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Learning Splines for Sparse Tomographic Reconstruction
[chapter]
2014
Lecture Notes in Computer Science
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We present a spline-based sparse tomographic reconstruction algorithm where content-adaptive patch sparsity is integrated into the reconstruction process. ...
The experiments show that enforcing patch-based sparsity, in terms of a learned dictionary, on higher order spline representations, outperforms existing methods that utilize pixelbasis for image representation ...
Fig. 6 . 6 Enforcing adaptive sparsity with spline learning, (d), outperforms sparse wavelet tomographic reconstruction, (c), in a few-view setup. ...
doi:10.1007/978-3-319-14249-4_1
fatcat:mjvb3qodm5e3hbwgyplrek4a7i
Sparse image representation for jet neutron and gamma tomography
2013
Fusion engineering and design
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The JET gamma/neutron profile monitor plasma coverage of the emissive region enables tomographic reconstruction. ...
A new reconstruction method, based on the sparse representation of the reconstructed image in an over-complete dictionary, has been developed and applied to JET neutron/gamma tomography. ...
Figure 2 : 2 The dictionary D learned from 50 tomographic reconstructions obtained using the ML method. This dictionary was used as an initial guess for solving Eq.4. ...
doi:10.1016/j.fusengdes.2013.03.024
fatcat:o5mnjtzmwvabhc7tdj3h54ne3y
CoronARe: A Coronary Artery Reconstruction Challenge
[chapter]
2017
Lecture Notes in Computer Science
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CoronARe ranks state-of-the-art methods in symbolic and tomographic coronary artery reconstruction from interventional C-arm rotational angiography. ...
Acknowledgement The authors would like to thank Zach Mullen, Kitware, for his support in hosting this challenge. ...
Submission formats for tomographic and symbolic reconstructions are held as simple as possible. For tomographic data we rely on the previously established CAVAREV format. ...
doi:10.1007/978-3-319-67564-0_10
fatcat:nze5kbdasvc2xmucyft4apmski
An over-complete dictionary based regularized reconstruction of a field of ensemble average propagators
2012
2012 9th IEEE International Symposium on Biomedical Imaging (ISBI)
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We present a dictionary learning framework for achieving a smooth EAP reconstruction across the field wherein, the dictionary atoms are learned from the data via an initial regression using adaptive spline ...
The formulation involves a two stage optimization where the first stage involves optimizing for a sparse dictionary using a K-SVD based updating and the second stage involves a quadratic cost function ...
The problem of searching for the proper kernel function as well as the sparse weighting coefficients can be solved by a dictionary learning paradigm. ...
doi:10.1109/isbi.2012.6235711
pmid:23227275
pmcid:PMC3515658
dblp:conf/isbi/YeVE12
fatcat:jx2m3ulevvhsvc63z76f6ralbe
Evaluation of interpolation methods for surface-based motion compensated tomographic reconstruction for cardiac angiographic C-arm data
2013
Medical Physics (Lancaster)
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In general, the framework of motion estimation using a surface model and motion interpolation to a dense MVF provides the ability for tomographic reconstruction using a motion compensation technique. ...
Conclusions: In this work, the influence of different motion interpolation methods on left ventricle motion compensated tomographic reconstructions was investigated. ...
The interpolation is used to compute a dense motion vector field from a sparse one for the purpose of motion compensation in left ventricle tomographic reconstruction. ...
doi:10.1118/1.4789593
pmid:23464287
pmcid:PMC3598768
fatcat:iwydjw2ufnhlvfiifpgijwjb2y
Cubic-Spline Interpolation for Sparse-View CT Image Reconstruction With Filtered Backprojection in Dynamic Myocardial Perfusion Imaging
2019
Tomography
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We interpolated the sparse-view (quarter) projections to a full-view setting using a cubic-spline interpolation method before applying FBP to reconstruct the DCE heart images (synthesized full-view). ...
This method may facilitate the application of sparse-view dynamic acquisition for ultralow-dose quantitative computed tomography (CT) myocardial perfusion (MP) imaging. ...
The cubic-spline view interpolation method allows the standard FBP algorithm to be used for sparse-view image reconstruction without the need of implementing iterative reconstruction algorithms such as ...
doi:10.18383/j.tom.2019.00013
pmid:31572791
pmcid:PMC6752292
fatcat:3ijqaul2svbfbaagjbbclijbce
Limited Tomography Reconstruction via Tight Frame and Sinogram Extrapolation
[article]
2016
arXiv
pre-print
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X-ray computed tomography (CT) is one of widely used diagnostic tools for medical and dental tomographic imaging of the human body. ...
In this paper, we consider two dimensional CT reconstruction using the horizontally truncated projections. ...
R Λ P u = f 0 . (10) When W is a B-spline framelet, then by [5] , (10) can be viewed as a finite difference approximation of sparse model based approach (7) with HOT k (u) for some k ∈ N. ...
arXiv:1602.07049v1
fatcat:tzkmsb6chfdv3hlgvh2qhsx654
Framelet pooling aided deep learning network: the method to process high dimensional medical data
2020
Machine Learning: Science and Technology
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The purpose of this paper is to introduce a framelet-pooling aided deep learning method for mitigating computational bundles caused by large dimensionality. ...
learning tasks. ...
Figure 8 . 8 Qualitative comparison of the proposed method for sparse-view CT reconstruction problem. ...
doi:10.1088/2632-2153/ab592b
fatcat:mep6loatdfbdxldpwaonjz2lhe
Deep Microlocal Reconstruction for Limited-Angle Tomography
[article]
2021
arXiv
pre-print
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We present a deep learning-based algorithm to jointly solve a reconstruction problem and a wavefront set extraction problem in tomographic imaging. ...
We find strong numerical evidence for the effectiveness of this approach. ...
Learned Primal-Dual network in (2.5) for tomographic reconstruction that was first introduced in [2] . ...
arXiv:2108.05732v1
fatcat:273mpr5zpzhnnibqhkks4vylqe
Compressed sensing for STEM tomography
2017
Ultramicroscopy
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We then propose a regularized tomographic reconstruction framework to recover volumes from RB-STEM measurements. ...
This application of compressed sensing principles to STEM paves the way for a practical implementation of RB-STEM and opens new perspectives for high-quality reconstructions in STEM tomography. (L. ...
Acknowledgments The authors acknowledge the PICT-IBiSA for providing access to their chemical imaging equipment. The work of L. Donati and M. ...
doi:10.1016/j.ultramic.2017.04.003
pmid:28411510
fatcat:3fsyvuecnnh27h32vlfjs3q63e
Front Matter: Volume 9413
2015
Medical Imaging 2015: Image Processing
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Base 36 numbering is employed for the last two digits and indicates the order of articles within the volume. Numbers start with 00, ...
sparse tomographic reconstruction with Besov priors [9413-14] SESSION 4 COMPRESSED SENSING/SPARSE METHODS 9413 0G Rank-sparsity constrained atlas construction and phenotyping [9413-15] 9413 0H Compressed ...
[9413-17] 9413 0J Alternating minimization algorithm with iteratively reweighted quadratic penalties for compressive transmission tomography [9413-18] SESSION 5 MACHINE LEARNING 9413 0K Revealing [9413 ...
doi:10.1117/12.2194368
fatcat:lbd4hy3s2rbodfhutf34bmarhu
Studies on the sparsifying operator in compressive digital holography
2017
Optics Express
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sparse wavefields perfectly, and the robustness of the reconstructions to additive noise and sparsity defects. ...
In particular, we recommend the CDF 9/7 and 17/11 wavelet transformations, as well as their reverse counterparts, because they yield sufficiently sparse representations for most accustomed wavefields in ...
We learn from this observation that the selected metrics only predict the reconstruction error coarsely. ...
doi:10.1364/oe.25.018656
pmid:29041062
fatcat:nfm4pbioqjct5lqcfqxakn3qcu
Deep Learning-Based Reconstruction of Interventional Tools from Four X-Ray Projections for Tomographic Interventional Guidance
[article]
2020
arXiv
pre-print
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In this work we propose a deep learning-based pipeline for real-time tomographic (four-dimensional) interventional guidance at acceptable dose levels. ...
Our pipeline is capable of reconstructing interventional tools from only four x-ray projections without the need for a patient prior with very high accuracy. ...
Fig. 1 . 1 Illustration of a deep learning-based tomographic interventional guidance. ...
arXiv:2009.10993v1
fatcat:2rfbgjjbvfgwjap6lhty3pwlpu
A Bayesian fusion model for space-time reconstruction of finely resolved velocities in turbulent flows from low resolution measurements
2015
Journal of Statistical Mechanics: Theory and Experiment
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The study of turbulent flows calls for measurements with high resolution both in space and in time. ...
The model is compared to other conventional methods such as Linear Stochastic Estimation and cubic spline interpolation. ...
Sparse LTHS and HTLS measurements are subsampled from HTHS data to learn the fusion model. HTHS is used as the ground truth to estimate reconstruction errors. ...
doi:10.1088/1742-5468/2015/10/p10008
fatcat:jn4ilca6hzgsbcyb6xuamdps4e
Dictionary Learning on the Manifold of Square Root Densities and Application to Reconstruction of Diffusion Propagator Fields
[chapter]
2013
Lecture Notes in Computer Science
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In this paper, we present a novel dictionary learning framework for data lying on the manifold of square root densities and apply it to the reconstruction of diffusion propagator (DP) fields given a multi-shell ...
Unlike most of the existing dictionary learning algorithms which rely on the assumption that the data points are vectors in some Euclidean space, our dictionary learning algorithm is designed to incorporate ...
imaging (DSI) proposed in [12] and the tomographic reconstruction methods in [13] [14] [15] . ...
doi:10.1007/978-3-642-38868-2_52
pmid:24684004
pmcid:PMC4000552
fatcat:qhrrc6a5uzh3dcoqmsuq3ike5y
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