MRI to CT Synthesis of the Lumbar Spine from a Pseudo-3D Cycle GAN

In this paper, we introduce a fully unsupervised approach for the synthesis of CT images of the lumbar spine, used for image-guided surgical procedures, from a T2-weighted MRI acquired for diagnostic purposes. Our approach makes use of a trainable pre-processing pipeline using a low-capacity fully convolutional network, to normalize the input MRI data, in cascade with FC-ResNets, to segment the vertebral bodies and pedicles. A pseudo-3D Cycle GAN architecture is proposed to include neighboring slices in the synthesis process, along with a cyclic loss function ensuring consistency between MRI and CT synthesis. Clinical experiments were performed on the SpineWeb dataset, totalling 18 patients with both MRI and CT. Quantitative comparison to expert CT segmentations yields an average Dice score of 83 ± 1.6 on synthetic CTs, while a comparison to CT annotations yielded a landmark localization error of 2.2 ± 1.4mm. Intensity distributions and mean absolute errors in Hounsfield units also show promising results, illustrating the strong potential and versatility of the pipeline by achieving clinically viable CT scans which can be used for surgical guidance.