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检索条件"主题词=Reconstruction algorithms"

共 6394 条记录，以下是151-160 订阅

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Fast and accurate rotator for rotation-based iterative reconstruction algorithms

Fast and accurate rotator for rotation-based iterative recon...

引用

IEEE Nuclear Science Symposium and Medical Imaging Conference

作者： Krzysztof Kacperski Jakub Pietrzak Maria Sklodowska-Curie Memorial Cancer Centre Institute of Oncology Warsaw Poland

We present an image rotation algorithm in which the values of pixels of the rotated image are computed as weighted averages of the original grid pixels, with the weights proportional to the overlapping areas of each corresponding pixel pair. The method preserves virtually perfectly the image uniformity and single pixel pulse value in the rotated image. These properties are particularly important to accurately model projections in iterative reconstruction algorithms. The matrix of pixel redistribution coefficients can be precomputed analytically and the algorithm is much faster that e.g. the widely used Gaussian rotator.

关键词： reconstruction algorithms Fast accurate rotator

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Theoretical and numerical study of MLEM and OSEM reconstruction algorithms for motion correction in emission tomography

Theoretical and numerical study of MLEM and OSEM reconstruct...

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IEEE Symposium on Nuclear Science (NSS/MIC)

作者： Joyoni Dey Michael A. King Division of Nuclear Medicine Department of Radiology University of Massachusetts Medical School Worcester MA USA

Patient body-motion and respiratory-motion impacts the image quality of cardiac PET or SPECT perfusion images. Several algorithms exist in the literature to correct for motion within the iterative maximum-likelihood reconstruction framework. In this work, three algorithms are derived using Poisson statistics to correct for patient motion. The first one is a motion compensated MLEM algorithm (MC-MLEM). The next two algorithms called MGEM-1 and MGEM-2 (short for Motion Gated EM, 1 and 2) use the motion states as subsets, in two different ways. Experiments were performed with NCAT phantoms with exactly known motion as the source and attenuation distributions. The SIMIND Monte Carlo simulation software was used to create SPECT projection images of the NCAT phantoms. The projection images were then modified to have Poisson noise levels equivalent to that of clinical acquisition. We investigated application of these algorithms to correction of (1) a large body-motion of 2 cm in Superior-Inferior (SI) and Anterior-Posterior (AP) directions each and (2) respiratory motion of 2 cm in SI and 0.6 cm in AP. We obtained the bias with respect to the NCAT phantom activity for noiseless reconstructions as well as the bias-variance for noisy reconstructions. The MGEM-1 advanced along the biasvariance curve faster than the MC-MLEM with iterations. The MGEM-1 also lowered the noiseless bias (with respect to NCAT truth) faster with iterations, compared to the MC-MLEM algorithms, as expected with subset algorithms. For the body motion correction with two motion states, after the 9 th iteration the bias was close to that of MC-MLEM at iteration 17, reducing the number of iterations by a factor of 1.89. For the respiratory motion correction with 9 motion states, based on the noiseless bias, the iteration reduction factor was approximately 7. For the MGEM-2, however, bias-plot or the bias-variance-plot saturates with iteration because of successive interpolation error.

关键词： reconstruction algorithms Iterative algorithms Image reconstruction Imaging phantoms Image quality Positron emission tomography Statistics Attenuation Noise level Application software

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Whole-Body dynamic PET: Effect of temporal gaps on FDG Ki quantification from 3D and 4D reconstruction algorithms

Whole-Body dynamic PET: Effect of temporal gaps on FDG Ki qu...

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IEEE Symposium on Nuclear Science (NSS/MIC)

作者： Zacharias Chalampalakis Simon Stute Marina Filipović Thibaut Merlin Claude Comtat Imagerie Moleculaire In Vivo CEA SHFJ Inserm CNRS Univ. Paris Sud Univ. Paris Saclay Orsay France LaTIM France

ISBN: (数字)9781728141640

ISBN: (纸本)9781728141657

In whole-body dynamic PET imaging, acquisition is performed sequentially over bed positions to achieve whole-body coverage, introducing large temporal gaps in the acquired data. The objective of this work is to study the effect of temporal gaps on quantification of FDG net influx rate K i from Patlak analysis using 3D and 4D reconstruction methods. A brain FDG scan was simulated for a single bed dynamic protocol and for 3 different cases of whole-body dynamic protocols. reconstructions were performed using 3D and 4D MLEM algorithms. Two different dynamic models were considered for 4D reconstructions, a Patlak model for direct reconstruction of K i and a spectral model for temporal regularisation. Except in the case of the direct Patlak 4D reconstruction, post reconstruction least squares fitting of the Patlak model was performed on the reconstructed frame activity images, at both the ROI and voxel level. K i estimates derived from ROI level analysis showed similar level of bias for all simulations, and all reconstruction methods evaluated. In contrary estimates from voxel level analysis (parametric imaging) resulted to higher bias and noise for the whole-body protocols with 3D reconstruction, compared to the single bed protocol. For all whole-body protocols both 4D reconstruction methods outperformed 3D, by providing comparable bias and RMSE to the single bed case with 3D reconstruction. In all protocols with temporal gaps, evaluation of the standard-deviation bias trade-off showed that for 3D reconstruction standard deviation rises fast, with increasing iterations, with no convergence of the mean K i estimate to a level of bias, while in contrary 4D reconstructions provided results with smaller standard deviation at matched bias and converged to a certain level of bias. These results suggest that behaviour of 4D reconstruction algorithms is preferable for use in FDG K i quantification in parametric imaging, particularly for the case of whole-body dynamic imagi

关键词： Image reconstruction Protocols Three-dimensional displays reconstruction algorithms Brain modeling Heuristic algorithms

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Comparison of rotation-based methods for iterative reconstruction algorithms

Comparison of rotation-based methods for iterative reconstru...

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IEEE Symposium on Nuclear Science (NSS/MIC)

作者： E.V.R. Di Bella A.B. Barclay R.L. Eisner R.W. Schafer Georgia Institute of Technology Carlyle Fraser Heart Center/Emory Crawford Long Hospital USA

Rotation-based approaches to iterative reconstruction algorithms offer unique trade-offs between accuracy, computational complexity, and smoothing. We analyzed six different rotation methods for generating projections and reconstructions of simulated and clinical data. Nearest neighbor, upsampled nearest neighbor, bilinear interpolation, and bicubic interpolation were all used to determine the values at rotated grid points. A decomposition of the rotation transformation matrix into three components was also investigated. Linear and cubic interpolation were used in the three pass method. For all of the methods, mean normalized square errors were computed as was a measure of smoothness. Our results demonstrate the trade-offs associated with the different methods and suggest that the method of shears with cubic interpolation offers a computationally efficient approach with accuracy between that of bilinear and bicubic interpolation.

关键词： Iterative methods reconstruction algorithms Interpolation Image reconstruction Nearest neighbor searches Smoothing methods Matrix decomposition Shearing Heart Hospitals

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Simulation Study of Wavefront reconstruction algorithms in DCAO System

Simulation Study of Wavefront Reconstruction Algorithms in D...

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International Symposium on Knowledge Acquisition and Modeling, KAM

作者： Jing Li Jian Rong Xiaochun Zhong School of Physical Electronic UESTC Chengdu Sichuan China Department of Applied Physics Southwest Jiaotong University Chengdu Sichuan China

In this paper, we present a analytical study of the wavefront reconstruction algorithm for DCAO system. This algorithm is based on Zernike polynomials. We simulate the wavefront reconstruction using Zernike expansion in classical AO system and in DCAO system, and compare the reconstruction performance of the two systems. Through the simulation, we can see, the wavefront we reconstructed in DCAO system is more similar to the initial wavefront. With the analysis of the simulation results, we come to the conclusion that this algorithm has good effect in wavefront reconstruction in DCAO system.

关键词： reconstruction algorithms Polynomials Optical sensors Delta modulation Optical distortion Mirrors Adaptive optics Sensor systems Algorithm design and analysis Optical devices

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Concurrent iterative reconstruction algorithms (CIRAs) in multi-headed SPECT systems

Concurrent iterative reconstruction algorithms (CIRAs) in mu...

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IEEE Symposium on Nuclear Science (NSS/MIC)

作者： X. Wu J.K. Brown H.R. Tang S.C. Blankespoor B.H. Hasegawa Department of Radiology University of California San Francisco San Francisco CA USA

We are developing concurrent iterative reconstruction algorithms (CIRAs) that use partial sets of projection data to perform iterative reconstruction concurrently with data acquisition. In this study, we investigate CIRAs for multi-headed SPECT systems by simulating data for clinically relevant configurations. We study CIRAs for multi-headed SPECT under three different clinically feasible instances and identify the camera geometries and data acquisition protocols that are favorable for the convergence rates of CIRAs. We demonstrate that CIRA converges faster if projections from widely differing angular views, rather than from consecutive angular views, are available early during the data acquisition. Specifically, we show that CIRAs with single-headed and triple-headed systems show similar convergence rates, and are more efficient than CIRAs with the opposed dual-headed systems. In all cases, CIRAs take less total time to acquire and reconstruct images than conventional reconstruction methods with single-headed or multi-headed SPECT systems.

关键词： reconstruction algorithms Data acquisition Image reconstruction Convergence Cameras Electrical capacitance tomography Iterative algorithms Geometry Protocols Heart

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Effective Sparse reconstruction algorithms for Compressed ECG Sensing with Deterministic Binary Block Diagonal Sensing Matrix

Effective Sparse Reconstruction Algorithms for Compressed EC...

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International Conference on Electronics, Computers and Artificial Intelligence (ECAI)

作者： Jayarani Medakal Anirudhan M. Sabarimalai Manikandan Subrahmanyam Mula Department of Electrical Engineering Indian Institute of Technology Palakkad Palakkad Kerala India

ISBN: (数字)9798350371154

ISBN: (纸本)9798350371161

For low-power, energy-efficient wearable edge health devices and health monitoring systems, compressed sensing (CS) achieves significant advances by sampling at sub-Nyquist rates for sparse signals such as electrocardiograms (ECG). The paper attempts to explore the best sparse reconstruction algorithm, along with a sparsifying matrix composed of a discrete cosine (C) and a discrete sine (S) basis [C S] and a deterministic binary block diagonal (DBBD) sensing matrix. The six different sparse reconstruction algorithms used for the recovery of CS ECG are orthogonal matching pursuit (OMP), approximate message passing (AMP), L1-minimization (L1-min), compressive sampling matching pursuit (CoSaMP), iterative hard thresholding (IHT), and iterative soft thresholding (IST). The ECG signals from 48 records of the MIT-BIH arrhythmia database (mitdb) were tested for CS reconstruction at compression ratios (CR) of 2.4, 3, 4, 4.8, 6 and 8. Further results showed that the combination of the OMP algorithm, the DBBD sensing matrix, and the basis [C S] surpassed the performance of other recovery algorithms in terms of lower average percentage root mean square difference (PRD). This combination achieved a lowest average PRD of 1.04 for the mitdb record 213, at a CR of 2.4. Although L1-min exhibits competitive performance with OMP in terms of PRD, OMP is computationally simpler than L1-min with a shorter recovery time.

关键词： Databases Arrhythmia Matching pursuit algorithms Electrocardiography reconstruction algorithms Iterative algorithms Sensors

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Comparing short scan CT reconstruction algorithms regarding cone-beam artifact performance

Comparing short scan CT reconstruction algorithms regarding ...

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IEEE Symposium on Nuclear Science (NSS/MIC)

作者： Clemens Maaß Frank Dennerlein Frédéric Noo Marc Kachelrieß Institute of Medical Physics (IMP) University of Erlangen-Nu¨rnberg Henkestr. 91 91052 Erlangen Germany Frank Dennerlein: Siemens AG Healthcare Sector Forchheim Germany Department of Radiology University of Utah Salt Lake City UT USA

While circular cone-beam computed tomography (CBCT) has numerous advantages in CT scanner design, the image reconstruction from circular cone-beam data is known to be approximate since the data sufficiency condition is not fulfilled. This is an unresolved issue yet and may become a problem if the number of detector rows is further increased in the future. The purpose of this work is to evaluate different CBCT reconstruction algorithms regarding their cone-beam artifact performance. For the comparison the two standard methods Feldkamp and SART are used. Additionally the factorization approach, which is designed for reconstruction with low cone-beam artifacts and a new method for cone-beam artifact reduction (CBAR) based on iterative filtered backprojection reconstruction are used. Among the compared algorithms, the Feldkamp reconstruction has the lowest computational load and shows the worst cone-beam artifacts while the factorization approach shows the best trade-off between cone-beam artifacts and computation time.

关键词： Phantoms Image reconstruction reconstruction algorithms Detectors Pixel Computed tomography Thorax

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FAST reconstruction algorithms FOR OPTICAL TOMOGRAPHY USING SPARSE MATRIX REPRESENTATIONS

FAST RECONSTRUCTION ALGORITHMS FOR OPTICAL TOMOGRAPHY USING ...

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IEEE International Symposium on Biomedical Imaging

作者： Guangzhi Cao Charles A. Bouman School of Electrical and Computer Engineering Purdue University West Lafayette IN USA

ISBN: (纸本)1424406714

This paper introduces a novel method for reconstructing optical tomography images using pre-computed transforms. Our approach is to pre-compute and store the inverse matrix required for MAP reconstruction using lossy source coding techniques. We show how lossy source coding techniques can be used to store the large and non-sparse matrix by applying a wavelet transform in the image space and appropriate orthonormal transforms in the sensor space. Lossy coding dramatically reduces the number of non-zero coefficients, thereby proportionately reducing both the required storage and computation time. However, if the number of sensor measurements is large, the storage and computation of the orthonormal transforms can become prohibitive. For this purpose, we introduce a general method for approximating any orthonormal transform by a series of sparse binary transforms. This sparse matrix transform technique is then used together with lossy coding to result in a fast reconstruction algorithm for optical tomography. Simulations indicate that the technique can dramatically reduce the storage and computation requirements in reconstruction by exploiting redundancy in the transformed matrices.

关键词： reconstruction algorithms Tomography Sparse matrices Optical sensors Image reconstruction Optical losses Source coding Wavelet transforms Image sensors Computational modeling

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Influence of Three reconstruction algorithms on the Estimation of the Standardize Uptake Value in ~(18)F-Fluoride PET

Influence of Three Reconstruction Algorithms on the Estimati...

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IEEE Nuclear Science Symposium

作者： Elisabetta Grecchi Kris Thielemans Gary J. Cook Charalampos Tsoumpas Division of Imaging Sciences and Biomedical Engineering King's College London UK King's College London London UK

In clinical practice accurate and precise quantification of PET images is of crucial importance especially for tumor staging when comparing pre- and post- treatment scans. For example a change in the standardized uptake values (SUV) of 10-20% can discriminate whether a treatment is effective or not. However, several factors cause degradation of the accuracy and precision of quantitative information. Iterative reconstruction methods can help towards more accurate tumor delineation and potentially quantification. However, the most commonly used reconstruction technique (Ordered Subset Expectation Maximization, OSEM) has pitfalls that are critical for quantification: bias (e.g. at early iterations and at low count statistics) and noise (e.g. at late iterations). The aim of this work is to evaluate the quantitative performance of existing reconstruction algorithms with acquired phantom data and PET/CT (~(18)F-fluoride and ~(11)C-choline) scans of patients with metastatic prostate cancer. Data are reconstructed with three different ordered subset (OS) methods: OSEM, OS Maximum A Posteriori One Step Late (OSMAPOSL) with the Median Root Prior and OS Separable Paraboloidal Surrogate (OSSPS) with the Quadratic Prior. These three algorithms are available with the open source Software for Tomographic Image reconstruction (STIR, ). We investigate the SUVmax and SUVmean for each of the three reconstruction methods and how they change over sub-iteration. Our findings show that the subset limit cycle behavior does not affect the comparison of the SUV estimates for identical acquisition. On the other hand fine parameter tuning is necessary to achieve better tradeoff between convergence, bias and noise.

关键词： Image reconstruction Positron emission tomography reconstruction algorithms Phantoms Standards

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