Dual-energy contrast-enhanced digital mammography (DE-CEDM): optimization on digital subtraction with practical x-ray low/high-energy spectra

2006 ◽  
Author(s):  
Biao Chen ◽  
Zhenxue Jing ◽  
Andrew P. Smith ◽  
Samir Parikh ◽  
Yuri Parisky
Keyword(s):  
Digital Mammography ◽  
High Energy ◽  
Dual Energy ◽  
Energy Spectra ◽  
Digital Subtraction ◽  
X Ray ◽  
Contrast Enhanced

scholarly journals High Energy Resolution Hyperspectral X-Ray Imaging for Low-Dose Contrast-Enhanced Digital Mammography

2017 ◽  
Vol 36 (9) ◽  
pp. 1784-1795 ◽  
Author(s):  
Silvia Pani ◽  
Sarene C. Saifuddin ◽  
Filipa I.M. Ferreira ◽  
Nicholas Henthorn ◽  
Paul Seller ◽  
...  
Keyword(s):  
Energy Resolution ◽  
Digital Mammography ◽  
Low Dose ◽  
High Energy ◽  
High Energy Resolution ◽  
X Ray ◽  
X Ray Imaging ◽  
Contrast Enhanced

scholarly journals Combination of Dual-Energy X-ray Transmission and Variable Gas-Ejection for the In-Line Automatic Sorting of Many Types of Scrap in One Measurement

2021 ◽  
Vol 11 (10) ◽  
pp. 4349
Author(s):  
Tianzhong Xiong ◽  
Wenhua Ye ◽  
Xiang Xu
Keyword(s):  
High Efficiency ◽  
Nearest Neighbor ◽  
High Energy ◽  
Dual Energy ◽  
Center Of Gravity ◽  
Low Energy ◽  
Identification Accuracy ◽  
Flow Process ◽  
X Ray ◽  
Automatic Sorting

As an important part of pretreatment before recycling, sorting has a great impact on the quality, efficiency, cost and difficulty of recycling. In this paper, dual-energy X-ray transmission (DE-XRT) combined with variable gas-ejection is used to improve the quality and efficiency of in-line automatic sorting of waste non-ferrous metals. A method was proposed to judge the sorting ability, identify the types, and calculate the mass and center-of-gravity coordinates according to the shading of low-energy, the line scan direction coordinate and transparency natural logarithm ratio of low energy to high energy (R_value). The material identification was satisfied by the nearest neighbor algorithm of effective points in the material range to the R_value calibration surface. The flow-process of identification was also presented. Based on the thickness of the calibration surface, the material mass and center-of-gravity coordinates were calculated. The feasibility of controlling material falling points by variable gas-ejection was analyzed. The experimental verification of self-made materials showed that identification accuracy by count basis was 85%, mass and center-of-gravity coordinates calculation errors were both below 5%. The method proposed features high accuracy, high efficiency, and low operation cost and is of great application value even to other solid waste sorting, such as plastics, glass and ceramics.

scholarly journals New technologies in breast cancer detection – contrast enhanced dual-energy spectral mammography

2019 ◽  
pp. 49-61
Author(s):  
A. V. Chernaya ◽  
S. N. Novikov ◽  
P. V. Krivorotko ◽  
R. Kh. Ulyanova ◽  
V. V. Danilov
Keyword(s):  
Breast Cancer ◽  
Digital Mammography ◽  
Predictive Value ◽  
New Technologies ◽  
Malignant Tumors ◽  
Dual Energy ◽  
Post Contrast ◽  
Morphological Studies ◽  
Contrast Enhanced ◽  
Sensitivity Specificity

Purpose: to study the possibilities of contrast enhanced dual-energy spectral mammography (CESM) in the diagnostics of malignant tumors in the breast.Material and methods. Forty-seven patients with suspicious for breast cancer (BC) lesions underwent CESM. Digital mammography (MMG) and post-contrast images were correlated with the results of path morphological studies after surgery or puncture biopsy was performed.Results. Sensitivity, specificity and overall accuracy in the diagnostics of breast cancer were 83.3%, 85.7%, 85.1% for digital mammography and 91.6%, 91.4%, 91.4% for CESM, respectively. The positive predictive value was 66.6% for digital MMG and 78.5% for CESM. The negative predictive value (NPV) was 96.9% for the CESM and exceeded NPV of the digital MMG, which was 93.7%.Conclusion. Thus, these findings suggest that CESM is an effective method for the diagnostics of malignant tumors in the breast.

scholarly journals Exploring silver as a contrast agent for contrast-enhanced dual-energy X-ray breast imaging

2014 ◽  
Vol 87 (1041) ◽  
pp. 20140081 ◽  
Author(s):  
R Karunamuni ◽  
A Tsourkas ◽  
A D A Maidment
Keyword(s):  
Contrast Agent ◽  
Breast Imaging ◽  
Dual Energy ◽  
X Ray ◽  
Contrast Enhanced

WE-G-103-01: Dual-Energy Temporal Study of Contrast-Enhanced Digital Mammography in 20 Patients: The Use of a Pixel-By-Pixel Weighting Formalism for the Subtraction

2013 ◽  
Vol 40 (6Part31) ◽  
pp. 509-509
Author(s):  
ME Brandan ◽  
JP Cruz-Bastida ◽  
IM Rosado-Mendez ◽  
H Perez-Ponce ◽  
MJ Mateos ◽  
...  
Keyword(s):  
Digital Mammography ◽  
Dual Energy ◽  
Temporal Study ◽  
Contrast Enhanced

Energy spectrometer on a diffractometer using a charge-coupled device X-ray detector

2006 ◽  
Vol 39 (5) ◽  
pp. 767-770
Author(s):  
Hiroshi Abe ◽  
Hiroyuki Saitoh ◽  
Hironori Nakao ◽  
Kazuki Ito ◽  
Ken-ichi Ohshima
Keyword(s):  
Pyrolytic Graphite ◽  
High Energy ◽  
Energy Spectra ◽  
Time Data ◽  
Charge Coupled Device ◽  
X Ray ◽  
High Energy Accelerator ◽  
X Ray Scattering ◽  
Energy Spectrometer ◽  
A Charge

A charge-coupled device (CCD) X-ray detector for inelastic X-ray scattering was installed at beamline BL-4C of the Photon Factory at the High Energy Accelerator Research Organization in Japan. A wavelength-dispersive X-ray spectrometer was mounted on a six-circle diffractometer. Energy spectra were obtained by the CCD X-ray detector and a curved highly oriented pyrolytic graphite analyser. By the combination of energy spectroscopy and diffraction, simultaneous real-time data acquisition of both the momentum and the energy transfer was performed.

scholarly journals Flexible Learning-Free Segmentation and Reconstruction for Sparse Neuronal Circuit Tracing

2018 ◽  
Author(s):  
Ali Shahbazi ◽  
Jeffery Kinnison ◽  
Rafael Vescovi ◽  
Ming Du ◽  
Robert Hill ◽  
...  
Keyword(s):  
High Energy ◽  
Dominant Strategy ◽  
Machine Learning Algorithms ◽  
Biological Study ◽  
Learning Methods ◽  
Single Experiment ◽  
Flexible Learning ◽  
X Ray ◽  
Contrast Enhanced ◽  
Robust To Noise

AbstractImaging is a dominant strategy for data collection in neuroscience, yielding stacks of images that often scale to gigabytes of data for a single experiment. Machine learning algorithms from computer vision can serve as a pair of virtual eyes that tirelessly processes these images, automatically constructing more complete and realistic circuits. In practice, such algorithms are often too error-prone and computationally expensive to be immediately useful. We address these shortcomings with a new fast, flexible, learning-free method for sparse segmentation and reconstruction of neural volumes. Unlike learning methods, our Flexible Learning-free Reconstruction of Imaged Neural volumes (FLoRIN) pipeline exploits structure-specific contextual clues and requires no training. This approach generalizes across different modalities, including serially-sectioned scanning electron microscopy (sSEM) of genetically labeled and contrast enhanced processes, spectral confocal reflectance (SCoRe) microscopy, and high-energy synchrotron X-ray microtomography (μCT) of large tissue volumes. We deploy the FLoRIN pipeline on newly published and novel mouse datasets, demonstrating the high biological fidelity of the pipeline’s reconstructions, which are of sufficient quality for preliminary biological study. Compared to existing supervised learning methods, it is both significantly faster (up to several orders of magnitude) and produces high-quality reconstructions that are robust to noise and artifacts.

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