Real-time neutron radiography imaging system

1994 ◽  
Author(s):  
Ailton F. Dias ◽  
Arnaldo de Albuquerque Araujo
2021 ◽  
Vol 187 (1) ◽  
pp. 145-153
Author(s):  
Conor R. Lanahan ◽  
Bridget N. Kelly ◽  
Michele A. Gadd ◽  
Michelle C. Specht ◽  
Carson L. Brown ◽  
...  

Abstract Purpose Safe breast cancer lumpectomies require microscopically clear margins. Real-time margin assessment options are limited, and 20–40% of lumpectomies have positive margins requiring re-excision. The LUM Imaging System previously showed excellent sensitivity and specificity for tumor detection during lumpectomy surgery. We explored its impact on surgical workflow and performance across patient and tumor types. Methods We performed IRB-approved, prospective, non-randomized studies in breast cancer lumpectomy procedures. The LUM Imaging System uses LUM015, a protease-activated fluorescent imaging agent that identifies residual tumor in the surgical cavity walls. Fluorescent cavity images were collected in real-time and analyzed using system software. Results Cavity and specimen images were obtained in 55 patients injected with LUM015 at 0.5 or 1.0 mg/kg and in 5 patients who did not receive LUM015. All tumor types were distinguished from normal tissue, with mean tumor:normal (T:N) signal ratios of 3.81–5.69. T:N ratios were 4.45 in non-dense and 4.00 in dense breasts (p = 0.59) and 3.52 in premenopausal and 4.59 in postmenopausal women (p = 0.19). Histopathology and tumor receptor testing were not affected by LUM015. Falsely positive readings were more likely when tumor was present < 2 mm from the adjacent specimen margin. LUM015 signal was stable in vivo at least 6.5 h post injection, and ex vivo at least 4 h post excision. Conclusions Intraoperative use of the LUM Imaging System detected all breast cancer subtypes with robust performance independent of menopausal status and breast density. There was no significant impact on histopathology or receptor evaluation.


2005 ◽  
Vol 49 (1) ◽  
pp. 380-387 ◽  
Author(s):  
Yan Q. Xiong ◽  
Julie Willard ◽  
Jagath L. Kadurugamuwa ◽  
Jun Yu ◽  
Kevin P. Francis ◽  
...  

ABSTRACT Therapeutic options for invasive Staphylococcus aureus infections have become limited due to rising antimicrobial resistance, making relevant animal model testing of new candidate agents more crucial than ever. In the present studies, a rat model of aortic infective endocarditis (IE) caused by a bioluminescently engineered, biofilm-positive S. aureus strain was used to evaluate real-time antibiotic efficacy directly. This strain was vancomycin and cefazolin susceptible but gentamicin resistant. Bioluminescence was detected and quantified daily in antibiotic-treated and control animals with IE, using a highly sensitive in vivo imaging system (IVIS). Persistent and increasing cardiac bioluminescent signals (BLS) were observed in untreated animals. Three days of vancomycin therapy caused significant reductions in both cardiac BLS (>10-fold versus control) and S. aureus densities in cardiac vegetations (P < 0.005 versus control). However, 3 days after discontinuation of vancomycin therapy, a greater than threefold increase in cardiac BLS was observed, indicating relapsing IE (which was confirmed by quantitative culture). Cefazolin resulted in modest decreases in cardiac BLS and bacterial densities. These microbiologic and cardiac BLS differences during therapy correlated with a longer time-above-MIC for vancomycin (>12 h) than for cefazolin (∼4 h). Gentamicin caused neither a reduction in cardiac S. aureus densities nor a reduction in BLS. There were significant correlations between cardiac BLS and S. aureus densities in vegetations in all treatment groups. These data suggest that bioluminescent imaging provides a substantial advance in the real-time monitoring of the efficacy of therapy of invasive S. aureus infections in live animals.


Inventions ◽  
2021 ◽  
Vol 6 (2) ◽  
pp. 42
Author(s):  
Worasit Sangjan ◽  
Arron H. Carter ◽  
Michael O. Pumphrey ◽  
Vadim Jitkov ◽  
Sindhuja Sankaran

Sensor applications for plant phenotyping can advance and strengthen crop breeding programs. One of the powerful sensing options is the automated sensor system, which can be customized and applied for plant science research. The system can provide high spatial and temporal resolution data to delineate crop interaction with weather changes in a diverse environment. Such a system can be integrated with the internet to enable the internet of things (IoT)-based sensor system development for real-time crop monitoring and management. In this study, the Raspberry Pi-based sensor (imaging) system was fabricated and integrated with a microclimate sensor to evaluate crop growth in a spring wheat breeding trial for automated phenotyping applications. Such an in-field sensor system will increase the reproducibility of measurements and improve the selection efficiency by investigating dynamic crop responses as well as identifying key growth stages (e.g., heading), assisting in the development of high-performing crop varieties. In the low-cost system developed here-in, a Raspberry Pi computer and multiple cameras (RGB and multispectral) were the main components. The system was programmed to automatically capture and manage the crop image data at user-defined time points throughout the season. The acquired images were suitable for extracting quantifiable plant traits, and the images were automatically processed through a Python script (an open-source programming language) to extract vegetation indices, representing crop growth and overall health. Ongoing efforts are conducted towards integrating the sensor system for real-time data monitoring via the internet that will allow plant breeders to monitor multiple trials for timely crop management and decision making.


2017 ◽  
Vol T170 ◽  
pp. 014027 ◽  
Author(s):  
A Huber ◽  
D Kinna ◽  
V Huber ◽  
G Arnoux ◽  
I Balboa ◽  
...  

2001 ◽  
Vol 125 (4) ◽  
pp. 1743-1753 ◽  
Author(s):  
Shoichiro Kiyomiya ◽  
Hiromi Nakanishi ◽  
Hiroshi Uchida ◽  
Atsunori Tsuji ◽  
Shingo Nishiyama ◽  
...  

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