Rui Pedro Azeredo Gomes Teixeira

A dedicated neonatal brain imaging system

The goal of the Developing Human Connectome Project is to acquire MRI in 1000 neonates to create a dynamic map of human brain connectivity during early development. High‐quality imaging in this cohort without sedation presents a number of technical and practical challenges.

Characterizing the modulation transfer function (MTF) of proton/carbon radiography using Monte Carlo simulations

PURPOSE To characterize the modulation transfer function (MTF) of proton/carbon radiography using Monte Carlo simulations. To assess the spatial resolution of proton/carbon radiographic imaging. METHODS A phantom was specifically modeled with inserts composed of two materials with three different densities of bone and lung. The basic geometry of the phantom consists of cube-shaped inserts placed in water. The thickness of the water, the thickness of the cubes, the depth of the cubes in the water, and the particle beam energy have all been varied and studied. There were two phantom thicknesses considered 20 and 28 cm. This represents an average patient thickness and a thicker sized patient. Radiographs were produced for proton beams at 230 and 330 MeV and for a carbon ion beam at 400 MeV per nucleon. The contrast-to-noise ratio (CNR) was evaluated at the interface of two materials on the radiographs, i.e., lung-water and bone-water. The variation in CNR at interface between lung-water and bone-water were study, where a sigmoidal fit was performed between the lower and the higher CNR values. The full width half-maximum (FWHM) value was then obtained from the sigmoidal fit. Ultimately, spatial resolution was defined by the 10% point of the modulation-transfer-function (MTF10%), in units of line-pairs per mm (lp/mm). RESULTS For the 20 cm thick phantom, the FWHM values varied between 0.5 and 0.7 mm at the lung-water and bone-water interfaces, for the proton beam energies of 230 and 330 MeV and the 400 MeV/n carbon beam. For the 28 cm thick phantom, the FWHM values varied between 0.5 and 1.2 mm at the lung-water and bone-water interface for the same inserts and beam energies. For the 20 cm phantom the MTF10% for lung-water interface is 2.3, 2.4, and 2.8 lp/mm, respectively, for 230, 330, and 400 MeV/n beams. For the same 20 cm thick phantom but for the bone-water interface the MTF10% yielded 1.9, 2.3, and 2.7 lp/mm, respectively, for 230, 330, and 400 MeV/n beams. In the case of the thicker 28 cm phantom, the authors observed that at the lung-water interface the MTF10% is 1.6, 1.9, and 2.6 lp/mm, respectively, for 230, 330, and 400 MeV/n beams. While for the bone-water interface the MTF10% was 1.4, 1.9, and 2.9 lp/mm, respectively, for 230, 330, and 400 MeV/n beams. CONCLUSIONS Carbon radiography (400 MeV/n) yielded best spatial resolution, with MTF10% = 2.7 and 2.8 lp/mm, respectively, at the lung-water and bone-water interfaces. The spatial resolution of the 330 MeV proton beam was better than the 230 MeV proton, because higher incident proton energy suffer smaller deflections within the patient and thus yields better proton radiographic images. The authors also observed that submillimeter resolution can be obtained with both proton and carbon beams.

Joint system relaxometry (JSR) and Crámer-Rao lower bound optimization of sequence parameters: A framework for enhanced precision of DESPOT T1 and T2 estimation

This study aims to increase the precision of single‐compartment DESPOT relaxometry by two means: (i) a joint system relaxometry (JSR) approach that estimates parameters in a single step using all available data; and (ii) optimizing acquisition parameters by deploying a robust design tool based on the Crámer‐Rao lower bound (CRLB).

Joint system relaxometry (JSR) and Crámer-Rao lower bound optimization of sequence parameters

This study aims to increase the precision of single‐compartment DESPOT relaxometry by two means: (i) a joint system relaxometry (JSR) approach that estimates parameters in a single step using all available data; and (ii) optimizing acquisition parameters by deploying a robust design tool based on the Crámer‐Rao lower bound (CRLB).

Impaired development of the cerebral cortex in infants with congenital heart disease is correlated to reduced cerebral oxygen delivery

Neurodevelopmental impairment is the most common comorbidity associated with complex congenital heart disease (CHD), while the underlying biological mechanism remains unclear. We hypothesised that impaired cerebral oxygen delivery in infants with CHD is a cause of impaired cortical development, and predicted that cardiac lesions most associated with reduced cerebral oxygen delivery would demonstrate the greatest impairment of cortical development. We compared 30 newborns with complex CHD prior to surgery and 30 age-matched healthy controls using brain MRI. The cortex was assessed using high resolution, motion-corrected T2-weighted images in natural sleep, analysed using an automated pipeline. Cerebral oxygen delivery was calculated using phase contrast angiography and pre-ductal pulse oximetry, while regional cerebral oxygen saturation was estimated using near-infrared spectroscopy. We found that impaired cortical grey matter volume and gyrification index in newborns with complex CHD was linearly related to reduced cerebral oxygen delivery, and that cardiac lesions associated with the lowest cerebral oxygen delivery were associated with the greatest impairment of cortical development. These findings suggest that strategies to improve cerebral oxygen delivery may help reduce brain dysmaturation in newborns with CHD, and may be most relevant for children with CHD whose cardiac defects remain unrepaired for prolonged periods after birth.

Unbiased construction of a temporally consistent morphological atlas of neonatal brain development

Premature birth increases the risk of developing neurocognitive and neurobehavioural disorders. The mechanisms of altered brain development causing these disorders are yet unknown. Studying the morphology and function of the brain during maturation provides us not only with a better understanding of normal development, but may help us to identify causes of abnormal development and their consequences. A particular difficulty is to distinguish abnormal patterns of neurodevelopment from normal variation. The Developing Human Connectome Project (dHCP) seeks to create a detailed four-dimensional (4D) connectome of early life. This connectome may provide insights into normal as well as abnormal patterns of brain development. As part of this project, more than a thousand healthy fetal and neonatal brains will be scanned in vivo. This requires computational methods which scale well to larger data sets. We propose a novel groupwise method for the construction of a spatio-temporal model of mean morphology from cross-sectional brain scans at different gestational ages. This model scales linearly with the number of images and thus improves upon methods used to build existing public neonatal atlases, which derive correspondence between all pairs of images. By jointly estimating mean shape and longitudinal change, the atlas created with our method overcomes temporal inconsistencies, which are encountered when mean shape and intensity images are constructed separately for each time point. Using this approach, we have constructed a spatio-temporal atlas from 275 healthy neonates between 35 and 44 weeks post-menstrual age (PMA). The resulting atlas qualitatively preserves cortical details significantly better than publicly available atlases. This is moreover confirmed by a number of quantitative measures of the quality of the spatial normalisation and sharpness of the resulting template brain images.

Construction of a neonatal cortical surface atlas using Multimodal Surface Matching in the Developing Human Connectome Project

&NA; We propose a method for constructing a spatio‐temporal cortical surface atlas of neonatal brains aged between 36 and 44 weeks of post‐menstrual age (PMA) at the time of scan. The data were acquired as part of the Developing Human Connectome Project (dHCP), and the constructed surface atlases are publicly available. The method is based on a spherical registration approach: Multimodal Surface Matching (MSM), using cortical folding for driving the alignment. Templates have been generated for the anatomical cortical surface and for the cortical feature maps: sulcal depth, curvature, thickness, T1w/T2w myelin maps and cortical regions. To achieve this, cortical surfaces from 270 infants were first projected onto the sphere. Templates were then generated in two stages: first, a reference space was initialised via affine alignment to a group average adult template. Following this, templates were iteratively refined through repeated alignment of individuals to the template space until the variability of the average feature sets converged. Finally, bias towards the adult reference was removed by applying the inverse of the average affine transformations on the template and de‐drifting the template. We used temporal adaptive kernel regression to produce age‐dependant atlases for 9 weeks (36–44 weeks PMA). The generated templates capture expected patterns of cortical development including an increase in gyrification as well as an increase in thickness and T1w/T2w myelination with increasing age. HighlightsCreation of spatio‐temporal cortical surface atlas of the developing brain (36‐44 weeks PMA).Atlas captures patterns of cortical development in the neonatal dHCP population.Includes surface features: sulcal depth, curvature, thickness, T1w/T2w myelin, cortical labels.

Extended Phase Graph formalism for systems with Magnetization Transfer and Exchange

An extended phase graph framework (EPG‐X) for modeling systems with exchange or magnetization transfer (MT) is proposed.

Grey matter biomarker identification in Schizophrenia: detecting regional alterations and their underlying substrates

State-of-the-art approaches in Schizophrenia research investigate neuroanatomical biomarkers using structural Magnetic Resonance Imaging. However, current models are 1) voxel-wise, 2) difficult to interpret in biologically meaningful ways, and 3) difficult to replicate across studies. Here, we propose a machine learning framework that enables the identification of sparse, region-wise grey matter neuroanatomical biomarkers and their underlying biological substrates by integrating well-established statistical and machine learning approaches. We address the computational issues associated with application of machine learning on structural MRI data in Schizophrenia, as discussed in recent reviews, while promoting transparent science using widely available data and software. In this work, a cohort of patients with Schizophrenia and healthy controls was used. It was found that the cortical thickness in left pars orbitalis seems to be the most reliable measure for distinguishing patients with Schizophrenia from healthy controls. Highlights We present a sparse machine learning framework to identify biologically meaningful neuroanatomical biomarkers for Schizophrenia Our framework addresses methodological pitfalls associated with application of machine learning on structural MRI data in Schizophrenia raised by several recent reviews Our pipeline is easy to replicate using widely available software packages The presented framework is geared towards identification of specific changes in brain regions that relate directly to the pathology rather than classification per se

Integrated and efficient diffusion-relaxometry using ZEBRA

The emergence of multiparametric diffusion models combining diffusion and relaxometry measurements provides powerful new ways to explore tissue microstructure, with the potential to provide new insights into tissue structure and function. However, their ability to provide rich analyses and the potential for clinical translation critically depends on the availability of efficient, integrated, multi-dimensional acquisitions. We propose a fully integrated sequence simultaneously sampling the acquisition parameter spaces required for T1 and T2* relaxometry and diffusion MRI. Slice-level interleaved diffusion encoding, multiple spin/gradient echoes and slice-shuffling are combined for higher efficiency, sampling flexibility and enhanced internal consistency. In-vivo data was successfully acquired on healthy adult brains. Obtained parametric maps as well as clustering results demonstrate the potential of the technique to provide eloquent data with an acceleration of roughly 20 compared to conventionally used approaches. The proposed integrated acquisition, which we call ZEBRA, offers significant acceleration and flexibility compared to existing diffusion-relaxometry studies, and thus facilitates wider use of these techniques both for research-driven and clinical applications.