Multimodal imaging has proven to be critical in the study of a broad range of diseases and numerous surgical disciplines. As new and diverse imaging data becomes available, it is possible to understand the multiple mechanisms that cause complex morphological and physiological diseases. Surgically, multimodal data has allowed for the ability to perform more complex interventions with minimized risk. However, analyzing multimodal data required a sequential and chronological review of multiple sources of discrete 3-dimensional imaging data. Utilizing multimodal data in this sequential manner for surgical planning requires significant and rapid visuospatial memory to recall and reconstruct spatial relationships during surgery. Furthermore, this stream of collected imaging data is viewed on a 2-dimensional computer screen, skewing proportions and ignoring the spatial relationships of the 3-dimensional (3D) data. As the amount of multimodal data available for medical research and surgical planning continues to grow, so are the challenges of understanding, comprehending, and synthesizing meaningful results from the ever-increasing wealth of available data.
Motivated by the need to develop more informative and data-rich patient-specific presurgical planning models, we present a high-resolution method that enables the tangible replication of multi-modal medical data. By leveraging voxel-level control of multi-material three-dimensional (3D) printing, our method allows for the digital integration of disparate medical data types such as fMRI, Positron Electron Tomography, Magnetoencephalography, Diffusion Tensor Imaging, and 4D flow, overlaid upon traditional MRI and CT data. While permitting the explicit translation of multi-modal medical data into physical objects, this approach also bypasses the need to process data into mesh-based boundary representations, alleviating the potential loss and remodeling of information. After evaluating the optical characteristics of test specimens generated with our correlative data-driven method, we culminate with multi-modal real-world 3D-printed examples, highlighting current and potential applications for improved surgical planning, communication, and clinical decision-making through this approach.
Learning Objectives:
- Upon completion, participants will be able to describe, demonstrate and apply new opportunities for the application of bitmap 3d printing for medical applications.
- Upon completion, participants will be able to apply new techniques for 3D bitmap printing to conduct clinical applications involving multimodal data.
- Upon completion, participants will be able to conduct collaborations between engineers, computer scientists, and medical professionals to advance 3D printing and patient-specific care.
