scholarly journals MR-PET head motion correction based on co-registration of multi-contrast MR images

2018 ◽  
Author(s):  
Zhaolin Chen ◽  
Francesco Sforazzini ◽  
Jakub Baran ◽  
Thomas Close ◽  
N. Jon Shah ◽  
...  
Keyword(s):  
Motion Correction ◽  
Estimation Method ◽  
Correction Method ◽  
Head Motion ◽  
Mr Images ◽  
Image Sharpness ◽  
Positron Emission ◽  
Quantitative Accuracy ◽  
Slow Infusion ◽  
Pet Image Reconstruction

AbstractHead motion is a major source of image artefacts in neuroimaging studies and can lead to degradation of the quantitative accuracy of reconstructed PET images. Simultaneous Magnetic Resonance-Positron Emission Tomography (MR-PET) makes it possible to estimate head motion information from high-resolution MR images and then correct motion artefacts in PET images. In this paper, we introduce a fully automated PET motion correction method, MR-guided MAF, based on the co-registration of multi-contrast MR images. The performance of the MR-guided MAF method was evaluated using MR-PET data acquired from a cohort of ten healthy participants who received a slow infusion of fluorodeoxyglucose ([18-F]FDG). Compared with conventional methods, MR guided PET image reconstruction can reduce head motion introduced artefacts and improve the image sharpness and quantitative accuracy of PET images acquired using simultaneous MR-PET scanners. The fully automated motion estimation method has been implemented as a publicly available web-service.

scholarly journals MR-PET head motion correction based on co-registration of multicontrast MR images

2019 ◽  
Author(s):  
Zhaolin Chen ◽  
Francesco Sforazzini ◽  
Jakub Baran ◽  
Thomas Close ◽  
Nadim Jon Shah ◽  
...  
Keyword(s):  
Motion Correction ◽  
Head Motion ◽  
Mr Images

Motion correction in PET/CT

2005 ◽  
Vol 44 (S 01) ◽  
pp. S46-S50 ◽  
Author(s):  
M. Dawood ◽  
N. Lang ◽  
F. Büther ◽  
M. Schäfers ◽  
O. Schober ◽  
...  
Keyword(s):  
Optical Flow ◽  
Motion Correction ◽  
Motion Vector ◽  
Emission Tomography ◽  
Cardiac Pet ◽  
Novel Approach ◽  
Positron Emission ◽  
Pet Ct ◽  
Flow Algorithm ◽  
Motion Vector Field

Summary:Motion in PET/CT leads to artifacts in the reconstructed PET images due to the different acquisition times of positron emission tomography and computed tomography. The effect of motion on cardiac PET/CT images is evaluated in this study and a novel approach for motion correction based on optical flow methods is outlined. The Lukas-Kanade optical flow algorithm is used to calculate the motion vector field on both simulated phantom data as well as measured human PET data. The motion of the myocardium is corrected by non-linear registration techniques and results are compared to uncorrected images.

scholarly journals Influence of patient motion on quantitative accuracy in cardiac 15O-water positron emission tomography

2021 ◽  
Author(s):  
Jonny Nordström ◽  
Hendrik J. Harms ◽  
Tanja Kero ◽  
Jens Sörensen ◽  
Mark Lubberink
Keyword(s):  
Motion Detection ◽  
Detection Algorithm ◽  
Clinical Effects ◽  
Average Deviation ◽  
Patient Motion ◽  
Motion Data ◽  
Positron Emission ◽  
Quantitative Accuracy ◽  
Pet Ct ◽  
Using Data

Abstract Background Patient motion is a common problem during cardiac PET. The purpose of the present study was to investigate to what extent motions influence the quantitative accuracy of cardiac 15O-water PET/CT and to develop a method for automated motion detection. Method Frequency and magnitude of motion was assessed visually using data from 50 clinical 15O-water PET/CT scans. Simulations of 4 types of motions with amplitude of 5 to 20 mm were performed based on data from 10 scans. An automated motion detection algorithm was evaluated on clinical and simulated motion data. MBF and PTF of all simulated scans were compared to the original scan used as reference. Results Patient motion was detected in 68% of clinical cases by visual inspection. All observed motions were small with amplitudes less than half the LV wall thickness. A clear pattern of motion influence was seen in the simulations with a decrease of myocardial blood flow (MBF) in the region of myocardium to where the motion was directed. The perfusable tissue fraction (PTF) trended in the opposite direction. Global absolute average deviation of MBF was 3.1% ± 1.8% and 7.3% ± 6.3% for motions with maximum amplitudes of 5 and 20 mm, respectively. Automated motion detection showed a sensitivity of 90% for simulated motions ≥ 10 mm but struggled with the smaller (≤ 5 mm) simulated (sensitivity 45%) and clinical motions (accuracy 48%). Conclusion Patient motion can impair the quantitative accuracy of MBF. However, at typically occurring levels of patient motion, effects are similar to or only slightly larger than inter-observer variability, and downstream clinical effects are likely negligible.

scholarly journals Accurate Transmission-Less Attenuation Correction Method for Amyloid-β Brain PET Using Deep Neural Network

Electronics ◽  
2021 ◽  
Vol 10 (15) ◽  
pp. 1836
Author(s):  
Bo-Hye Choi ◽  
Donghwi Hwang ◽  
Seung-Kwan Kang ◽  
Kyeong-Yun Kim ◽  
Hongyoon Choi ◽  
...  
Keyword(s):  
Neural Network ◽  
Attenuation Correction ◽  
Complex Structure ◽  
Scatter Correction ◽  
Amyloid Β ◽  
Correction Method ◽  
Percentage Error ◽  
Similarity Coefficients ◽  
Brain Pet ◽  
Positron Emission

The lack of physically measured attenuation maps (μ-maps) for attenuation and scatter correction is an important technical challenge in brain-dedicated stand-alone positron emission tomography (PET) scanners. The accuracy of the calculated attenuation correction is limited by the nonuniformity of tissue composition due to pathologic conditions and the complex structure of facial bones. The aim of this study is to develop an accurate transmission-less attenuation correction method for amyloid-β (Aβ) brain PET studies. We investigated the validity of a deep convolutional neural network trained to produce a CT-derived μ-map (μ-CT) from simultaneously reconstructed activity and attenuation maps using the MLAA (maximum likelihood reconstruction of activity and attenuation) algorithm for Aβ brain PET. The performance of three different structures of U-net models (2D, 2.5D, and 3D) were compared. The U-net models generated less noisy and more uniform μ-maps than MLAA μ-maps. Among the three different U-net models, the patch-based 3D U-net model reduced noise and cross-talk artifacts more effectively. The Dice similarity coefficients between the μ-map generated using 3D U-net and μ-CT in bone and air segments were 0.83 and 0.67. All three U-net models showed better voxel-wise correlation of the μ-maps compared to MLAA. The patch-based 3D U-net model was the best. While the uptake value of MLAA yielded a high percentage error of 20% or more, the uptake value of 3D U-nets yielded the lowest percentage error within 5%. The proposed deep learning approach that requires no transmission data, anatomic image, or atlas/template for PET attenuation correction remarkably enhanced the quantitative accuracy of the simultaneously estimated MLAA μ-maps from Aβ brain PET.

Integrated positron emission tomography and magnetic resonance imaging–guided resection of brain tumors: a report of 103 consecutive procedures

2006 ◽  
Vol 104 (2) ◽  
pp. 238-253 ◽  
Author(s):  
Benoît Pirotte ◽  
Serge Goldman ◽  
Olivier Dewitte ◽  
Nicolas Massager ◽  
David Wikler ◽  
...  
Keyword(s):  
Positron Emission Tomography ◽  
Brain Tumors ◽  
Mr Imaging ◽  
Tumor Resection ◽  
Tracer Uptake ◽  
Emission Tomography ◽  
Mr Images ◽  
Pet Tracer ◽  
Positron Emission ◽  
Pet Scanning

Object The aim of this study was to evaluate the integration of positron emission tomography (PET) scanning data into the image-guided resection of brain tumors. Methods Positron emission tomography scans obtained using fluorine-18 fluorodeoxyglucose (FDG) and l-[methyl-11C]methionine (MET) were combined with magnetic resonance (MR) images in the navigational planning of 103 resections of brain tumors (63 low-grade gliomas [LGGs] and 40 high-grade gliomas [HGGs]). These procedures were performed in 91 patients (57 males and 34 females) in whom tumor boundaries could not be accurately identified on MR images for navigation-based resection. The level and distribution of PET tracer uptake in the tumor were analyzed to define the lesion contours, which in turn yielded a PET volume. The PET scanning–demonstrated lesion volume was subsequently projected onto MR images and compared with MR imaging data (MR volume) to define a final target volume for navigation-based resection—the tumor contours were displayed in the microscope’s eyepiece. Maximal tumor resection was accomplished in each case, with the intention of removing the entire area of abnormal metabolic activity visualized during surgical planning. Early postoperative MR imaging and PET scanning studies were performed to assess the quality of tumor resection. Both pre- and postoperative analyses of MR and PET images revealed whether integrating PET data into the navigational planning contributed to improved tumor volume definition and tumor resection. Metabolic information on tumor heterogeneity or extent was useful in planning the surgery. In 83 (80%) of 103 procedures, PET studies contributed to defining a final target volume different from that obtained with MR imaging alone. Furthermore, FDG-PET scanning, which was performed in a majority of HGG cases, showed that PET volume was less extended than the MR volume in 16 of 21 cases and contributed to targeting the resection to the hypermetabolic (anaplastic) area in 11 (69%) of 16 cases. Performed in 59 LGG cases and 23 HGG cases, MET-PET demonstrated that the PET volume did not match the MR volume and improved the tumor volume definition in 52 (88%) of 59 and 18 (78%) of 23, respectively. Total resection of the area of increased PET tracer uptake was achieved in 54 (52%) of 103 procedures. Conclusions Imaging guidance with PET scanning provided independent and complementary information that helped to assess tumor extent and plan tumor resection better than with MR imaging guidance alone. The PET scanning guidance could help increase the amount of tumor removed and target image-guided resection to tumor portions that represent the highest evolving potential.

Direct conversion semiconductor detectors in positron emission tomography

2015 ◽  
Vol 30 (14) ◽  
pp. 1530011 ◽  
Author(s):  
Joshua W. Cates ◽  
Yi Gu ◽  
Craig S. Levin
Keyword(s):  
Positron Emission Tomography ◽  
Direct Detection ◽  
Direct Conversion ◽  
Image Resolution ◽  
Emission Tomography ◽  
Semiconductor Detectors ◽  
Ongoing Research ◽  
Tumor Delineation ◽  
Positron Emission ◽  
Quantitative Accuracy

Semiconductor detectors are playing an increasing role in ongoing research to improve image resolution, contrast, and quantitative accuracy in preclinical applications of positron emission tomography (PET). These detectors serve as a medium for direct detection of annihilation photons. Early clinical translation of this technology has shown improvements in image quality and tumor delineation for head and neck cancers, relative to conventional scintillator-based systems. After a brief outline of the basics of PET imaging and the physical detection mechanisms for semiconductor detectors, an overview of ongoing detector development work is presented. The capabilities of semiconductor-based PET systems and the current state of these devices are discussed.

scholarly journals The efficacy of different preprocessing steps in reducing motion-related confounds in diffusion MRI connectomics

2020 ◽  
Author(s):  
Stuart Oldham ◽  
Aurina Arnatkevic̆iūtė ◽  
Robert E. Smith ◽  
Jeggan Tiego ◽  
Mark A. Bellgrove ◽  
...  
Keyword(s):  
Diffusion Mri ◽  
Motion Correction ◽  
Healthy Adult ◽  
Structural Connectivity ◽  
Head Motion ◽  
List Type ◽  
Edge Weighting ◽  
Quantitative Metrics ◽  
The Impact ◽  
Connectivity Strength

AbstractHead motion is a major confounding factor in neuroimaging studies. While numerous studies have investigated how motion impacts estimates of functional connectivity, the effects of motion on structural connectivity measured using diffusion MRI have not received the same level of attention, despite the fact that, like functional MRI, diffusion MRI relies on elaborate preprocessing pipelines that require multiple choices at each step. Here, we report a comprehensive analysis of how these choices influence motion-related contamination of structural connectivity estimates. Using a healthy adult sample (N = 252), we evaluated 240 different preprocessing pipelines, devised using plausible combinations of different choices related to explicit head motion correction, tractography propagation algorithms, track seeding methods, track termination constraints, quantitative metrics derived for each connectome edge, and parcellations. We found that an approach to motion correction that includes outlier replacement and within-slice volume correction led to a dramatic reduction in cross-subject correlations between head motion and structural connectivity strength, and that motion contamination is more severe when quantifying connectivity strength using mean tract fractional anisotropy rather than streamline count. We also show that the choice of preprocessing strategy can significantly influence subsequent inferences about network organization, with the location of network hubs varying considerably depending on the specific preprocessing steps applied. Our findings indicate that the impact of motion on structural connectivity can be successfully mitigated using recent motion-correction algorithms that include outlier replacement and within-slice motion correction.HighlightsWe assess how motion affects structural connectivity in 240 preprocessing pipelinesMotion contamination of structural connectivity depends on preprocessing choicesAdvanced motion correction tools reduce motion confoundsFA edge weighting is more susceptible to motion effects than streamline count

Truck mass estimation method based on the on-board sensor

2020 ◽  
Vol 234 (10-11) ◽  
pp. 2429-2443
Author(s):  
Beijia Wang ◽  
Hongliang Wang ◽  
Lei Wu ◽  
Liuliu Cai ◽  
Dawei Pi ◽  
...  
Keyword(s):  
Error Correction ◽  
Estimation Error ◽  
Estimation Method ◽  
Estimation Algorithm ◽  
Correction Method ◽  
Mass Estimation ◽  
The Road ◽  
Vehicle Mass ◽  
Error Correction Method ◽  
Road Gradient

Vehicle mass estimation is the key technology to improve vehicle stability. However, the existing mass estimation accuracy is easily affected by the change of road gradient, and there are few studies on the mass estimation method of the light truck. Aiming at this problem, this paper uses sensors to measure road gradient and rear suspension deformation and proposes a sensor-based vehicle mass estimation algorithm. First, factors that affect the mass estimation are analyzed, road gradient error correction method and mass estimation error correction method are established. Besides, the suspension deformation is decoupled from the road gradient. Second, the mass estimation algorithm model was established in Matlab/Simulink platform and compared with the mass estimation iterative algorithm. Finally, the road test was carried out under various conditions, the results show that the proposed mass estimation algorithm is robust, and the accuracy of the mass estimation will not be affected by the sudden change of road gradient.

scholarly journals Analytic time-of-flight positron emission tomography reconstruction: two-dimensional case

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Gengsheng L. Zeng ◽  
Ya Li ◽  
Qiu Huang
Keyword(s):  
Positron Emission Tomography ◽  
Image Reconstruction ◽  
Iterative Algorithm ◽  
Emission Tomography ◽  
Two Dimensional ◽  
Profile Function ◽  
Positron Emission ◽  
Pet Image Reconstruction ◽  
2D Filter ◽  
Tof Pet

AbstractIn a positron emission tomography (PET) scanner, the time-of-flight (TOF) information gives us rough event position along the line-of-response (LOR). Using the TOF information for PET image reconstruction is able to reduce image noise. The state-of-the-art TOF PET image reconstruction uses iterative algorithms. Analytical image reconstruction algorithm exits for TOF PET which emulates the iterative Landweber algorithm. This paper introduces such an algorithm, focusing on two-dimensional (2D) reconstruction. The proposed algorithm is in the form of backprojection filtering, in which the backprojection is performed first, and then a 2D filter is applied to the backprojected image. For the list-mode data, the backprojection is carried out in the event-by-event fashion, and a profile function may be used along the projection LOR. The 2D filter depends on the TOF timing resolution as well as the backprojection profile function. In order to emulate the iterative algorithm effects, a Fourier-domain window function is suggested. This window function has a parameter, k, which corresponds to the iteration number in an iterative algorithm.

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