3-D In Vitro Acoustic Super-Resolution and Super-Resolved Velocity Mapping Using Microbubbles

Standard clinical ultrasound (US) imaging frequencies are unable to resolve microvascular structures due to the fundamental diffraction limit of US waves. Recent demonstrations of 2D super-resolution both in vitro and in vivo have demonstrated that fine vascular structures can be visualized using acoustic single bubble localization. Visualization of more complex and disordered 3D vasculature, such as that of a tumor, requires an acquisition strategy which can additionally localize bubbles in the elevational plane with high precision in order to generate super-resolution in all three dimensions. Furthermore, a particular challenge lies in the need to provide this level of visualization with minimal acquisition time. In this work, we develop a fast, coherent US imaging tool for microbubble localization in 3D using a pair of US transducers positioned at 90°. This allowed detection of point scatterer signals in 3 dimensions with average precisions equal to 1.9 µm in axial and elevational planes, and 11 µm in the lateral plane, compared to the diffraction limited point spread function full widths at half maximum of 488 µm, 1188 µm and 953 µm of the original imaging system with a single transducer. Visualization and velocity mapping of 3D in vitro structures was demonstrated far beyond the diffraction limit. The capability to measure the complete flow pattern of blood vessels associated with disease at depth would ultimately enable analysis of in vivo microvascular morphology, blood flow dynamics and occlusions resulting from disease states

Test of the chiral structure and FCNC in the quark sector by radiative B meson decays

We study the effects of a vector-like SU(2) quark doublet as a fourth generation. In this model we examine the chiral structure and the FCNC in the quark sector by using radiative B meson decays in the allowed region for parameters from $R_b = {\Gamma_{qq}}/{\Gamma_{had.}}$. We compute the ratio $R = {Br (b \to d \gamma)}/{Br (b \to s \gamma)}$ in the model which realizes a different chiral structure as well as FCNC. The constraints has been extracted from the experimental results of B meson decays, the $T_{new}$ parameter of oblique corrections and $R_b$. Under the natural assumption that the violation of the $V-A$ structure in the light-quark sector is small, we can determine the allowed region for most of the mixings parameters and the vector-like quark masses. We show that there will be significant deviations in $R$ from the SM prediction due to the FCNC's and the violation of the $V-A$ structure.Comment: 19 pages, figures included, revised version for publication (accepted in Int. Jour. Mod. Phys. A

A tract-specific approach to assessing white matter in preterm infants.

Diffusion-weighted imaging (DWI) is becoming an increasingly important tool for studying brain development. DWI analyses relying on manually-drawn regions of interest and tractography using manually-placed waypoints are considered to provide the most accurate characterisation of the underlying brain structure. However, these methods are labour-intensive and become impractical for studies with large cohorts and numerous white matter (WM) tracts. Tract-specific analysis (TSA) is an alternative WM analysis method applicable to large-scale studies that offers potential benefits. TSA produces a skeleton representation of WM tracts and projects the group's diffusion data onto the skeleton for statistical analysis. In this work we evaluate the performance of TSA in analysing preterm infant data against results obtained from native space tractography and tract-based spatial statistics. We evaluate TSA's registration accuracy of WM tracts and assess the agreement between native space data and template space data projected onto WM skeletons, in 12 tracts across 48 preterm neonates. We show that TSA registration provides better WM tract alignment than a previous protocol optimised for neonatal spatial normalisation, and that TSA projects FA values that match well with values derived from native space tractography. We apply TSA for the first time to a preterm neonatal population to study the effects of age at scan on WM tracts around term equivalent age. We demonstrate the effects of age at scan on DTI metrics in commissural, projection and association fibres. We demonstrate the potential of TSA for WM analysis and its suitability for infant studies involving multiple tracts

Multimodal image analysis of clinical influences on preterm brain development.

OBJECTIVE: Premature birth is associated with numerous complex abnormalities of white and gray matter and a high incidence of long-term neurocognitive impairment. An integrated understanding of these abnormalities and their association with clinical events is lacking. The aim of this study was to identify specific patterns of abnormal cerebral development and their antenatal and postnatal antecedents. METHODS: In a prospective cohort of 449 infants (226 male), we performed a multivariate and data-driven analysis combining multiple imaging modalities. Using canonical correlation analysis, we sought separable multimodal imaging markers associated with specific clinical and environmental factors and correlated to neurodevelopmental outcome at 2 years. RESULTS: We found five independent patterns of neuroanatomical variation that related to clinical factors including age, prematurity, sex, intrauterine complications, and postnatal adversity. We also confirmed the association between imaging markers of neuroanatomical abnormality and poor cognitive and motor outcomes at 2 years. INTERPRETATION: This data-driven approach defined novel and clinically relevant imaging markers of cerebral maldevelopment, which offer new insights into the nature of preterm brain injury. Ann Neurol 2017;82:233-246

Exploring the multiple-hit hypothesis of preterm white matter damage using diffusion MRI.

Background: Preterm infants are at high risk of diffuse white matter injury and adverse neurodevelopmental outcome. The multiple hit hypothesis suggests that the risk of white matter injury increases with cumulative exposure to multiple perinatal risk factors. Our aim was to test this hypothesis in a large cohort of preterm infants using diffusion weighted magnetic resonance imaging (dMRI). Methods: We studied 491 infants (52% male) without focal destructive brain lesions born at < 34 weeks, who underwent structural and dMRI at a specialist Neonatal Imaging Centre. The median (range) gestational age (GA) at birth was 30+ 1 (23+ 2-33+ 5) weeks and median postmenstrual age at scan was 42+ 1 (38-45) weeks. dMRI data were analyzed using tract based spatial statistics and the relationship between dMRI measures in white matter and individual perinatal risk factors was assessed. We tested the hypothesis that increased exposure to perinatal risk factors was associated with lower fractional anisotropy (FA), and higher radial, axial and mean diffusivity (RD, AD, MD) in white matter. Neurodevelopmental performance was investigated using the Bayley Scales of Infant and Toddler Development, Third Edition (BSITD-III) in a subset of 381 infants at 20 months corrected age. We tested the hypothesis that lower FA and higher RD, AD and MD in white matter were associated with poorer neurodevelopmental performance. Results: Identified risk factors for diffuse white matter injury were lower GA at birth, fetal growth restriction, increased number of days requiring ventilation and parenteral nutrition, necrotizing enterocolitis and male sex. Clinical chorioamnionitis and patent ductus arteriosus were not associated with white matter injury. Multivariate analysis demonstrated that fetal growth restriction, increased number of days requiring ventilation and parenteral nutrition were independently associated with lower FA values. Exposure to cumulative risk factors was associated with reduced white matter FA and FA values at term equivalent age were associated with subsequent neurodevelopmental performance. Conclusion: This study suggests multiple perinatal risk factors have an independent association with diffuse white matter injury at term equivalent age and exposure to multiple perinatal risk factors exacerbates dMRI defined, clinically significant white matter injury. Our findings support the multiple hit hypothesis for preterm white matter injury

Microbubble Axial Localization Errors in Ultrasound Super-Resolution Imaging

Acoustic super-resolution imaging has allowed visualization of microvascular structure and flow beyond the diffraction limit using standard clinical ultrasound systems through the localization of many spatially isolated microbubble signals. The determination of each microbubble position is typically performed by calculating the centroid, finding a local maximum, or finding the peak of a 2-D Gaussian function fit to the signal. However, the backscattered signal from a microbubble depends not only on diffraction characteristics of the waveform, but also on the microbubble behavior in the acoustic field. Here, we propose a new axial localization method by identifying the onset of the backscattered signal. We compare the accuracy of localization methods using in vitro experiments performed at 7 cm depth and 2.3 MHz center frequency. We corroborate these findings with simulated results based on the Marmottant model. We show experimentally and in simulations that detecting the onset of the returning signal provides considerably increased accuracy for super-resolution. Resulting experimental cross-sectional profiles in super-resolution images demonstrate at least 5.8 times improvement in contrast ratio and more than 1.8 reduction in spatial spread (provided by 90% of the localizations) for the onset method over centroiding, peak detection and 2D Gaussian fitting methods. Simulations estimate that these latter methods could create errors in relative bubble positions as high as 900 μ m at these experimental settings, while the onset method reduced the interquartile range of these errors by a factor of over 2.2. Detecting the signal onset is therefore expected to considerably improve the accuracy of super-resolution

Automatic volumetry on MR brain images can support diagnostic decision making.

Background: Diagnostic decisions in clinical imaging currently rely almost exclusively on visual image interpretation. This can lead to uncertainty, for example in dementia disease, where some of the changes resemble those of normal ageing. We hypothesized that extracting volumetric data from patients MR brain images, relating them to reference data and presenting the results as a colour overlay on the grey scale data would aid diagnostic readers in classifying dementia disease versus normal ageing. Methods: A proof-of-concept forced-choice reader study was designed using MR brain images from 36 subjects. Images were segmented into 43 regions using an automatic atlas registration-based label propagation procedure. Seven subjects had clinically probable AD, the remaining 29 of a similar age range were used as controls. Seven of the control subject data sets were selected at random to be presented along with the seven AD datasets to two readers, who were blinded to all clinical and demographic information except age and gender. Readers were asked to review the grey scale MR images and to record their choice of diagnosis (AD or non-AD) along with their confidence in this decision. Afterwards, readers were given the option to switch on a false-colour overlay representing the relative size of the segmented structures. Colorization was based on the size rank of the test subject when compared with a reference group consisting of the 22 control subjects who were not used as review subjects. The readers were then asked to record whether and how the additional information had an impact on their diagnostic confidence. Results: The size rank colour overlays were useful in 18 of 28 diagnoses, as determined by their impact on readers diagnostic confidence. A not useful result was found in 6 of 28 cases. The impact of the additional information on diagnostic confidence was significant (p < 0.02). Conclusion: Volumetric anatomical information extracted from brain images using automatic segmentation and presented as colour overlays can support diagnostic decision making. © 2008 Heckemann et al; licensee BioMed Central Ltd.Published versio