Abstract
Objective: To compare a deep learning (DL)-accelerated non-enhanced abbreviated MRI (AMRIDL) protocol with standard AMRI (AMRISTD) of the liver in terms of image quality and malignant focal lesion detection. Materials and Methods: This retrospective study included 155 consecutive patients (110 male; mean age 62.4 ± 11 years) from two sites who underwent standard liver MRI and additional AMRIDL sequences, specifically DL-accelerated single-shot fast-spin echo (SSFSEDL) and DL-accelerated diffusion-weighted imaging (DWIDL). Additional MRI phantom experiments assessed signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and apparent diffusion coefficient (ADC) values. Three reviewers evaluated AMRIDL and AMRISTD protocols for image quality using a five-point Likert scale and identified malignant hepatic lesions. Image quality scores and per-lesion sensitivities were compared between AMRIDL and AMRISTD using the Wilcoxon signed-rank test and logistic regression with generalized estimating equations, respectively. Results: Phantom experiments demonstrated comparable SNR and higher CNR for SSFSEDL compared to SSFSESTD, with similar ADC values for DWIDL and DWISTD. Among the 155 patients, 130 (83.9%) had chronic liver disease or a history of intra-or extrahepatic malignancy. Of 104 malignant focal lesions in 64 patients, 58 (55.8%) were hepatocellular carcinomas (HCCs), 38 (36.5%) were metastases, four (3.8%) were cholangiocarcinomas, and four (3.8%) were lymphomas. The pooled per-lesion sensitivity across three readers was 97.6% for AMRIDL, comparable to 97.6% for AMRISTD. Compared with AMRISTD, AMRIDL demonstrated superior image quality regarding structural sharpness, artifacts, and noise (all P < 0.001) and reduced the average scan time by approximately 50% (2 min 29 sec vs. 4 min 11 sec). In patients with chronic liver disease, AMRIDL achieved a 96.6% per-lesion sensitivity for HCC detection, similar to 96.5% for AMRISTD (P > 0.05). Conclusion: The AMRIDL protocol offers comparable sensitivity for detecting malignant focal lesions, including HCC while significantly enhancing image quality and reducing scan time by approximately 50% compared to AMRISTD.
| Original language | English |
|---|---|
| Pages (from-to) | 333-345 |
| Number of pages | 13 |
| Journal | Korean Journal of Radiology |
| Volume | 26 |
| Issue number | 4 |
| DOIs | |
| State | Published - Apr 2025 |
Bibliographical note
Publisher Copyright:© 2025 The Korean Society of Radiology.
Keywords
- Deep learning
- Hepatocellular carcinoma
- Liver
- Magnetic resonance imaging
- Sensitivity
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