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Abstract #2764

Fast hepatobiliary phase gadoxetate-enhanced imaging under breath-holding utilizing DL reconstruction (Sonic DL): preliminary experience

Keisuke Sato1, Shinji Tanaka1, Ryo Murayama1, Yukihisa Takayama1, Atsushi Nozaki2, Xucheng Zhu3, Ty Cashen4, Arnaud Guidon5, Tetsuya Wakayama2, and Kengo Yoshimitsu1
1Fukuoka University, Fukuoka prefecture, Japan, 2GE HealthCare, Hino, Japan, Tokyo prefecture, Japan, 3GE HealthCare, Menlo Park, CA, USA, Menlo Park, CA, United States, 4GE Healthcare, Madison, WI, USA, Madison, WI, United States, 5GE Healthcare, Boston, MA, USA, Bostno, MA, United States

Synopsis

Keywords: AI/ML Image Reconstruction, AI/ML Image Reconstruction, DL Speed, AIR Recon DL

Motivation: DL Speed (DLS), a modified Sonic DL, is a new deep learning technology, reconstructing high-quality MRI images from under-sampled k-space data, providing minimal image degradation compared to standard methods.

Goal(s): To assess the performance of DLS-LAVA for breath-holding hepatobiliary phase (HBP) imaging in comparison with conventional LAVA.

Approach: In 20 liver MRI cases, DLS-LAVA and conventional LAVA were evaluated by two radiologists using qualitative and quantitative measures, including a liver-spleen intensity ratio (LSR).

Results: DLS-LAVA provided superior overall image quality, sharpness, and aliasing though graininess was slightly less favorable in some highly undersampled settings. DLS-LAVA achieved high-quality HBP images with shorter breath-holding times.

Impact: DLS-LAVA enables radiologists to obtain high-quality HBP images with reduced scan time, enhancing patient comfort and diagnostic precision compared to conventional LAVA. This advancement is especially valuable for patients with limited breath-holding capacity.

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Keywords