scholarly journals Deep learning‐based method for reducing residual motion effects in diffusion parameter estimation

2020 ◽  
Vol 85 (4) ◽  
pp. 2278-2293
Author(s):  
Ting Gong ◽  
Qiqi Tong ◽  
Zhiwei Li ◽  
Hongjian He ◽  
Hui Zhang ◽  
...  
Keyword(s):  
Parameter Estimation ◽  
Deep Learning ◽  
Diffusion Parameter ◽  
Residual Motion

scholarly journals Improved Shape Parameter Estimation in K Clutter with Neural Networks and Deep Learning

2016 ◽  
Vol 3 (7) ◽  
pp. 96 ◽  
Author(s):  
José Raúl Machado Fernández ◽  
Jesús Concepción Bacallao Vidal
Keyword(s):  
Neural Networks ◽  
Parameter Estimation ◽  
Deep Learning ◽  
Shape Parameter

scholarly journals Statistically-informed deep learning for gravitational wave parameter estimation

2021 ◽  
Author(s):  
Hongyu Shen ◽  
Eliu Huerta ◽  
Eamonn O’Shea ◽  
Prayush Kumar ◽  
Zhizhen Zhao
Keyword(s):  
Neural Network ◽  
Parameter Estimation ◽  
Deep Learning ◽  
Gravitational Wave ◽  
Network Models ◽  
Wave Parameter ◽  
Posterior Distributions ◽  
Neural Network Models ◽  
The Masses ◽  
Bayesian Methodologies

Abstract We introduce deep learning models to estimate the masses of the binary components of black hole mergers, (m1, m2), and three astrophysical properties of the post-merger compact remnant, namely, the final spin, af, and the frequency and damping time of the ringdown oscillations of the fundamental (l=m=2) bar mode, (ωR, ωI). Our neural networks combine a modified WaveNet architecture with contrastive learning and normalizing flow. We validate these models against a Gaussian conjugate prior family whose posterior distribution is described by a closed analytical expression. Upon confirming that our models produce statistically consistent results, we used them to estimate the astrophysical parameters (m1, m2, af, ωR, ωI) of five binary black holes: GW150914, GW170104, GW170814, GW190521 and GW190630. We use PyCBC Inference to directly compare traditional Bayesian methodologies for parameter estimation with our deep learning based posterior distributions. Our results show that our neural network models predict posterior distributions that encode physical correlations, and that our data-driven median results and 90\% confidence intervals are similar to those produced with gravitational wave Bayesian analyses. This methodology requires a single V100 NVIDIA GPU to produce median values and posterior distributions within two milliseconds for each event. This neural network, and a tutorial for its use, are available at the Data and Learning Hub for Science.

scholarly journals Cosmological parameter estimation from large-scale structure deep learning

2020 ◽  
Vol 63 (11) ◽  
Author(s):  
ShuYang Pan ◽  
MiaoXin Liu ◽  
Jaime Forero-Romero ◽  
Cristiano G. Sabiu ◽  
ZhiGang Li ◽  
...  
Keyword(s):  
Parameter Estimation ◽  
Deep Learning ◽  
Large Scale ◽  
Large Scale Structure ◽  
Scale Structure ◽  
Cosmological Parameter

scholarly journals Evaluation of the accuracy and precision of the diffusion parameter EStImation with Gibbs and NoisE removal pipeline

NeuroImage ◽  
2018 ◽  
Vol 183 ◽  
pp. 532-543 ◽  
Author(s):  
Benjamin Ades-Aron ◽  
Jelle Veraart ◽  
Peter Kochunov ◽  
Stephen McGuire ◽  
Paul Sherman ◽  
...  
Keyword(s):  
Parameter Estimation ◽  
Noise Removal ◽  
Diffusion Parameter ◽  
Accuracy And Precision

scholarly journals Streamlined Magnetic Resonance Fingerprinting: Fast Whole-brain Coverage with Deep-learning Based Parameter Estimation

2020 ◽  
Author(s):  
Mahdi Khajehim ◽  
Thomas Christen ◽  
Fred Tam ◽  
Simon J. Graham
Keyword(s):  
Parameter Estimation ◽  
Deep Learning ◽  
Magnetic Resonance ◽  
Quantitative Mri ◽  
Parameter Estimates ◽  
Single Shot ◽  
Whole Brain ◽  
Dictionary Matching ◽  
Scan Time

AbstractMagnetic resonance fingerprinting (MRF) is a novel quantitative MRI (qMRI) framework that provides simultaneous estimates of multiple relaxation parameters as well as metrics of field inhomogeneity in a single acquisition. However, current bottlenecks exist in the forms of (1) scan time; (2) need for custom image reconstruction; (3) large dictionary sizes; (4) long dictionary-matching time. The aim of this study is to introduce a novel streamlined magnetic-resonance fingerprinting (sMRF) framework that is based on a single-shot echo-planar imaging (EPI) sequence to simultaneously estimate tissue T1, T2, and T2* with integrated B1+ correction. Encouraged by recent work on EPI-based MRF, we developed a method that combines spin-echo EPI with gradient-echo EPI to achieve T2 in addition to T1 and T2* quantification. To this design, we add simultaneous multi-slice (SMS) acceleration to enable full-brain coverage in a few minutes. Moreover, in the parameter-estimation step, we use deep learning to train a deep neural network (DNN) to accelerate the estimation process by orders of magnitude. Notably, due to the high image quality of the EPI scans, the training process can rely simply on Bloch-simulated data. The DNN also removes the need for storing large dictionaries. Phantom scans along with in-vivo multi-slice scans from seven healthy volunteers were acquired with resolutions of 1.1×1.1×3 mm3 and 1.7×1.7×3 mm3, and the results were validated against ground truth measurements. Excellent correspondence was found between our T1, T2, and T2* estimates and results obtained from standard approaches. In the phantom scan, a strong linear relationship (R=1-1.04, R2>0.96) was found for all parameter estimates, with a particularly high agreement for T2 estimation (R2>0.99). Similar findings are reported for the in-vivo human data for all of our parameter estimates. Incorporation of DNN results in a reduction of parameter estimation time on the order of 1000 x and a reduction in storage requirements on the order of 2500 x while achieving highly similar results as conventional dictionary matching (%differences of 7.4±0.4%, 3.6±0.3% and 6.0±0.4% error in T1, T2, and T2* estimation). Thus, sMRF has the potential to be the method of choice for future MRF studies by providing ease of implementation, fast whole-brain coverage, and ultra-fast T1/T2/T2* estimation.

scholarly journals Deep learning-based parameter estimation in fetal diffusion-weighted MRI

NeuroImage ◽  
2021 ◽  
Vol 243 ◽  
pp. 118482
Author(s):  
Davood Karimi ◽  
Camilo Jaimes ◽  
Fedel Machado-Rivas ◽  
Lana Vasung ◽  
Shadab Khan ◽  
...  
Keyword(s):  
Parameter Estimation ◽  
Deep Learning ◽  
Diffusion Weighted Mri ◽  
Diffusion Weighted
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