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Author(s):  
Tao Sun ◽  
Yaping Wu ◽  
Yan Bai ◽  
Zhenguo Wang ◽  
Chushu Shen ◽  
...  

Abstract As a non-invasive imaging tool, Positron Emission Tomography (PET) plays an important role in brain science and disease research. Dynamic acquisition is one way of brain PET imaging. Its wide application in clinical research has often been hindered by practical challenges, such as patient involuntary movement, which could degrade both image quality and the accuracy of the quantification. This is even more obvious in scans of patients with neurodegeneration or mental disorders. Conventional motion compensation methods were either based on images or raw measured data, were shown to be able to reduce the effect of motion on the image quality. As for a dynamic PET scan, motion compensation can be challenging as tracer kinetics and relatively high noise can be present in dynamic frames. In this work, we propose an image-based inter-frame motion compensation approach specifically designed for dynamic brain PET imaging. Our method has an iterative implementation that only requires reconstructed images, based on which the inter-frame subject movement can be estimated and compensated. The method utilized tracer-specific kinetic modelling and can deal with simple and complex movement patterns. The synthesized phantom study showed that the proposed method can compensate for the simulated motion in scans with 18F-FDG, 18F-Fallypride and 18F-AV45. Fifteen dynamic 18F-FDG patient scans with motion artifacts were also processed. The quality of the recovered image was superior to the one of the non-corrected images and the corrected images with other image-based methods. The proposed method enables retrospective image quality control for dynamic brain PET imaging, hence facilitates the applications of dynamic PET in clinics and research.


2021 ◽  
Author(s):  
Seif Eddine Guerbas ◽  
Nathan Crombez ◽  
Guillaume Caron ◽  
El Mustapha Mouaddib

2021 ◽  
Vol 8 (1) ◽  
pp. 93-103
Author(s):  
Jin-Liang Wu ◽  
Jun-Jie Shi ◽  
Lei Zhang

AbstractImage and video processing based on geometric principles typically changes the rectangular shape of video frames to an irregular shape. This paper presents a warping based approach for rectangling such irregular frame boundaries in space and time, i.e., making them rectangular again. To reduce geometric distortion in the rectangling process, we employ content-preserving deformation of a mesh grid with line structures as constraints to warp the frames. To conform to the original inter-frame motion, we keep feature trajectory distribution as constraints during motion compensation to ensure stability after warping the frames. Such spatially and temporally optimized warps enable the output of regular rectangular boundaries for the video frames with low geometric distortion and jitter. Our experiments demonstrate that our approach can generate plausible video rectangling results in a variety of applications.


2021 ◽  
Author(s):  
Azar Tolouee

Dynamic magnetic resonance imaging requires rapid data acquisition to provide an appropriate combination of spatial and temporal resolution, and volumetric coverage for clinical studies. In the most challenging clinical situations, conventional dynamic MR scanners are often incapable of simultaneously providing images with sufficient temporal resolution and high spatial resolution. In practice, clinicians are often forced to compromise between these parameters, often resulting in sub-optimal performance. Cardiac MRI is the most challenging and inspiring dynamic MRI application. In cardiac MRI, the main challenge is the sensitivity of reconstruction methods to large inter frame motion. The reconstructions often suffer from temporal blurring and motion related artifacts at high acceleration factors. In this dissertation, three novel approaches are proposed specifically designed to minimize the sensitivity of the reconstructions to inter frame motion. First, a compressed sensing (CS) based image reconstruction method in conjunction with spiral sampling is developed for the reconstruction of dynamic MRI data from highly accelerated / under-sampled Fourier measurements. In the second algorithm, the problem of motion artifacts including respiratory motion and cardiac motion in compressed sensing reconstructions is addressed. A motion estimation/motion compensation algorithm based on a modified search that aids block matching and results in improved residual reconstruction is incorporated into the CS reconstruction for dynamic MRI. In the third algorithm, a novel formulation for the joint estimation of the deformation and the dynamic images in cardiac cine MR imaging is introduced. The motion estimation algorithm estimates the deformation by registering the dynamic data to a reference dataset that is free of respiratory motion, which is derived from the measurements themselves. A variable splitting framework is used to minimize the objective function, and thus derive the deformation and the dynamic images. The validation of the proposed algorithms is illustrated using a numerical phantom and in-vivo cine MRI data to show the feasibility in precisely recovering cardiac MRI data from extensively under-sampled data.


2021 ◽  
Author(s):  
Azar Tolouee

Dynamic magnetic resonance imaging requires rapid data acquisition to provide an appropriate combination of spatial and temporal resolution, and volumetric coverage for clinical studies. In the most challenging clinical situations, conventional dynamic MR scanners are often incapable of simultaneously providing images with sufficient temporal resolution and high spatial resolution. In practice, clinicians are often forced to compromise between these parameters, often resulting in sub-optimal performance. Cardiac MRI is the most challenging and inspiring dynamic MRI application. In cardiac MRI, the main challenge is the sensitivity of reconstruction methods to large inter frame motion. The reconstructions often suffer from temporal blurring and motion related artifacts at high acceleration factors. In this dissertation, three novel approaches are proposed specifically designed to minimize the sensitivity of the reconstructions to inter frame motion. First, a compressed sensing (CS) based image reconstruction method in conjunction with spiral sampling is developed for the reconstruction of dynamic MRI data from highly accelerated / under-sampled Fourier measurements. In the second algorithm, the problem of motion artifacts including respiratory motion and cardiac motion in compressed sensing reconstructions is addressed. A motion estimation/motion compensation algorithm based on a modified search that aids block matching and results in improved residual reconstruction is incorporated into the CS reconstruction for dynamic MRI. In the third algorithm, a novel formulation for the joint estimation of the deformation and the dynamic images in cardiac cine MR imaging is introduced. The motion estimation algorithm estimates the deformation by registering the dynamic data to a reference dataset that is free of respiratory motion, which is derived from the measurements themselves. A variable splitting framework is used to minimize the objective function, and thus derive the deformation and the dynamic images. The validation of the proposed algorithms is illustrated using a numerical phantom and in-vivo cine MRI data to show the feasibility in precisely recovering cardiac MRI data from extensively under-sampled data.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sagi Monin ◽  
Evgeny Hahamovich ◽  
Amir Rosenthal

AbstractSingle-pixel imaging (SPI) enables the visualization of objects with a single detector by using a sequence of spatially modulated illumination patterns. For natural images, the number of illumination patterns may be smaller than the number of pixels when compressed-sensing algorithms are used. Nonetheless, the sequential nature of the SPI measurement requires that the object remains static until the signals from all the required patterns have been collected. In this paper, we present a new approach to SPI that enables imaging scenarios in which the imaged object, or parts thereof, moves within the imaging plane during data acquisition. Our algorithms estimate the motion direction from inter-frame cross-correlations and incorporate it in the reconstruction model. Moreover, when the illumination pattern is cyclic, the motion may be estimated directly from the raw data, further increasing the numerical efficiency of the algorithm. A demonstration of our approach is presented for both numerically simulated and measured data.


Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2608
Author(s):  
Héctor Espinós-Morató ◽  
David Cascales-Picó ◽  
Marina Vergara ◽  
Ángel Hernández-Martínez ◽  
José María Benlloch Baviera ◽  
...  

Positron emission tomography (PET) is a functional non-invasive imaging modality that uses radioactive substances (radiotracers) to measure changes in metabolic processes. Advances in scanner technology and data acquisition in the last decade have led to the development of more sophisticated PET devices with good spatial resolution (1–3 mm of full width at half maximum (FWHM)). However, there are involuntary motions produced by the patient inside the scanner that lead to image degradation and potentially to a misdiagnosis. The adverse effect of the motion in the reconstructed image increases as the spatial resolution of the current scanners continues improving. In order to correct this effect, motion correction techniques are becoming increasingly popular and further studied. This work presents a simulation study of an image motion correction using a frame-based algorithm. The method is able to cut the acquired data from the scanner in frames, taking into account the size of the object of study. This approach allows working with low statistical information without losing image quality. The frames are later registered using spatio-temporal registration developed in a multi-level way. To validate these results, several performance tests are applied to a set of simulated moving phantoms. The results obtained show that the method minimizes the intra-frame motion, improves the signal intensity over the background in comparison with other literature methods, produces excellent values of similarity with the ground-truth (static) image and is able to find a limit in the patient-injected dose when some prior knowledge of the lesion is present.


2021 ◽  
Author(s):  
Vertti Tarvus ◽  
Lucile Turc ◽  
Markus Battarbee ◽  
Jonas Suni ◽  
Xóchitl Blanco-Cano ◽  
...  

<p>Foreshock cavitons are transient structures forming in Earth's foreshock as a result of non-linear interaction of ultra-low frequency waves. Cavitons are characterised by simultaneous density and magnetic field depressions with sizes of the order of 1 Earth radius. These transients are advected by the solar wind towards the bow shock, where they may accumulate shock-reflected suprathermal ions and become spontaneous hot flow anomalies (SHFAs), which are characterised by an enhanced temperature and a perturbed bulk flow inside them.<br>    Both spacecraft measurements and hybrid simulations have shown that while cavitons and SHFAs are carried towards the bow shock by the solar wind, their motion in the solar wind rest frame is directed upstream. In this work, we have made a statistical analysis of the propagation properties of cavitons and SHFAs using Vlasiator, a hybrid-Vlasov simulation model. In agreement with previous studies, we find the transients propagating upstream in the solar wind rest frame. Our results show that the solar wind rest frame motion of cavitons is aligned with the direction of the interplanetary magnetic field, while the motion of SHFAs deviates from this direction. We find that SHFAs have a faster solar wind rest frame propagation speed than cavitons, which is due to an increase in the sound speed near the bow shock, affecting the speed of the waves in the foreshock.</p>


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