State-of-the-art radiation detectors for medical imaging: Demands and trends

2006 ◽  
Vol 569 (2) ◽  
pp. 153-158 ◽  
Author(s):  
Dimitra G. Darambara
Keyword(s):  
Medical Imaging ◽  
State Of The Art ◽  
Radiation Detectors

Entropy as a measure of the performance of phosphor materials used in medical imaging radiation detectors

2001 ◽  
Vol 72 (1) ◽  
pp. 67-72 ◽  
Author(s):  
D. Cavouras ◽  
I. Kandarakis ◽  
T. Maris ◽  
G.S. Panayiotakis ◽  
C.D. Nomicos
Keyword(s):  
Medical Imaging ◽  
Radiation Detectors ◽  
Materials Used

Ultra Low Noise Epitaxial 4H-SiC X-Ray Detectors

2009 ◽  
Vol 615-617 ◽  
pp. 845-848 ◽  
Author(s):  
Giuseppe Bertuccio ◽  
S. Caccia ◽  
Filippo Nava ◽  
Gaetano Foti ◽  
Donatella Puglisi ◽  
...  
Keyword(s):  
Detection Efficiency ◽  
State Of The Art ◽  
Reverse Current ◽  
Radiation Detectors ◽  
Low Noise ◽  
Active Volume ◽  
X Ray ◽  
Pixel Detectors ◽  
Schottky Junctions ◽  
Sic Wafer

The design and the experimental results of some prototypes of SiC X-ray detectors are presented. The devices have been manufactured on a 2’’ 4H-SiC wafer with 115 m thick undoped high purity epitaxial layer, which constitutes the detection’s active volume. Pad and pixel detectors based on Ni-Schottky junctions have been tested. The residual doping of the epi-layer was found to be extremely low, 3.7 x 1013 cm-3, allowing to achieve the highest detection efficiency and the lower specific capacitance of the detectors. At +22°C and in operating bias condition, the reverse current densities of the detector’s Schottky junctions have been measured to be between J=0.3 pA/cm2 and J=4 pA/cm2; these values are more than two orders of magnitude lower than those of state of the art silicon detectors. With such low leakage currents, the equivalent electronic noise of SiC pixel detectors is as low as 0.5 electrons r.m.s at room temperature, which represents a new state of the art in the scenario of semiconductor radiation detectors.

scholarly journals State of the Art: Eye-Tracking Studies in Medical Imaging

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 37023-37034 ◽  
Author(s):  
Lucie Leveque ◽  
Hilde Bosmans ◽  
Lesley Cockmartin ◽  
Hantao Liu
Keyword(s):  
Medical Imaging ◽  
Eye Tracking ◽  
State Of The Art

A counting and integrating pixel readout chip for amorphous selenium direct radiation detectors for medical imaging applications

2009 ◽  
Author(s):  
A. H. Goldan ◽  
Bahman Hadji ◽  
K. S. Karim ◽  
G. DeCrescenzo ◽  
J. A. Rowlands ◽  
...  
Keyword(s):  
Medical Imaging ◽  
Radiation Detectors ◽  
Amorphous Selenium ◽  
Direct Radiation

Evaluating Zn 2 SiO 4 :Mn phosphor for use in medical imaging radiation detectors

1998 ◽  
Vol 67 (5) ◽  
pp. 521-525 ◽  
Author(s):  
I. Kandarakis ◽  
D. Cavouras ◽  
P. Prassopoulos ◽  
E. Kanellopoulos ◽  
C.D. Nomicos ◽  
...  
Keyword(s):  
Medical Imaging ◽  
Radiation Detectors

scholarly journals Non-I.I.D. Multi-Instance Learning for Predicting Instance and Bag Labels with Variational Auto-Encoder

2021 ◽  
Author(s):  
Weijia Zhang
Keyword(s):  
Medical Imaging ◽  
Supervised Learning ◽  
Real World ◽  
State Of The Art ◽  
Experimental Results ◽  
Weakly Supervised Learning ◽  
Variational Autoencoder ◽  
Label Prediction ◽  
Weakly Supervised ◽  
Better Than

Multi-instance learning is a type of weakly supervised learning. It deals with tasks where the data is a set of bags and each bag is a set of instances. Only the bag labels are observed whereas the labels for the instances are unknown. An important advantage of multi-instance learning is that by representing objects as a bag of instances, it is able to preserve the inherent dependencies among parts of the objects. Unfortunately, most existing algorithms assume all instances to be identically and independently distributed, which violates real-world scenarios since the instances within a bag are rarely independent. In this work, we propose the Multi-Instance Variational Autoencoder (MIVAE) algorithm which explicitly models the dependencies among the instances for predicting both bag labels and instance labels. Experimental results on several multi-instance benchmarks and end-to-end medical imaging datasets demonstrate that MIVAE performs better than state-of-the-art algorithms for both instance label and bag label prediction tasks.

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