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Deshan Yang

Publications and source records attributed to Deshan Yang.

At least 19 recordsLinked to original sources
arXiv2026

Develop and Optimize 5DCT Imaging Simulation and Reconstruction Methods

Purpose: To develop and optimize a 5DCT (3D + cardiac phase + respiratory phase) imaging simulation and reconstruction pipeline, and to compare two sinogram-space interpolation methods for reconstructing images at arbitrary combinations of cardiac and respiratory phase. Methods: Helical CT projections were simulated from the 4D XCAT phantom across a range of cardiac and respiratory motion states, with Poisson and electronic noise added. Ground-truth-matched volumes were generated at 5 cardiac phases and 10 respiratory amplitudes (50 total phase combinations). Because acquired projections are sparsely and unevenly distributed across this joint phase space, each target slice was reconstructed by interpolating rebinned sinogram rows to the target cardiac phase and respiratory amplitude, using either 2D scattered barycentric interpolation or 2D scattered local linear interpolation with a circular kernel for cardiac phase. Reconstructed volumes were compared to phantom ground truth using mean absolute error (MAE), and to conventional respiratory-gated 4DCT (r4DCT) reconstructed from the same simulated data. Results: Both interpolation methods eliminated the severe axial misalignment artifacts present when helical projections were reconstructed without phase-space interpolation. Local linear interpolation achieved lower MAE than barycentric interpolation across most tested conditions, with the largest improvement at low pitch. The 5DCT pipeline also produced respiratory-only volumes with fewer residual cardiac-motion artifacts than conventional r4DCT reconstructed from the same projection data, including at standard clinical pitch (0.1). Conclusions: 5DCT reconstruction using sinogram-space interpolation is feasible and can jointly resolve cardiac and respiratory motion with better accuracy than conventional 4DCT reconstruction.

physics.med-ph↗
arXiv2026

PIVM: Diffusion-Based Prior-Integrated Variation Modeling for Anatomically Precise Abdominal CT Synthesis

Abdominal CT data are limited by high annotation costs and privacy constraints, which hinder the development of robust segmentation and diagnostic models. We present a Prior-Integrated Variation Modeling (PIVM) framework, a diffusion-based method for anatomically accurate CT image synthesis. Instead of generating full images from noise, PIVM predicts voxel-wise intensity variations relative to organ-specific intensity priors derived from segmentation labels. These priors and labels jointly guide the diffusion process, ensuring spatial alignment and realistic organ boundaries. Unlike latent-space diffusion models, our approach operates directly in image space while preserving the full Hounsfield Unit (HU) range, capturing fine anatomical textures without smoothing. Source code is available at https://github.com/BZNR3/PIVM.

cs.CV↗
arXiv2025

A Respiratory Motion Analysis for Guiding Stereotactic Arrhythmia Radiotherapy Motion Management

Stereotactic Arrhythmia Radiotherapy (STAR) treats ventricular tachycardia (VT) but requires internal target volume (ITV) expansions to compensate for cardiorespiratory motion. Current clinical r4DCT imaging methods are limited, and the reconstructed r4DCTs suffer from unmanaged cardiac motion artifacts that affect the quantitative assessment of respiratory motion. A groupwise surface-to-surface deformable image registration (DIR) algorithm, named gCGF, was developed. A novel principal component filtering (PCF) mechanism and a spatial smoothing mechanism were developed and incorporated into gCGF to iteratively register heart contours from an average respiratory-phase CT to ten r4DCT phases while removing random cardiac motion from the cyclic respiratory motion. The performance of the groupwise DIR was quantitatively validated using 8 digital phantoms with simulated cardiac artifacts. An ablation study was conducted to compare gCGF to another comparable state-of-the-art groupwise DIR method. gCGF was applied to r4DCTs of 20 STAR patients to analyze the respiratory motion of the heart. Validation on digital phantoms showed that gCGF achieved a mean target registration error of 0.63+-0.51 mm while successfully achieving phase smoothness and reducing cardiac motion artifacts. Among all STAR patients, the heart's maximum and mean respiratory motion magnitudes ranged from 3.6 to 7.9 mm and 1.0 mm to 2.6 mm. The peak-to-peak motion range was from 6.2 to 14.7 mm. For VT targets, the max and mean motion magnitude ranges were 3.0 to 6.7 mm and 0.8 to 2.9 mm, respectively. The peak-to-peak range was from 4.7 to 11.8 mm. Significant dominance of the first principal component of the motion direction was observed (p = 0).

physics.med-ph↗
arXiv2025

A Vessel Bifurcation Landmark Pair Dataset for Abdominal CT Deformable Image Registration (DIR) Validation

Deformable image registration (DIR) is an enabling technology in many diagnostic and therapeutic tasks. Despite this, DIR algorithms have limited clinical use, largely due to a lack of benchmark datasets for quality assurance during development. To support future algorithm development, here we introduce our first-of-its-kind abdominal CT DIR benchmark dataset, comprising large numbers of highly accurate landmark pairs on matching blood vessel bifurcations. Abdominal CT image pairs of 30 patients were acquired from several public repositories as well as the authors' institution with IRB approval. The two CTs of each pair were originally acquired for the same patient on different days. An image processing workflow was developed and applied to each image pair: 1) Abdominal organs were segmented with a deep learning model, and image intensity within organ masks was overwritten. 2) Matching image patches were manually identified between two CTs of each image pair 3) Vessel bifurcation landmarks were labeled on one image of each image patch pair. 4) Image patches were deformably registered, and landmarks were projected onto the second image. 5) Landmark pair locations were refined manually or with an automated process. This workflow resulted in 1895 total landmark pairs, or 63 per case on average. Estimates of the landmark pair accuracy using digital phantoms were 0.7+/-1.2mm. The data is published in Zenodo at https://doi.org/10.5281/zenodo.14362785. Instructions for use can be found at https://github.com/deshanyang/Abdominal-DIR-QA. This dataset is a first-of-its-kind for abdominal DIR validation. The number, accuracy, and distribution of landmark pairs will allow for robust validation of DIR algorithms with precision beyond what is currently available.

cs.CV↗
arXiv2024

Small metal artifact detection and inpainting in cardiac CT images

Background: Quantification of cardiac motion on pre-treatment CT imaging for stereotactic arrhythmia radiotherapy patients is difficult due to the presence of image artifacts caused by metal leads of implantable cardioverter-defibrillators (ICDs). New methods are needed to accurately reduce the metal artifacts in already reconstructed CTs to recover the otherwise lost anatomical information. Purpose: To develop a methodology to automatically detect metal artifacts in cardiac CT scans and inpaint the affected volume with anatomically consistent structures and values. Methods: ECG-gated 4DCT scans of 12 patients who underwent cardiac radiation therapy for treating ventricular tachycardia were collected. The metal artifacts in the images were manually contoured. A 2D U-Net deep learning (DL) model was developed to segment the metal artifacts. A dataset of synthetic CTs was prepared by adding metal artifacts from the patient images to artifact-free CTs. A 3D image inpainting DL model was trained to refill the metal artifact portion in the synthetic images with realistic values. The inpainting model was evaluated by analyzing the automated segmentation results of the four heart chambers on the synthetic dataset. Additionally, the raw cardiac patient cases were qualitatively inspected. Results: The artifact detection model produced a Dice score of 0.958 +- 0.008. The inpainting model was able to recreate images with a structural similarity index of 0.988 +- 0.012. With the chamber segmentations improved surface Dice scores from 0.684 +- 0.247 to 0.964 +- 0.067 and the Hausdorff distance reduced from 3.4 +- 3.9 mm to 0.7 +- 0.7 mm. The inpainting model's use on cardiac patient CTs was visually inspected and the artifact-inpainted images were visually plausible. Conclusion: We successfully developed two deep models to detect and inpaint metal artifacts in cardiac CT images.

physics.med-ph↗
arXiv2024

A comprehensive liver CT landmark pair dataset for evaluating deformable image registration algorithms

Purpose: Evaluating deformable image registration (DIR) algorithms is vital for enhancing algorithm performance and gaining clinical acceptance. However, there's a notable lack of dependable DIR benchmark datasets for assessing DIR performance except for lung images. To address this gap, we aim to introduce our comprehensive liver computed tomography (CT) DIR landmark dataset library. Acquisition and Validation Methods: Thirty CT liver image pairs were acquired from several publicly available image archives as well as authors' institutions under institutional review board approval. The images were processed with a semi-automatic procedure to generate landmark pairs: 1) for each case, liver vessels were automatically segmented on one image; 2) landmarks were automatically detected at vessel bifurcations; 3) corresponding landmarks in the second image were placed using the deformable image registration method; 4) manual validation was applied to reject outliers and confirm the landmarks' positional accuracy. This workflow resulted in an average of ~68 landmark pairs per image pair, in a total of 2028 landmarks for all 30 cases. The general landmarking accuracy of this procedure was evaluated using digital phantoms. Estimates of the mean and standard deviation of landmark pair target registration errors (TRE) on digital phantoms were 0.64 and 0.40 mm. 99% of landmark pairs had TREs below 2 mm. Data Format and Usage Notes: All data are publicly available at Zenodo. Instructions for using our data and MATLAB code can be found on our GitHub page. Potential Applications: The landmark dataset generated in this work is the first collection of large-scale liver CT DIR landmarks prepared on real patient images. This dataset can provide researchers with a dense set of ground truth benchmarks for the quantitative evaluation of DIR algorithms within the liver.

physics.med-ph↗
arXiv2021

Gluon fragmentation into $B_c^{(*)}$ in NRQCD factorization

The universal fragmentation functions of gluon into the flavored quarkonia $B_c$ and (polarized) $B_c^*$ are computed within NRQCD factorization framework, at the lowest order in velocity expansion and strong coupling constant. It is mandatory to invoke the DGLAP renormalization program to render the NRQCD short-distance coefficients UV finite in a point-wise manner. The calculation is facilitated with the sector decomposition method, with the final results presented with high numerical accuracy. This knowledge is useful to enrich our understanding toward the large-$p_T$ behavior of $B_c^{(*)}$ production at LHC experiment.

hep-ph↗
arXiv2020

GroupRegNet: A Groupwise One-shot Deep Learning-based 4D Image Registration Method

Accurate deformable 4-dimensional (4D) (3-dimensional in space and time) medical images registration is essential in a variety of medical applications. Deep learning-based methods have recently gained popularity in this area for the significant lower inference time. However, they suffer from drawbacks of non-optimal accuracy and the requirement of a large amount of training data. A new method named GroupRegNet is proposed to address both limitations. The deformation fields to warp all images in the group into a common template is obtained through one-shot learning. The use of the implicit template reduces bias and accumulated error associated with the specified reference image. The one-shot learning strategy is similar to the conventional iterative optimization method but the motion model and parameters are replaced with a convolutional neural network (CNN) and the weights of the network. GroupRegNet also features a simpler network design and a more straightforward registration process, which eliminates the need to break up the input image into patches. The proposed method was quantitatively evaluated on two public respiratory-binned 4D-CT datasets. The results suggest that GroupRegNet outperforms the latest published deep learning-based methods and is comparable to the top conventional method pTVreg. To facilitate future research, the source code is available at https://github.com/vincentme/GroupRegNet.

eess.IV↗
arXiv2020

Automatic segmentation of CT images for ventral body composition analysis

Purpose: Body composition is known to be associated with many diseases including diabetes, cancers and cardiovascular diseases. In this paper, we developed a fully automatic body tissue decomposition procedure to segment three major compartments that are related to body composition analysis - subcutaneous adipose tissue (SAT), visceral adipose tissue (VAT) and muscle. Three additional compartments - the ventral cavity, lung and bones were also segmented during the segmentation process to assist segmentation of the major compartments. Methods: A convolutional neural network (CNN) model with densely connected layers was developed to perform ventral cavity segmentation. An image processing workflow was developed to segment the ventral cavity in any patient's CT using the CNN model, then further segment the body tissue into multiple compartments using hysteresis thresholding followed by morphological operations. It is important to segment ventral cavity firstly to allow accurate separation of compartments with similar Hounsfield unit (HU) inside and outside the ventral cavity. Results: The ventral cavity segmentation CNN model was trained and tested with manually labelled ventral cavities in 60 CTs. Dice scores (mean +/- standard deviation) for ventral cavity segmentation were 0.966+/-0.012. Tested on CT datasets with intravenous (IV) and oral contrast, the Dice scores were 0.96+/-0.02, 0.94+/-0.06, 0.96+/-0.04, 0.95+/-0.04 and 0.99+/-0.01 for bone, VAT, SAT, muscle and lung, respectively. The respective Dice scores were 0.97+/-0.02, 0.94+/-0.07, 0.93+/-0.06, 0.91+/-0.04 and 0.99+/-0.01 for non-contrast CT datasets. Conclusion: A body tissue decomposition procedure was developed to automatically segment multiple compartments of the ventral body. The proposed method enables fully automated quantification of 3D ventral body composition metrics from CT images.

eess.IV↗
arXiv2018

Goldstone Modes and Clebsch-Gordan Coefficients

We solve explicitly the Goldstone modes in spontaneously symmetry breaking models with supersymmetry. We find that, when more than one fields or representations contribute to the symmetry breaking, there exist identities among the Clebsch-Gordan coefficients which can be used as consistent checks on the calculations.

hep-ph↗
arXiv2017

The leading twist light-cone distribution amplitudes for the S-wave and P-wave quarkonia and their applications in single quarkonium exclusive productions

In this paper, we calculate twist-2 light-cone distribution amplitudes (LCDAs) of the S-wave and P-wave quarkonia (namely $^1S_0$ state $η_{Q}$, $^3S_1$ state $J/ψ(Υ)$, $^1P_1$ state $h_{Q}$ and $^3P_J$ states $χ_{QJ}$ with $J=0,1,2$ and $Q=c,b$) to the next-leading order of the strong coupling $α_s$ and leading order of the velocity expansion $v$ in the non-relativistic QCD (NRQCD). We apply these LCDAs to some single quarkonium exclusive productions at large center-of-mass energy, such as $γ^*\to η_Qγ, χ_{QJ}γ~(J=0,1,2)$, $Z\to η_Qγ, χ_{QJ}γ~(J=0,1,2), J/ψ(Υ)γ, h_Qγ$ and $h\to J/ψγ$, by adopting the collinear factorization. The asymptotic behaviors of those processes obtained in NRQCD factorization are reproduced.

hep-ph↗
arXiv2017

Relativistic corrections to light-cone distribution amplitudes of S-wave $B_c$ mesons and heavy quarkonia

In collinear factorization, light-cone distribution amplitudes (LCDAs) are key ingredients to calculate the production rate of a hadron in high energy exclusive processes. For a doubly-heavy meson system (such as $B_c, J/ψ, Υ$ etc), the LCDAs contain perturbative scales that can be integrated out and then are re-factorized into products of perturbatively calculable distribution parts and non-relativistic QCD matrix elements. In this re-factorization scheme, the LCDAs are known at next-to-leading order in the strong coupling constant $α_s$ and at leading order in the velocity expansion. In this work, we calculate the ${\cal O}( { v}^2)$ corrections to twist-2 LCDAs of S-wave $B_c$ mesons. These results are applicable to heavy quarkonia like $η_{c,b}$, $J/ψ$ and $Υ$ by setting $m_b=m_c$. We apply these relativistically corrected LCDAs to study their inverse moments and a few Gegenbauer moments which are important for phenomenological study. We point out that the relativistic corrections are sizable, and comparable with the next-to-leading order radiative corrections. These results for LCDAs are useful in future theoretical analyses of the productions of heavy quarkonia and $B_c$ mesons.

hep-ph↗
arXiv2016

The leading twist light-cone distribution amplitudes for the S-wave and P-wave Bc mesons

The light-cone distribution amplitudes (LCDAs) serve as important non-perturbative inputs for the study of hard exclusive processes. In this paper, we calculate ten LCDAs at twist-2 for the S-wave and P-wave $B_c$ mesons up to the next-to-leading order (NLO) of the strong coupling $α_s$ and leading order of the velocity expansion. Each one of these ten LCDAs is expressed as a product of a perturbatively calculable distribution and a universal NRQCD matrix-element. By use of the spin symmetry, only two NRQCD matrix-elements will be involved. The reduction of the number of non-perturbative inputs will improve the predictive power of collinear factorization.

hep-ph↗
arXiv2014

$χ_{QJ} \to\ell^+\ell^-$ within and beyond the Standard Model

We revisit $χ_{QJ}\to \ell^+\ell^-$ (with $J=0,1,2$ and $Q=b,c$) within the Standard Model (SM). The electro-magnetic contributions are given in color-singlet model with non-vanishing lepton masses at the leading order of $v$. Numerically, the branching ratios of $χ_{QJ}\to\ell^{+}\ell^{-}$ predicted within the SM are so small that such decays are barely possible to be detected at future BESIII and SuperB experiments, but may be possible to be observed at the LHC. We investigate $χ_{b0}\to\ell^+\ell^-$ in Type-II 2HDM with large $\tanβ$, and $χ_{b2}\to\ell^+\ell^-$ in the Randall-Sundrum model, to see their chance to be observed in future experiments.

hep-ph↗
arXiv2013

$B_c$ Exclusive Decays to Charmonium and a Light Meson at Next-to-Leading Order Accuracy

In this paper the next-to-leading order (NLO) corrections to $B_c$ meson exclusive decays to S-wave charmonia and light pseudoscalar or vector mesons, i.e. $π$, $K$, $ρ$, and $K^*$, are performed within non-relativistic (NR) QCD approach. The non-factorizable contribution is included, which is absent in traditional naive factorization (NF). And the theoretical uncertainties for their branching ratios are reduced compared with that of direct tree level calculation. Numerical results show that NLO QCD corrections markedly enhance the branching ratio with a K factor of 1.75 for $B_{c}^{\pm}\to η_{c} π^{\pm}$ and 1.31 for $B_{c}^{\pm}\to J/ψπ^{\pm}$. In order to investigate the asymptotic behavior, the analytic form is obtained in the heavy quark limit, i.e. $m_b \to \infty$. We note that annihilation topologies contribute trivia in this limit, and the corrections at leading order in $z= m_c/m_b$ expansion come from form factors and hard spectator interactions. At last, some related phenomenologies are also discussed.

hep-ph↗
arXiv2011

Bridging light-cone and NRQCD approaches: asymptotic behavior of $B_c$ electromagnetic form factor

This work aims at illustrating that, for a class of leading-twist hard exclusive reactions involving two heavy quarkonia, the light-cone approach, when equipped with the strategy of refactorization of the light-cone distribution amplitude of quarkonium, can be employed to elegantly reproduce the corresponding predictions made in the nonrelativistic QCD (NRQCD) factorization approach, order by order in perturbative expansion. Taking the electromagnetic form factor of the $B_c$ meson at large momentum transfer, $Q^2$, as a concrete example, we compare the results obtained from both NRQCD-based and light-cone-based calculations through the next-to-leading order (NLO) in $α_s$, while at the leading order (LO) in both velocity and $1/Q^2$ expansion, and explicitly confirm their mutual agreement. As a byproduct, we apply our NLO result to explore certain features about the asymptotic behavior of the heavy-light meson form factor. We also address the major theoretical obstacles that prevent us from establishing an analogous equivalence between these two approaches for the double charmonium production process of phenomenological interest, $e^+e^-\to J/ψ+η_c$.

hep-ph↗
arXiv2010

Broken S_3 Flavor Symmetry of Leptons and Quarks: Mass Spectra and Flavor Mixing Patterns

We apply the discrete S_3 flavor symmetry to both lepton and quark sectors of the standard model extended by introducing one Higgs triplet and realizing the type-II seesaw mechanism for finite neutrino masses. The resultant mass matrices of charged leptons (M_l), neutrinos (M_nu), up-type quarks (M_u) and down-type quarks (M_d) have a universal form consisting of two terms: one is proportional to the identity matrix I and the other is proportional to the democracy matrix D. We argue that the textures of M_l, M_u and M_d are dominated by the D term, while that of M_nu is dominated by the I term. This hypothesis implies a near mass degeneracy of three neutrinos and can naturally explain why the mass matrices of charged fermions are strongly hierarchical, why the quark mixing matrix is close to I and why the lepton mixing matrix contains two large angles. We discuss a rather simple perturbation ansatz to break the S_3 symmetry and obtain more realistic mass spectra of leptons and quarks as well as their flavor mixing patterns. We stress that the I term, which used to be ignored from M_l, M_u and M_d, is actually important because it can significantly modify the smallest lepton flavor mixing angle theta_13 or three quark flavor mixing angles.

hep-ph↗