Future Trends in Spinal Imaging

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.