Advancements and Clinical Implications of Deep Learning-Based Synthetic CT Generation from MRI for Spine Surgery: A Literature Review

Abstract

This study reviews the transformative impact of deep learning (DL) in generating synthetic computed tomography (sCT) images from magnetic resonance imaging (MRI) datasets, particularly in spine surgery. It explores how DL-driven sCT aims to enhance surgical planning, improve diagnostic capabilities, and potentially integrate with navigation and robotic systems, while also critically evaluating current methodologies, performance metrics, and challenges to widespread clinical adoption. The overarching goal is to reduce patient radiation exposure and streamline clinical workflows by providing CT-equivalent bone visualization from MRI data.

Keywords

artificial intelligence - deep learning - magnetic resonance imaging - neuronavigation - spine surgery - surgical planning - synthetic CT

Publikationsverlauf

Artikel online veröffentlicht:
03. September 2025

• References

• 1 Abumoussa A, Gopalakrishnan V, Succop B. et al. Machine learning for automated and real-time two-dimensional to three-dimensional registration of the spine using a single radiograph. Neurosurg Focus 2023; 54 (06) E16
• 2 Bahrami A, Karimian A, Arabi H. Comparison of different deep learning architectures for synthetic CT generation from MR images. Phys Med 2021; 90: 99-107
• 3 Cole KL, Findlay MC, Kundu M, Johansen C, Rawanduzy C, Lucke-Wold B. The role of advanced imaging in neurosurgical diagnosis. J Mod Med Imaging 2023;
• 4 Ahern DP, Gibbons D, Schroeder GD, Vaccaro AR, Butler JS. Image-guidance, robotics, and the future of spine surgery. Clin Spine Surg 2020; 33 (05) 179-184
• 5 Boulanger M, Nunes JC, Chourak H. et al. Deep learning methods to generate synthetic CT from MRI in radiotherapy: a literature review. Phys Med 2021; 89: 265-281
• 6 Chen G, Xu Z. Usage of intelligent medical aided diagnosis system under the deep convolutional neural network in lumbar disc herniation. Appl Soft Comput 2021; 111
• 7 Li W, Kazemifar S, Bai T. et al. Synthesizing CT images from MR images with deep learning: model generalization for different datasets through transfer learning. Biomed Phys Eng Express 2021; 7 (02)
• 8 Gotoh M, Nakaura T, Funama Y. et al. Virtual magnetic resonance lumbar spine images generated from computed tomography images using conditional generative adversarial networks. Radiography (Lond) 2022; 28 (02) 447-453
• 9 Hong KT, Cho Y, Kang CH. et al. Lumbar spine computed tomography to magnetic resonance imaging synthesis using generative adversarial network: Visual Turing Test. Diagnostics (Basel) 2022; 12 (02) 530
• 10 Burström G, Buerger C, Hoppenbrouwers J. et al. Machine learning for automated 3-dimensional segmentation of the spine and suggested placement of pedicle screws based on intraoperative cone-beam computer tomography. J Neurosurg Spine 2019; 31 (01) 147-154
• 11 Cao G, Li Y, Wu S. et al. Clinical feasibility of MRI-based synthetic CT imaging in the diagnosis of lumbar disc herniation: a comparative study. Acta Radiol 2024; 65 (01) 41-48
• 12 van Stralen M, van der Kolk B, Zijlstra F. et al. BoneMRI of the cervical spine: Deep learning-based radiodensity contrast generation for selective visualization of osseous structures. ISMRM 27th Annual Meeting and Exhibition; May 11–16, 2019; Montreal, QC, Canada; 2019
• 13 Staartjes VE, Seevinck PR, Vandertop WP, van Stralen M, Schröder ML. Magnetic resonance imaging-based synthetic computed tomography of the lumbar spine for surgical planning: a clinical proof-of-concept. Neurosurg Focus 2021; 50 (01) E13
• 14 van der Kolk BBYM, Slotman DJJ, Nijholt IM. et al. Bone visualization of the cervical spine with deep learning-based synthetic CT compared to conventional CT: a single-center noninferiority study on image quality. Eur J Radiol 2022; 154: 110414
• 15 Bajger M, To M-S, Lee G. et al. Lumbar spine CT synthesis from MR images using CycleGAN—a preliminary study. In: 2021 Digital Image Computing: Techniques and Applications (DICTA). Australia: IEEE Publication; 2021: 1-8
• 16 Roberts M, Hinton G, Wells AJ. et al. Imaging evaluation of a proposed 3D generative model for MRI to CT translation in the lumbar spine. Spine J 2023; 23 (11) 1602-1612
• 17 Oulbacha R, Kadoury S. MRI to CT synthesis of the lumbar spine from a pseudo-3D Cycle GAN. IEEE 2020; 17: 1784-1787
• 18 Jans LBO, Chen M, Elewaut D. et al. MRI-based synthetic CT in the detection of structural lesions in patients with suspected sacroiliitis: comparison with MRI. Radiology 2021; 298 (02) 343-349
• 19 Morbée L, Chen M, Herregods N, Pullens P, Jans LBO. MRI-based synthetic CT of the lumbar spine: geometric measurements for surgery planning in comparison with CT. Eur J Radiol 2021; 144: 109999
• 20 Fritz J. Automated and radiation-free generation of synthetic CT from MRI data: does AI help to cross the finish line?. Radiology 2021; 298 (02) 350-352