For Immediate Release
Bitmap-Based 3D Printing Achieves More Accurate and Precise Physical Models from Patient Imaging Data
Contact: Danielle Giordano
New Rochelle, NY, July 3, 2018—Prior to performing a medical procedure, physicians are increasingly relying on access to 3D printed models created using patient-specific medical data. Unfortunately, current 3D data processing workflows can be extremely time consuming, and frequently, the resulting 3D-printed models fail to accurately depict the anatomical details of interest. Motivated by these inherent limitations, an international team of researchers describes a rapid method for creating extremely detailed physical models directly from volumetric data stacks in an article published in 3D Printing and Additive Manufacturing, a peer-reviewed journal from Mary Ann Liebert, Inc., publishers. The article is available free on the 3D Printing and Additive Manufacturing website through August 3, 2018.
The article entitled “From Improved Diagnostics to Presurgical Planning: High-Resolution Functionally Graded Multimaterial 3D Printing of Biomedical Tomographic Data Sets” is coauthored by James Weaver, Wyss Institute for Biologically Inspired Engineering, Harvard University (Cambridge, MA) and colleagues from MIT (Cambridge, MA), Massachusetts General Hospital and Harvard Medical School (Boston, MA), University of Washington (Seattle, WA), Brigham and Women’s Hospital (Boston, MA), Isomics (Cambridge, MA), Universitätsmedizin Berlin (Germany), Beth Israel Deaconess Medical Center (Boston, MA), and Max Planck Institute of Colloids and Interfaces (Potsdam, Germany).
The bitmap-based workflow relies on the use of cross-sectional image stacks – images acquired for diagnostic purposes or in advance of a surgical procedure, for example. These bitmap slices can be processed using standard photo editing workflows and are then fed directly into an ink jet-like 3D printer, resulting in extremely detailed physical models with the ability to incorporate grayscale, transparency, or mechanical property gradients, and thus more closely mimic the patient’s specific anatomy.
Research reported in this publication was supported by the Human Frontier Science Program and National Institutes of Health. The content is solely the responsibility of the authors and does not necessarily represent the official views of the funding organizations.
About the Journal
3D Printing and Additive Manufacturing is the only peer-reviewed journal focused on the rapidly moving field of 3D printing and related technologies. Led by Editor-in-Chief Skylar Tibbits Director, Self-Assembly Lab, MIT, and Founder & Principal, SJET LLC., the Journal explores emerging challenges and opportunities ranging from new developments of processes and materials, to new simulation and design tools, and informative applications and case studies. Published quarterly online with open access options and in print, the Journal spans a broad array of disciplines to address the challenges and discover new breakthroughs and trends within this groundbreaking technology. Tables of content and a sample issue may be viewed on the 3D Printing and Additive Manufacturing website.
About the Publisher
Mary Ann Liebert, Inc., publishers is a privately held, fully integrated media company known for establishing authoritative medical and biomedical peer-reviewed journals, including Big Data, Soft Robotics, New Space, and Tissue Engineering. Its biotechnology trade magazine GEN (Genetic Engineering & Biotechnology News) was the first in its field and is today the industry’s most widely read publication worldwide. A complete list of the firm’s more than 80 journals, newsmagazines, and books is available on the Mary Ann Liebert, Inc., publishers website.