Abstract #0528

VISIBLE: Improvement in Vessel Visibility and Application of Machine Learning to Detect Brain Metastases

Kazufumi Kikuchi¹, Osamu Togao², Koji Yamashita³, Makoto Obara⁴, and Kousei Ishigami¹

¹Department of Clinical Radiology, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan, ²Department of Molecular Imaging & Diagnosis, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan, ³Department of Radiology Informatics & Network, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan, ⁴Philips Japan, Tokyo, Japan

Synopsis

Keywords: Tumors, Machine Learning/Artificial Intelligence, Brain metastasesThis study aimed to improve vessel visibility by modifying k-space filling and to verify the usefulness of volume isotropic simultaneous interleaved bright- and black-blood examination (VISIBLE) in detecting brain metastases using machine learning (ML). We tested three types of VISIBLE in different k-space fillings, and counted the number of vessels. We also tested the ML model by using VISIBLE. The number of vessels was lower in Centric and Reversed centric sequences than that in MPRAGE, but comparable in the Startup echo 30 sequence. Our ML model was achieved high sensitivity (97%) and there were no differences among three sequences.

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