scholarly journals A Doubly Constrained TV Algorithm for Image Reconstruction

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Zhiwei Qiao ◽  
Gage Redler ◽  
Boris Epel ◽  
Howard Halpern
Keyword(s):  
Image Reconstruction ◽  
Convergence Rate ◽  
Simulated Data ◽  
Reconstruction Algorithm ◽  
Model Parameters ◽  
Resonance Imaging ◽  
Reconstruction Accuracy ◽  
Paramagnetic Resonance ◽  
Positron Emission ◽  
Tv Model

Purpose. The total variation (TV) minimization algorithm is an effective image reconstruction algorithm capable of accurately reconstructing images from sparse and/or noisy data. The TV model consists of two terms: a data fidelity term and a TV regularization term. Two constrained TV models, data divergence-constrained TV minimization (DDcTV) and TV-constrained data divergence minimization (TVcDM), have been successfully applied to computed tomography (CT) and electron paramagnetic resonance imaging (EPRI). In this work, we propose a new constrained TV model, a doubly constrained TV (dcTV) model, which has the potential to further improve the reconstruction accuracy for the two terms which are both of constraint forms. Methods. We perform an inverse crime study to validate the model and its Chambolle-Pock (CP) solver and characterize the performance of the dcTV-CP algorithm in the context of CT. To demonstrate the superiority of the dcTV model, we compare the convergence rate and the reconstruction accuracy with the DDcTV and TVcDM models via simulated data. Results and Conclusions. The performance-characterizing study shows that the dcTV-CP algorithm is an accurate and convergent algorithm, with the model parameters impacting the reconstruction accuracy and the algorithm parameters impacting the convergence path and rate. The comparison studies show that the dcTV-CP algorithm has a relatively fast convergence rate and can achieve higher reconstruction accuracy from sparse projections or noisy projections relative to the other two single-constrained TV models. The knowledge and insights gained in the work may be utilized in the application of the new model in other imaging modalities including divergence-beam CT, magnetic resonance imaging (MRI), positron emission tomography (PET), and EPRI.

scholarly journals Review and Prospect: Artificial Intelligence in Advanced Medical Imaging

2021 ◽  
Vol 1 ◽  
Author(s):  
Shanshan Wang ◽  
Guohua Cao ◽  
Yan Wang ◽  
Shu Liao ◽  
Qian Wang ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Image Reconstruction ◽  
Medical Imaging ◽  
Imaging Data ◽  
Resonance Imaging ◽  
Volumetric Imaging ◽  
Positron Emission ◽  
Potential Applications ◽  
Magnetic Resonance Imaging Mri

Artificial intelligence (AI) as an emerging technology is gaining momentum in medical imaging. Recently, deep learning-based AI techniques have been actively investigated in medical imaging, and its potential applications range from data acquisition and image reconstruction to image analysis and understanding. In this review, we focus on the use of deep learning in image reconstruction for advanced medical imaging modalities including magnetic resonance imaging (MRI), computed tomography (CT), and positron emission tomography (PET). Particularly, recent deep learning-based methods for image reconstruction will be emphasized, in accordance with their methodology designs and performances in handling volumetric imaging data. It is expected that this review can help relevant researchers understand how to adapt AI for medical imaging and which advantages can be achieved with the assistance of AI.

Rapid Gradient Projection Algorithm for Image Reconstruction

2014 ◽  
Vol 530-531 ◽  
pp. 443-446
Author(s):  
Hai Xia Yan ◽  
Yan Jun Liu
Keyword(s):  
Image Reconstruction ◽  
Compressed Sensing ◽  
Gradient Descent ◽  
Target Function ◽  
Reconstruction Algorithm ◽  
Gradient Projection ◽  
Projection Algorithm ◽  
Descent Direction ◽  
Reconstruction Accuracy ◽  
Gradient Projection Algorithm

In order to improve the speed of compressed sensing image reconstruction algorithm, a rapid gradient projection algorithm for image reconstruction is proposed. In traditional Gradient Projection algorithm, the pursuit direction is alternating, in rapid gradient projection algorithm, we use the Newton's method to calculate the gradient descent direction, thus the constraints conditions of gradient projection is satisfied. And the target function is updated in each iteration computing. The effect of approximation matrix to target function is reduced. The iteration computing times is reduced, because the algorithm works in accurate search direction. Experiment results show that, compared with the GPSR algorithm, the RGPSR algorithm improves the signals reconstruction accuracy, improves PSNR of reconstruction signals, and exhibits higher robustness under different noise intensities.

scholarly journals MRI-guided PET Reconstruction with Adaptive Prior Strength: Study on Image Quality at Different Levels of Statistics

2021 ◽  
Author(s):  
Jorge Cabello ◽  
Udunna Anazodo
Keyword(s):  
Image Reconstruction ◽  
Image Quality ◽  
Reconstruction Algorithm ◽  
Optimization Methods ◽  
Low Noise ◽  
Measured Data ◽  
Similarity Coefficients ◽  
Positron Emission ◽  
Pet Reconstruction ◽  
Different Levels

Abstract BackgroundThe combination of magnetic resonance imaging (MRI)-anatomical information with positron emission tomography (PET) image reconstruction has been shown to improve PET image quality in terms of spatial resolution and image noise, especially in brain PET imaging. There are different approaches to combine MRI and PET available, being the use of a Bayesian framework the most extended. Generally, the strength of the prior is controlled by a hyperparameter that needs to be tuned depending on the acquired statistics/counts and the desired image quality in the resulting PET image. However, comparisons between methods is scant, and it is not clear how sensitive they are to the different levels of statistics that can be measured in a PET scan. MethodsIn this work we employed maximum a posteriori (MAP) reconstruction with MRI information to guide the PET reconstruction, and evaluated the performance of several prior models and optimization methods with a fixed and adaptive hyperparameter. Different simulated scenarios, and measured data using different radiotracers at different levels of statistics were employed for the evaluation. Comparisons in image quality and quanti cation between methods were performed in different brain cortical and sub-cortical regions. ResultsSimulated data showed that an adaptive hyperparameter consistently outperformed a fixed hyperparameter for every image reconstruction algorithm implemented. The best performance was achieved with a model combining the Bowsher prior weighted by similarity coefficients based on joint entropy between PET and MRI. One-step-late (OSL) and preconditioned gradient ascent (PGA) optimization methods performed similarly at any level of statistics and number of iterations, so long as the hyperparameter was adaptive. ConclusionsResults with simulated and measured data agreed that MAP reconstruction outperformed OSEM reconstruction, especially at low level of statistics, without any need of tuning. High resolution and low noise images were obtained using MAP reconstruction for 5{30 minutes scan times, showing negligible image quality difference for different radiotracers.

scholarly journals Synergistic motion compensation strategies for positron emission tomography when acquired simultaneously with magnetic resonance imaging

2021 ◽  
Vol 379 (2204) ◽  
pp. 20200207
Author(s):  
Irene Polycarpou ◽  
Georgios Soultanidis ◽  
Charalampos Tsoumpas
Keyword(s):  
Magnetic Resonance Imaging ◽  
Positron Emission Tomography ◽  
Magnetic Resonance ◽  
Image Reconstruction ◽  
Motion Correction ◽  
Kinetic Modelling ◽  
Image Resolution ◽  
Emission Tomography ◽  
Resonance Imaging ◽  
Positron Emission

Subject motion in positron emission tomography (PET) is a key factor that degrades image resolution and quality, limiting its potential capabilities. Correcting for it is complicated due to the lack of sufficient measured PET data from each position. This poses a significant barrier in calculating the amount of motion occurring during a scan. Motion correction can be implemented at different stages of data processing either during or after image reconstruction, and once applied accurately can substantially improve image quality and information accuracy. With the development of integrated PET-MRI (magnetic resonance imaging) scanners, internal organ motion can be measured concurrently with both PET and MRI. In this review paper, we explore the synergistic use of PET and MRI data to correct for any motion that affects the PET images. Different types of motion that can occur during PET-MRI acquisitions are presented and the associated motion detection, estimation and correction methods are reviewed. Finally, some highlights from recent literature in selected human and animal imaging applications are presented and the importance of motion correction for accurate kinetic modelling in dynamic PET-MRI is emphasized. This article is part of the theme issue ‘Synergistic tomographic image reconstruction: part 2’.

Sign in / Sign up

Export Citation Format

Share Document