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Updated: Apr 27, 2026
Utilizing 3D Printing Technology to Merge MRI with Histology: A Protocol for Brain Sectioning
Published on: December 6, 2016
Postmortem magnetic resonance imaging to guide the pathologic cut: individualized, 3-dimensionally printed cutting
Martina Absinta1, Govind Nair, Massimo Filippi
1From the Translational Neuroradiology Unit (MA, GN, DSR) and Surgical Neurology Branch (AR-C), National Institute of Neurological Disorders and Stroke, National Institutes of Health; and Department of Neurology, Johns Hopkins School of Medicine (MIR-M, CAP, DSR), Baltimore, Maryland; and Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy (MA, MF).
None:
Interfacing magnetic resonance imaging (MRI) with pathology is critically important for understanding the pathologic basis of MRI signal changes in vivo and for clinicopathologic correlations. Postmortem MRI is an intermediate step in this process; unfortunately, however, relating the data to standard pathologic sections, which are relatively thick and often nonparallel, is both time-consuming and insufficiently accurate. The aim of this project was to develop technology to integrate postmortem, high-resolution, whole-brain MRI into the planning and execution of pathologic analysis through precise localization of the target and coordinates of cut. Compared with standard pathologic sectioning, the use of an individualized, 3-dimensionally printed cutting box-designed based on postmortem MRI of formalin-fixed whole brains-improved the speed, quality, and accuracy of radiologic-pathologic correlations and, specifically, the histopathologic localization of imaging findings. The technology described herein is easily implemented, applicable to any brain disorder, and potentially extendable to other organs. From the point of view of the pathologist, this technique can improve localization of small or subtle abnormalities, whereas from the point of view of the radiologist, it has the potential to improve understanding of MRI signal changes observed in diseases.
