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Evaluation of Image Quality and Detectability of Deep Learning Image Reconstruction (DLIR) Algorithm in Single- and Dual-energy CT.
- Source :
- Journal of Digital Imaging; Aug2023, Vol. 36 Issue 4, p1390-1407, 18p, 2 Diagrams, 5 Charts, 9 Graphs
- Publication Year :
- 2023
-
Abstract
- This study is aimed to evaluate effects of deep learning image reconstruction (DLIR) on image quality in single-energy CT (SECT) and dual-energy CT (DECT), in reference to adaptive statistical iterative reconstruction-V (ASIR-V). The Gammex 464 phantom was scanned in SECT and DECT modes at three dose levels (5, 10, and 20 mGy). Raw data were reconstructed using six algorithms: filtered back-projection (FBP), ASIR-V at 40% (AV-40) and 100% (AV-100) strength, and DLIR at low (DLIR-L), medium (DLIR-M), and high strength (DLIR-H), to generate SECT 120kVp images and DECT 120kVp-like images. Objective image quality metrics were computed, including noise power spectrum (NPS), task transfer function (TTF), and detectability index (d′). Subjective image quality evaluation, including image noise, texture, sharpness, overall quality, and low- and high-contrast detectability, was performed by six readers. DLIR-H reduced overall noise magnitudes from FBP by 55.2% in a more balanced way of low and high frequency ranges comparing to AV-40, and improved the TTF values at 50% for acrylic inserts by average percentages of 18.32%. Comparing to SECT 20 mGy AV-40 images, the DECT 10 mGy DLIR-H images showed 20.90% and 7.75% improvement in d′ for the small-object high-contrast and large-object low-contrast tasks, respectively. Subjective evaluation showed higher image quality and better detectability. At 50% of the radiation dose level, DECT with DLIR-H yields a gain in objective detectability index compared to full-dose AV-40 SECT images used in daily practice. [ABSTRACT FROM AUTHOR]
- Subjects :
- DIAGNOSTIC imaging
NOISE
RESEARCH funding
COMPUTED tomography
DESCRIPTIVE statistics
DEEP learning
COMPUTERS in medicine
STATISTICS
DIGITAL image processing
QUALITY assurance
RADIATION doses
HEALTH outcome assessment
DATA analysis software
MULTIDETECTOR computed tomography
ALGORITHMS
CONTRAST media
Subjects
Details
- Language :
- English
- ISSN :
- 08971889
- Volume :
- 36
- Issue :
- 4
- Database :
- Complementary Index
- Journal :
- Journal of Digital Imaging
- Publication Type :
- Academic Journal
- Accession number :
- 169808801
- Full Text :
- https://doi.org/10.1007/s10278-023-00806-z