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Medicine and Health

Building cost-effective prototypes

PHIDIAS brought together an SME, an interdisciplinary university research group and two of Europe's largest companies to adapt the latest 'rapid prototyping' technologies from manufacturing industry to surgery.
As a result, surgeons can now have highly accurate models of their patients' internal organs just a few hours after performing CT or MR scans. The models improve both diagnosis and surgical planning. They can also be used as masters for surgical implants or prothesis.
By mid-1996 the SME leading the project had sold 20 licenses world-wide and opened a sales office in the USA, while both large companies have improved their product range. The three companies have applied for at least seven patents between them.

3D medical imaging systems, such as Computerised Tomogaphy (CT) scanners, have been allowing surgeons to 'see' inside their patients and plan appropriate treatments since the 1970s. For surgeons planning complex operations, however, a solid model of their patients' organs and bones would offer many advantages over the images displayed on the scanners' computer screens.
To begin with, a model would make interpreting the scan data far easier, ensuring that the surgeons are better informed on what they will see on the operating table. Having a model to work with also improves communication within the medical team and between doctor and patient, and allows the surgeons the luxury of a 'dry run' before many operations.
Until recently, unfortunately, there was no way of creating such models. In the late 1980s, however, manufacturing industry commercialised rapid prototyping, a new technique with intriguing possibilities. The BRITE-EURAM project PHIDIAS was launched to apply rapid prototyping to surgical applications.

Rapid prototyping

Manufacturers had always either carved their design models out of solid blocks ('milling') or made them by hand. Rapid prototyping (RP), by contrast, builds models upwards, layer by layer. There are several techniques - one, for example, involves laying down a series of carefully cut pieces of paper - and all translate computer-generated 3D images into solid objects in a few hours.
The technique can, unlike milling, create objects with internal spaces - such as the sinus cavities in a human skull. The Belgian SME Materialise NV, recognising the potential for medical applications, bought a stereo photolithography RP system from 3D-Systems in 1990. The machine essentially builds each layer of the model from a resin which is 'cured', or solidified, by a laser.
After first confirming their idea's basic feasibility, Materialise launched the PHIDIAS project in 1993 with Siemens Medical Systems, Europe's largest producer of medical scanners; Zeneca, a world supplier of stereo lithography resins, and the interdisciplinary medical imaging research group of the Katholieke Universiteit Leuven (KUL), a Belgian university.
One of the first steps was to find out what surgeons wanted from the models, so the KUL surveyed 30 surgeons from Europe and the USA. As expected, while some requirements were universal (non-toxic resins, reasonable accuracy, hardness), different specialists had different demands - dental surgeons need sub-millimetre accuracy, for example, while emergency spinal or skull surgery often requires the models within 12 hours.

Higher resolution and accuracy

The project structure was straightforward, with one partner responsible for each stage of the process. Siemens, for example, focused on data quality, as solid models require much higher resolution and accuracy than normal scans.
They concentrated on 'spiral CT' imaging, where the CT scanner is moved continuously around the patient in a spiral pattern. Unlike normal scans, which requires the patient to lie motionless for up to 20 minutes, a spiral CT scan only takes around 30 seconds, significantly reducing inaccuracies caused by patient movement.
The disadvantage is that, unlike normal scans, spiral CT data has to be heavily processed into a series of 2D images, each representing a thin slice of the patient. In addition, Siemens had to develop a suite of mathematical techniques to 'fill in the gaps' between the data points.
By the end of the project, Siemens had successfully developed high-power algorithms which both improved the resolution of each slice by a factor of 100 and filled in the gaps between each slice, producing the smooth curves required for medical models. Apart from making an essential contribution to the production of medical models, their work has improved their entire product range and resulted in two new patent applications.

Sophisticated software

Apart from coordinating the project and marketing its results, Materialise developed the software for the Medical Modelling Workstation. This equipment takes the data, displays it to the radiologist and surgeon, helps them select the objects (such as bones, muscles and organs) they want produced and prepares the data for the rapid prototyping machine.
A significant challenge was to automate the selection of the body parts to be transformed into a model. Dedicated software was developed so that if the surgeons selected just one data point - representing a fragment of the lower jaw, for example - the workstation could identify all the data points representing the entire jaw. In this way the surgeons can select whatever they want modelled in a few minutes, rather than laboriously specifying hundreds of data points manually.
Another problem is that many body parts are shaped so that they cannot stand upright as they are being created within the Rapid Prototyping machine. The partners therefore wrote software to help the users add special support structures into the model.

Extra dimensions of data

Zeneca, naturally, contributed the resins. As for Siemens, the new application set them some challenging requirements - the resins had to be entirely non-toxic, for example, as they would be used in surgical theatres.
More importantly, almost all of the surgeons surveyed by the KUL agreed that simple 3D models faced one outstanding problem: they could not show the difference between tissue types. For a surgeon planning to remove bone tumour from the surrounding bone, for example, this distinction is literally a matter of life and death.
The partners took their cue from the medical scanners, which display bone, cartilage and other tissue types using different colours on their screens. But how to add colour to the models? Zeneca solved this problem by studying resins that can be coloured, as well as cured, by laser beams.
The resulting non-toxic resin becomes a transparent solid when cured at normal laser beam intensity, and goes darker and darker red as the beam intensity increases. In this way different organs, or tumours can be shown in red throughout the transparent model. Zeneca has applied for at least two patents on this new product.

Proven success

KUL validated the technology throughout the project, providing more than 20 surgeons with experimental models for almost 50 different patients. The surgeons rated the models as Not Useful (6% of the time), Useful (33%), Very Useful (40%) and Essential (21%).
Materialise and its partners have therefore developed, tested and patented a new technology which looks set to radically improve complex surgery. For Materialise, which has already sold around 20 licenses, opened a sales office in the USA and increased its size from five to 40 employees, this is just the first step. The future, they believe, will see larger scale applications in customising implants and prostheses.
Today, for example, inserting a knee implant involves opening up the knee and selecting the 'best fit' implant from one of five standard sizes and shapes. PHIDIAS technology will allow surgeons to design each implant for each patient, ensuring a perfect fit and improving the implant's lifetime and effectiveness enormously.


Project Title:  
Laser photo- polymerization models based on medical imaging - a development improving the accuracy of surgery.

Industrial and Materials Technologies (BRITE-EURAM/CRAFT/SMT)

Contract Reference: BE-5930

Cordis DatabaseFor more information on this project,
go to the CORDIS Database Record