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Role of 3D Printing and Modeling to Aid in Neuroradiology Education for Medical Trainees

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Background: Applications of 3-dimensional (3D) printing in medical imaging and health care are expanding. Currently, primary uses involve presurgical planning and patient and medical trainee education. Neuroradiology is a complex subdiscipline of radiology that requires further training beyond radiology residency. This review seeks to explore the clinical value of 3D printing and modeling specifically in enhancing neuroradiology education for radiology physician residents and medical trainees.

Methods: A brief review summarizing the key steps from radiologic image to 3D printed model is provided, including storage of computed tomography and magnetic resonance imaging data as digital imaging and communications in medicine files; conversion to standard tessellation language (STL) format; manipulation of STL files in interactive medical image control system software (Materialise) to create 3D models; and 3D printing using various resins via a Formlabs 2 printer.

Results: For the purposes of demonstration and proof of concept, neuroanatomy models deemed crucial in early radiology education were created via open-source hardware designs under free or open licenses. 3D-printed objects included a sphenoid bone, cerebellum, skull base, middle ear labyrinth and ossicles, mandible, circle of Willis, carotid aneurysm, and lumbar spine using a combination of clear, white, and elastic resins.

Conclusions: Based on this single-institution experience, 3D-printed complex neuroanatomical structures seem feasible and may enhance resident education and patient safety. These same steps and principles may be applied to other subspecialties of radiology. Artificial intelligence also has the potential to advance the 3D process.



Applications of 3-dimensional (3D) printing in medical imaging and health care are expanding. 3D printing may serve a variety of roles and is used increasingly in the context of presurgical planning, as specific medical models may be created using individual patient imaging data.1 These patient-specific models may assist in medical trainee education, decrease operating room time, improve patient education for potential planned surgery, and guide clinicians for optimizing therapy.1,2 This article discusses the utility of 3D printing at a single institution to serve in enhancing specifically neuroradiology education.


As digital imaging and 3D printing have increased in popularity, the potential application of using imaging data to guide patient therapy has shown significant promise. Computed tomography (CT) is a commonly used modality that can be used to create 3D anatomical models, as it is frequently used in the medical setting, demonstrates excellent resolution to the millimeter scale, and can readily pinpoint pathology on imaging.

Image Acquisition

CT scans can be rapidly obtained, which adds significant value, particularly in the context of point-of-care 3D printing. Another modality commonly used for 3D printing is magnetic resonance imaging (MRI), which unlike CT, does not expose the patient to ionizing radiation. The 3D printing process is initiated with patient-specific CT or MRI data stored in the digital imaging and communications in medicine (DICOM) format, which is the international standard for communication and management of medical imaging information and related data. DICOM allows for faster and robust collaboration among imaging professionals.3

Image Processing

To print 3D anatomical models, patient-specific data must be converted from DICOM into standard tessellation language (STL) format, which can be created and edited with a variety of softwares.3 At James A. Haley Veterans’ Hospital in Tampa, Florida, we use an image processing package that includes the Materialise 3-matic and interactive medical image control system. Image quality is essential; therefore, careful attention to details such as pixel dimensions, slice thickness, and slice increments must be considered.3,4

An STL file creates a 3D image from triangle approximations. The entire 3D shape will be made of numerous large or small triangles, depending on the slice thickness, therefore, quality of the original radiologic image. The size and position of the triangles used to make the model can be varied to approximate the object’s shape. The smaller the triangles, the better the image quality and vice versa. This concept is analogous to approximating a circle using straight lines of equal length—more, smaller lines will result in better approximation of a circle (Figure 1).5,6 Similarly, using smaller triangles allows for better approximation of the image. As the human body is a complex structure, mimicking the body requires a system able to create nongeometrical shapes, which is made possible via these triangle approximations in a 3D STL file.

The creation of an STL file from DICOM data starts with a threshold-based segmentation process followed by additional fine-tuning and edits, and ends in the creation of a 3D part. The initial segmentation can be created with the threshold tool, using a Hounsfield unit range based on the area of interest desired (eg, bone, blood, fat). This is used to create an initial mask, which can be further optimized. The region grow tool allows the user to focus the segmentation by discarding areas that are not directly connected to the region of interest. In contrast, the split mask tool divides areas that are connected. Next, fine-tuning the segmentation using tools such as multiple slice edit helps to optimize the model. After all edits are made, the calculate part tool converts the mask into a 3D component that can be used in downstream applications. For the purposes of demonstration and proof of concept, the models provided in this article were created via open-source hardware designs under free or open licenses.7-9

3D Printing in Neuroradiology Education

Neuroradiologists focus on diagnosing pathology related to the brain, head and neck, and spine. CT and MRI scans are the primary modalities used to diagnose these conditions. 3D printing is a useful tool for the trainee who wishes to fully understand neuroanatomy and obtain further appreciation of imaging pathology as it relates to 3D anatomy. Head and neck imaging are a complex subdiscipline of neuroradiology that often require further training beyond radiology residency. A neuroradiology fellowship that focuses on head and neck imaging extends the training.


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