2004 Fiscal Year Final Research Report Summary
Clinical Application of Scatter Correction with Artificial Neural Network in Myocardial and Brain SPECT
| Project/Area Number |
15591302
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Radiation science
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| Research Institution | Keio University |
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Principal Investigator |
HASHIMOTO Jun Keio University, Department of Medicine, Assistant Professor, 医学部, 講師 (20228414)
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| Co-Investigator(Kenkyū-buntansha) |
OGAWA Koichi Hosei University, Department of Electrical Engineering, Professor, 工学部, 教授 (00158817)
KUBO Atsushi Keio University, Department of Medicine, Professor, 医学部, 教授 (90051771)
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| Project Period (FY) |
2003 – 2004
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| Keywords | SPECT / Scatter Correction / Attenuation Correction / Brain SPECT / Myocardial SPECT / Artificial Neural Network |
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| Research Abstract |
We have developed a novel scatter correction algorithm with an artificial neural network(ANN) and it was validated in dual isotope SPECT for separating the primary photons of Tc-99m from those of I-123.
Initially, we arranged an ANN with one input layer comprising 10 units that requires 10 energy windows for data acquisition. This method resulted in errors within 4% in SPECT quantification in phantom experiments. However, the signal-to-noise ratios were not satisfactory, and the energy setting is not achievable in current gamma camera systems.
A renewed ANN containing one input layer with 3 units for 3 energy window acquisition was designed to overcome the above problems. Phantom experiments yielded images with reduced noise, and errors of SPECT quantification within 5%.
Clinical trials of the ANN were conducted in brain and myocardial SPECT for the simultaneous evaluation of rest and stress brain perfusion, myocardial perfusion and fatty acid metabolism, and myocardial perfusion and cardiac sympathetic nerve function. Technetium-99m and I-123 images were clearly separated and images with clinically acceptable noises were obtained.
This method enables us to obtain images including two different pieces of information of various kinds by just one-time acquisition.
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