scholarly journals Compensating Positron Range Effects of Ga-68 in Preclinical PET Imaging by Using Convolutional Neural Network: A Monte Carlo Simulation Study

Diagnostics ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 2275
Author(s):  
Ching-Ching Yang
Keyword(s):  
Neural Network ◽  
Monte Carlo ◽  
Convolutional Neural Network ◽  
Pet Imaging ◽  
Coefficient Of Variation ◽  
Recovery Coefficient ◽  
Monte Carlo Simulation Study ◽  
Positron Range ◽  
Preclinical Pet ◽  
Range Correction

This study aimed to investigate the feasibility of positron range correction based on three different convolutional neural network (CNN) models in preclinical PET imaging of Ga-68. The first model (CNN1) was originally designed for super-resolution recovery, while the second model (CNN2) and the third model (CNN3) were originally designed for pseudo CT synthesis from MRI. A preclinical PET scanner and 30 phantom configurations were modeled in Monte Carlo simulations, where each phantom configuration was simulated twice, once for Ga-68 (CNN input images) and once for back-to-back 511-keV gamma rays (CNN output images) with a 20 min emission scan duration. The Euclidean distance was used as the loss function to minimize the difference between CNN input and output images. According to our results, CNN3 outperformed CNN1 and CNN2 qualitatively and quantitatively. With regard to qualitative observation, it was found that boundaries in Ga-68 images became sharper after correction. As for quantitative analysis, the recovery coefficient (RC) and spill-over ratio (SOR) were increased after correction, while no substantial increase in coefficient of variation of RC (CVRC) or coefficient of variation of SOR (CVSOR) was observed. Overall, CNN3 should be a good candidate architecture for positron range correction in Ga-68 preclinical PET imaging.

scholarly journals Monte Carlo Dose Calculation Using MRI Based Synthetic CT Generated by Fully Convolutional Neural Network for Gamma Knife Radiosurgery

2021 ◽  
Vol 20 ◽  
pp. 153303382110464
Author(s):  
Jiankui Yuan ◽  
Elisha Fredman ◽  
Jian-Yue Jin ◽  
Serah Choi ◽  
David Mansur ◽  
...  
Keyword(s):  
Neural Network ◽  
Monte Carlo ◽  
Convolutional Neural Network ◽  
Gamma Knife ◽  
Learning Algorithm ◽  
Gamma Knife Radiosurgery ◽  
Dice Similarity Coefficient ◽  
Mr Images ◽  
The Difference ◽  
Synthetic Ct

The aim of this work is to study the dosimetric effect from generated synthetic computed tomography (sCT) from magnetic resonance (MR) images using a deep learning algorithm for Gamma Knife (GK) stereotactic radiosurgery (SRS). The Monte Carlo (MC) method is used for dose calculations. Thirty patients were retrospectively selected with our institution IRB’s approval. All patients were treated with GK SRS based on T1-weighted MR images and also underwent conventional external beam treatment with a CT scan. Image datasets were preprocessed with registration and were normalized to obtain similar intensity for the pairs of MR and CT images. A deep convolutional neural network arranged in an encoder–decoder fashion was used to learn the direct mapping from MR to the corresponding CT. A number of metrics including the voxel-wise mean error (ME) and mean absolute error (MAE) were used for evaluating the difference between generated sCT and the true CT. To study the dosimetric accuracy, MC simulations were performed based on the true CT and sCT using the same treatment parameters. The method produced an MAE of 86.6 ± 34.1 Hundsfield units (HU) and a mean squared error (MSE) of 160.9 ± 32.8. The mean Dice similarity coefficient was 0.82 ± 0.05 for HU > 200. The difference for dose-volume parameter D95 between the ground true dose and the dose calculated with sCT was 1.1% if a synthetic CT-to-density table was used, and 4.9% compared with the calculations based on the water-brain phantom.

scholarly journals A Monte Carlo Simulation Approach in Non-linear Structural Dynamics Using Convolutional Neural Networks

2021 ◽  
Vol 7 ◽  
Author(s):  
Franz Bamer ◽  
Denny Thaler ◽  
Marcus Stoffel ◽  
Bernd Markert
Keyword(s):  
Neural Network ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Convolutional Neural Network ◽  
Ground Motion ◽  
Structural Response ◽  
Structural Failure ◽  
Non Linear ◽  
Time Histories ◽  
Sample Set

The evaluation of the structural response statistics constitutes one of the principal tasks in engineering. However, in the tail region near structural failure, engineering structures behave highly non-linear, making an analytic or closed form of the response statistics difficult or even impossible. Evaluating a series of computer experiments, the Monte Carlo method has been proven a useful tool to provide an unbiased estimate of the response statistics. Naturally, we want structural failure to happen very rarely. Unfortunately, this leads to a disproportionately high number of Monte Carlo samples to be evaluated to ensure an estimation with high confidence for small probabilities. Thus, in this paper, we present a new Monte Carlo simulation method enhanced by a convolutional neural network. The sample-set used for this Monte Carlo approach is provided by artificially generating site-dependent ground motion time histories using a non-linear Kanai-Tajimi filter. Compared to several state-of-the-art studies, the convolutional neural network learns to extract the relevant input features and the structural response behavior autonomously from the entire time histories instead of learning from a set of hand-chosen intensity inputs. Training the neural network based on a chosen input sample set develops a meta-model that is then used as a meta-model to predict the response of the total Monte Carlo sample set. This paper presents two convolutional neural network-enhanced strategies that allow for a practical design approach of ground motion excited structures. The first strategy enables for an accurate response prediction around the mean of the distribution. It is, therefore, useful regarding structural serviceability. The second strategy enables for an accurate prediction around the tail end of the distribution. It is, therefore, beneficial for the prediction of the probability of failure.

scholarly journals Study of the Compton scattering effect of soft tissue in PET imaging by Monte Carlo method

2019 ◽  
Author(s):  
Narges Araste ◽  
Hossein TavakoliAnbaran
Keyword(s):  
Monte Carlo ◽  
Soft Tissue ◽  
Point Source ◽  
Compton Scattering ◽  
Pet Imaging ◽  
Monte Carlo Simulation Study ◽  
Spherical Source ◽  
Attenuation Effect ◽  
Scattering Effect ◽  
Simple Geometry

Introduction:In PET imaging, one or both of two annihilation photons may change the direction before reaching the detector due to Compton scattering interaction in body. .Methods:This article, a Monte Carlo simulation study, examined the effect of soft tissue on this error.In this work, the PET BiographTM 6 scanner, a simple geometry of soft tissue -including a sphere of soft tissue in center of PET ringwith various radii (from 0.5 to 30cm)- and two kinds of 511keV gamma source –point source and spherical source - were simulated by Monte Carlo MCNPX code to investigate scattering effect of soft tissue on PET imaging. Results:Analysis of the results of the simulation showed that, the majority scattered photons fell within the energy window without much loss of energy. Soft tissue around the point source at a distance of 8 to 12cm from the source and soft tissue around the spherical source at a distance of 8cm from the center had the most scattering effect in PET imaging. The scattering effect of soft tissue around the point source was more than the spherical source. Conclusion:The scattering effect of the adjacent organs is more than the non-adjacent organs.For high thicknessof soft tissue (more than20cm of radius), the attenuation effect is as obvious as the scattering effect. According to the results of this study, the patient's body thickness -in the abdominal region-could be a more accurate alternative for the patient's weight for increasing the injected dose into obese patients.

scholarly journals Direct pixel to pixel principal strain mapping from tagging MRI using end to end deep convolutional neural network (DeepStrain)

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Khaled Z. Abd-Elmoniem ◽  
Inas A. Yassine ◽  
Nader S. Metwalli ◽  
Ahmed Hamimi ◽  
Ronald Ouwerkerk ◽  
...  
Keyword(s):  
Neural Network ◽  
Monte Carlo ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Ground Truth ◽  
Image Resolution ◽  
Principal Strain ◽  
Strain Mapping ◽  
End To End

AbstractRegional soft tissue mechanical strain offers crucial insights into tissue's mechanical function and vital indicators for different related disorders. Tagging magnetic resonance imaging (tMRI) has been the standard method for assessing the mechanical characteristics of organs such as the heart, the liver, and the brain. However, constructing accurate artifact-free pixelwise strain maps at the native resolution of the tagged images has for decades been a challenging unsolved task. In this work, we developed an end-to-end deep-learning framework for pixel-to-pixel mapping of the two-dimensional Eulerian principal strains $$\varvec{{\varepsilon }}_{\boldsymbol{p1}}$$ ε p 1 and $$\varvec{{\varepsilon }}_{\boldsymbol{p2}}$$ ε p 2 directly from 1-1 spatial modulation of magnetization (SPAMM) tMRI at native image resolution using convolutional neural network (CNN). Four different deep learning conditional generative adversarial network (cGAN) approaches were examined. Validations were performed using Monte Carlo computational model simulations, and in-vivo datasets, and compared to the harmonic phase (HARP) method, a conventional and validated method for tMRI analysis, with six different filter settings. Principal strain maps of Monte Carlo tMRI simulations with various anatomical, functional, and imaging parameters demonstrate artifact-free solid agreements with the corresponding ground-truth maps. Correlations with the ground-truth strain maps were R = 0.90 and 0.92 for the best-proposed cGAN approach compared to R = 0.12 and 0.73 for the best HARP method for $$\varvec{{\varepsilon }}_{\boldsymbol{p1}}$$ ε p 1 and $$\varvec{{\varepsilon }}_{\boldsymbol{p2}}$$ ε p 2 , respectively. The proposed cGAN approach's error was substantially lower than the error in the best HARP method at all strain ranges. In-vivo results are presented for both healthy subjects and patients with cardiac conditions (Pulmonary Hypertension). Strain maps, obtained directly from their corresponding tagged MR images, depict for the first time anatomical, functional, and temporal details at pixelwise native high resolution with unprecedented clarity. This work demonstrates the feasibility of using the deep learning cGAN for direct myocardial and liver Eulerian strain mapping from tMRI at native image resolution with minimal artifacts.

A monte carlo simulation study to evaluate septal spacing using triple-head hybrid pet imaging

2003 ◽  
Vol 50 (5) ◽  
pp. 1339-1346 ◽  
Author(s):  
C.J. Groiselle ◽  
Y. D'Asseler ◽  
J.A. Kolthammer ◽  
C.G. Matthews ◽  
S.J. Glick
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Simulation Study ◽  
Pet Imaging ◽  
Monte Carlo Simulation Study
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