Prior Guided Deep Difference Meta-Learner for Fast Adaptation to Stylized Segmentation

When a pre-trained general auto-segmentation model is deployed at a new institution, a support framework in the proposed Prior-guided DDL network will learn the systematic difference between the model predictions and the final contours revised and approved by clinicians for an initial group of patients. The learned style feature differences are concatenated with the new patients (query) features and then decoded to get the style-adapted segmentations. The model is independent of practice styles and anatomical structures. It meta-learns with simulated style differences and does not need to be exposed to any real clinical stylized structures during training. Once trained on the simulated data, it can be deployed for clinical use to adapt to new practice styles and new anatomical structures without further training. To show the proof of concept, we tested the Prior-guided DDL network on six different practice style variations for three different anatomical structures. Pre-trained segmentation models were adapted from post-operative clinical target volume (CTV) segmentation to segment CTVstyle1, CTVstyle2, and CTVstyle3, from parotid gland segmentation to segment Parotidsuperficial, and from rectum segmentation to segment Rectumsuperior and Rectumposterior. The mode performance was quantified with Dice Similarity Coefficient (DSC). With adaptation based on only the first three patients, the average DSCs were improved from 78.6, 71.9, 63.0, 52.2, 46.3 and 69.6 to 84.4, 77.8, 73.0, 77.8, 70.5, 68.1, for CTVstyle1, CTVstyle2, and CTVstyle3, Parotidsuperficial, Rectumsuperior, and Rectumposterior, respectively, showing the great potential of the Priorguided DDL network for a fast and effortless adaptation to new practice style

Coarse-Super-Resolution-Fine Network (CoSF-Net): A Unified End-to-End Neural Network for 4D-MRI with Simultaneous Motion Estimation and Super-Resolution

Four-dimensional magnetic resonance imaging (4D-MRI) is an emerging technique for tumor motion management in image-guided radiation therapy (IGRT). However, current 4D-MRI suffers from low spatial resolution and strong motion artifacts owing to the long acquisition time and patients' respiratory variations; these limitations, if not managed properly, can adversely affect treatment planning and delivery in IGRT. Herein, we developed a novel deep learning framework called the coarse-super-resolution-fine network (CoSF-Net) to achieve simultaneous motion estimation and super-resolution in a unified model. We designed CoSF-Net by fully excavating the inherent properties of 4D-MRI, with consideration of limited and imperfectly matched training datasets. We conducted extensive experiments on multiple real patient datasets to verify the feasibility and robustness of the developed network. Compared with existing networks and three state-of-the-art conventional algorithms, CoSF-Net not only accurately estimated the deformable vector fields between the respiratory phases of 4D-MRI but also simultaneously improved the spatial resolution of 4D-MRI with enhanced anatomic features, yielding 4D-MR images with high spatiotemporal resolution

3D dictionary learning based iterative cone beam CT reconstruction

Purpose: This work is to develop a 3D dictionary learning based cone beam CT (CBCT) reconstruction algorithm on graphic processing units (GPU) to improve the quality of sparse-view CBCT reconstruction with high efficiency. Methods: A 3D dictionary containing 256 small volumes (atoms) of 3 × 3 × 3 was trained from a large number of blocks extracted from a high quality volume image. On the basis, we utilized cholesky decomposition based orthogonal matching pursuit algorithm to find the sparse representation of each block. To accelerate the time-consuming sparse coding in the 3D case, we implemented the sparse coding in a parallel fashion by taking advantage of the tremendous computational power of GPU. Conjugate gradient least square algorithm was adopted to minimize the data fidelity term. Evaluations are performed based on a head-neck patient case. FDK reconstruction with full dataset of 364 projections is used as the reference. We compared the proposed 3D dictionary learning based method with tight frame (TF) by performing reconstructions on a subset data of 121 projections. Results: Compared to TF based CBCT reconstruction that shows good overall performance, our experiments indicated that 3D dictionary learning based CBCT reconstruction is able to recover finer structures, remove more streaking artifacts and also induce less blocky artifacts. Conclusion: 3D dictionary learning based CBCT reconstruction algorithm is able to sense the structural information while suppress the noise, and hence to achieve high quality reconstruction under the case of sparse view. The GPU realization of the whole algorithm offers a significant efficiency enhancement, making this algorithm more feasible for potential clinical application.-------------------------------Cite this article as: Bai T, Yan H, Shi F, Jia X, Lou Y, Xu Q, Jiang S, Mou X. 3D dictionary learning based iterative cone beam CT reconstruction. Int J Cancer Ther Oncol 2014; 2(2):020240. DOI: 10.14319/ijcto.0202.4

Ultrafast cone-beam CT scatter correction with GPU-based Monte Carlo simulation

Purpose: Scatter artifacts severely degrade image quality of cone-beam CT (CBCT). We present an ultrafast scatter correction framework by using GPU-based Monte Carlo (MC) simulation and prior patient CT image, aiming at automatically finish the whole process including both scatter correction and reconstruction within 30 seconds.Methods: The method consists of six steps: 1) FDK reconstruction using raw projection data; 2) Rigid Registration of planning CT to the FDK results; 3) MC scatter calculation at sparse view angles using the planning CT; 4) Interpolation of the calculated scatter signals to other angles; 5) Removal of scatter from the raw projections; 6) FDK reconstruction using the scatter-corrected projections. In addition to using GPU to accelerate MC photon simulations, we also use a small number of photons and a down-sampled CT image in simulation to further reduce computation time. A novel denoising algorithm is used to eliminate MC noise from the simulated scatter images caused by low photon numbers. The method is validated on one simulated head-and-neck case with 364 projection angles.Results: We have examined variation of the scatter signal among projection angles using Fourier analysis. It is found that scatter images at 31 angles are sufficient to restore those at all angles with < 0.1% error. For the simulated patient case with a resolution of 512 × 512 × 100, we simulated 5 × 106 photons per angle. The total computation time is 20.52 seconds on a Nvidia GTX Titan GPU, and the time at each step is 2.53, 0.64, 14.78, 0.13, 0.19, and 2.25 seconds, respectively. The scatter-induced shading/cupping artifacts are substantially reduced, and the average HU error of a region-of-interest is reduced from 75.9 to 19.0 HU.Conclusion: A practical ultrafast MC-based CBCT scatter correction scheme is developed. It accomplished the whole procedure of scatter correction and reconstruction within 30 seconds.----------------------------Cite this article as: Xu Y, Bai T, Yan H, Ouyang L, Wang J, Pompos A, Zhou L, Jiang SB, Jia X. Ultrafast cone-beam CT scatter correction with GPU-based Monte Carlo simulation. Int J Cancer Ther Oncol 2014; 2(2):020245. DOI: 10.14319/ijcto.0202.4

Cellular immunity in children with successful immunoprophylactic treatment for mother-to-child transmission of hepatitis B virus

Background: The administration of hepatitis B immunoglobulin followed by hepatitis B vaccine can result in a protective efficacy of almost 90% in mother-to-child transmission of hepatitis B virus (HBV). However, little is known about immunity against HBV infection in children after immunoprophylactic treatment. We tried to assess the association between T-cell responses and viremia in children after successful prophylactic treatment. Methods: Thirteen children and their 8 HBV carrier mothers (8 families), who were positive for human leukocyte antigen (HLA)-A24, were enrolled in this study. All of the 13 children received immunoprophylactic treatment and became negative for hepatitis B surface antigen (HBsAg) after birth. HBV-specific cytotoxic T lymphocyte (CTL) responses were evaluated using IFNγ - enzyme-linked immunosorbent spot (ELISPOT) and major histocompatibility complex class I peptide pentamer assays. Serum HBV DNA was measured by real-time PCR. Results: Significant HBV-specific T-cell responses were detected in 2 (15%) of the 13 children by ELISPOT. However, the frequency of HLA-A24-HBV-specific CTLs was very low in both HBV carrier mothers and children using pentamers. Of the 13 children, 4 (31%) were positive for serum HBV DNA. However, the levels of serum HBV DNA were 100 copies/ml or less. One of the 2 children in whom significant HBV-specific CTL responses were detectable was positive for serum HBV DNA. Conclusions: HBV core and polymerase-specific T-cell responses were detected and a low-dose viremia was observed in children after successful immunoprophylaxis treatment. Although the presence of viremia was not related to HBV-specific T-cell responses, CTLs might play a role in the control of HBV infection in children born to HBsAg-positive mothers after immunoprophylactic treatment. </p

Measurement of the inclusive and dijet cross-sections of b-jets in pp collisions at sqrt(s) = 7 TeV with the ATLAS detector

The inclusive and dijet production cross-sections have been measured for jets containing b-hadrons (b-jets) in proton-proton collisions at a centre-of-mass energy of sqrt(s) = 7 TeV, using the ATLAS detector at the LHC. The measurements use data corresponding to an integrated luminosity of 34 pb^-1. The b-jets are identified using either a lifetime-based method, where secondary decay vertices of b-hadrons in jets are reconstructed using information from the tracking detectors, or a muon-based method where the presence of a muon is used to identify semileptonic decays of b-hadrons inside jets. The inclusive b-jet cross-section is measured as a function of transverse momentum in the range 20 < pT < 400 GeV and rapidity in the range |y| < 2.1. The bbbar-dijet cross-section is measured as a function of the dijet invariant mass in the range 110 < m_jj < 760 GeV, the azimuthal angle difference between the two jets and the angular variable chi in two dijet mass regions. The results are compared with next-to-leading-order QCD predictions. Good agreement is observed between the measured cross-sections and the predictions obtained using POWHEG + Pythia. MC@NLO + Herwig shows good agreement with the measured bbbar-dijet cross-section. However, it does not reproduce the measured inclusive cross-section well, particularly for central b-jets with large transverse momenta.Comment: 10 pages plus author list (21 pages total), 8 figures, 1 table, final version published in European Physical Journal

The mitochondrial DNA 4,977-bp deletion and its implication in copy number alteration in colorectal cancer

<p>Abstract</p> <p>Background</p> <p>Qualitative and quantitative changes in human mitochondrial DNA (mtDNA) have been implicated in various cancer types. A 4,977 bp deletion in the major arch of the mitochondrial genome is one of the most common mutations associated with a variety of human diseases and aging.</p> <p>Methods</p> <p>We conducted a comprehensive study on clinical features and mtDNA of 104 colorectal cancer patients in the Wenzhou area of China. In particular, using a quantitative real time PCR method, we analyzed the 4,977 bp deletion and mtDNA content in tumor tissues and paired non-tumor areas from these patients.</p> <p>Results</p> <p>We found that the 4,977 bp deletion was more likely to be present in patients of younger age (≤65 years, p = 0.027). In patients with the 4,977 bp deletion, the deletion level decreased as the cancer stage advanced (p = 0.031). Moreover, mtDNA copy number in tumor tissues of patients with this deletion increased, both compared with that in adjacent non-tumor tissues and with in tumors of patients without the deletion. Such mtDNA content increase correlated with the levels of the 4,977 bp deletion and with cancer stage (p < 0.001).</p> <p>Conclusions</p> <p>Our study indicates that the mtDNA 4,977 bp deletion may play a role in the early stage of colorectal cancer, and it is also implicated in alteration of mtDNA content in cancer cells.</p