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We proposed a maximum a posterior (MAP) framework for incorporating information from co-registered anatomical images into PET image reconstruction through a novel anato-functional joint prior. The characteristic of the utilized hyperbolic potential function is determinate by the voxel intensity differences within the anatomical image, while the penalization is computed based on voxel intensity differences...
While helical scanning is well established in the clinical arena, most micro-CT scanners use circular cone beam trajectories and approximate reconstructions based on a filtered backprojection (FBP) algorithm. This may be sufficient for some applications, but in studies of larger animals, such as rats, the size of the detector can constrain the field of view and extend scan time. To address this problem,...
In image-guided neurosurgery, preoperatively acquired diagnostic images (e.g., brain MRI) should be accurately registered to the physical space that is specific to the patient's intraoperative neuroanatomy. A popular framework of registration requires manual defining corresponding positions of fiducial markers on the patient head and the preoperative brain MRI. The procedure is time-consuming and...
We propose a new T2 mapping method to improve the CS reconstruction based on the theory of structured sparse representation. The proposed method learns the PCA sub-dictionaries for adaptive sparse representation and suppresses the sparse coding noise to obtain good reconstructions. Experimental results demonstrate that the proposed method capable of delivering state-of-the-art performance at CS reconstruction...
The European Space Agency (ESA) Soil Moisture and Ocean Salinity mission (SMOS) is intended to provide global maps of soil moisture and ocean surface salinity. Its payload MIRAS (Microwave Imaging Radiometer with Aperture Synthesis) is an L-band interferometric radiometer which achieves unprecedented resolution. It was successfully launched in November 2009 under the European Space Agency Earth Explorers...
In this paper, we develop efficient deconvolution and super-resolution methodologies and apply these techniques to reduce image blurring and distortion inherent in an aperture synthesis system. Such a system produces ringing at sharp edges and other transitions in the observed field. The conventional approach to suppressing sidelobes is to apply linear apodization, which has the undesirable side effect...
Detection of pixels corrupted by noise and assessing the degree to which the pixels are corrupted intrinsically fuzzy processes, involve uncertainty and imprecision. The paper aims at reconstruction of the image with ensured quality after removing noise from the original image. Here region marking process has been introduced to obtain number of clusters automatically which partition the whole image...
In today's world peoples are clicking lots of pictures and also trying to preserve there past pictures, but as the time passes that pictures got damaged. To restore that damages like scratches, overlaid text or graphics can be remove by using technique called Image Inpainting. Images Inpainting is a set of techniques for making undetectable modifications to images. Applications of image inpainting...
We present a novel, general-purpose method for deconvolving and denoising images from gridded radio interferometric visibilities using Bayesian inference based on a Gaussian process model. The method automatically takes into account incomplete coverage of the uv-plane and mode coupling due to the beam. Our method uses Gibbs sampling to efficiently explore the full posterior distribution of the underlying...
The ability of having a sparse representation for a certain class of signals has many applications in data analysis, image processing, and other research fields. Among sparse representations, the cosparse analysis model has recently gained increasing interest. Many signals exhibit a multidimensional structure, e.g. images or three-dimensional MRI scans. Most data analysis and learning algorithms use...
Compressive sensing exploits the structure of signals to acquire them with fewer measurements than required by the Nyquist-Shannon theory. However, the design of practical compressive sensing hardware raises several issues. First, one has to elicit a measurement mechanism that exhibits adequate incoherence properties. Second, the system should be robust to noise, whether it be measurement noise, or...
Near Infrared Spectroscopy is a method that measures the brain's haemodynamic response. It is of interest in brain-computer interfaces where haemodynamic patterns in motor tasks are exploited to detect movement. However, the NIRS signal is usually corrupted with background biological processes, some of which are periodic or quasi-periodic in nature. Singular spectrum analysis (SSA) is a time-series...
We present a method for producing an accurate and compact 3-D face model in real time using a low cost RGB-D sensor like the Kinect camera. We extend and use Bump Images for highly accurate and low memory consumption 3-D reconstruction of the human face. Bump Images are generated by representing the Cartesian coordinates of points on the face in the spherical coordinate system whose origin is the...
Signal and image reconstruction from Fourier Transform magnitude is a difficult inverse problem. Fourier transform magnitude can be measured in many practical applications, but the phase may not be measured. Since the autocorrelation of an image or a signal can be expressed as convolution of x(n) with x(−n), it is possible to formulate the inverse problem as a non-negative matrix factorization problem...
In traditional compressed sensing theory, the dictionary matrix is given a priori, while in real applications this matrix suffers from random noise and fluctuations. This paper considers a signal model where each column in the dictionary matrix is affected by a structured noise. This formulation is common in radar related applications and direction-of-arrival estimation. We propose to use joint-sparse...
The placement of knot vector and the determination of control points are two fundamental issues in B-spline surface reconstruction. This paper presents a variational approach to construct B-spline surfaces from a set of data points. The approach finds the optimal placement of knots and control points simultaneously while most previous methods determine the knots heuristically or in a separate step...
Coherent change detection between multiple synthetic aperture sonar (SAS) images is reliant on the images being co-registered with sub-pixel accuracy. In this paper we suggest a technique using available navigation data to reconstruct the images onto a common grid. Data obtained using the MUD SAS system is used to demonstrate this method. We show that even with high quality navigation data there is...
At the Heidelberg Ion-Beam Therapy Center (HIT), post-irradiation PET/CT imaging with a Biograph mCT scanner is used to verify in-vivo the treatment delivery. One main challenge is the extremely low number of true coincidences of 80 · 103 – 1.3 · 106. This work investigates the performance of the scanner in terms of precision and accuracy in activity quantification and shape recovery at varying counting...
Statistical iterative reconstruction (SIR) algorithms have shown advantages over the conventional filtered back-projection method for low-dose computed tomography (CT) reconstruction. For the SIR algorithms, the regularization term plays a critical role on determining the performance. One commonly used regularization is the quadratic-form Gaussian Markov random field (MRF), which penalizes differences...
PET scanner calibration is a routine procedure that is performed on a daily basis. The normalization data acquisition ideally should take as little time as possible. Consequently, normalization data are noisy and a component-based model is used to battle related issues. Most normalization components except scanner crystal efficiencies are fixed for a given scanner type. In this paper we propose a...
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