Abstract #4659

Deep Learning Reconstruction for Abdomen Diagnosis: Improvement of Image Quality and Diagnostic Performance

Po-Ting Chen^1,2, Cheng-Ya Yeh², Yi-Chen Chen², Chia-Wei Li³, Charng-Chyi Shieh³, Chien-Yuan Lin³, and Kao-Lang Liu^1,2

¹Department of Medical Imaging, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan, ²Department of Medical Imaging, National Taiwan University Cancer Center, Taipei, Taiwan, ³GE Healthcare, Taipei, Taiwan

Synopsis

There has been no study that demonstrated the quality improvement in abdomen images by DLrecon. We evaluate the image quality comparison, included SNR, CNR, and clinical scoring, between DL and non-DL abdomen images. DLrecon are found to be less artefacts, higher SNR, CNR, and clinical scores than those on non-DL images. This study proves the improvement of image quality by DLrecon, and DLrecon shows its potential power in clinical diagnosis of abdomen images to overcome classical MRI trade-off resolution, SNR, and scan time.

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