| Autor: |
Takahiro Ueda, Kaori Yamamoto, Natsuka Yazawa, Ikki Tozawa, Masato Ikedo, Masao Yui, Hiroyuki Nagata, Masahiko Nomura, Yoshiyuki Ozawa, Yoshiharu Ohno |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
European Radiology Experimental, Vol 8, Iss 1, Pp 1-9 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2509-9280 |
| DOI: |
10.1186/s41747-024-00506-5 |
| Popis: |
Abstract Background We aimed to determine the capabilities of compressed sensing (CS) and deep learning reconstruction (DLR) with those of conventional parallel imaging (PI) for improving image quality while reducing examination time on female pelvic 1.5-T magnetic resonance imaging (MRI). Methods Fifty-two consecutive female patients with various pelvic diseases underwent MRI with T1- and T2-weighted sequences using CS and PI. All CS data was reconstructed with and without DLR. Signal-to-noise ratio (SNR) of muscle and contrast-to-noise ratio (CNR) between fat tissue and iliac muscle on T1-weighted images (T1WI) and between myometrium and straight muscle on T2-weighted images (T2WI) were determined through region-of-interest measurements. Overall image quality (OIQ) and diagnostic confidence level (DCL) were evaluated on 5-point scales. SNRs and CNRs were compared using Tukey’s test, and qualitative indexes using the Wilcoxon signed-rank test. Results SNRs of T1WI and T2WI obtained using CS with DLR were higher than those using CS without DLR or conventional PI (p |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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