Introduction

Imaging methods of the spine have greatly expanded since the advent of X-rays for the use of plain radiographs (c.1895), providing anatomical clarity for diagnosis and treatment of the cervical, thoracic, lumbar, and sacral vertebrae. The complex anatomy of the vertebrae and irregular contours and geometry of the spinal elements have influenced the rapid development of more precise imaging modalities. Various advancements to two- and three-dimensional (i.e., 2D, 3D, respectively) imaging through plain radiographs, computed tomography (CT), magnetic resonance imaging (MRI), ultrasound, and EOS imaging along with multiple dimensional views of the spine are continuously optimized. Future developments in imaging may improve the assessment of pedicle screw placement and image definition, reduce radiation, provide quantitative information about disc and other soft tissue composition, and provide autonomous spinal mapping using artificial intelligence (AI).

Future of Plain Radiographs

Plain radiographs utilize a heterogeneous emission of X-rays projected in the direction of a detector, ultimately curating an image established through components of the intervening objects, including composition as well as density. Plain radiographs may see improvements by providing image intensification, digital detectors, and radiosensitive screen amplifications. Although plain radiographs demonstrate a substantial degree of reliability, computed tomography (CT) has become the primary choice in imaging spinal injuries while obtaining similar measurements to plain radiographs. Many may transpose their diagnostic care toward CT imaging over plain radiographs. Computed tomography utilizes a computerized X-ray imaging method in which a beam of X-rays is directed at the patient and rapidly circumvolved around the patient to develop cross-sectional images of the patient’s body. Plain radiographs are increasingly replaced by contemporary imaging methods, including, but not limited to, intraoperative CT (ICT), 3D imaging, and the newfound application of AI and augmented reality (AR). Several reasons exist for the gradual phasing out of plain radiographs. Foremost are the limitations on types of features visible in the image. Such limitations are evident when soft tissue, high image resolution, and 3D characteristics are desired. For example, identification of bone loss on plain radiographs is typically not appreciable until over 30%–40% loss has occurred. Furthermore, these limitations persist when detecting pedicle screws with a “safe” misguided freehand placement. Instead, CT technology boasts a higher accuracy and, therefore, a better-suited imaging technique for the confirmation of suspected incorrect screw positioning. Plain radiographs have also proven inadequate in terms of cervical spine assessment following blunt trauma due to poor visibility of the entire cervical spine. An accompanying CT scan is often required to assay the cervical spine accurately, eliminating the need for a plain radiograph. These circumstances have indicated the urgency to adopt alternative and more modern methods in spinal imaging.

Future of Computed Tomography

The use of CT imaging in the United States has significantly increased in the past two decades, although its growth has slowed in recent years. Additionally, pediatric CT usage has seen a significant decrease due to concerns for excessive radiation exposure. The increase in adult CT usage has brought about concerns about the health risks associated with radiation exposure. Regardless, CT remains an often-used tool for adult patients to obtain detailed images of the spine and, in some cases, even offers reduced radiation doses compared to some standard techniques.

Fluoroscopy and CT are standard techniques for surgical navigation. Computed tomography has been shown to expose physicians to less radiation than fluoroscopy; however, radiation exposure for patients was higher. Intraoperative CT systems have improved accuracy and success in spine surgery. Comparative studies with fluoroscopic-based techniques have repeatedly found intraoperative CT imaging to have superior accuracy and less radiation exposure for the surgeon. Additionally, modifications to amperage, scan, and scout length radiation exposure for patients can also be reduced compared to standard CT imaging. However, a novel technique using a detachable pedicle marker and probe combined with pulsed fluoroscopy has been shown to considerably reduce radiation exposure for both patients and physicians while maintaining comparable accuracy to standard fluoroscopy and intraoperative CT imaging.

Co-registration or image fusion of CT images with preoperative MRI can enhance navigation for spinal procedures. A problem with attempting to fuse MRI with CT arises from patient positioning changing between the different imaging procedures. The study by Hille et al. utilized a multisegmented approach for image registration to account for this issue. The fusion of MRI with CT using this multisegmented approach may provide the greatest benefit to spinal metastases being targeted by radiofrequency ablations. The multisegmented approach takes significantly less time for registration compared to the landmark-based directive (an average of 24 s per vertebra vs an average of 8 min per vertebra), illustrating that this technique can be integrated practically into the clinical workflow.

A case study of two patients analyzed the usage of co-registered intraoperative CT and MRI for the resection of intradural spinal tumors. For one of the patients, additional preoperative diffusion tensor imaging (DTI) was also co-registered with the other imaging modalities. In each case, the co-registration of intraoperative CT with MRI and DTI enhanced navigation during tumor removal. The study reported that the addition of DTI with intraoperative CT helped with the localization of the tumor and allowed for about 90% removal, which would have been unlikely without the co-registration of DTI.

Machine learning applications for spinal imaging are continually evolving. A team of medical researchers at Pusan National University Hospital in Busan, Korea, developed a machine learning model to predict osteoporosis from Hounsfield units of lumbar CT images. Machine learning models for the identification and classification of vertebral fractures from CT images have also been developed. Deep learning has also been utilized for the automatic segmentation of lumbosacral nerves from CT images to aid in the 3D reconstruction of targeted areas for the viability assessment of transforaminal steroid injection. Compared to manual segmentation, the deep learning model saves considerable time. There are additional models being developed. Machine learning for spinal imaging will be an area of rapid growth in the upcoming years.

Future of Magnetic Resonance Imaging

Magnetic resonance imaging provides high-resolution multiplanar images of vertebral and soft tissue anatomy without posing any risks associated with radiation exposure, proving to be the diagnostic procedure of choice for most spinal pathologies. This imaging modality offers relatively high sensitivity and specificity for the assessment of infections, tumors, disc degeneration, pathologic fractures, and disc herniations. However, MRI is relatively expensive and has a variable degree of utility in obese, claustrophobic, and pacemaker-dependent patients.

Future of EOS Biplanar X-Ray

The EOS imaging system operates by performing two simultaneous X-rays of the patient from the anteroposterior and lateral views. These 2D images are utilized to reconstruct a 3D model of the spine. Another significant benefit is the reduced radiation dose compared to traditional X-rays and other imaging methods. These benefits make EOS imaging an enticing alternative to other 3D imaging methods. However, since the 3D image is a reconstruction produced from X-rays, the 3D reconstruction does not provide information on soft tissues. Additionally, the 3D model requires manual reconstruction by a radiologist.

EOS boasts high interobserver reproducibility and intraobserver repeatability in its applications for scoliosis and sagittal balance. These involve measurements from the 2D X-rays as well as the 3D reconstruction of the spine. The reproducibility and repeatability of apical vertebral orientation measurements from the EOS 3D spine reconstruction and 3D CT scans showed no statistically significant difference. However, it is essential to note that patient positioning can affect EOS measurements as the apparatus requires patients to be standing upright and facing a particular direction. The patient can be improperly positioned up to ± 10 degrees and still result in acceptable images.

EOS has also been shown to be more accurate for vertebral osteoporosis than dual-energy X-ray absorptiometry. For axial spondyloarthritis, EOS was comparable to conventional radiography. However, the EOS imaging system was not an excellent alternative to MRI for analyzing disc degeneration and other soft tissues. As expected, MRI had greater image visibility and better detection of soft tissue pathology.

X-ray and CT angiography are both typically utilized to garner bone information; however, each comes with its limitations. X-ray provides only 2D images, while CT risks a significant radiation dose. As an alternative, the EOS system provides a method to produce 3D models of the spine with low radiation exposure. As previously mentioned, the 3D image must be manually reconstructed. This requires the radiologist to manually mark the vertebral bodies’ positions, which can take more than an hour, limiting the number of patients that can be imaged daily. This limitation can be overcome using convolution neural networks. By applying a convolution neural network to the acquired X-rays, the 3D reconstruction can be automated and performed much more quickly, allowing for more patients to be imaged each day.

Future of Ultrasound Imaging

Ultrasound imaging employs high-frequency sound waves to image internal organs. Ultrasound can be noninvasive by operating the transducer on the skin. In other cases, invasive ultrasound, such as endoscopic or transvaginal ultrasound, can be performed to obtain better images of the organs of interest. Compared to other imaging modalities, the risk posed to patients by ultrasound is low since it has no ionizing radiation. The few risks involve heating of tissue and cavitation. Due to this overall low risk, ultrasound is an attractive technology to aid in surgical and diagnostic applications for spinal diseases.

Assessing Intervertebral Disc Metabolism and Transport for Precision Spine Care

The intervertebral disc is the largest avascular structure in the human body, requiring disc cells to be uniquely suited to survival in a harsh environment, including a low nutrient supply of glucose and oxygen and high metabolite concentration of lactic acid, leading to low pH. Despite the suitability of IVD cells for this environment, changes in disc structure and subsequent small changes in nutrient supply can threaten the survival of endogenous IVD cells as well as transplanted cells. As the cells die, due to a mixture of nutrient limitations, biomechanical problems, and signaling cascades, the ability of the disc to remodel its extracellular matrix (ECM) declines and the disc degenerates. All discs undergo some structural changes with age, including reorganization of ECM components and calcification of the disc endplate, an important part of the disc nutrient supply. Capillaries are lost, and contact with the marrow cavities decreases. Additionally, the ECM changes in the annulus fibrosus (AF) and nucleus pulposus (NP) also cause decreased diffusion in those regions. Mature disc cells use glucose and produce lactic acid. Although they do not require much oxygen to live, cell activity and ECM synthesis are reduced at low concentrations.

Fundamental studies of the disc often focus on regenerative medicine. Disc regeneration strategies focus on targeting molecular pathways of ECM degeneration. This could theoretically be accomplished by supplementing anabolic factors (usually by adding cells), increasing production of anabolic factors (by growth factors), or decreasing production of catabolic factors (through growth factor or cytokine therapy). One major challenge to these approaches is their reliance on increasing cell activity or density in a nutrient-starved environment. In order to make these regenerative treatments successful, there will likely need to be an imaging approach to determine the nutrient capacity for each disc being considered for a biological therapy.

There are several imaging methods that are being used to study disc metabolism and transport phenomena. Magnetic resonance imaging can be used to study water diffusion in the disc in vivo using diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) mapping. In a study of 100 patients’ MRIs, ADC maps of NPs in 435 discs showed diffusion coefficients varied by up to 75% among discs with the same Pfirrmann grade. Recently, DTI studies have investigated diffusion in the disc and detected pathological changes at early stages of cartilage degeneration. There have been a few excellent studies that have answered disc transport phenomena-related questions using medical imaging of small-molecule surrogates in the disc.

Ratio of R1р dispersion and -OH chemical exchange saturation transfer (RROC) imaging is sensitive to pH changes in ex vivo porcine discs without being confounded by variation in GAG concentration. Endogenous glucose changes are apparent on T1р-weighted dynamic glucose-enhanced (DGE) MRI. Glucose changes could also theoretically be determined using [ ¹⁸ F]2-fluoro-2-deoxy- d -glucose ( ¹⁸ F-FGD) positron emission tomography. Finally, magnetic resonance spectroscopy has been demonstrated to quantify metabolites, specifically lactate and proteoglycans, and has been applied to aid surgeons in identifying painful discs for surgical intervention.

Applications of Artificial Intelligence

Artificial intelligence (AI) is an overarching field that contains subjects, such as machine learning (ML) and deep learning (DL). Machine learning is a statistical method that absorbs data and their results to train a computer algorithm, which may later perform predictions on unseen data, while deep learning has a layered structure that creates an artificial neural network. This artificial neural network has autonomy, which allows it to learn the complexity further, performing intelligent decisions on its own. The applications of AI in spinal imaging are continuously studied and endlessly evolving although the full extent of their applications has yet to be applied. It is well known that the potential of AI may allow for enhancements to existing techniques and modalities of spine care. Machine learning and deep learning, in conjunction with current technologies, augment traditional approaches further to enhance the scope of a clinician’s accuracy, further optimizing pre-, post-, and perioperative treatment. The applications of AI present itself through 3D construction of the spine, enhanced image resolution, improvements to perioperative navigation techniques, and postoperative patient satisfaction outcome predictions.

Conclusions

Improved image quality, decreased radiation exposure, faster imaging and analysis, improved reliability, and intraoperative use have been guiding factors in the evolution of spinal imaging. Less reliance on plain radiographs while exploring new technologies, such as ML reconstructions or using standard tools in novel ways, such as intraoperative ultrasound, has decreased radiation risk while improving surgical techniques. Intraoperative CT provides surgeons with great guidance in real time to assist in navigating challenging anatomy. Magnetic resonance imaging has long been an expensive and time-consuming imaging technology. New MRI techniques being developed improve both image acquisition time and diagnostic information that can be obtained. Novel imaging can further assess metabolism and transport of materials into and out of the disc, allowing for refined patient selection/stratification that can also inform dose and approach for regenerative biologics of the disc as well as facilitate precision spine care. Machine learning is at the forefront of impressive automatic classification, reconstruction, and analysis of spinal features. The quick automatic analysis provided by machine learning can intraoperatively assist navigation during surgery. These developments in spinal imaging are reducing intra- and postoperative complications for both patients and physicians, leading to overall superior satisfaction and outcomes, as well as identifying the source of the pain for operative planning, managing patient expectations, and improved patient outcomes. The utility between advances in imaging coupled with the unraveling and implications of AI and ML approaches toward imaging and predictive modeling will continue to expand in the years to come and usher in a new age of precision spine care.