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IMPROVEMENTS OF 111IN SPECT IMAGES RECONSTRUCTED WITH SPARSELY ACQUIRED PROJECTIONS BY DEEP LEARNING GENERATED SYNTHETIC PROJECTIONS
- Source :
- Radiation Protection Dosimetry
- Publication Year :
- 2021
- Publisher :
- Oxford University Press (OUP), 2021.
-
Abstract
- The aim was to improve single-photon emission computed tomography (SPECT) quality for sparsely acquired 111In projections by adding deep learning generated synthetic intermediate projections (SIPs). Method: The recently constructed deep convolutional network for generating synthetic intermediate projections (CUSIP) was used for improving 20 sparsely acquired 111In-octreotide SPECTs. Reconstruction was performed with 120 (120P) or 30 (30P) projections, or 120 projections with 90 SIPs generated from 30 projections (30–120SIP). The SPECT reconstructions were performed with attenuation, scatter and collimator response corrections. Postfiltered 30P reconstructed SPECT was also analyzed. Image quality were quantitatively evaluated with root-mean-square error, peak signal-to-noise ratio and structural similarity index metrics. Result: The 30–120SIP reconstructed SPECT had statistically significant improved image quality parameters compared to 30P reconstructed SPECT with and without post filtering. The images visual appearance was similar to slightly filtered 120P SPECTs. Thereby, substantial acquisition time reduction with SIPs seems possible without image quality degradation.
- Subjects :
- Paper
Computer science
Image quality
030218 nuclear medicine & medical imaging
law.invention
03 medical and health sciences
Deep Learning
0302 clinical medicine
Post filtering
law
Image Processing, Computer-Assisted
medicine
Radiology, Nuclear Medicine and imaging
Tomography, Emission-Computed, Single-Photon
Image quality degradation
AcademicSubjects/SCI00180
Radiation
Radiological and Ultrasound Technology
medicine.diagnostic_test
Phantoms, Imaging
business.industry
Deep learning
Attenuation
Indium Radioisotopes
Public Health, Environmental and Occupational Health
Collimator
Pattern recognition
General Medicine
030220 oncology & carcinogenesis
Acquisition time
Artificial intelligence
business
Emission computed tomography
Subjects
Details
- ISSN :
- 17423406 and 01448420
- Volume :
- 195
- Database :
- OpenAIRE
- Journal :
- Radiation Protection Dosimetry
- Accession number :
- edsair.doi.dedup.....d8f3b4849b3709d4fca3f840f66db00a