3D printing has the potential to improve the understanding of various imaging pathologies by providing the trainee with a more in-depth appreciation of the anterior, middle, and posterior cranial fossa, the skull base foramina (ie, foramen ovale, spinosum, rotundum), and complex 3D areas, such as the pterygopalatine fossa, which are all critical areas to investigate on imaging. Figure 2 highlights how a complex anatomical structure, such as the sphenoid bone when printed in 3D, can be correlated with CT cross-sectional images to supplement the educational experience.
Furthermore, the various lobes, sulci, and gyri of the brain and cerebellum and how they interrelate to nearby vasculature and bony structures can be difficult to conceptualize for early trainees. A 3D-printed cerebellum and its relation to the brainstem is illustrated in Figure 3A. Additional complex head and neck structures of the middle ear membranous and bony labyrinth and ossicles and multiple views of the mandible are shown in Figures 3B through 3E.
3D printing in the context of neurovascular pathology holds great promise, particularly as these models may provide the trainee, patient, and proceduralist essential details such as appearance and morphology of an intracranial aneurysm, relationship and size of the neck of aneurysm, incorporation of vessels emanating from the aneurysmal sac, and details of the dome of the aneurysm. For example, the normal circle of Willis in Figure 4A is juxtaposed with an example of a saccular internal carotid artery aneurysm (Figure 4B).
A variety of conditions can affect the bony spine from degenerative, trauma, neoplastic, and inflammatory etiologies. A CT scan of the spine is readily used to detect these different conditions and often is used in the initial evaluation of trauma as indicated in the American College of Radiology appropriateness criteria.10 In addition, MRI is used to evaluate the spinal cord and to further define spinal stenosis as well as evaluate radiculopathy. An appreciation of the bony and soft tissue structures within the spine can be garnered with the use of 3D models (Figure 5).
Trainees can further their understanding of approaches in spinal procedures, including lumbar puncture, myelography, and facet injections. A variety of approaches to access the spinal canal have been documented, such as interspinous, paraspinous, and interlaminar oblique; 3D-printed models can aid in practicing these procedures.11 For example, a water-filled tube can be inserted into the vertebral canal to provide realistic tactile feedback for simulation of a lumbar puncture. An appreciation of the 3D anatomy can guide the clinician on the optimal approach, which can help limit time and potentially improve outcomes.
Future Directions
Artificial Intelligence (AI) offers the ability to teach computers to perform tasks that ordinarily require human intelligence. In the context of 3D printing, the ability to use AI to readily convert and process DICOM data into printable STL models holds significant promise. Currently, the manual conversion of a DICOM file into a segmented 3D model may take several days, necessitating a number of productive hours even from the imaging and engineering champion. If machines could aid in this process, the ability to readily scale clinical 3D printing and promote widespread adoption would be feasible. Several studies already are looking into this concept to determine how deep learning networks may automatically recognize lesions on medical imaging to assist a human operator, potentially cutting hours from the clinical 3D printing workflow.12,13
Furthermore, there are several applications for AI in the context of 3D printing upstream or before the creation of a 3D model. A number of AI tools are already in use at the CT and MRI scanner. Current strategies leverage deep learning and advances in neural networks to improve image quality and create thin section DICOM data, which can be converted into printable 3D files. Additionally, the ability to automate tasks using AI can improve production capacity by assessing material costs and ensuring cost efficiency, which will be critical as point-of-care 3D printing develops widespread adoption. AI also can reduce printing errors by using automated adaptive feedback, using machine learning to search for possible print errors, and sending feedback to the computer to ensure appropriate settings (eg, temperature settings/environmental conditions).
Conclusions
Based on this single-institution experience, 3D-printed complex neuroanatomical structures seems feasible and may enhance resident education and patient safety. Interested trainees may have the opportunity to learn and be involved in the printing process of new and innovative ideas. Further studies may involve printing various pathologic processes and applying these same steps and principles to other subspecialties of radiology. Finally, AI has the potential to advance the 3D printing process in the future.
