Optimizing contrast-enhanced abdominal MRI: A comparative study of deep learning and standard VIBE techniques.

OBJECTIVE

To validate a deep learning (DL) reconstruction technique for faster post-contrast enhanced coronal Volume Interpolated Breath-hold Examination (VIBE) sequences and assess its image quality compared to conventionally acquired coronal VIBE sequences.

METHODS

This prospective study included 151 patients undergoing clinically indicated upper abdominal MRI acquired on 3 T scanners. Two coronal T1 fat-suppressed VIBE sequences were acquired: a DL-reconstructed sequence (VIBEDL) and a standard sequence (VIBESD). Three radiologists independently evaluated six image quality parameters: overall image quality, perceived signal-to-noise ratio, severity of artifacts, liver edge sharpness, liver vessel sharpness, and lesion conspicuity, using a 4-point Likert scale. Inter-reader agreement was assessed using Gwet's AC2. Ordinal mixed-effects regression models were used to compare VIBEDL and VIBESD.

RESULTS

Acquisition times were 10.2 s for VIBEDL compared to 22.3 s for VIBESD. VIBEDL demonstrated superior overall image quality (OR 1.95, 95 % CI: 1.44-2.65, p < 0.001), reduced image noise (OR 3.02, 95 % CI: 2.26-4.05, p < 0.001), enhanced liver edge sharpness (OR 3.68, 95 % CI: 2.63-5.15, p < 0.001), improved liver vessel sharpness (OR 4.43, 95 % CI: 3.13-6.27, p < 0.001), and better lesion conspicuity (OR 9.03, 95 % CI: 6.34-12.85, p < 0.001) compared to VIBESD. However, VIBEDL showed increased severity of peripheral artifacts (OR 0.13, p < 0.001). VIBEDL detected 137/138 (99.3 %) focal liver lesions, while VIBESD detected 131/138 (94.9 %). Inter-reader agreement ranged from good to very good for both sequences.

CONCLUSION

The DL-reconstructed VIBE sequence significantly outperformed the standard breath-hold VIBE in image quality and lesion detection, while reducing acquisition time. This technique shows promise for enhancing the diagnostic capabilities of contrast-enhanced abdominal MRI.