Learn More
The contrast-to-noise ratio (CNR) was used to determine the detectability of objects within reconstructed images from diffuse near-infrared tomography. It was concluded that there was a maximal value of CNR near the location of an object within the image and that the size of the true region could be estimated from the CNR. Experimental and simulation(More)
Three-dimensional (3D), multiwavelength near-infrared tomography has the potential to provide new physiological information about biological tissue function and pathological transformation. Fast and reliable measurements of multiwavelength data from multiple planes over a region of interest, together with adequate model-based nonlinear image reconstruction,(More)
Calf blood flow was measured simultaneously in healthy human subjects (n = 7) during cuff inflation and deflation using near-infrared diffuse correlation spectroscopy (DCS) and arterial spin labeled perfusion MRI (ASL-MRI). The DCS and ASL-MRI data exhibited highly correlated absolute and relative dynamic flow responses in each individual (p < 0.001). Peak(More)
Near-Infrared (NIR) tomographic image reconstruction is a non-linear, ill-posed and ill-conditioned problem, and so in this study, different ways of penalizing the objective function with structural information were investigated. A simple framework to incorporate structural priors is presented, using simple weight matrices that have either Laplacian or(More)
A multispectral direct chromophore and scattering reconstruction technique has been implemented for near-infrared frequency-domain tomography in recovering images of total hemoglobin, oxygen saturation, water, and scatter parameters. The method applies the spectral constraint of the chromophores and scattering spectra directly in the reconstruction(More)
A promising method to incorporate tissue structural information into the reconstruction of diffusion-based fluorescence imaging is introduced. The method regularizes the inversion problem with a Laplacian-type matrix, which inherently smoothes pre-defined tissue, but allows discontinuities between adjacent regions. The technique is most appropriately used(More)
Flexible objects are a challenge to manipulate. Their motions are hard to predict, and the high number of degrees of freedom makes sensing, control, and planning difficult. Additionally, they have more complex friction and contact issues than rigid bodies, and they may stretch and compress. In this thesis, I explore two major types of flexible materials:(More)
We investigate fiber placement issues associated with a hybrid magnetic resonance imaging (MRI) near-infrared (NIR) imaging technique for small animal brain studies. Location of the optical fibers on the cranium is examined, with an emphasis on maximizing the recovered resolution and contrast in the region of interest, which in this case is the murine(More)
We present an image reconstruction method for diffuse optical tomography (DOT) by using the sparsity regularization and expectation-maximization (EM) algorithm. Typical image reconstruction approaches in DOT employ Tikhonov-type regularization, which imposes restrictions on the L 2 norm of the optical properties (absorption/scattering coefficients). It(More)
  • S Mehmet, Alexia Uzen, Turgut Giannoula, Durduran, O D R J Leff, L C Warren +95 others
  • 2010
Diffuse optical tomography (DOT) allows tomographic (3D), non-invasive reconstructions of tissue optical properties for biomedical applications. Severe under-sampling is a common problem in DOT which leads to image artifacts. A large number of measurements is needed in order to minimize these artifacts. In this work, we introduce a compressed sensing (CS)(More)