Rapid LAVA imaging with deep learning reconstruction: evaluation of image quality and diagnostic performance in patients with liver cancer

Motivation: 3D gradient-echo based liver acceleration volume acquisition (LAVA) sequence is widely used for dynamic contrast imaging in liver. LAVA usually requires breath-holding for over 16 seconds, posing a challenge for individuals with difficulty in prolonged breath-holding. Goal(s): To investigate whether deep learning reconstruction (DLR) allows for LAVA imaging with reduced scan time but without sacrificing image diagnostic quality. Approach: SNR, CNR, and subjective analysis using 5-point Likert scales were compared to evaluate the image quality and diagnostic performance between DLR-LAVA and conventional LAVA. Results: Compared to conventional LAVA, DLR-LAVA showed similar SNR, CNR, and qualitative image quality scores. Impact: Deep learning reconstruction based rapid LAVA imaging is promising for reducing breath-hold time while maintaining similar image quality compared with conventional LAVA imaging.