A new method for inpainting of depth maps from time-of-flight sensors based on a modified closing by reconstruction algorithm

Author(s):  
Marco Antonio Garduño-Ramón ◽  
Ivan Ramon Terol-Villalobos ◽  
Roque Alfredo Osornio-Rios ◽  
Luis Alberto Morales-Hernandez
2018 ◽  
Vol 25 (3) ◽  
pp. 587-592 ◽  
Author(s):  
Liang Chen ◽  
Qiang Xiao ◽  
Wei Liang ◽  
Jingxian Hong ◽  
Xingjiang Zou

Abstract Lamb wave tomography can be used to evaluate structural integrity. The time-of-flight (TOF) data are usually recorded as input to the reconstruction algorithm. For composite materials, TOF estimation is complicated due to their anisotropy. To reduce the effects of anisotropy on image reconstruction, the TOF data of flawed plates are revised according to baseline data obtained from an unflawed plate. Tomographic images are reconstructed using the original and revised TOF data, respectively. Results show that images reconstructed using the revised TOF data have better visual quality and that TOF data revision can substantially reduce the artifacts resulting from anisotropy in defect detection of composite materials.


2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Paulo R. R. V. Caribé ◽  
M. Koole ◽  
Yves D’Asseler ◽  
B. Van Den Broeck ◽  
S. Vandenberghe

Abstract Purpose Q.Clear is a block sequential regularized expectation maximization (BSREM) penalized-likelihood reconstruction algorithm for PET. It tries to improve image quality by controlling noise amplification during image reconstruction. In this study, the noise properties of this BSREM were compared to the ordered-subset expectation maximization (OSEM) algorithm for both phantom and patient data acquired on a state-of-the-art PET/CT. Methods The NEMA IQ phantom and a whole-body patient study were acquired on a GE DMI 3-rings system in list mode and different datasets with varying noise levels were generated. Phantom data was evaluated using four different contrast ratios. These were reconstructed using BSREM with different β-factors of 300–3000 and with a clinical setting used for OSEM including point spread function (PSF) and time-of-flight (TOF) information. Contrast recovery (CR), background noise levels (coefficient of variation, COV), and contrast-to-noise ratio (CNR) were used to determine the performance in the phantom data. Findings based on the phantom data were compared with clinical data. For the patient study, the SUV ratio, metabolic active tumor volumes (MATVs), and the signal-to-noise ratio (SNR) were evaluated using the liver as the background region. Results Based on the phantom data for the same count statistics, BSREM resulted in higher CR and CNR and lower COV than OSEM. The CR of OSEM matches to the CR of BSREM with β = 750 at high count statistics for 8:1. A similar trend was observed for the ratios 6:1 and 4:1. A dependence on sphere size, counting statistics, and contrast ratio was confirmed by the CNR of the ratio 2:1. BSREM with β = 750 for 2.5 and 1.0 min acquisition has comparable COV to the 10 and 5.0 min acquisitions using OSEM. This resulted in a noise reduction by a factor of 2–4 when using BSREM instead of OSEM. For the patient data, a similar trend was observed, and SNR was reduced by at least a factor of 2 while preserving contrast. Conclusion The BSREM reconstruction algorithm allowed a noise reduction without a loss of contrast by a factor of 2–4 compared to OSEM reconstructions for all data evaluated. This reduction can be used to lower the injected dose or shorten the acquisition time.


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