Sinogram-based deep learning image reconstruction technique in abdominal CT: image quality considerations
Autor: | Anushri Parakh, Michael A. Blake, Cristy A. Savage, Avinash Kambadakone, Theodore T. Pierce, Jinjin Cao |
---|---|
Rok vydání: | 2021 |
Předmět: |
medicine.medical_specialty
Artifact (error) Image quality business.industry media_common.quotation_subject Deep learning Ultrasound General Medicine Iterative reconstruction 030218 nuclear medicine & medical imaging 03 medical and health sciences Noise 0302 clinical medicine 030220 oncology & carcinogenesis medicine Image noise Contrast (vision) Radiology Nuclear Medicine and imaging Radiology Artificial intelligence business media_common |
Zdroj: | European Radiology. 31:8342-8353 |
ISSN: | 1432-1084 0938-7994 |
DOI: | 10.1007/s00330-021-07952-4 |
Popis: | To investigate the image quality and perception of a sinogram-based deep learning image reconstruction (DLIR) algorithm for single-energy abdominal CT compared to standard-of-care strength of ASIR-V. In this retrospective study, 50 patients (62% F; 56.74 ± 17.05 years) underwent portal venous phase. Four reconstructions (ASIR-V at 40%, and DLIR at three strengths: low (DLIR-L), medium (DLIR-M), and high (DLIR-H)) were generated. Qualitative and quantitative image quality analysis was performed on the 200 image datasets. Qualitative scores were obtained for image noise, contrast, small structure visibility, sharpness, and artifact by three blinded radiologists on a 5-point scale (1, excellent; 5, very poor). Radiologists also indicated image preference on a 3-point scale (1, most preferred; 3, least preferred). Quantitative assessment was performed by measuring image noise and contrast-to-noise ratio (CNR). DLIR had better image quality scores compared to ASIR-V. Scores on DLIR-H for noise (1.40 ± 0.53), contrast (1.41 ± 0.55), small structure visibility (1.51 ± 0.61), and sharpness (1.60 ± 0.54) were the best (p 0.05). DLIRs did not influence subjective textural perceptions and were preferred over ASIR-V from the beginning. All DLIRs had a higher CNR (26.38–102.30%) and lower noise (20.64–48.77%) than ASIR-V. DLIR-H had the best objective scores. Sinogram-based deep learning image reconstructions were preferred over iterative reconstruction subjectively and objectively due to improved image quality and lower noise, even in large patients. Use in clinical routine may allow for radiation dose reduction. • Deep learning image reconstructions (DLIRs) have a higher contrast-to-noise ratio compared to medium-strength hybrid iterative reconstruction techniques. • DLIR may be advantageous in patients with large body habitus due to a lower image noise. • DLIR can enable further optimization of radiation doses used in abdominal CT. |
Databáze: | OpenAIRE |
Externí odkaz: |