A hybrid Eulerian-Lagrangian approach for thickness, correspondence, and gridding of annular tissues

Evolving Mural Defects, Dilatation, and Biomechanical Dysfunction in Angiotensin II-Induced Thoracic Aortopathies

BACKGROUND

Thoracic aortopathy associates with extracellular matrix remodeling and altered biomechanical properties. We sought to quantify the natural history of thoracic aortopathy in a common mouse model and to correlate measures of wall remodeling such as aortic dilatation or localized mural defects with evolving microstructural composition and biomechanical properties of the wall.

METHODS

We combined a high-resolution multimodality imaging approach (panoramic digital image correlation and optical coherence tomography) with histopathologic examinations and biaxial mechanical testing to correlate spatially, for the first time, macroscopic mural defects and medial degeneration within the ascending aorta with local changes in aortic wall composition and mechanical properties.

RESULTS

Findings revealed strong correlations between local decreases in elastic energy storage and increases in circumferential material stiffness with increasing proximal aortic diameter and especially mural defect size. Mural defects tended to exhibit a pronounced biomechanical dysfunction that is driven by an altered organization of collagen and elastic fibers.

CONCLUSIONS

While aneurysmal dilatation is often observed within particular segments of the aorta, dissection and rupture initiate as highly localized mechanical failures. We show that wall composition and material properties are compromised in regions of local mural defects, which further increases the dilatation and overall structural vulnerability of the wall. Identification of therapies focused on promoting robust collagen accumulation may protect the wall from these vulnerabilities and limit the incidence of dissection and rupture.

Local variations in material and structural properties characterize murine thoracic aortic aneurysm mechanics

We recently developed an approach to characterize local nonlinear, anisotropic mechanical properties of murine arteries by combining biaxial extension-distension testing, panoramic digital image correlation, and an inverse method based on the principle of virtual power. This experimental-computational approach was illustrated for the normal murine abdominal aorta assuming uniform wall thickness. Here, however, we extend our prior approach by adding an optical coherence tomography (OCT) imaging system that permits local reconstructions of wall thickness. This multimodality approach is then used to characterize spatial variations of material and structural properties in ascending thoracic aortic aneurysms (aTAA) from two genetically modified mouse models (fibrillin-1 and fibulin-4 deficient) and to compare them with those from angiotensin II-infused apolipoprotein E-deficient and wild-type control ascending aortas. Local values of stored elastic energy and biaxial material stiffness, computed from spatial distributions of the best fit material parameters, varied significantly with circumferential position (inner vs. outer curvature, ventral vs. dorsal sides) across genotypes and treatments. Importantly, these data reveal an inverse relationship between material stiffness and wall thickness that underlies a general linear relationship between stiffness and wall stress across aTAAs. OCT images also revealed sites of advanced medial degeneration, which were captured by the inverse material characterization. Quantification of histological data further provided high-resolution local correlations among multiple mechanical metrics and wall microstructure. This is the first time that such structural defects and local properties have been characterized mechanically, which can better inform computational models of aortopathy that seek to predict where dissection or rupture may initiate.

Bullseye's representation of cerebral white matter hyperintensities

BACKGROUND AND PURPOSE

Visual rating scales have limited capacities to depict the regional distribution of cerebral white matter hyperintensities (WMH). We present a regional-zonal volumetric analysis alongside a visualization tool to compare and deconstruct visual rating scales.

MATERIALS AND METHODS

3D T1-weighted, T2-weighted spin-echo and FLAIR images were acquired on a 3T system, from 82 elderly participants in a population-based study. Images were automatically segmented for WMH. Lobar boundaries and distance to ventricular surface were used to define white matter regions. Regional-zonal WMH loads were displayed using bullseye plots. Four raters assessed all images applying three scales. Correlations between visual scales and regional WMH as well as inter and intra-rater variability were assessed. A multinomial ordinal regression model was used to predict scores based on regional volumes and global WMH burdens.

RESULTS

On average, the bullseye plot depicted a right-left symmetry in the distribution and concentration of damage in the periventricular zone, especially in frontal regions. WMH loads correlated well with the average visual rating scores (e.g. Kendall's tau [Volume, Scheltens]=0.59 CI=[0.53 0.62]). Local correlations allowed comparison of loading patterns between scales and between raters. Regional measurements had more predictive power than global WMH burden (e.g. frontal caps prediction with local features: ICC=0.67 CI=[0.53 0.77], global volume=0.50 CI=[0.32 0.65], intra-rater=0.44 CI=[0.23 0.60]).

CONCLUSION

Regional-zonal representation of WMH burden highlights similarities and differences between visual rating scales and raters. The bullseye infographic tool provides a simple visual representation of regional lesion load that can be used for rater calibration and training.

White matter hyperintensities are seen only in GRN mutation carriers in the GENFI cohort

Genetic frontotemporal dementia is most commonly caused by mutations in the progranulin (GRN), microtubule-associated protein tau (MAPT) and chromosome 9 open reading frame 72 (C9orf72) genes. Previous small studies have reported the presence of cerebral white matter hyperintensities (WMH) in genetic FTD but this has not been systematically studied across the different mutations. In this study WMH were assessed in 180 participants from the Genetic FTD Initiative (GENFI) with 3D T1- and T2-weighed magnetic resonance images: 43 symptomatic (7 GRN, 13 MAPT and 23 C9orf72), 61 presymptomatic mutation carriers (25 GRN, 8 MAPT and 28 C9orf72) and 76 mutation negative non-carrier family members. An automatic detection and quantification algorithm was developed for determining load, location and appearance of WMH. Significant differences were seen only in the symptomatic GRN group compared with the other groups with no differences in the MAPT or C9orf72 groups: increased global load of WMH was seen, with WMH located in the frontal and occipital lobes more so than the parietal lobes, and nearer to the ventricles rather than juxtacortical. Although no differences were seen in the presymptomatic group as a whole, in the GRN cohort only there was an association of increased WMH volume with expected years from symptom onset. The appearance of the WMH was also different in the GRN group compared with the other groups, with the lesions in the GRN group being more similar to each other. The presence of WMH in those with progranulin deficiency may be related to the known role of progranulin in neuroinflammation, although other roles are also proposed including an effect on blood-brain barrier permeability and the cerebral vasculature. Future studies will be useful to investigate the longitudinal evolution of WMH and their potential use as a biomarker as well as post-mortem studies investigating the histopathological nature of the lesions.

•

In genetic FTD white matter hyperintensities (WMH) are found most prominently in symptomatic patients with GRN mutations.

•

Frontal and occipital lobes are the most affected regions.

•

WMH are more likely to occur close to the ventricles.

•

WMH have a more homogenous appearance possibly suggestive of inflammatory rather than vascular lesions.

Identification of a rectal subregion highly predictive of rectal bleeding in prostate cancer IMRT

BACKGROUND AND PURPOSE

To identify rectal subregions at risks (SRR) highly predictive of 3-year rectal bleeding (RB) in prostate cancer IMRT.

MATERIALS AND METHODS

Overall, 173 prostate cancer patients treated with IMRT/IGRT were prospectively analyzed, divided into "training" (n=118) and "validation" cohorts (n=53). Dose-volume histograms (DVHs) were calculated in three types of rectal subregions: "geometric", intuitively defined (hemi-rectum,…); "personalized", obtained by non-rigid registration followed by voxel-wise statistical analysis (SRRp); "generic", mapped from SRRps, located within 8×8 rectal subsections (SRRg). DVHs from patients with and without RB were compared and used for toxicity prediction.

RESULTS

Training cohort SRRps were primarily within the inferior anterior hemi-rectum and upper anal canal, with 3.8Gy mean dose increase for Grade⩾1 RB patients. The SRRg, representing 15% of the absolute rectal volume, was located in 10 inferior-anterior rectal subsections. V18-V70 for SRRps and V58-V65 for SRRg were significantly higher for RB patients than non-RB. Maximum areas under the curve (AUCs) for SRRp and SRRg RB prediction were 71% and 64%, respectively. The validation cohort confirmed the predictive value of SRRg for Grade⩾1 RB. The total cohort confirmed the predictive value of SRRg for Grade⩾2 RB. Geometrical subregions were not RB predictors.

CONCLUSION

The inferior-anterior hemi anorectum was highly predictive of RB.

An adaptive grid for graph-based segmentation in retinal OCT

Graph-based methods for retinal layer segmentation have proven to be popular due to their efficiency and accuracy. These methods build a graph with nodes at each voxel location and use edges connecting nodes to encode the hard constraints of each layer's thickness and smoothness. In this work, we explore deforming the regular voxel grid to allow adjacent vertices in the graph to more closely follow the natural curvature of the retina. This deformed grid is constructed by fixing node locations based on a regression model of each layer's thickness relative to the overall retina thickness, thus we generate a subject specific grid. Graph vertices are not at voxel locations, which allows for control over the resolution that the graph represents. By incorporating soft constraints between adjacent nodes, segmentation on this grid will favor smoothly varying surfaces consistent with the shape of the retina. Our final segmentation method then follows our previous work. Boundary probabilities are estimated using a random forest classifier followed by an optimal graph search algorithm on the new adaptive grid to produce a final segmentation. Our method is shown to produce a more consistent segmentation with an overall accuracy of 3.38 μm across all boundaries.

Cortical Surface Reconstruction from High-Resolution MR Brain Images

Reconstruction of the cerebral cortex from magnetic resonance (MR) images is an important step in quantitative analysis of the human brain structure, for example, in sulcal morphometry and in studies of cortical thickness. Existing cortical reconstruction approaches are typically optimized for standard resolution (~1 mm) data and are not directly applicable to higher resolution images. A new PDE-based method is presented for the automated cortical reconstruction that is computationally efficient and scales well with grid resolution, and thus is particularly suitable for high-resolution MR images with submillimeter voxel size. The method uses a mathematical model of a field in an inhomogeneous dielectric. This field mapping, similarly to a Laplacian mapping, has nice laminar properties in the cortical layer, and helps to identify the unresolved boundaries between cortical banks in narrow sulci. The pial cortical surface is reconstructed by advection along the field gradient as a geometric deformable model constrained by topology-preserving level set approach. The method's performance is illustrated on exvivo images with 0.25–0.35 mm isotropic voxels. The method is further evaluated by cross-comparison with results of the FreeSurfer software on standard resolution data sets from the OASIS database featuring pairs of repeated scans for 20 healthy young subjects.

Automated voxel-based 3D cortical thickness measurement in a combined Lagrangian-Eulerian PDE approach using partial volume maps

Accurate cortical thickness estimation is important for the study of many neurodegenerative diseases. Many approaches have been previously proposed, which can be broadly categorised as mesh-based and voxel-based. While the mesh-based approaches can potentially achieve subvoxel resolution, they usually lack the computational efficiency needed for clinical applications and large database studies. In contrast, voxel-based approaches, are computationally efficient, but lack accuracy. The aim of this paper is to propose a novel voxel-based method based upon the Laplacian definition of thickness that is both accurate and computationally efficient. A framework was developed to estimate and integrate the partial volume information within the thickness estimation process. Firstly, in a Lagrangian step, the boundaries are initialized using the partial volume information. Subsequently, in an Eulerian step, a pair of partial differential equations are solved on the remaining voxels to finally compute the thickness. Using partial volume information significantly improved the accuracy of the thickness estimation on synthetic phantoms, and improved reproducibility on real data. Significant differences in the hippocampus and temporal lobe between healthy controls (NC), mild cognitive impaired (MCI) and Alzheimer's disease (AD) patients were found on clinical data from the ADNI database. We compared our method in terms of precision, computational speed and statistical power against the Eulerian approach. With a slight increase in computation time, accuracy and precision were greatly improved. Power analysis demonstrated the ability of our method to yield statistically significant results when comparing AD and NC. Overall, with our method the number of samples is reduced by 25% to find significant differences between the two groups.

Registration based cortical thickness measurement

Cortical thickness is an important biomarker for image-based studies of the brain. A diffeomorphic registration based cortical thickness (DiReCT) measure is introduced where a continuous one-to-one correspondence between the gray matter-white matter interface and the estimated gray matter-cerebrospinal fluid interface is given by a diffeomorphic mapping in the image space. Thickness is then defined in terms of a distance measure between the interfaces of this sheet like structure. This technique also provides a natural way to compute continuous estimates of thickness within buried sulci by preventing opposing gray matter banks from intersecting. In addition, the proposed method incorporates neuroanatomical constraints on thickness values as part of the mapping process. Evaluation of this method is presented on synthetic images. As an application to brain images, a longitudinal study of thickness change in frontotemporal dementia (FTD) spectrum disorder is reported.