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IEEE Transactions on Medical Imaging
Article . 2021 . Peer-reviewed
License: CC BY
Data sources: Crossref
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Self-Collimating SPECT With Multi-Layer Interspaced Mosaic Detectors

Authors: Tianyu Ma; Qingyang Wei; Zhenlei Lyu; Debin Zhang; Hongyang Zhang; Rui Wang; Jiahong Dong; +3 Authors

Self-Collimating SPECT With Multi-Layer Interspaced Mosaic Detectors

Abstract

Conventional single photon emission computed tomography (SPECT) relies on mechanical collimation whose resolution and sensitivity are interdependent, the best performance a SPECT system can attain is only a compromise of these two equally desired properties. To simultaneously achieve high resolution and sensitivity, we propose to use sensitive detectors constructed in a multi-layer in ter spaced mosaicdetectors (MATRICES) architecture to accomplish part of the collimation needed. We name this new approach self-collimation. We evaluate three self-collimating SPECT systems and report their imaging performance: 1) A simulated human brain SPECT achieves 3.88% sensitivity, it clearly resolves 0.5-mm and 1.0-mm hot-rod patterns at noise-free and realistic count-levels, respectively; 2) a simulated mouse SPECT achieves 1.25% sensitivity, it clearly resolves 50- [Formula: see text] and 100- [Formula: see text] hot-rod patterns at noise-free and realistic count-levels, respectively; 3) a SPECT prototype achieves 0.14% sensitivity and clearly separates 0.3-mm-diameter point sources of which the center-to-center neighbor distance is also 0.3 mm. Simulated contrast phantom studies show excellent resolution and signal-to-noise performance. The unprecedented system performance demonstrated by these 3 SPECT scanners is a clear manifestation of the superiority of the self-collimating approach over conventional mechanical collimation. It represents a potential paradigm shift in SPECT technology development.

Keywords

Radioisotopes, Tomography, Emission-Computed, Single-Photon, Phantoms, Imaging, Mice, Animals, Humans, Tomography, X-Ray Computed

  • BIP!
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    citations
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    16
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Top 10%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 10%
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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
16
Top 10%
Top 10%
Top 10%
hybrid