Computed tomography (CT) is widely used for the diagnosis and surgical treatment of spinal pathologies, particularly for pedicle screw placement. However, CT’s limitations, notably radiation exposure, necessitate the development of alternative imaging techniques. Synthetic CT (sCT), which generates CT-like images from existing magnetic resonance imaging (MRI) scans, offers a promising alternative to reduce radiation exposure. This study examines the emerging role of sCT in spinal surgery, focusing on usability, efficiency, and potential impact on surgical outcomes. This qualitative literature review evaluated various sCT generation methods, encompassing traditional atlas-based and bulk-density models, as well as advanced convolutional neural network (CNN) architectures, including U-net, V-net, and generative adversarial network models. The review assessed sCT accuracy and clinical feasibility across different medical disciplines, particularly oncology and surgery, with potential applications in orthopedic, neurosurgical, and spinal surgery. sCT has shown significant promise across various medical disciplines. CNN-based techniques enable rapid and accurate generation of sCT from MRI scans, rendering clinical use feasible. sCT has been used to identify pathologies and monitor disease progression, suggesting that MRI alone may suffice for diagnosis and planning in the future. In spinal surgery, sCTs are particularly useful in visualizing key anatomical features like vertebral dimensions and spinal canal diameter. However, challenges persist, especially in visualizing complex structures and larger spinal regions, like the lumbar spine. Additional limitations include inaccuracies stemming from surgical implants and image variability. The application of sCT technology in spinal surgery holds great promise, improving diagnostics, planning, and treatment outcomes. Although further research is required to improve its precision, it offers a viable alternative to traditional CT in many clinical contexts, with the potential for broader application as the technology matures.

Computed tomography (CT) is widely used for the diagnosis and surgical treatment of spinal pathologies, particularly for pedicle screw placement. However, CT’s limitations, notably radiation exposure, necessitate the development of alternative imaging techniques. Synthetic CT (sCT), which generates CT-like images from existing magnetic resonance imaging (MRI) scans, offers a promising alternative to reduce radiation exposure. This study examines the emerging role of sCT in spinal surgery, focusing on usability, efficiency, and potential impact on surgical outcomes. This qualitative literature review evaluated various sCT generation methods, encompassing traditional atlas-based and bulk-density models, as well as advanced convolutional neural network (CNN) architectures, including U-net, V-net, and generative adversarial network models. The review assessed sCT accuracy and clinical feasibility across different medical disciplines, particularly oncology and surgery, with potential applications in orthopedic, neurosurgical, and spinal surgery. sCT has shown significant promise across various medical disciplines. CNN-based techniques enable rapid and accurate generation of sCT from MRI scans, rendering clinical use feasible. sCT has been used to identify pathologies and monitor disease progression, suggesting that MRI alone may suffice for diagnosis and planning in the future. In spinal surgery, sCTs are particularly useful in visualizing key anatomical features like vertebral dimensions and spinal canal diameter. However, challenges persist, especially in visualizing complex structures and larger spinal regions, like the lumbar spine. Additional limitations include inaccuracies stemming from surgical implants and image variability. The application of sCT technology in spinal surgery holds great promise, improving diagnostics, planning, and treatment outcomes. Although further research is required to improve its precision, it offers a viable alternative to traditional CT in many clinical contexts, with the potential for broader application as the technology matures.

Computed tomography (CT) is widely used for the diagnosis and surgical treatment of spinal pathologies, particularly for pedicle screw placement. However, CT’s limitations, notably radiation exposure, necessitate the development of alternative imaging techniques. Synthetic CT (sCT), which generates CT-like images from existing magnetic resonance imaging (MRI) scans, offers a promising alternative to reduce radiation exposure. This study examines the emerging role of sCT in spinal surgery, focusing on usability, efficiency, and potential impact on surgical outcomes. This qualitative literature review evaluated various sCT generation methods, encompassing traditional atlas-based and bulk-density models, as well as advanced convolutional neural network (CNN) architectures, including U-net, V-net, and generative adversarial network models. The review assessed sCT accuracy and clinical feasibility across different medical disciplines, particularly oncology and surgery, with potential applications in orthopedic, neurosurgical, and spinal surgery. sCT has shown significant promise across various medical disciplines. CNN-based techniques enable rapid and accurate generation of sCT from MRI scans, rendering clinical use feasible. sCT has been used to identify pathologies and monitor disease progression, suggesting that MRI alone may suffice for diagnosis and planning in the future. In spinal surgery, sCTs are particularly useful in visualizing key anatomical features like vertebral dimensions and spinal canal diameter. However, challenges persist, especially in visualizing complex structures and larger spinal regions, like the lumbar spine. Additional limitations include inaccuracies stemming from surgical implants and image variability. The application of sCT technology in spinal surgery holds great promise, improving diagnostics, planning, and treatment outcomes. Although further research is required to improve its precision, it offers a viable alternative to traditional CT in many clinical contexts, with the potential for broader application as the technology matures.