We developed a novel method to accelerate diffusion spectrum imaging using compressed sensing. The method can be applied to either reduce acquisition time of diffusion spectrum imaging acquisition without losing critical information or to improve the resolution in diffusion space without increasing scan time. Unlike parallel imaging, compressed sensing can be applied to reconstruct a sub-Nyquist sampled dataset in domains other than the spatial one. Simulations of fiber crossings in 2D and 3D were performed to systematically evaluate the effect of compressed sensing reconstruction with different types of undersampling patterns (random, gaussian, Poisson disk) and different acceleration factors on radial and axial diffusion information. Experiments in brains of healthy volunteers were performed, where diffusion space was undersampled with different sampling patterns and reconstructed using compressed sensing. Essential information on diffusion properties, such as orientation distribution function, diffusion coefficient, and kurtosis is preserved up to an acceleration factor of R Key words: compressed sensing; q-space; diffusion spectrum imaging; kurtosis; undersampling; orientation distribution function Over the last decade the application of diffusionweighted MR imaging to the central nervous system has gained significant attention. Recently, Inglese and Bester (1) reviewed the importance of diffusion in clinical evaluation of multiple sclerosis. Similarly, earlier studies indicated that diffusion tensor imaging could be used to detect evidence of traumatic brain injury (2). Diffusion tensor imaging samples only a very small subset of the full diffusion information encoded in q-space and describes diffusion as single compartment gaussian (3). This assumption however falls short for instance in fiber crossings or in biological tissue (4), which may exhibit restricted, non-gaussian diffusion. The concept of full qspace imaging to study molecular diffusion and tissue microstructure was introduced by Callaghan et al. (5) and first applied to brain tissue by King et al. (6); its modulus Fourier transform variant using finite gradient pulse widths is known as diffusion spectrum MR imaging (DSI) (7). DSI samples the full q-space and can be related to a center-of-mass weighted displacement space (8) by Fourier transform. Despite the large information content of DSI, its high dimensionality (three dimensions in the spatial domain [k-space] and three dimensions in the q-space) leading to very long acquisition times, severely limited its clinical application in vivo. And indeed the application of DSI has been reported only a few times in biological systems (6,9), although the nonlocalized analysis of q-space is commonly used in porous media (10). It can however be envisioned that using the full potential of diffusion information of full q-space to derive and evaluate surrogate markers for multiple sclerosis (MS) and traumatic brain injury would add significant clinical benefit and indeed more extended sampling of diffusion...

Active shape models (ASMs) are often limited by the inability of relatively few eigenvectors to capture the full range of biological shape variability. This paper presents a method that overcomes this limitation, by using a hierarchical formulation of active shape models, using the wavelet transform. The statistical properties of the wavelet transform of a deformable contour are analyzed via principal component analysis, and used as priors in the contour's deformation. Some of these priors reflect relatively global shape characteristics of the object boundaries, whereas, some of them capture local and high-frequency shape characteristics and, thus, serve as local smoothness constraints. This formulation achieves two objectives. First, it is robust when only a limited number of training samples is available. Second, by using local statistics as smoothness constraints, it eliminates the need for adopting ad hoc physical models, such as elasticity or other smoothness models, which do not necessarily reflect true biological variability. Examples on magnetic resonance images of the corpus callosum and hand contours demonstrate that good and fully automated segmentations can be achieved, even with as few as five training samples.

Optical aberrations due to the inhomogeneous refractive index of tissue degrade the resolution and brightness of images in deep-tissue imaging. We introduce a confocal fluorescence microscope with adaptive optics, which can correct aberrations based on direct wavefront measurements using a Shack-Hartmann wavefront sensor with a fluorescent bead used as a point source reference beacon. The results show a 4.3× improvement in the Strehl ratio and a 240% improvement in the signal intensity for fixed mouse tissues at depths of up to 100 μm.

This presentation will be on the use of adaptive optics (AO) with direct wavefront sensing for biological imaging. Adaptive optics have been used in ground based astronomy to correct image aberrations caused by refraction as light passes through Earth's turbulent atmosphere. As shown on the left in Figure One, light from the telescope has a distorted wavefront, as indicated by the wavy lines. A wavefront sensor measures these distortions and applies the opposite shape on an adaptive mirror using a feedback control system. After reflection from the adaptive mirror a corrected wavefront is generated and is recorded by a highresolution camera. An image of the planet Neptune before and after AO correction is shown on the right in Figures 1(a) and 1(b). After correction the cloud structure on Neptune can be resolved in 1(b)