Automatic localization of needles in real-time images can facilitate MR-guided percutaneous interventions. It enables automatic slice repositioning and targeting support and, thus, allows for faster workflows. The improvement of deep learning based passive needle tracking by using both, anatomical and positive contrast images as input was investigated. A prototype bSSFP sequence for interleaved acquisition of k-space lines for conventional and positive contrast with Cartesian readout was implemented and evaluated ex-vivo and in-vivo. The U-Net segmentation algorithm showed superior performance when using both contrasts. In conclusion, this method is a promising approach for robust needle localization in real-time interventional workflows.
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