Deep Learning based prediction of the planes for automated planning of MRI imaging of cervical neural foramina and lumbar pars interarticularis

We present a generalized DL-based intelligent slice placement framework for planes of cervical neural foramina (CF) and lumbar pars interarticularis (PI) for spine MRI. CF and PI scan improves assessment of foraminal stenosis and lumbar spondylolysis respectively, but requires highly skilled operator for accurate prescription. Our approach enables automatic patient-specific scan plane prescription from routine axial T2W images. In our test, it achieves mean error of <0.7 mm and <0.2 degrees and demonstrates similar/better contiguous anatomical visualization to manual scans on retrospective reformatting of 3D data. These results indicate that our approach is accurate and suitable for clinical usage.