Physics-Driven Mask R-CNN for Physical Needle Localization in MRI-Guided Percutaneous Interventions

• Published in: IEEE access Vol. 9; pp. 161055 - 161068
• Main Authors: Li, Xinzhou, Lee, Yu-Hsiu, Lu, David S., Tsao, Tsu-Chin, Wu, Holden H.
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial neural networks; convolutional neural network; Data acquisition; deep learning; device tracking; Image segmentation; Imaging; Interventional MRI; Localization; Location awareness; Magnetic resonance imaging; needle susceptibility; Needles; Percutaneous treatment; Phantoms; Physics; Training

Discrepancies between the needle feature position on magnetic resonance imaging (MRI) and the underlying physical needle position could increase localization errors during needle-based targeting procedures in MRI-guided percutaneous interventions. This work aimed to develop a deep learning-based fra...