Literature DB >> 15742938

High-resolution PET detector design: modelling components of intrinsic spatial resolution.

Jennifer R Stickel1, Simon R Cherry.   

Abstract

The development of dedicated small animal PET (positron emission tomography) scanners has led to significantly higher spatial resolution and comparable sensitivity to clinical scanners. However, it is not clear whether we are approaching the fundamental limit of spatial resolution. This work aims to understand what is currently limiting spatial resolution during data formation and collection and how to apply that knowledge to obtain the best possible resolution for small animal PET without sacrificing sensitivity. Monte Carlo simulations were performed of the interactions of a 511 keV photon in a variety of detector materials to evaluate the modulation transfer function of the materials. Positron range, non-colinearity and pixel size were modelled to determine the contribution of additional components of data formation and collection on the complete modulation transfer function of a PET system. These simulations are shown to predict the intrinsic detector resolution of current high resolution systems very well. They also show that current detectors are not limited by inter-crystal scatter. An intrinsic resolution of 0.5 mm can be achieved, but would require a detector with a pixel size of around 250 microm that can be read out unambiguously. It is shown that a range of different detector materials, both scintillators and semiconductors, can be used in these high-resolution detectors. While this design relies on thin (approximately 3 mm) pieces of material, stacks of the material are shown to simultaneously provide spatial resolution near 0.5 mm and 60% efficiency. This work has shown that detectors with significantly better resolution and sensitivity can be developed for small animal PET applications.

Mesh:

Year:  2005        PMID: 15742938     DOI: 10.1088/0031-9155/50/2/001

Source DB:  PubMed          Journal:  Phys Med Biol        ISSN: 0031-9155            Impact factor:   3.609


  39 in total

Review 1.  Instrumentation for molecular imaging in preclinical research: Micro-PET and Micro-SPECT.

Authors:  Arion F Chatziioannou
Journal:  Proc Am Thorac Soc       Date:  2005

2.  A prototype of very high resolution small animal PET scanner using silicon pad detectors.

Authors:  Sang-June Park; W Leslie Rogers; Sam Huh; Harris Kagan; Klaus Honscheid; Don Burdette; Enrico Chesi; Carlos Lacasta; Gabriela Llosa; Marko Mikuz; Andrej Studen; Peter Weilhammer; Neal H Clinthorne
Journal:  Nucl Instrum Methods Phys Res A       Date:  2007-01-21       Impact factor: 1.455

3.  Virtual-pinhole PET.

Authors:  Yuan-Chuan Tai; Heyu Wu; Debashish Pal; Joseph A O'Sullivan
Journal:  J Nucl Med       Date:  2008-02-20       Impact factor: 10.057

Review 4.  Novel detector technology for clinical PET.

Authors:  Roger Lecomte
Journal:  Eur J Nucl Med Mol Imaging       Date:  2009-03       Impact factor: 9.236

5.  Realistic PET Monte Carlo Simulation With Pixelated Block Detectors, Light Sharing, Random Coincidences and Dead-Time Modeling.

Authors:  Bastein Guérin; Georges El Fakhri
Journal:  IEEE Trans Nucl Sci       Date:  2008       Impact factor: 1.679

6.  Statistical image reconstruction from correlated data with applications to PET.

Authors:  Adam Alessio; Ken Sauer; Paul Kinahan
Journal:  Phys Med Biol       Date:  2007-10-01       Impact factor: 3.609

7.  Experimental characterization and system simulations of depth of interaction PET detectors using 0.5 mm and 0.7 mm LSO arrays.

Authors:  Sara St James; Yongfeng Yang; Yibao Wu; Richard Farrell; Purushottam Dokhale; Kanai S Shah; Simon R Cherry
Journal:  Phys Med Biol       Date:  2009-06-30       Impact factor: 3.609

8.  A residual correction method for high-resolution PET reconstruction with application to on-the-fly Monte Carlo based model of positron range.

Authors:  Lin Fu; Jinyi Qi
Journal:  Med Phys       Date:  2010-02       Impact factor: 4.071

9.  Image reconstructions from super-sampled data sets with resolution modeling in PET imaging.

Authors:  Yusheng Li; Samuel Matej; Scott D Metzler
Journal:  Med Phys       Date:  2014-12       Impact factor: 4.071

Review 10.  Machine learning in quantitative PET: A review of attenuation correction and low-count image reconstruction methods.

Authors:  Tonghe Wang; Yang Lei; Yabo Fu; Walter J Curran; Tian Liu; Jonathon A Nye; Xiaofeng Yang
Journal:  Phys Med       Date:  2020-07-29       Impact factor: 2.685

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