Stabilizing deep tomographic reconstruction: Part A. Hybrid framework and experimental results
Patterns, 2022 Summary: A recent PNAS paper reveals that several popular deep reconstruction networks are unstable. Specifically, three kinds of instabilities were reported: (1) strong image artefacts from tiny perturbations, (2) small features missed in a deeply ...
Weiwen Wu +9 more
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Approximate k-space models and Deep Learning for fast photoacoustic reconstruction [PDF]
, 2018 We present a framework for accelerated iterative reconstructions using a fast and approximate forward model that is based on k-space methods for photoacoustic tomography. The approximate model introduces aliasing artefacts in the gradient information for
Arridge, Simon +6 more
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Fast Parallel Imaging Reconstruction Method Based on SIDWT and Iterative Self-Consistency
Shanghai Jiaotong Daxue xuebao, 2023 To improve the reconstruction speed of parallel magnetic resonance imaging, an efficient reconstruction method named fSIDWT-SPIRiT is proposed based on shift-invariant discrete wavelets transform (SIDWT) and the iterative self-consistent parallel imaging
DUAN Jizhong, QIAN Qingqing
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An Accelerated Iterative Cone Beam Computed Tomography Image Reconstruction Approach
مجلة النهرين للعلوم الهندسية, 2019 Cone-beam computed tomography (CBCT) is an indispensable method that reconstructs three dimensional (3D) images. CBCT employs a mathematical technique of reconstruction, which reveals the anatomy of the patient’s body through the measurements of ...
Shimaa Abdulsalam Khazal +1 more
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Model-Driven Enhanced Analytic Learned Iterative Shrinkage Threshold Algorithm
IEEE Access, 2022 The application of deep learning in compressed sensing reconstruction has achieved some excellent results. The deep neural network based on iterative algorithm can not only reflect the excellent performance of deep learning, but also reflect the ...
Jun Li +4 more
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Direct 3D Tomographic Reconstruction and Phase-Retrieval of Far-Field Coherent Diffraction Patterns [PDF]
, 2019 We present an alternative numerical reconstruction algorithm for direct tomographic reconstruction of a sample refractive indices from the measured intensities of its far-field coherent diffraction patterns.
Andersen, Martin Skovgaard +3 more
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Definitions, 2020 Parallel MRI techniques utilize the inherent encoding effect of receiver coil sensitivity for complementing gradient-driven Fourier encoding. As a consequence of this hybrid encoding approach, parallel techniques require advanced reconstruction ...
K. Pruessmann
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The Impact of Iterative Reconstruction on Computed Tomography Radiation Dosimetry: Evaluation in a Routine Clinical Setting. [PDF]
PLoS ONE, 2015 To evaluate the effect of introduction of iterative reconstruction as a mandated software upgrade on radiation dosimetry in routine clinical practice over a range of computed tomography examinations.Random samples of scanning data were extracted from a ...
Rachael E Moorin +3 more
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Low-dose CT Reconstruction Algorithm Based on Iterative Asymmetric Blind Spot Network [PDF]
Jisuanji kexue, 2023 Aiming at the problem that the method of low-dose CT reconstruction by machine learning method relies too much on pairwise legends,a low-dose CT reconstruction algorithm based on iterative asymmetric blind spot network is proposed.Firstly,low-dose CT is ...
GUO Guangxing, YIN Guimei, LIU Chenxu, DUAN Yonghong, QIANG Yan, WANG Yanfei, WANG Tao
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ADMM-SVNet: An ADMM-Based Sparse-View CT Reconstruction Network
Photonics, 2022 In clinical medical applications, sparse-view computed tomography (CT) imaging is an effective method for reducing radiation doses. The iterative reconstruction method is usually adopted for sparse-view CT.
Sukai Wang, Xuan Li, Ping Chen
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