Cylindrical Shock Waves in Space Superthermal Fluids

Hesham Gomaa Abdelwahed,Emad Kheder El-Shewy,Ali Abd El-Rahman,Noura Fakhry Abdo 한국물리학회 2019 THE JOURNAL OF THE KOREAN PHYSICAL SOCIETY Vol.75 No.9

The three-dimensional Burgers equation is used to count the effects of the spectral index param- eter , the ion, the negative (positive) kinematics viscosity coefficients of negatively (positively) charged grains on the dissipation of cylindrical shocks. The obtained results may useful for both laboratory and space applications of plasmas.

Multivalent mannose-decorated NIR nanoprobes for targeting pan lymph nodes

Wada, Hideyuki,Hyun, Hoon,Bao, Kai,Lee, Jeong Heon,El Fakhri, Georges,Choi, Yongdoo,Choi, Hak Soo Elsevier 2018 CHEMICAL ENGINEERING JOURNAL -LAUSANNE- Vol.340 No.-

<P><B>Abstract</B></P> <P>Lymphadenectomy is a prerequisite for most malignancies to define the precise staging of cancer, as well as resect the possible metastases completely. While it improves prognosis, lymphadenectomy often causes postoperative edema or bleeding because of unclear surgical margins. In this study, we synthesized near-infrared (NIR) fluorescent nanoprobes with conjugating various mannose moieties on the surface to target macrophages in the lymph node. Armed with these NIR nanoprobes, we demonstrated the feasibility of intraoperative pan lymph nodes (PLN) mapping and real-time optical imaging under the NIR fluorescence imaging system. We found that even single mannose-conjugated ZW800-1 showed specific uptake in lymph nodes within 4 h, and multiple mannose-employed polyrotaxanes highlighted PLN efficiently with low background signals in major organs. This technology can help surgeons perform lymphadenectomy with ease and safety by identifying all regional lymph nodes proficiently after a single intravenous injection of NIR nanoprobes.</P> <P><B>Highlights</B></P> <P> <UL> <LI> NIR fluorescent nanoprobes to target macrophages in the lymph node. </LI> <LI> Renal clearable nanoprobes enhance the SBR of target tissue. </LI> <LI> Intraoperative pan lymph nodes mapping is demonstrated. </LI> <LI> Surgeons can perform lymphadenectomy with ease and safety using NIR. </LI> </UL> </P> <P><B>Graphical abstract</B></P> <P>[DISPLAY OMISSION]</P>

Sparse-View Spectral CT Reconstruction Using Spectral Patch-Based Low-Rank Penalty

Kyungsang Kim,Jong Chul Ye,Worstell, William,Jinsong Ouyang,Rakvongthai, Yothin,El Fakhri, Georges,Quanzheng Li IEEE 2015 IEEE transactions on medical imaging Vol.34 No.3

<P>Spectral computed tomography (CT) is a promising technique with the potential for improving lesion detection, tissue characterization, and material decomposition. In this paper, we are interested in kVp switching-based spectral CT that alternates distinct kVp X-ray transmissions during gantry rotation. This system can acquire multiple X-ray energy transmissions without additional radiation dose. However, only sparse views are generated for each spectral measurement; and the spectra themselves are limited in number. To address these limitations, we propose a penalized maximum likelihood method using spectral patch-based low-rank penalty, which exploits the self-similarity of patches that are collected at the same position in spectral images. The main advantage is that the relatively small number of materials within each patch allows us to employ the low-rank penalty that is less sensitive to intensity changes while preserving edge directions. In our optimization formulation, the cost function consists of the Poisson log-likelihood for X-ray transmission and the nonconvex patch-based low-rank penalty. Since the original cost function is difficult to minimize directly, we propose an optimization method using separable quadratic surrogate and concave convex procedure algorithms for the log-likelihood and penalty terms, which results in an alternating minimization that provides a computational advantage because each subproblem can be solved independently. We performed computer simulations and a real experiment using a kVp switching-based spectral CT with sparse-view measurements, and compared the proposed method with conventional algorithms. We confirmed that the proposed method improves spectral images both qualitatively and quantitatively. Furthermore, our GPU implementation significantly reduces the computational cost.</P>

Bias Atlases for Segmentation-Based PET Attenuation Correction Using PET-CT and MR

Jinsong Ouyang,Se Young Chun,Petibon, Yoann,Bonab, Ali A.,Alpert, Nathaniel,El Fakhri, Georges IEEE 2013 IEEE transactions on nuclear science Vol.60 No.5

<P>This study was to obtain voxel-wise PET accuracy and precision using tissue-segmentation for attenuation correction. We applied multiple thresholds to the CTs of 23 patients to classify tissues. For six of the 23 patients, MR images were also acquired. The MR fat/in-phase ratio images were used for fat segmentation. Segmented tissue classes were used to create attenuation maps, which were used for attenuation correction in PET reconstruction. PET bias images were then computed using the PET reconstructed with the original CT as the reference. We registered the CTs for all the patients and transformed the corresponding bias images accordingly. We then obtained the mean and standard deviation bias atlas using all the registered bias images. Our CT-based study shows that four-class segmentation (air, lungs, fat, other tissues), which is available on most PET-MR scanners, yields 15.1%, 4.1%, 6.6%, and 12.9% RMSE bias in lungs, fat, non-fat soft-tissues, and bones, respectively. An accurate fat identification is achievable using fat/in-phase MR images. Furthermore, we have found that three-class segmentation (air, lungs, other tissues) yields less than 5% standard deviation of bias within the heart, liver, and kidneys. This implies that three-class segmentation can be sufficient to achieve small variation of bias for imaging these three organs. Finally, we have found that inter- and intra-patient lung density variations contribute almost equally to the overall standard deviation of bias within the lungs.</P>

Endocrine-specific NIR fluorophores for adrenal gland targeting

Ashitate, Yoshitomo,Levitz, Andrew,Park, Min Ho,Hyun, Hoon,Venugopal, Vivek,Park, GwangLi,El Fakhri, Georges,Henary, Maged,Gioux, Sylvain,Frangioni, John V.,Choi, Hak Soo The Royal Society of Chemistry 2016 Chemical communications Vol.52 No.67

<P>The adrenal glands (AGs) are relatively small yet require definitive identification during their resection, or more commonly their avoidance. To enable image-guided surgery involving the AGs, we have developed novel near-infrared (NIR) fluorophores that target the AGs after a single intravenous injection, which provided dual-NIR image-guided resection or avoidance of the AGs during both open and minimally-invasive surgery.</P>

Penalized PET Reconstruction Using Deep Learning Prior and Local Linear Fitting

Kim, Kyungsang,Wu, Dufan,Gong, Kuang,Dutta, Joyita,Kim, Jong Hoon,Son, Young Don,Kim, Hang Keun,El Fakhri, Georges,Li, Quanzheng IEEE 2018 IEEE transactions on medical imaging Vol.37 No.6

<P>Motivated by the great potential of deep learning in medical imaging, we propose an iterative positron emission tomography reconstruction framework using a deep learning-based prior. We utilized the denoising convolutional neural network (DnCNN) method and trained the network using full-dose images as the ground truth and low dose images reconstructed from downsampled data by Poisson thinning as input. Since most published deep networks are trained at a predetermined noise level, the noise level disparity of training and testing data is a major problem for their applicability as a generalized prior. In particular, the noise level significantly changes in each iteration, which can potentially degrade the overall performance of iterative reconstruction. Due to insufficient existing studies, we conducted simulations and evaluated the degradation of performance at various noise conditions. Our findings indicated that DnCNN produces additional bias induced by the disparity of noise levels. To address this issue, we propose a local linear fitting function incorporated with the DnCNN prior to improve the image quality by preventing unwanted bias. We demonstrate that the resultant method is robust against noise level disparities despite the network being trained at a predetermined noise level. By means of bias and standard deviation studies via both simulations and clinical experiments, we show that the proposed method outperforms conventional methods based on total variation and non-local means penalties. We thereby confirm that the proposed method improves the reconstruction result both quantitatively and qualitatively.</P>