This is the authors' abstract. We don't add key points for this paper.
Purpose To evaluate and compare the performance of diffusion-weighted imaging (DWI) using compressed sensing (CS) and DWI using CS with model-based deep learning reconstruction (DL-DWI) in detecting and differentiating liver metastases from hepatic hemangiomas. Materials and methods We retrospectively analyzed data from 53 patients with metastases or hemangiomas (34 men and 19 women, mean age, 65.9 years) who underwent abdominal DWI. Two radiologists evaluated liver contour and distortion, artifact, noise, overall image quality, and lesion conspicuity using a five-point scale. Signal-to-noise ratio (SNR) and apparent diffusion coefficient (ADC) of the liver, as well as contras-to-noise ratio (CNR) and ADC of metastases (n = 59) and hemangiomas (n = 33) were assessed and statistically compared. A receiver operating characteristic (ROC) analysis was performed to assess the diagnostic performance of the two sequences for differentiating metastases and hemangiomas. Results DL-DWI provided significantly better conspicuity of metastasis than CS-DWI (p < 0.05 in both radiologists), whereas no significant difference was observed in the conspicuity of hemangioma between DL-DWI and CS-DWI. The SNR of liver parenchyma and the CNR of metastases and hemangiomas were higher in DL-DWI than in CS-DWI (p < 0.05). ADC values of liver parenchyma, metastases, and hemangiomas were lower in DL-DWI than in CS-DWI (p < 0.05). The ADC cutoff value for differentiating between metastases and hemangiomas was 1.693 × 10 –3 mm 2 /s in DL-DWI and 1.411 × 10 –3 mm 2 /s in CS-DWI. No significant differences were observed in the area under the ROC curve, sensitivity, and specificity between the two methods (p > 0.05). Conclusion DL-DWI enhanced both qualitative and quantitative aspects of image quality in abdominal DWI. However, its diagnostic performance, including ADC cutoff values for differentiating between metastases and hemangiomas, is comparable to that of CS-DWI.
Answers are grounded in this paper and cite their sources.
No takes yet. Share an insight, caveat, or question.
Ozaki et al. (2025) studied this question.
Synapse has enriched 10 related papers. Consider them for comparative context: