

Background

Rates of durable aneurysm occlusion following coil embolization vary widely, and a better understanding of coil mass mechanics is desired. The goal of this study is to evaluate the impact of packing density and coil uniformity on aneurysm permeability.

Methods

Aneurysm models were coiled using either Guglielmi detachable coils or Target coils. The permeability was assessed by taking the ratio of microspheres passing through the coil mass to those in the working fluid. Aneurysms containing coil masses were sectioned for image analysis to determine surface area fraction and coil uniformity.

Results

All aneurysms were coiled to a packing density of at least 27%. Packing density, surface area fraction of the dome and neck, and uniformity of the dome were significantly correlated (p<0.05). Hence, multivariate principal components-based partial least squares regression models were used to predict permeability. Similar loading vectors were obtained for packing and uniformity measures. Coil mass permeability was modeled better with the inclusion of packing and uniformity measures of the dome (r²=0.73) than with packing density alone (r²=0.45). The analysis indicates the importance of including a uniformity measure for coil distribution in the dome along with packing measures.

Conclusions

A densely packed aneurysm with a high degree of coil mass uniformity will reduce permeability.