Dynamic elasticity model for inter-subject non-rigid registration of 3D MRI brain scans

Multifaceted atlases of the human brain in its infancy

Brain atlases are spatial references for integrating, processing, and analyzing brain features gathered from different individuals, sources, and scales. Here we introduce a collection of joint surface-volume atlases that chart postnatal development of the human brain in a spatiotemporally dense manner from two weeks to two years of age. Our month-specific atlases chart normative patterns and capture key traits of early brain development and are therefore conducive to identifying aberrations from normal developmental trajectories. These atlases will enhance our understanding of early structural and functional development by facilitating the mapping of diverse features of the infant brain to a common reference frame for precise multifaceted quantification of cortical and subcortical changes.

Medical decision making for 5D cardiac model: Template matching technique and simulation of the fifth dimension

The purpose of this paper is to develop a 5D cardiac model which is inspired from the 5D model for the lungs. This model depends on five variables: the anatomical structure of the 3D heart, temporal dimension and the function of blood flow as the fifth dimension. To test this hypothesis, we took the same mathematical modeling as a reference for the fifth dimension of pulmonary flow where r→_ρ(t)=r→_v(t)+r_f→(t) wherer→_v(t) is the displacement vectors with approximate magnitudes by linear functions of the tidal volume and r_f→(t) is the blood flow. The scans were acquired for 10 patients,in the 404 series for a total of 18,483 images studied in three cases: healthy patient, case of heart failure and aortic stenosis. Where r→_vand r→_f are the unit vectors along the volume of ejection and the blood flow axes, indicating the direction of motion of the object due to heart volume ejection and blood flow variations, respectively. The quantities of α and β coefficients are determined from real-time patient image data. The alpha and beta coefficients are derived from the following dimension equations[mm / ml] [mm*ms / ml] . Since the cardiac system has two diastolic and systolic phases, we have calculated α₁ and β₁ for telediatolic volume and α₂ and β₂ for telesystolic volume throughout the cardiac cycle as a function of the location of the cuts chosen randomly. Fifth-dimensional experiments are used to track, simulate the behavior of blood flow to detect preliminary indications for the identification of stenosis or valve leakage. The average discrepancy was tabulated as the global fraction of systolic ejection. The results shown in Fig. 3 detect a correspondence between the hunting chamber cut and the flow sequence through the orifice of aorta for this patient with suspicious of having an aortic stenosis disease and an ejection fraction about 71% with a maximum of velocity (Vmax) detected=250 (cm / ms) = 2.5 (m / 10-3 s). In this case this patient has a minor stenosis in the aorta. It should be referred that the normalization of this measures is classified such as : Minor stenosis: area 1.5 cm2, Vmax <3 m / moderate stenosis: area 1.0 - 1.5 cm2, Vmax 3 - 4 m / severe stenosis: area <1.0 cm2, Vmax> 4 m / s. For a patient who has an aortic stenosis the cloud of the points is accumulated comparing to the origin of the axis while the patient with a symptom of insufficiency the points are widened with a remarkable gap in the trajectory. To solve the issue of the bad prediction, the inaccuracy of the clouds points of the model 5D, the lack of the exact measurements to estimate the degree of cardiac insufficiency (leakage or stenosis), a solution of 5D imagery was depicted. Our main contribution is to test the validity of the template-matching algorithm and the fifth dimension simulation to provide more clues to detect the aortic stenosis and cardiac insufficiency in the context of medical decision support.

Surface-constrained volumetric registration for the early developing brain

The T1-weighted and T2-weighted MRI contrasts of the infant brain evolve drastically during the first year of life. This poses significant challenges to inter- and intra-subject registration, which is key to subsequent statistical analyses. Existing registration methods that do not consider temporal contrast changes are ineffective for infant brain MRI data. To address this problem, we present in this paper a method for deformable registration of infant brain MRI. The key advantage of our method is threefold: (i) To deal with appearance changes, registration is performed based on segmented tissue maps instead of image intensity. Segmentation is performed by using an infant-centric algorithm previously developed by our group. (ii) Registration is carried out with respect to both cortical surfaces and volumetric tissue maps, thus allowing precise alignment of both cortical and subcortical structures. (iii) A dynamic elasticity model is utilized to allow large non-linear deformation. Experimental results in comparison with well-established registration methods indicate that our method yields superior accuracy in both cortical and subcortical alignment.