scholarly journals Electromagnetic corrections to leptonic pion decay from lattice QCD using infinite-volume reconstruction method

2020 ◽  
Author(s):  
Xu Feng
2021 ◽  
Vol 2021 (7) ◽  
Author(s):  
Luke Gayer ◽  
Nicolas Lang ◽  
Sinéad M. Ryan ◽  
David Tims ◽  
Christopher E. Thomas ◽  
...  

Abstract Isospin-1/2 Dπ scattering amplitudes are computed using lattice QCD, working in a single volume of approximately (3.6 fm)3 and with a light quark mass corresponding to mπ ≈ 239 MeV. The spectrum of the elastic Dπ energy region is computed yielding 20 energy levels. Using the Lüscher finite-volume quantisation condition, these energies are translated into constraints on the infinite-volume scattering amplitudes and hence enable us to map out the energy dependence of elastic Dπ scattering. By analytically continuing a range of scattering amplitudes, a $$ {D}_0^{\ast } $$ D 0 ∗ resonance pole is consistently found strongly coupled to the S-wave Dπ channel, with a mass m ≈ 2200 MeV and a width Γ ≈ 400 MeV. Combined with earlier work investigating the $$ {D}_{s0}^{\ast } $$ D s 0 ∗ , and $$ {D}_0^{\ast } $$ D 0 ∗ with heavier light quarks, similar couplings between each of these scalar states and their relevant meson-meson scattering channels are determined. The mass of the $$ {D}_0^{\ast } $$ D 0 ∗ is consistently found well below that of the $$ {D}_{s0}^{\ast } $$ D s 0 ∗ , in contrast to the currently reported experimental result.


2015 ◽  
Vol 75 (2) ◽  
Author(s):  
Ho Wei Yong ◽  
Abdullah Bade ◽  
Rajesh Kumar Muniandy

Over the past thirty years, a number of researchers have investigated on 3D organ reconstruction from medical images and there are a few 3D reconstruction software available on the market. However, not many researcheshave focused on3D reconstruction of breast cancer’s tumours. Due to the method complexity, most 3D breast cancer’s tumours reconstruction were done based on MRI slices dataeven though mammogram is the current clinical practice for breast cancer screening. Therefore, this research will investigate the process of creating a method that will be able to reconstruct 3D breast cancer’s tumours from mammograms effectively.  Several steps were proposed for this research which includes data acquisition, volume reconstruction, andvolume rendering. The expected output from this research is the 3D breast cancer’s tumours model that is generated from correctly registered mammograms. The main purpose of this research is to come up with a 3D reconstruction method that can produce good breast cancer model from mammograms while using minimal computational cost.


2014 ◽  
Vol 732 ◽  
pp. 41-48 ◽  
Author(s):  
R. Horsley ◽  
Y. Nakamura ◽  
A. Nobile ◽  
P.E.L. Rakow ◽  
G. Schierholz ◽  
...  

1993 ◽  
Vol 301 (2-3) ◽  
pp. 224-230 ◽  
Author(s):  
M. Fukugita ◽  
N. Ishizuka ◽  
H. Mino ◽  
M. Okawa ◽  
A. Ukawa

2000 ◽  
Vol 62 (9) ◽  
Author(s):  
S. Aoki ◽  
M. Fukugita ◽  
S. Hashimoto ◽  
K.-I. Ishikawa ◽  
N. Ishizuka ◽  
...  

2017 ◽  
Vol 39 (4) ◽  
pp. 240-259 ◽  
Author(s):  
Tiexiang Wen ◽  
Ling Li ◽  
Qingsong Zhu ◽  
Wenjian Qin ◽  
Jia Gu ◽  
...  

Volume reconstruction method plays an important role in improving reconstructed volumetric image quality for freehand three-dimensional (3D) ultrasound imaging. By utilizing the capability of programmable graphics processing unit (GPU), we can achieve a real-time incremental volume reconstruction at a speed of 25-50 frames per second (fps). After incremental reconstruction and visualization, hole-filling is performed on GPU to fill remaining empty voxels. However, traditional pixel nearest neighbor–based hole-filling fails to reconstruct volume with high image quality. On the contrary, the kernel regression provides an accurate volume reconstruction method for 3D ultrasound imaging but with the cost of heavy computational complexity. In this paper, a GPU-based fast kernel regression method is proposed for high-quality volume after the incremental reconstruction of freehand ultrasound. The experimental results show that improved image quality for speckle reduction and details preservation can be obtained with the parameter setting of kernel window size of [Formula: see text] and kernel bandwidth of 1.0. The computational performance of the proposed GPU-based method can be over 200 times faster than that on central processing unit (CPU), and the volume with size of 50 million voxels in our experiment can be reconstructed within 10 seconds.


2019 ◽  
Vol 69 (1) ◽  
pp. 65-107 ◽  
Author(s):  
Maxwell T. Hansen ◽  
Stephen R. Sharpe

Most strong-interaction resonances have decay channels involving three or more particles, including many of the recently discovered X, Y, and Z resonances. In order to study such resonances from first principles using lattice QCD, one must understand finite-volume effects for three particles in the cubic box used in calculations. We review efforts to develop a three-particle quantization condition that relates finite-volume energies to infinite-volume scattering amplitudes. We describe in detail the three approaches that have been followed, and present new results on the relationship between the corresponding results. We show examples of the numerical implementation of all three approaches and point out the important issues that remain to be resolved.


2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Qian Ni ◽  
Yi Zhang ◽  
Tiexiang Wen ◽  
Ling Li

Slice-to-volume reconstruction (SVR) method can deal well with motion artifacts and provide high-quality 3D image data for fetal brain MRI. However, the problem of sparse sampling is not well addressed in the SVR method. In this paper, we mainly focus on the sparse volume reconstruction of fetal brain MRI from multiple stacks corrupted with motion artifacts. Based on the SVR framework, our approach includes the slice-to-volume 2D/3D registration, the point spread function- (PSF-) based volume update, and the adaptive kernel regression-based volume update. The adaptive kernel regression can deal well with the sparse sampling data and enhance the detailed preservation by capturing the local structure through covariance matrix. Experimental results performed on clinical data show that kernel regression results in statistical improvement of image quality for sparse sampling data with the parameter setting of the structure sensitivity 0.4, the steering kernel size of 7 × 7 × 7 and steering smoothing bandwidth of 0.5. The computational performance of the proposed GPU-based method can be over 90 times faster than that on CPU.


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