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A framework for fast multiview fusion of Single Plane Illumination Microscopy (SPIM) images based on a spatially-variant point spread function (PSF) model is presented. For the multiview fusion a new algorithm based on the regularized Lucy-Richardson deconvolution and the Overlap-Save method is developed and tested on SPIM images. In the algorithm the image is decomposed into small blocks which are...
We develop optimal forward and inverse variance-stabilizing trans formations for the Rice distribution, in order to approach the problem of magnetic resonance (MR) image filtering by means of standard denoising algorithms designed for homoskedastic observations. Further, we present a stable and fast iterative procedure for robustly estimating the noise level from a single Rician-distributed image...
We present a unified approach to Expectation-Maximization (EM) and Level Set image segmentation that combines the advantages of the two algorithms via a geometric prior that encourages local classification similarity. Compared to level sets, our method increases the information returned by providing probabilistic soft decisions, is easily extensible to multiple regions, and does not require solving...
Real-time cardiovascular MRI is a useful and challenging dynamic imaging application. The partial separability (PS) model enables reconstruction of dynamic cardiac images from highly undersampled (k, t)-space data. However, the underlying PS model-based reconstruction problem is ill-conditioned, so regularization is often necessary to stabilize its solution. It has been shown that ℓ1 regularization...
Compressive sensing (CS) is an emerging technique to speed up the data acquisition in MRI. CS relies on the sparsity constraint of the underlying image. Currently total variation (TV) is being used ubiquitously in CS-MRI as a sparsity measurement. TV is based on the first-order difference, which works well for piece-wise constant images. In this paper, a sparsifying transform based on the second-order...
The Blood-Oxygen-Level-Dependent (BOLD) signal that is measured by functional magnetic resonance imaging (fMRI) has been the subject of extensive research since the development of the first balloon model. While there are definite benefits to moving from the Canonical Hemodynamic Response function to a physiologically inspired BOLD model, significant barriers remain. Optimizing the simplest balloon...
Medical images contain information about vital organic tissues inside of human body and are widely used for diagnoses of disease or for surgical purposes. Due to vital rule of image reconstruction in medical sciences the corresponding algorithms with better efficiency and higher speed is desirable. Most algorithms in image reconstruction are operated on frequency domain such as filtered back projection...
This paper describes a methodology for integrating confocal microscopy images acquired from two different illumination sources over time obtained from experiments of cell migration and vessel formation. The three-dimensional fluorescent images of stained cell nuclei and two dimensional bright field images of the extracellular matrix, supplement one another as the outline of the conduit formed by the...
X-ray CT images have various applications, including CT-based attenuation correction (CTAC) for PET. Low-dose CT imaging is particularly desirable for CTAC. Dual-energy (DE) CT imaging methods may improve the accuracy of attenuation correction in PET. However, conventional DE CT approaches to sinogram material decomposition use logarithmic transforms that are sensitive to noise in low-dose scans....
In this paper we introduce a new algorithm for reconstruction of low-dose CT images. The approach, called multi-resolution feature fusion (MRFF), combines the textural qualities of conventional filtered-back projection images, with the noise suppression ability of non-quadratic regularized iterative reconstructions, to form a fast image reconstruction with good noise texture properties. Low-dose abdominal...
Recently we developed two image reconstruction approaches aimed to unify gated imaging and dynamic imaging in nuclear cardiac imaging, the goal being to obtain an image sequence from a single acquisition which shows both cardiac motion and tracer distribution change over the course of imaging. While both approaches exploit the similarity among the different cardiac gate intervals, they differ in the...
Noise is an important concern in high-angular resolution diffusion imaging studies because it can lead to errors in downstream analyses of white matter structure. To address this issue, we investigate a new approach for denoising diffusion-weighted data sets based on the K-SVD algorithm. We analyze its characteristics using both simulated and biological data and compare its performance with existing...
We introduce a variational framework and a numerical method for simultaneous reconstruction and regularization of orientation distribution functions (ODF). The regularization is performed both angularly and spatially. The spatial regularization is based on the sparsity of MR images in finite difference domain and wavelet domain. The angular regularization is performed using Laplace-Beltrami operator...
Using Positron Emission Tomography for dynamic tracer mapping requires the knowledge of the arterial plasmatic activity concentration (PTAC). The reference method to obtain it is arterial sampling, not applicable in clinical studies because invasive and dangerous. The Simultaneous Estimation (SIME) method aims to estimate simultaneously the PTAC and the organ pharmacokinetic parameters from the time-activity...
Joint use of partial separability (PS) and spatial-spectral sparsity constraints has previously been demonstrated useful for image reconstruction from undersampled data. This paper extends our early work in this area by proposing a new method for jointly enforcing the PS and spatial total variation (TV) constraints for dynamic MR image reconstruction. An algorithm is also described to solve the underlying...
GRAPPA has been widely used as a k-space-based parallel MRI reconstruction technique. It linearly combines the acquired k-space signals to estimate the missing k-space signals where the coefficients are obtained by linear regression using auto-calibration signals. At high acceleration factors, GRAPPA reconstruction can suffer from a high level of noise even with a large number of auto-calibration...
A new method to estimate the variance of noise from the composite magnitude signal of GRAPPA reconstructed images is presented. Parallel imaging methods allow to increase the acquisition rate via subsampled acquisitions of the k-space. However, the reconstruction process yields to a variance of noise value which is dependent on the position within the image. The proposed method uses information of...
In fluoroscopic tracking for fractured femur bone fixation, a precise identification of the femoral head provides valuable guidance for positioning the implant instruments such as nails and screws. Noise, occlusions and weak edges challenge the task of automatically segmenting the femoral head. In this paper, a fast and fully automated method to precisely delineate the femoral head in fluoroscopic...
The rapid assessment of how cells respond to pathologic, biological, environmental, and endogenous agents is critical for understanding how such responses may increase genomic instability, disease development and, ultimately, affect quality of life. Recently, we have applied the technique of DNA fiber analysis to increase our understanding of how DNA damaging agents influence DNA replication [1]....
It has been know for at least one decade that functional MRI time series display long-memory properties, such as power-law scaling in the frequency spectrum. Concomitantly, multivariate model-free analysis of spatial patterns, such as spatial Independent Component Analysis (sICA), has been successfully used to segment from spontaneous activity Resting-State Networks (RSN) that correspond to known...
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