The aim of this study was to examine the use of R2* mapping in maternal and fetal sub-regions of the placenta with the aim of providing a reference for blood oxygenation levels during normative development. There have been a number of MR relaxation studies of placental tissues in-utero, but none have reported R2* value changes with age, or examined differences in sub-regions of the placenta. Here specialized long-duration Multi-frame R2* imaging was used to create a stable estimate for R2* values in different placental regions in healthy pregnant volunteers not imaged for clinical reasons. 27 subjects were recruited and scanned up to 3 times during their pregnancy. A multi-slice dual echo EPI based BOLD acquisition was employed and repeated between 90 and 150 times over 3 to 5 minutes to provide a high accuracy estimate of the R2* signal level. Acquisitions were also repeated in 13 cases within a visit to evaluate reproducibility of the method in a given subject. Experimental results showed R2* measurements were highly repeatable within a visit with standard deviation of (0.76). Plots of all visits against gestational age indicated clear correlations showing decreases in R2* with age. This increase was consistent was also consistent over time in multiple visits of the same volunteer during their pregnancy. Maternal and fetal regional changes with gestational age followed the same trend with increase in R2* over the gestational age.
Understanding when and how resting state brain functional activity begins in the human brain is an increasing area of interest in both basic neuroscience and in the clinical evaluation of the brain during pregnancy and after premature birth. Although fMRI studies have been carried out on pregnant women since the 1990's, reliable mapping of brain function in utero is an extremely challenging problem due to the unconstrained fetal head motion. Recent studies have employed scrubbing to exclude parts of the time series and whole subjects from studies in order to control the confounds of motion. Fundamentally, even after correction of the location of signals due to motion, signal intensity variations are a fundamental limitation, due to coil sensitivity and spin history effects. An alternative technique is to use a more parametric MRI signal derived from multiple echoes that provides a level of independence from basic MRI signal variation. Here we examine the use of R2* mapping combined with slice based multi echo geometric distortion correction for in-utero studies. The challenges for R2* mapping arise from the relatively low signal strength of in-utero data. In this paper we focus on comparing activation detection in-utero using T2W and R2* approaches. We make use a subset of studies with relatively limited motion to compare the activation patterns without the additional confound of significant motion. Results at different gestational ages indicate comparable agreement in many activation patterns when limited motion is present, and the detection of some additional networks in the R2* data, not seen in the T2W results.
One of the most common approaches to MRI brain tissue segmentation is to employ an atlas prior to initialize an Expectation- Maximization (EM) image labeling scheme using a statistical model of MRI intensities. This prior is commonly derived from a set of manually segmented training data from the population of interest. However, in cases where subject anatomy varies significantly from the prior anatomical average model (for example in the case where extreme developmental abnormalities or brain injuries occur), the prior tissue map does not provide adequate information about the observed MRI intensities to ensure the EM algorithm converges to an anatomically accurate labeling of the MRI. In this paper, we present a novel approach for automatic segmentation of such cases. This approach augments the atlas-based EM segmentation by exploring methods to build a hybrid tissue segmentation scheme that seeks to learn where an atlas prior fails (due to inadequate representation of anatomical variation in the statistical atlas) and utilize an alternative prior derived from a patch driven search of the atlas data. We describe a framework for incorporating this patch-based augmentation of EM (PBAEM) into a 4D age-specific atlas-based segmentation of developing brain anatomy. The proposed approach was evaluated on a set of MRI brain scans of premature neonates with ages ranging from 27.29 to 46.43 gestational weeks (GWs). Results indicated superior performance compared to the conventional atlas-based segmentation method, providing improved segmentation accuracy for gray matter, white matter, ventricles and sulcal CSF regions.
Traditional bone atlas modelling is carried out using linear methods such as PCA. Such linear models use a
mean shape and principal modes to represent the atlas. A new shape, which is a high dimensional data vector,
is then described using this mean and a weighted combination of the principal modes. The use of alternate
methods for modelling statistical atlases have not been explored very much. Recently, there has been a lot of
new work in the areas of multilinear modelling and nonlinear modelling. They present new ways of modelling
high dimensional data. In this work, we compare and contrast several linear, multilinear and nonlinear methods
for bone atlas modelling.
Endoscopy is an invaluable tool for several surgical and diagnostic applications. It permits minimally invasive
visualization of internal structures thus involving little or no injury to internal structures. This method of visualization
however restricts the size of the imaging device and therefore compromises on the field of view captured in a single
image. The problem of a narrow field of view can be solved by capturing video sequences and stitching them to generate
a mosaic of the scene under consideration. Registration of images in the sequence is therefore a crucial step. Existing methods compute frame-to-frame registration estimates and use these to resample images in order to generate a mosaic. The complexity of the appearance of internal structures and accumulation of registration error in frame to frame estimates however can be large enough to cause a cumulative drift that can misrepresent the scene. These errors can be reduced by application of global adjustment schemes. In this paper, we present a set of techniques for overcoming this problem of drift for pixel based registration in order to achieve global consistency of mosaics. The algorithm uses the frame-to-frame estimate as an initialization and subsequently corrects these estimates by setting up a large scale optimization problem which simultaneously solves for all corrections of estimates. In addition we set up a graph and introduce loop closure constraints in order to ensure consistency of registration. We present our method and results in semi global and fully global graph based adjustment methods as well as validation of our results.