Investigating the maturation of microstructure and radial orientation in the preterm human cortex with diffusion MRI

Preterm birth disrupts and alters the complex developmental processes in the cerebral cortex. This disruption may be a contributing factor to widespread delay and cognitive difficulties in the preterm population. Diffusion-weighted magnetic resonance imaging (DW MRI) is a noninvasive imaging technique that makes inferences about cellular structures, at scales smaller than the imaging resolution. One established finding is that DW MRI shows a transient radial alignment in the preterm cortex. In this study, we quantify this maturational process with the “radiality index”, a parameter that measures directional coherence, which we expect to change rapidly in the perinatal period. To measure this index, we used structural T2-weighted MRI to segment the cortex and generate cortical meshes. We obtained normal vectors for each face of the mesh and compared them to the principal diffusion direction, calculated by both the DTI and DIAMOND models, to generate the radiality index. The subjects included in this study were 89 infants born at fewer than 34 weeks completed gestation, each imaged at up to four timepoints between 27 and 42 weeks gestational age. In this manuscript, we quantify the longitudinal trajectory of radiality, fractional anisotropy and mean diffusivity from the DTI and DIAMOND models. For the radiality index and fractional anisotropy, the DIAMOND model offers improved sensitivity over the DTI model. The radiality index has a consistent progression across time, with the rate of change depending on the cortical lobe. The occipital lobe changes most rapidly, and the frontal and temporal least: this is commensurate with known developmental anatomy. Analysing the radiality index offers information complementary to other diffusion parameters

Topology Correction of Segmented Medical Images using a Fast Marching Algorithm

Mirzejewski, Julien (Invention - Telephone) / 1281 Orchard Pl

Topology Correction of Segmented Medical Images using a Fast Marching Algorithm

We present here a new method for correcting the topology of objects segmented from medical images. Whereas previous techniques alter a surface obtained from a binary segmentation of the object, our technique can be applied directly to the image intensities of a probabilistic or fuzzy segmentation, thereby propagating the topology for all isosurfaces of the object. From an analysis of topological changes and critical points in implicit surfaces, we derive a topology propagation algorithm that enforces any desired topology using a fast marching technique. The method has been applied successfully to the correction of the cortical gray matter / white matter interface in segmented brain images and is publicly released as a software plug-in or the MIPAV package

Preprocessing methods for morphometric brain analysis and quality assurance of structural magnetic resonance images

Gegenstand der Dissertation ist die Neuentwicklung und Validierung von Verfahren zur Aufbereitung von anatomischen Daten, die mittels Magnetresonanztomographie gewonnen wurden. Ziel ist dabei die Erfassung von morphometrischen Kennwerten zur Beschreibung der Struktur und Form des Gehirns, wie beispielsweise Volumen, Fläche, Dicke oder Faltung der Großhirnrinde. Die Kennwerte erlauben sowohl die Erforschung individueller gesunder und pathologischer Entwicklung als auch der evolutionären Anpassung des Gehirns. Die zur Datenanalyse notwendige Vorverarbeitung beinhaltet dabei die Angleichung von Bildeigenschaften und individueller Anatomie. Die fortlaufende Weiterentwicklung der Scanner- und Rechentechnik ermöglicht eine zunehmend genauere Bildgebung, erfordert aber die kontinuierliche Anpassung existierender Verfahren. Die Schwerpunkte dieser Dissertation lagen in der Entwicklung neuer Verfahren zur (i) Klassifikation der Hirngewebe (Segmentierung), (ii) räumlichen Abbildung des individuellen Gehirns auf ein Durchschnittsgehirn (Registrierung), (iii) Bestimmung der Dicke der Großhirnrinde und Rekonstruktion einer repräsentativen Oberfläche und (iv) Qualitätssicherung der Eingangsdaten. Die Segmentierung gleicht die Bildeigenschaften unterschiedlicher Protokolle an, während die Registrierung anatomische Merkmale normalisiert und so den Vergleich verschiedener Gehirne ermöglicht. Die Rekonstruktion von Oberflächen erlaubt wiederum die Gewinnung einer Vielzahl weiterer morphometrischer Maße zur spezifischen Charakterisierung des Gehirns und seiner Entwicklung. Anhand von simulierten und realen Daten wird die Validität der neuen Methoden belegt und mit anderen Ansätzen verglichen. Die Verfahren sind Bestandteil der Computational Anatomy Toolbox (CAT; http://dbm.neuro.uni-jena.de/cat), deren Schwerpunkt die Vorverarbeitung von strukturellen Daten ist und die Teil des Statistical Parametric Mapping (SPM) Softwarepaketes in MATLAB ist.This Ph.D. thesis focuses on the development, optimization and validation of preprocessing methods of structural magnetic resonance images of the brain. The preprocessing describes the creation of morphometric data that support a statistical analysis of brain anatomy. Image interferences have to be removed to allow a tissue classification (segmentation). In order to compare different subjects a spatial normalization to an average-shaped brain (template) is required, where atlas maps allow identification of specific brain structures and regions of interest. Beside the analysis in a voxel-grid, the cortex can be represented by surfaces that allow further measures such as the cortical thickness or folding. The derived brain features (such as volume, area, and thickness) permit the individual study of normal and pathological development during the lifespan but also of the evolutionary adaption of the brain. The ongoing progress of imaging and computing technology demands continous enhancement of preprocessing tools but also facilitates the exploration of novel approaches and models. The basis of this thesis is the development of a method that uses a tissue segmentation to estimate the cortical thickness and the central surface in one integrated step. Further essential improvements of surface reconstruction algorithms were achieved by specific refinement of processing steps such as (i) the classification of brain tissue (segmentation), (ii) the spatial mapping of the individual brain to an average brain (registration), (iii) determining the thickness of the cerebral cortex and reconstructing a representative surface and (iv) the quality assurance of input data. The validity of the new methods is proven and compared with other approaches by simulated and real data. The procedures are part of the Computational Anatomy Toolbox (CAT; http://dbm.neuro.uni-jena.de/cat), which focuses on the preprocessing of structural data and is part of the Statistical Parametric Mapping (SPM) software package in MATLAB

Colin27 high resolution cortical surfaces

This data set includes high-resolution (0.5mm) maps of the cerebral cortical surfaces reconstructed from the Colin27 brain atlas [Aubert-Brohe et al., 2006]. The tissue segmentation provided with the atlas was manually edited to merge sub-cortical structures and lateral ventricles with the white matter (WM), then the WM mask was topology-corrected [Bazin et al., 2007] and the WM and cortical gray matter (GM) masks were used to estimate cortical surfaces with the CRUISE algorithm [Han et al., 2004] for the GM/WM boundary (gwb), CSF/GM boundary (cgb), and an average surface (avg) equidistant to both cortical boundaries. Surfaces were reconstructed as levet sets of a signed distance function, and triangular meshes were extracted with the connectivity consistent marching cubes algorithm [Han et al., 2003] and inflated to obtain smooth cortical maps [Tosun et al., 2004]. <br><br>Included here are the left and right cortical segmentations (1=GM, 2=WM), level set surfaces (gwb, cgb, avg) in compressed NIFTI format, and the corresponding extracted and inflated (inf) meshes in VTK ascii format. The NIFTI images are co-aligned with the Colin27 atlas in voxel space, and the surfaces follow the MIPAV mesh space conventions.<br><br>B Aubert-Broche, AC Evans, and DL Collins, “A new improved version of the realistic digital brain phantom,” NeuroImage, vol. 32, no. 1, pp. 138–45, 2006. <a href="http://www.ncbi.nlm.nih.gov/pubmed/16750398" rel="nofollow">http://www.ncbi.nlm.nih.gov/pubmed/16750398</a><br><br><div>PL Bazin, DL Pham <a href="https://scholar.google.com/citations?view_op=view_citation&hl=en&user=g1EY49YAAAAJ&cstart=140&sortby=pubdate&citation_for_view=g1EY49YAAAAJ:Tyk-4Ss8FVUC">Topology correction of segmented medical images using a fast marching algorithm</a> Computer methods and programs in biomedicine 88 (2), 182-190, 2007<br></div><br><b>Tosun D</b>, Rettmann ME, Han X, <b>Tao X</b>, Xu C, Resnick SM, Pham DL, Prince JL. Cortical surface segmentation and mapping. Neuroimage. 2004; 23 Suppl 1:S108-18. PMID: 15501080; PMCID: PMC4587756.<br><br><div>X Han, C Xu, JL Prince <a href="https://scholar.google.com/citations?view_op=view_citation&hl=en&user=XGVV3gEAAAAJ&citation_for_view=XGVV3gEAAAAJ:u5HHmVD_uO8C">A topology preserving level set method for geometric deformable models</a> IEEE Transactions on Pattern Analysis and Machine Intelligence 25 (6), 755-768, 2003<br></div><br>X Han, DL Pham, D Tosun, ME Rettmann, C Xu, JL Prince <a href="https://scholar.google.com/citations?view_op=view_citation&hl=en&user=XGVV3gEAAAAJ&citation_for_view=XGVV3gEAAAAJ:2osOgNQ5qMEC">CRUISE: cortical reconstruction using implicit surface evolution</a> NeuroImage 23 (3), 997-1012, 200