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About 10,000 papers for “Iterative reconstruction”

Iterative Reconstruction

De Man, Bruno Kristiaan Bernard, Bouman, Charles A., Thibault, Jean-Baptiste et al. · University of Notre Dame · 2016

An improved iterative reconstruction method to reconstruct a first image includes generating an imaging beam, receiving said imaging beam on a detector array, generating projection data based on said imaging beams received by said detector array, providing said projection data to an image reconstructor, enlarging one of a plurality of voxels and a plurality of detectors of the provided projection data, reconstructing portions of the first image with the plurality of enlarged voxels or detectors, and iteratively reconstructing the portions of the first image to create a reconstructed image.

Cited by 0Open access

Iterative Reconstruction

National Cancer Institute · Definitions · 2020

A method of reconstruction used in PET and SPECT that repeats a series of calculation steps with image data such that there is an improvement in the image quality in each repetition. It is an alternative approach to filtered backprojection.

Cited by 0Open access

Iterative initial condition reconstruction

Marcel Schmittfull, Tobias Baldauf, Matías Zaldarriaga · Physical review. D/Physical review. D. · 2017

Motivated by recent developments in perturbative calculations of the nonlinear evolution of large-scale structure, we present an iterative algorithm to reconstruct the initial conditions in a given volume starting from the dark matter distribution in real space. In our algorithm, objects are first moved back iteratively along estimated potential gradients, with a progressively reduced smoothing scale, until a nearly uniform catalog is obtained. The linear initial density is then estimated as the divergence of the cumulative displacement, with an optional second-order correction. This algorithm should undo nonlinear effects up to one-loop order, including the higher-order infrared resummation piece. We test the method using dark matter simulations in real space. At redshift $z=0$, we find that after eight iterations the reconstructed density is more than 95% correlated with the initial density at $k\ensuremath{\le}0.35\text{ }\text{ }h{\mathrm{Mpc}}^{\ensuremath{-}1}$. The reconstruction also reduces the power in the difference between reconstructed and initial fields by more than 2 orders of magnitude at $k\ensuremath{\le}0.2\text{ }\text{ }h{\mathrm{Mpc}}^{\ensuremath{-}1}$, and it extends the range of scales where the full broadband shape of the power spectrum matches linear theory by a factor of 2--3. As a specific application, we consider measurements of the baryonic acoustic oscillation (BAO) scale that can be improved by reducing the degradation effects of large-scale flows. In our idealized dark matter simulations, the method improves the BAO signal-to-noise ratio by a factor of 2.7 at $z=0$ and by a factor of 2.5 at $z=0.6$, improving standard BAO reconstruction by 70% at $z=0$ and 30% at $z=0.6$, and matching the optimal BAO signal and signal-to-noise ratio of the linear density in the same volume. For BAO, the iterative nature of the reconstruction is the most important aspect.

Cited by 101Open access

Iterative Image Reconstruction Techniques

Young Jun Cho, U. Joseph Schoepf, Justin R. Silverman et al. · Journal of Thoracic Imaging · 2014

Iterative image reconstruction algorithms provide significant improvements over traditional filtered back projection in computed tomography (CT). Clinically available through recent advances in modern CT technology, iterative reconstruction enhances image quality through cyclical image calculation, suppressing image noise and artifacts, particularly blooming artifacts. The advantages of iterative reconstruction are apparent in traditionally challenging cases-for example, in obese patients, those with significant artery calcification, or those with coronary artery stents. In addition, as clinical use of CT has grown, so have concerns over ionizing radiation associated with CT examinations. Through noise reduction, iterative reconstruction has been shown to permit radiation dose reduction while preserving diagnostic image quality. This approach is becoming increasingly attractive as the routine use of CT for pediatric and repeated follow-up evaluation grows ever more common. Cardiovascular CT in particular, with its focus on detailed structural and functional analyses, stands to benefit greatly from the promising iterative solutions that are readily available.

Cited by 23

Iterative CT Reconstruction Using

Curvelet-based Regularization, Haibo Wu, Andreas Maier et al. · 2015

Abstract. There is a critical need to reconstruct clinically usable images at a low dose. One way of achieving this is to reconstruct with as few pro-jections as possible. Due to the undersampling, streak artifacts degrade image quality for traditional CT reconstruction. Compressed sensing (CS) [1] theory uses sparsity as a prior and improves the reconstruc-tion quality considerably using only few projections. CS formulates the reconstruction problem to an optimization problem. The objective func-tion consists of one data fidelity term and one regularization term which enforce the sparsity under a certain sparsifying transform. Curvelet is an effective sparse representation for objects [2]. In this work, we in-troduce to use curvelet as the sparsifying transform in the CS based re-construction framework. The algorithm was evaluated with one physical phantom dataset and one in vitro dataset and was compared against and two state-of-art approach, namely, wavelet-based regularization (WR) [3] and total variation based regularization methods (TVR) [4]. The results show that the reconstruction quality of our approach is superior to the reconstruction quality of WR and TVR. 1

Cited by 0

ITERATIVE RECONSTRUCTION ALGORITHMS 1 The implementation of iterative reconstruction algorithms in MATLAB

Van Hemelryck Tessa, Wuyts Sarah, Goossens Maggie et al. · 2007

Abstract—The mathematical problem posed by Computed Tomography (CT), which includes projecting radiation through an object resulting in an estimate of this object’s interior, is to calculate image data (the pixel values) from the projection values. Although for now the filtered back-projection algorithm is most widely used by manufacturers, efforts are being made to make iterative methods popular again due to their unique advantages, such as their performances with incomplete noisy data. The algebraic reconstruction technique (ART), the simultaneous algebraic reconstruction technique (SART) and the simultaneous iterative reconstruction technique (SIRT) are a few of those iterative methods and in this paper we discuss these techniques and how they can be implemented in MATLAB, a numerical computing environment and programming language, created by The MathWorks. We begin by creating the weight matrix. This is the matrix which holds the importance indicators per pixel. Then we move on to the actual reconstructions and we end by implementing these algorithms and calculations in a graphical user interface. I.

Cited by 0

Comparison of Iterative Reconstruction algorithms on PET-CT devices : Iterative CT -reconstruction

Iida Kulman · Trepo - Institutional Repository of Tampere University · 2023

Medical imaging utilizes radiation to examine a patient’s anatomy and internal structures. One imaging method is CT imaging, where an X-ray tube and detectors on the opposite side rotate around the patient to get images describing the patient’s internal structures in 3-D. The PET-CT device combines PET and CT imaging, where PET imaging provides information on the patient’s anatomy, and CT imaging can be used for attenuation correction of PET-imaging. In the imaging methods using ionizing radiation, the used dose affects the quality of the image. However, based on the purpose of the imaging, the dose should be as low as reasonably possible, according to the purpose of the image. For example, PET-CT imaging uses CT images for attenuation correction for the PET -image, so low-dose CT is enough. On the other hand, when the CT-image is needed to describe the patient’s anatomy to make a diagnosis, a higher dose is needed to distinguish the different structures well enough so that the patient can be diagnosed. The iterative reconstruction algorithms are mathematical methods in CT-device that improve the quality of CT-images and reduce the noise. Partly, through iterative reconstruction methods, the dose levels used in the scans can also be reduced. This work compares two iterative reconstruction methods, ADMIRE and SAFIRE, which are algorithms in Siemens PET-CT-devices. For image reconstruction, Catphan 500 was scanned, for which scans with the CT -device were performed using four different dose levels. Catphan 500 includes four modules that are used to study different characteristics of the image. In image reconstruction, SAFIRE and ADMIRE varying kernels, iteration strength and slice thicknesses. In image reconstruction, kernels are used as filters to smooth or sharpen the image. The comparison is made by examining image quality features such as image uniformity, contrast level, SNR and defining MTF-graphs. Analysis for CT -images were done using Python-based pylinac software. The results defined image quality parameters using mean or median values for different kernels, iteration strength, dose level and slice thickness combinations. The results showed differences between ADMIRE and SAFIRE algorithms. From the results, it is found that the use of the kernel, iteration strength and dose level significantly affects the image quality characteristics. From the results, it is seen that SAFIRE and ADMIRE differ when it comes to uniformity, contrast and detecting details from the image. For example, the spatial frequency between SAFIRE and ADMIRE was approximately the same when using kernel Br38 for SAFIRE but harder kernel Br40 for ADMIRE. On the other hand, a similar SNR is obtained with a kernel Br40 using iteration strength 3 for SAFIRE but strength 2 for ADMIRE. By studying the differences between iterative reconstruction algorithms, differences and similarities in image quality characteristics can be evaluated. When the differences between algorithms are detected, like in this work, which parameter combinations can be used for certain image quality parameters, they differ between ADMIRE and SAFIRE. Knowing the differences between these algorithms, image quality between two PET-CT scanners can be standardized and image quality optimized without increasing the dose level used in scanning.

Cited by 0Open access

Adaptive Statistical Iterative Reconstruction-V

Dominik C. Benz, Christoph Gräni, Fran Mikulicic et al. · Journal of Computer Assisted Tomography · 2016

ASiR-V yields substantial noise reduction and improved image quality enabling introduction of ultralow-dose CCTA.

Cited by 48Open access