Functional analysis of the placenta is important to analyze and understand its role in fetal growth and development. BOLD MR is a non-invasive technique that has been extensively used for functional analysis of the brain. During the last years, several studies have shown that this dynamic image modality is also useful to extract functional information of the placenta. We propose in this paper a method to track the placenta from a sequence of BOLD MR images acquired under normoxia and hyperoxia conditions with the goal of quantifying how the placenta adapts to oxygenation changes. The method is based on a spatiotemporal transformation model that ensures temporal coherence of the tracked structures. The method was initially applied to four patients with healthy pregnancies. An average MR signal increase of 16.96±8.39% during hyperoxia was observed. These automated results are in line with state-of-the-art reports using time-consuming manual segmentations subject to inter-observer errors.
Statistical shape models are commonly used to analyze the variability between similar anatomical structures and their use is established as a tool for analysis and segmentation of medical images. However, using a global model to capture the variability of complex structures is not enough to achieve the best results. The complexity of a proper global model increases even more when the amount of data available is limited to a small number of datasets. Typically, the anatomical variability between structures is associated to the variability of their physiological regions. In this paper, a complete pipeline is proposed for building a multi-region statistical shape model to study the entire variability from locally identified physiological regions of the inner ear. The proposed model, which is based on an extension of the Point Distribution Model (PDM), is built for a training set of 17 high-resolution images (24.5 μm voxels) of the inner ear. The model is evaluated according to its generalization ability and specificity. The results are compared with the ones of a global model built directly using the standard PDM approach. The evaluation results suggest that better accuracy can be achieved using a regional modeling of the inner ear.
It has been demonstrated that the acceleration signal has potential to monitor heart function and adaptively optimize Cardiac Resynchronization Therapy (CRT) systems. In this paper, we propose a non-invasive method for computing myocardial acceleration from 3D echocardiographic sequences. Displacement of the myocardium was estimated using a two-step approach: (1) 3D automatic segmentation of the myocardium at end-diastole using 3D Active Shape Models (ASM); (2) propagation of this segmentation along the sequence using non-rigid 3D+t image registration (temporal di eomorphic free-form-deformation, TDFFD). Acceleration was obtained locally at each point of the myocardium from local displacement. The framework has been tested on images from a realistic physical heart phantom (DHP-01, Shelley Medical Imaging Technologies, London, ON, CA) in which the displacement of some control regions was known. Good correlation has been demonstrated between the estimated displacement function from the algorithms and the phantom setup. Due to the limited temporal resolution, the acceleration signals are sparse and highly noisy. The study suggests a non-invasive technique to measure the cardiac acceleration that may be used to improve the monitoring of cardiac mechanics and optimization of CRT.
This paper combines different parallelization strategies for speeding up motion and deformation computation by
non-rigid registration of a sequence of images. The registration is performed in a two-level acceleration approach:
(1) parallelization of each registration process using MPI and/or threads, and (2) distribution of the sequential
registrations over a cluster.
On a 24-node double quad-core Intel Xeon (2.66 GHz CPU, 16 GB RAM) cluster, the method is demonstrated
to efficiently compute the deformation of a cardiac sequence reducing the computation time from more than 3
hours to a couple of minutes (for low downsampled images). It is shown that the distribution of the sequential
registrations over the cluster together with the parallelization of each pairwise registration by multithreading
lowers the computation time towards values compatible with clinical requirements (a few minutes per patient).
The combination of MPI and multithreading is only advantageous for large input data sizes.
Performances are assessed for the specific scenario of aligning cardiac sequences of taggedMagnetic Resonance
(tMR) images, with the aim of comparing strain in healthy subjects and hypertrophic cardiomyopathy (HCM)
patients. In particular, we compared the distribution of systolic strain in both populations. On average, HCM
patients showed lower average values of strain with larger deviation due to the coexistence of regions with
impaired deformation and regions with normal deformation.
A class of adaptive wavelet transforms that map integers to
integers based on the adaptive update lifting scheme is presented.
The main feature in the adaptive update lifting scheme is that the
update lifting step, which is considered as an averaging operator
and is performed prior to the prediction step, is adapted to the
underlying signal content and the adaptivity decisions can be
recovered at the synthesis transform without bookkeeping of the
adaptivity decisions. The perfect reconstruction criterion for the
integer realisation of such transforms are presented in this
paper. These adaptive integer-to-integer wavelet transforms can be
used in scalable lossless image coding applications. The lossless
image coding and spatially scalable decoding performances are