Advantage Of Data Redundancy Research Articles

Scatter correction using FBP with data rearrangement for a single full scan CBCT

A scattering plate consisting of multiple lead strips is an effective tool for scatter correction and dose reduction in cone-beam CT(CBCT). Existing scattering plate correction methods take advantage of data redundancy by partially blocking cone beam data from a single scan to make up and perform scattering correction. However, for geometric objects with large cone angles during CBCT scans, existing backprojection-filteration(BPF) algorithms cannot accurately reconstruct the object due to data inconsistency and truncation, and may produce additional image artifacts. In order to improve the image quality at large cone angles, this paper proposes a hybrid scatter correction method for filteration-backprojection (FBP) image reconstruction from partially blocked cone beam data using data rearrangement. The proposed method uses a blocker consisting of multiple lead strips that are placed between the light source and the object to provide scatter correction and image reconstruction with beam-blocked data. In combination with a data rearrangement algorithm, the cone-beam projection data is rearranged into a tilted parallel projection data, followed by weighted filtering and inverse projection reconstruction. The experimental results of CBCT show that the proposed method effectively corrects the scattering in each projection and reconstructs the scatter-corrected image from a single scan.

Memoization-based high-performance video frame processing

We present a high-performance improvement method for implementation of local processing algorithms for video frames by using the benefit of memoization technique. Memoization is a technique that uses the advantage of data redundancy to minimize the amount of computations performed by retrieving previous results instead of computing again, which leads to faster processing speed. In this method, the benefit of interframe redundancy in adjacent frames is used for memoizing where the pixels in a sequence of frames are correlated. We have developed this method in software and applied it to edge detection and median filters. The typical speedups achieved in the median filter range from 2.2× with exact results to 6.48× in tolerant methods and in edge detection filter range from 2.1× with exact results to 5.41× in tolerant method. The structural similarity index metric that is used for evaluating the perceived similarity of the tolerant result with ideal result was applied to each adjacent frame in sample stream frames. The typical values of this parameter were 0.96 in median filter and 0.71 in edge detection filter.

Exact Fan-Beam Reconstruction With Arbitrary Object Translations and Truncated Projections

This article proposes a new method for reconstructing two-dimensional (2D) computed tomography (CT) images from truncated and motion contaminated sinograms. The type of motion considered here is a sequence of rigid translations which are assumed to be known. The algorithm first identifies the sufficiency of angular coverage in each 2D point of the CT image to calculate the Hilbert transform from the local “virtual” trajectory which accounts for the motion and the truncation. By taking advantage of data redundancy in the full circular scan, our method expands the reconstructible region beyond the one obtained with chord-based methods. The proposed direct reconstruction algorithm is based on the Differentiated Back-Projection with Hilbert filtering (DBP-H). The motion is taken into account during backprojection which is the first step of our direct reconstruction, before taking the derivatives and inverting the finite Hilbert transform. The algorithm has been tested in a proof-of-concept study on Shepp-Logan phantom simulations with several motion cases and detector sizes.

Nonlocal linear minimum mean square error methods for denoising MRI

Abstract The presence of noise results in quality deterioration of magnetic resonance (MR) images and thus limits the visual inspection and influence the quantitative measurements from the data. In this work, an efficient two stage linear minimum mean square error (LMMSE) method is proposed for the enhancement of magnitude MR images in which data in the presence of noise follows a Rician distribution. The conventional Rician LMMSE estimator determines a closed-form analytical solution to the aforementioned inverse problem. Even-though computationally efficient, this approach fails to take advantage of data redundancy in the 3D MR data and hence leads to a suboptimal filtering performance. Motivated by this observation, we put forward the concept of nonlocal implementation with LMMSE estimation method. To select appropriate samples for the nonlocal version of the LMMSE estimation, the similarity weights are computed using Euclidean distance between either the gray level values in the spatial domain or the coefficients in the transformed domain. Assuming that the signal dependent component of the noise is optimally suppressed by this filtering and the rest is a white and uncorrelated noise with the image, we adopt a second stage LMMSE filtering in the principal component analysis (PCA) domain to further enhance the image and the noise variance is adaptively adjusted. Experiments on both simulated and real data show that the proposed filters have excellent filtering performance over other state-of-the-art methods.

A Novel Approach for RFID Data Cleansing Based on Bayesian Inference

Radio Frequency Identification (RFID) technologies are used in many applications for data collection. However, raw RFID readings are usually of low quality and may contain many anomalies. The solution should take advantage of the resulting data redundancy for data cleaning. In this paper we propose a Bayesian inference based approach for cleaning RFID raw data. Our approach takes full advantage of data redundancy. To capture the likelihood, we design a 3-state detection model and formally prove this model can maximize the system performance.

SU‐C‐BRA‐01: 4D Cone‐Beam CT Acquisition Using Respiratory Phase Predication Technique

Purpose: Respiration‐correlated CBCT reduces respiratory motion artifacts at the cost of increased view‐aliasing artifacts due to the gaps in projection data that are sorted to other phases. Respiratory‐gated acquisitions can cover the full scanning range but is inefficient (∼5 minutes per phase). In this study, we developed a 4D CBCT acquisition method using respiratory phase predication technique that can potentially acquire full projection data for each phase within a clinically acceptable (∼10 minutes or less) time. Methods: The proposed 4D CBCT continuously scans the target until sufficient number of projections is acquired for all phases. The respiratory phase predication technique consists of the following iterative steps: (1) training using respiratory signal for the first few (e.g., five) breathing cycles; (2) initial optimization of the gantry rotation speed; (3) adaptive control of the rotation speed and image acquisition during the scan (4) sorting the projection images into different phases. The method was validated using the 4D XCAT digital lung phantom with the motion driven by a sinusoid wave and a real patient respiration signal acquired with RPM system. CBCT images were reconstructed for each phase using the simultaneous arithmetic reconstruction technique and compared with the mixed‐phase reconstruction. Results: Sufficient projection images (an angular interval less than 1 degree) were acquired for all 10 phases after 10 gantry rotations. The acquisition time was estimated from 10–12 minutes assuming each gantry rotation took ∼1 minute. The CBCT images reconstructed from the mixed‐phase projections were blurred in comparison to those for individual phases. Conclusion: A respiratory prediction technique for 4D CBCT has been developed and validated with real patient breathing pattern. We plan to further reduce the number of gantry rotations and therefore the scanning time by improving the control of adaptive image acquisition and taking advantage of data redundancy as in 4D CT.

Fast inverse scattering solutions using the distorted Born iterative method and the multilevel fast multipole algorithm

The distorted Born iterative method (DBIM) computes iterative solutions to nonlinear inverse scattering problems through successive linear approximations. By decomposing the scattered field into a superposition of scattering by an inhomogeneous background and by a material perturbation, large or high-contrast variations in medium properties can be imaged through iterations that are each subject to the distorted Born approximation. However, the need to repeatedly compute forward solutions still imposes a very heavy computational burden. To ameliorate this problem, the multilevel fast multipole algorithm (MLFMA) has been applied as a forward solver within the DBIM. The MLFMA computes forward solutions in linear time for volumetric scatterers. The typically regular distribution and shape of scattering elements in the inverse scattering problem allow the method to take advantage of data redundancy and reduce the computational demands of the normally expensive MLFMA setup. Additional benefits are gained by employing Kaczmarz-like iterations, where partial measurements are used to accelerate convergence. Numerical results demonstrate both the efficiency of the forward solver and the successful application of the inverse method to imaging problems with dimensions in the neighborhood of ten wavelengths.