scholarly journals Extension of emission expectation maximization lookalike algorithms to Bayesian algorithms

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Gengsheng L. Zeng ◽  
Ya Li
Keyword(s):  
Computer Simulations ◽  
Expectation Maximization ◽  
Convergence Rates ◽  
Reconstruction Algorithms ◽  
Step Size ◽  
Transmission Tomography ◽  
Total Variation Norm ◽  
Simple Factor ◽  
Stable Performance ◽  
Image Reconstruction Algorithms

AbstractWe recently developed a family of image reconstruction algorithms that look like the emission maximum-likelihood expectation-maximization (ML-EM) algorithm. In this study, we extend these algorithms to Bayesian algorithms. The family of emission-EM-lookalike algorithms utilizes a multiplicative update scheme. The extension of these algorithms to Bayesian algorithms is achieved by introducing a new simple factor, which contains the Bayesian information. One of the extended algorithms can be applied to emission tomography and another to transmission tomography. Computer simulations are performed and compared with the corresponding un-extended algorithms. The total-variation norm is employed as the Bayesian constraint in the computer simulations. The newly developed algorithms demonstrate a stable performance. A simple Bayesian algorithm can be derived for any noise variance function. The proposed algorithms have properties such as multiplicative updating, non-negativity, faster convergence rates for bright objects, and ease of implementation. Our algorithms are inspired by Green’s one-step-late algorithm. If written in additive-update form, Green’s algorithm has a step size determined by the future image value, which is an undesirable feature that our algorithms do not have.

Incorporating known information into image reconstruction algorithms for transmission tomography

2002 ◽  
Author(s):  
Ryan Murphy ◽  
Joseph A. O'Sullivan ◽  
Jasenka Benac ◽  
Donald L. Snyder ◽  
Bruce R. Whiting ◽  
...  
Keyword(s):  
Image Reconstruction ◽  
Reconstruction Algorithms ◽  
Transmission Tomography ◽  
Image Reconstruction Algorithms

Three dimensional deblurring of transmitted-light brightfield micrographs

1993 ◽  
Vol 51 ◽  
pp. 564-565
Author(s):  
Santosh Bhattacharyya
Keyword(s):  
Image Reconstruction ◽  
Three Dimensional ◽  
Transmitted Light ◽  
Reconstruction Algorithms ◽  
3D Image Reconstruction ◽  
Structural Details ◽  
Spread Function ◽  
Early Testing ◽  
Linearized Model ◽  
Image Reconstruction Algorithms

Three dimensional microscopic structures play an important role in the understanding of various biological and physiological phenomena. Structural details of neurons, such as the density, caliber and volumes of dendrites, are important in understanding physiological and pathological functioning of nervous systems. Even so, many of the widely used stains in biology and neurophysiology are absorbing stains, such as horseradish peroxidase (HRP), and yet most of the iterative, constrained 3D optical image reconstruction research has concentrated on fluorescence microscopy. It is clear that iterative, constrained 3D image reconstruction methodologies are needed for transmitted light brightfield (TLB) imaging as well. One of the difficulties in doing so, in the past, has been in determining the point spread function of the system.We have been developing several variations of iterative, constrained image reconstruction algorithms for TLB imaging. Some of our early testing with one of them was reported previously. These algorithms are based on a linearized model of TLB imaging.

scholarly journals Radiation Dose Reduction in CT Torsion Measurement of the Lower Limb: Introduction of a New Ultra-Low Dose Protocol

Diagnostics ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 1209
Author(s):  
Gabriel Keller ◽  
Simon Götz ◽  
Mareen Sarah Kraus ◽  
Leonard Grünwald ◽  
Fabian Springer ◽  
...  
Keyword(s):  
Radiation Dose ◽  
Radiation Exposure ◽  
Lower Limb ◽  
Low Dose ◽  
Body Volume ◽  
Reconstruction Algorithms ◽  
Pelvic Radiograph ◽  
Lower Radiation Dose ◽  
Median Total Dose ◽  
Image Reconstruction Algorithms

This study analyzed the radiation exposure of a new ultra-low dose (ULD) protocol compared to a high-quality (HQ) protocol for CT-torsion measurement of the lower limb. The analyzed patients (n = 60) were examined in the period March to October 2019. In total, 30 consecutive patients were examined with the HQ and 30 consecutive patients with the new ULD protocol comprising automatic tube voltage selection, automatic exposure control, and iterative image reconstruction algorithms. Radiation dose parameters as well as the contrast-to-noise ratio (CNR) and diagnostic confidence (DC; rated by two radiologists) were analyzed and potential predictor variables, such as body mass index and body volume, were assessed. The new ULD protocol resulted in significantly lower radiation dose parameters, with a reduction of the median total dose equivalent to 0.17 mSv in the ULD protocol compared to 4.37 mSv in the HQ protocol (p < 0.001). Both groups showed no significant differences in regard to other parameters (p = 0.344–0.923). CNR was 12.2% lower using the new ULD protocol (p = 0.033). DC was rated best by both readers in every HQ CT and in every ULD CT. The new ULD protocol for CT-torsion measurement of the lower limb resulted in a 96% decrease of radiation exposure down to the level of a single pelvic radiograph while maintaining good image quality.

scholarly journals Comparison between silicon photomultiplier-based and conventional PET/CT in patients with suspected lung cancer—a pilot study

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Johan Economou Lundeberg ◽  
Jenny Oddstig ◽  
Ulrika Bitzén ◽  
Elin Trägårdh
Keyword(s):  
Lung Cancer ◽  
Expectation Maximization ◽  
Image Interpretation ◽  
Imaging Study ◽  
Reconstruction Algorithm ◽  
Tnm Staging ◽  
Reconstruction Algorithms ◽  
Silicon Photomultiplier ◽  
Standardized Uptake Values ◽  
Pet Ct

Abstract Background Lung cancer is one of the most common cancers in the world. Early detection and correct staging are fundamental for treatment and prognosis. Positron emission tomography with computed tomography (PET/CT) is recommended clinically. Silicon (Si) photomultiplier (PM)-based PET technology and new reconstruction algorithms are hoped to increase the detection of small lesions and enable earlier detection of pathologies including metastatic spread. The aim of this study was to compare the diagnostic performance of a SiPM-based PET/CT (including a new block-sequential regularization expectation maximization (BSREM) reconstruction algorithm) with a conventional PM-based PET/CT including a conventional ordered subset expectation maximization (OSEM) reconstruction algorithm. The focus was patients admitted for 18F-fluorodeoxyglucose (FDG) PET/CT for initial diagnosis and staging of suspected lung cancer. Patients were scanned on both a SiPM-based PET/CT (Discovery MI; GE Healthcare, Milwaukee, MI, USA) and a PM-based PET/CT (Discovery 690; GE Healthcare, Milwaukee, MI, USA). Standardized uptake values (SUV) and image interpretation were compared between the two systems. Image interpretations were further compared with histopathology when available. Results Seventeen patients referred for suspected lung cancer were included in our single injection, dual imaging study. No statically significant differences in SUVmax of suspected malignant primary tumours were found between the two PET/CT systems. SUVmax in suspected malignant intrathoracic lymph nodes was 10% higher on the SiPM-based system (p = 0.026). Good consistency (14/17 cases) between the PET/CT systems were found when comparing simplified TNM staging. The available histology results did not find any obvious differences between the systems. Conclusion In a clinical setting, the new SiPM-based PET/CT system with a new BSREM reconstruction algorithm provided a higher SUVmax for suspected lymph node metastases compared to the PM-based system. However, no improvement in lung cancer detection was seen.

scholarly journals Muon Tracing and Image Reconstruction Algorithms for Cosmic Ray Muon Computed Tomography

2019 ◽  
Vol 28 (1) ◽  
pp. 426-435 ◽  
Author(s):  
Zhengzhi Liu ◽  
Stylianos Chatzidakis ◽  
John M. Scaglione ◽  
Can Liao ◽  
Haori Yang ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Image Reconstruction ◽  
Cosmic Ray ◽  
Reconstruction Algorithms ◽  
Image Reconstruction Algorithms

scholarly journals A Benchmark Dataset and Deep Learning-Based Image Reconstruction for Electrical Capacitance Tomography

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3701 ◽  
Author(s):  
Jin Zheng ◽  
Jinku Li ◽  
Yi Li ◽  
Lihui Peng
Keyword(s):  
Machine Learning ◽  
Image Reconstruction ◽  
Benchmark Dataset ◽  
Electrical Capacitance Tomography ◽  
Electrical Capacitance ◽  
Reconstruction Algorithms ◽  
Reconstruction Methods ◽  
Image Reconstruction Algorithms ◽  
Capacitance Tomography ◽  
The Relationship

Electrical Capacitance Tomography (ECT) image reconstruction has developed for decades and made great achievements, but there is still a need to find a new theoretical framework to make it better and faster. In recent years, machine learning theory has been introduced in the ECT area to solve the image reconstruction problem. However, there is still no public benchmark dataset in the ECT field for the training and testing of machine learning-based image reconstruction algorithms. On the other hand, a public benchmark dataset can provide a standard framework to evaluate and compare the results of different image reconstruction methods. In this paper, a benchmark dataset for ECT image reconstruction is presented. Like the great contribution of ImageNet that transformed machine learning research, this benchmark dataset is hoped to be helpful for society to investigate new image reconstruction algorithms since the relationship between permittivity distribution and capacitance can be better mapped. In addition, different machine learning-based image reconstruction algorithms can be trained and tested by the unified dataset, and the results can be evaluated and compared under the same standard, thus, making the ECT image reconstruction study more open and causing a breakthrough.

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