paper-with-me

홈 › Papers

VLSI Implementation of TDC Architectures Used in PET Imaging Systems

2020-06-10 · Mehmet Akif Ozdemir, Ali Tangel

Positron emission tomography (PET) is a medical imaging method based on the measurement of concentrations of positron-emitting radionuclides in a living body. In the PET imaging system, glucose is labeled with a positron-emitting radionuclide and injected intravenously. Then, the positrons move through the tissue and collide with the electrons of the cells in which they interact. As a result of this interaction, two gamma rays are emitted in the opposite direction. Gama rays emitted from cancerous tissue that has retained radioactive glucose are detected through ring-shaped detectors. And the detected signals are converted into an electrical response. Subsequently, these responses are sampled with electronic circuits and recorded as histogram matrix to generate the image set. The gamma rays may not reach the detectors located in the opposite position in equal time. In PETs having TOF characteristics, it is aimed to obtain better positioning information by a method based on the principle of measuring the difference between the reach time of the two photons to detectors. The measurement of the flight time is carried out with TDC structures. The measurement of this time difference at the ps level is directly related to the spatial resolution of the PET system. In this study, 45 nm CMOS VLSI simulations of TDC structures that have various architectural approaches were performed for use in PET systems. With the designed TDC architectures, two gamma photons time reach to detectors have been simulated and the time difference has been successfully digitized. In addition, various performance metrics such as input and output voltages, time resolutions, measurement ranges, and power analysis of TDC architectures have been determined. Proposed Vernier oscillator-based TDC architecture has been reached 25 ps time resolution with a low power consumption of 1.62681 mW at 1V supply voltage.

📄 PDF Abstract BibTeX arXiv:2006.06034

Code (0)

등록된 구현이 없습니다.

Similar Papers 제목 키워드 기반

A VLSI Design and Implementation for a Real-Time Approximate Reasoning

2013-03-27 · Masaki Togai, Hiroyuki Watanabe

The role of inferencing with uncertainty is becoming more important in rule-based expert systems (ES), since knowledge given by a human expert is often uncertain or imprecise. We have succeeded in designing a VLSI chip w…

Decision Making

An improved wavelet-based signal-denoising architecture with less hardware consumption

2013-05-01 · 2019年12月15日 2013 5 · PankajGoel;MaheshChandraaAnkitaAnandbAsutoshKarc

This paper introduces a wavelet denoising architecture with adaptive thresholding for real-time 1D-systems and without the use of external memories for storing input data or wavelet coefficients. The Discrete Wavelet Tra…

DenoisingQuantization

A Design Methodology for Efficient Implementation of Deconvolutional Neural Networks on an FPGA

2017-05-07 · Xin-Yu Zhang, Srinjoy Das, Ojash Neopane, Ken Kreutz-Delgado

In recent years deep learning algorithms have shown extremely high performance on machine learning tasks such as image classification and speech recognition. In support of such applications, various FPGA accelerator arch…

CPUDenoisingGeneral ClassificationGenerative Adversarial Network+8

A compact aVLSI conductance-based silicon neuron

2015-09-03 · Runchun Wang, Chetan Singh Thakur, Tara Julia Hamilton, Jonathan Tapson 외

We present an analogue Very Large Scale Integration (aVLSI) implementation that uses first-order lowpass filters to implement a conductance-based silicon neuron for high-speed neuromorphic systems. The aVLSI neuron consi…

Fully-parallel Convolutional Neural Network Hardware

2020-06-22 · Christiam F. Frasser, Pablo Linares-Serrano, V. Canals, Miquel Roca 외

A new trans-disciplinary knowledge area, Edge Artificial Intelligence or Edge Intelligence, is beginning to receive a tremendous amount of interest from the machine learning community due to the ever increasing populariz…

BIG-bench Machine LearningEdge-computing