Doctors need MRI and CT scans to further evaluate, and diagnose, medical conditions. These scans produce high-resolution images in a series of slices which show the details of structures in a person’s body and can later be 3D printed into a detailed physical model.
Unfortunately, these images are so detailed that the part that needs to be 3D printed has to be isolated from the surrounding tissue, then converted into surface meshes. While a computer can quickly complete this in an automatic thresholding process, it can over- or under-exaggerate the object’s size, and also wash out important details.
Now, a new 3D printing technique by a collaborative research team allows these medical scans to be quickly converted into highly detailed, 3D printed models with ease – and at less cost.
“I nearly jumped out of my chair when I saw what this technology is able to do. It creates exquisitely detailed 3D-printed medical models with a fraction of the manual labor currently required, making 3D printing more accessible to the medical field as a tool for research and diagnosis,” said Beth Ripley, MD, PhD, Assistant Professor of Radiology at the University of Washington, and clinical radiologist at the Seattle VA.
It all started in 2016, when Steven Keating, PhD, had a brain tumor the size of a baseball removed while he was a graduate student in the MIT Media Lab’s Mediated Matter group. He wanted a better understanding of his diagnosis and treatment options, and to see what his brain looked like with the tumor, and started 3D printing his MRI and CT scans. Frustrated with how difficult, lengthy, and non-accurate the current methods were, he contacted some of the group’s collaborators, including members of the Wyss Institute at Harvard University.
Ahmed Hosny, a Research Fellow with the Wyss Institute at the time and now a Machine Learning Engineer at the Dana-Farber Cancer Institute, said, “It never occurred to us to use this approach for human anatomy until Steve came to us and said, ‘Guys, here’s my data, what can we do?’”
The collaboration grew to include Neri Oxman, PhD, Director of the Mediated Matter group and Associate Professor of Media Arts and Sciences; James Weaver, PhD, Senior Research Scientist at the Wyss Institute; and a team of physicians and researchers from the US and Germany. Supported by a grant from the Human Frontier Science Program, the National Heart, Lung, and Blood Institute, the National Institute of Biomedical Imaging and Bioengineering, and a Gottfried Wilhelm Leibniz-Preis 2010, they published a paper on their work in 3D Printing and Additive Manufacturing.
“Curiosity is one of the biggest drivers of innovation and change for the greater good, especially when it involves exploring questions across disciplines and institutions,” said Wyss Institute Founding Director Donald Ingber, MD, PhD, who is also the Judah Folkman Professor of Vascular Biology at Harvard Medical School (HMS) and the Vascular Biology Program at Boston Children’s Hospital, and Professor of Bioengineering at Harvard’s John A. Paulson School of Engineering and Applied Sciences (SEAS). “The Wyss Institute is proud to be a space where this kind of cross-field innovation can flourish.”
The team’s new method is an accurate, quick way to convert complex images into an easily 3D printable format. It uses a digital file format called dithered bitmaps, where each pixel from a grayscale image is converted into black and white pixels; the density of the black ones defines the various shades of gray.
Think of how newsprint images convey shading through differing sizes of black ink dots – the more black pixels in an area, the darker it looks. Dithered bitmaps simplify all of an image’s pixels from shades of gray into a black and white mixture, which means a 3D printer can use two materials to quickly produce complex medical images that preserve variations from the original data.
Weaver said, “Our approach not only allows for high levels of detail to be preserved and printed into medical models, but it also saves a tremendous amount of time and money. Manually segmenting a CT scan of a healthy human foot, with all its internal bone structure, bone marrow, tendons, muscles, soft tissue, and skin, for example, can take more than 30 hours, even by a trained professional – we were able to do it in less than an hour.”
The researchers created models of Keating’s brain and tumor through bitmap-based 3D printing that preserved all of the gradations from the raw MRI data in high-resolution detail. Their method was also used to 3D print a variable stiffness, multimaterial model of a human heart valve.
The goal now is make this method more feasible for patient education and routine exams.
“Right now, it’s just too expensive for hospitals to employ a team of specialists to go in and hand-segment image data sets for 3D printing, except in extremely high-risk or high-profile cases,” Hosny explained. “We’re hoping to change that.”
The team needs some help from the medical community to achieve this goal. It’s hard to get the raw MRI or CT scan files necessary for high-resolution 3D printing, as most of this data is compressed for space-saving purposes.
In addition, the team worked with Stratasys to use its 3D printer’s intrinsic bitmap printing capabilities, but new software packages need to be developed so others can have access to these capabilities.
However, they believe that their work could “present a significant value to the medical community.”
Weaver said, “I imagine that sometime within the next 5 years, the day could come when any patient that goes into a doctor’s office for a routine or non-routine CT or MRI scan will be able to get a 3D-printed model of their patient-specific data within a few days.
“The ability to understand what’s happening inside of you, to actually hold it in your hands and see the effects of treatment, is incredibly empowering.”
Co-authors of the paper include Hosny and Keating; Joshua Dilley, MD, from Massachusetts General Hospital (MGH); Ripley; Tatiana Kelil, MD, with Brigham and Women’s Hospital (BWH); Steve Pieper, PhD, from the Surgical Planning Laboratory at BWH and the CEO of Isomics, Inc.; Dominik Kolb, MS, a former Research Assistant at the MIT Media Lab; Christoph Bader, MS, with the MIT Media Lab; Anne-Marie Pobloth, DVM, with the Julius Wolff Institute for Biomechanics and Musculoskeletal Regeneration at Charité – Universitätsmedizin Berlin; Molly Griffin, from the Gillette Center for Women’s Cancer at MGH; Reza Nezafat, PhD, with Beth Israel Deaconess Medical Center; Georg Duda, PhD, with Biomechanics and Musculoskeletal Regeneration at Charité – Universitätsmedizin Berlin and the Director of the Julius Wolff Institute; Ennio Ciocca, MD, PhD, the Co-Director at the Institute for the Neurosciences and a professor at HMS; James Stone, MD, PhD, with MGH and HMS; James Michaelson, PhD, Director of the Laboratory for Quantitative Medicine at MGH; Mason Dean, PhD; from the Max Planck Institute of Colloids and Interfaces; Oxman; and Weaver.
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[Source: Wyss Institute / Images: James Weaver, Ahmed Hosny, and Steven Keating]