We have developed a point-of-care imaging method for non-melanoma skin cancer surgery whereby excised tissues are imaged with a smart near infrared quenched protease probe (6qcNIR) that fluoresces in the presence of overexpressed cathepsin proteases in basal cell carcinoma (BCC) and squamous cell carcinoma (SCC), and determine if margins are clear of cancer. Here we report our imaging system and our method to validate the detection of skin cancer. We imaged skin samples with an inverted, flying spot fluorescence scanner (LI-COR Odyssey CLx). Scatter in Odyssey system was greatly reduced giving an 80% improvement in the step response as compared to a previously used macroscopic imaging system with imaging of a fluorescence phantom. We developed a validation scheme for careful comparison of fluorescent cancer signal to histology annotation, involving image segmentation, fiducial based registration and non-rigid free-form deformation, using our LI-COR fluorescence images, corresponding color images, bread-loafed tissue images, H&E slides and pathologist annotation. Spatial accuracy in the bulk of the sample was ∼500 μm. Extrapolated with a linear stretch model suggests an error at the margin of <100 μm. Cancer annotations on H&E slides were transformed and superimposed on the probe fluorescence to generate the final result. In general, the fluorescence cancer signal corresponded with histological annotation.
Purpose: Dose calculation is a key component in radiation treatment planning systems. Its performance and accuracy are crucial to the quality of treatment plans as emerging advanced radiation therapy technologies are exerting ever tighter constraints on dose calculation. A common practice is to choose either a deterministic method such as the convolution/superposition (CS) method for speed or a Monte Carlo (MC) method for accuracy. The goal of this work is to boost the performance of a hybrid Monte Carlo convolution/superposition (MCCS) method by devising a graphics processing unit (GPU) implementation so as to make the method practical for day‐to‐day usage. Methods: Although the MCCS algorithm combines the merits of MC fluence generation and CS fluence transport, it is still not fast enough to be used as a day‐to‐day planning tool. To alleviate the speed issue of MC algorithms, the authors adopted MCCS as their target method and implemented a GPU‐based version. In order to fully utilize the GPU computing power, the MCCS algorithm is modified to match the GPU hardware architecture. The performance of the authors' GPU‐based implementation on an Nvidia GTX260 card is compared to a multithreaded software implementation on a quad‐core system. Results: A speedup in the range of 6.7–11.4× is observed for the clinical cases used. The less than 2% statistical fluctuation also indicates that the accuracy of the authors' GPU‐based implementation is in good agreement with the results from the quad‐core CPU implementation. Conclusions: This work shows that GPU is a feasible and cost‐efficient solution compared to other alternatives such as using cluster machines or field‐programmable gate arrays for satisfying the increasing demands on computation speed and accuracy of dose calculation. But there are also inherent limitations of using GPU for accelerating MC‐type applications, which are also analyzed in detail in this article.
The revolutionary technique cryoelectron tomography (cryo-ET) enables imaging of cellular structure and organization in a near-native environment at submolecular resolution, which is vital to subsequent data analysis and modeling. The conventional structure detection process first reconstructs the three-dimensional (3D) tomogram from a series of two-dimensional (2D) projections and then directly detects subcellular components found within the tomogram. However, this process is challenging due to potential structural information loss during the tomographic reconstruction and the limited scope of existing methods since most major state-of-the-art object detection methods are designed for 2D rather than 3D images. Therefore, in this article, as an alternative approach to complement the conventional process, we propose a novel 2D-to-3D framework that detects structures within 2D projection images before reconstructing the results back to 3D. We implemented the proposed framework as three specific algorithms for three individual tasks: semantic segmentation, edge detection, and object localization. As experimental validation of the 2D-to-3D framework for cryo-ET data, we applied the algorithms to the segmentation of mitochondrial calcium phosphate granules, detection of spherical edges, and localization of mitochondria. Quantitative and qualitative results show better performance for prediction tasks of segmentation on the 2D projections and promising performance on object localization and edge detection, paving the way for future studies in the exploration of cryo-ET for in situ structural biology.
Coronary artery calcification (CAC) as assessed with CT calcium score is the best biomarker of coronary artery disease. Dual energy x-ray provides an inexpensive, low radiation-dose alternative. A two shot system (GE Revolution-XRd) is used, raw images are processed with a custom algorithm, and a coronary calcium image (DECCI) is created, similar to the bone image, but optimized for CAC visualization, not lung visualization. In this report, we developed a physicsbased, digital-phantom containing heart, lung, CAC, spine, ribs, pulmonary artery, and adipose elements, examined effects on DECCI, suggested physics-inspired algorithms to improve CAC contrast, and evaluated the correlation between CT calcium scores and a proposed DE calcium score. In simulation experiment, Beam hardening from increasing adipose thickness (2cm to 8cm) reduced Cg by 19% and 27% in 120kVp and 60kVp images, but only reduced Cg by <7% in DECCI. If a pulmonary artery moves or pulsates with blood filling between exposures, it can give rise to a significantly confounding PA signal in DECCI similar in amplitude to CAC. Observations suggest modifications to DECCI processing, which can further improve CAC contrast by a factor of 2 in clinical exams. The DE score had the best correlation with "CT mass score" among three commonly used CT scores. Results suggest that DE x-ray is a promising tool for imaging and scoring CAC, and there still remains opportunity for further DECCI processing improvements.
Computer-aided detection or diagnosis (CAD) has been a promising area of research over the last two decades. Medical image analysis aims to provide a more efficient diagnostic and treatment process for the radiologists and clinicians. However, with the development of science and technology, data interpretation manually in the conventional CAD systems has gradually become a challenging task. Deep learning methods, especially convolutional neural networks (CNNs), are successfully used as tools to solve this problem. This includes applications such as breast cancer diagnosis, lung nodule detection and prostate cancer localization. In this overview, the current state-of-the-art medical image analysis techniques in CAD research are presented, which focus on the convolutional neural network (CNN) based methods. The commonly used medical image databases in literature are also listed. It is anticipated that this paper can provide researchers in radiomics, precision medicine, and imaging grouping with a systematic picture of the CNN-based methods used in CAD research.
Epidermal growth factor receptor (EGFR) is a promising target for the treatment of different types of malignant tumors. Therefore, a combined molecular modeling study was performed on a series of quinazoline derivatives as EGFR inhibitors. The optimum ligand-based CoMFA and CoMSIA models showed reliable and satisfactory predictability (with R2cv=0.681, R2ncv=0.844, R2pred=0.8702 and R2cv=0.643, R2ncv=0.874, R2pred=0.6423). The derived contour maps provide structural features to improve inhibitory activity. Furthermore, the contour maps, molecular docking, and molecular dynamics (MD) simulations have good consistency, illustrating that the derived models are reliable. In addition, MD simulations and binding free energy calculations were also carried out to understand the conformational fluctuations at the binding pocket of the receptor. The results indicate that hydrogen bond, hydrophobic and electrostatic interactions play significant roles on activity and selectivity. Furthermore, amino acids Val31, Lys50, Thr95, Leu149 and Asp160 are considered as essential residues to participate in the ligand-receptor interactions. Overall, this work would offer reliable theoretical basis for future structural modification, design and synthesis of novel EGFR inhibitors with good potency.Communicated by Ramaswamy H. Sarma
Focal adhesion kinase (FAK) is a promising target for developing more effective anticancer drugs. To better understand the structure-activity relationships and mechanism of actions of FAK inhibitors, a molecular modeling study using 3D-QSAR, molecular docking, molecular dynamics simulations, and binding free energy analysis were conducted. Two types of satisfactory 3D-QSAR models were generated, comprising the CoMFA model (R2cv = 0.528, R2pred = 0.7557) and CoMSIA model (R2cv = 0.757, R2pred = 0.8362), for predicting the inhibitory activities of novel inhibitors. The derived contour maps indicate structural characteristics for substituents on the template. Molecular docking, molecular dynamic simulations and binding free energy calculations further reveal that the binding of inhibitors to FAK is mainly contributed from hydrophobic, electrostatic and hydrogen bonding interactions. In addition, some key residues (Arg14, Glu88, Cys90, Arg138, Asn139, Leu141, and Leu155) responsible for ligand-receptor binding are highlighted. All structural information obtained from 3D-QSAR models and molecular dynamics is consist with the available experimental activities. All the results will facilitate the optimization of this series of FAK inhibitors with higher inhibitory activities.
With the improvement of SoC design flow, early system prototype is an efficient way in which designers can find function bugs and performance limitations. The SystemC transaction-level model, a high level system model, has been attracted great attentions in embedded system design community. In this paper, we present a system prototype methodology, its corrective modeling technique and development environment. Our transaction-level executable model covers the range from top-level specific application to low-level infrastructure and some OS-like support functions. We develop a tool called transaction viewer to analyze the simulation results. The demonstration of audio communication is provided as an example for highlighting the whole design process
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