We investigate the use of a deep neural network to automatically position imaging volumes for breast MR exams. The axial localizer images and scan volume information from a variety of MR scanners were used to train a deep neural network to replicate the clinical technologists’ placement. The average intersection over union between clinical placement and neural network predicted placement was 0.46 ± 0.21. The distance between volume centers was 7.4 cm on average and as low as 1.1 cm. These results show promise for improving consistency of imaging volume placement in breast MRI.
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