Improving Radiation Therapy Accuracy: 3D Printed Phantoms Help Plan Treatment of Liver Tumors

Posted on 10 May 2019 by Paul Fotheringham, Founder of  3D LifePrints

“As a specialist medical 3D printing organization, the ability to have a functionally rich and effective medical modeling application is crucial in order for 3D LifePrints to meet our client’s complex requirements for medical devices. Simpleware ScanIP application is an extremely powerful tool that allows us to do this.”

3D printed physical phantoms from 3D LifePrints that accurately recreate the organ provide one solution for enhancing the surgical planning process. 3D LifePrints use Synopsys Simpleware™ software to ensure accurate models are created for this medical 3D printing purpose, with this example focusing on radiation treatment for liver tumors. There are multiple approaches available for treating patients with liver tumors, including ablation, embolization, targeted therapy, immunotherapy, chemotherapy and radiation therapy. Radiation therapy is one of the most effective methods but comes with its own risks and complexities.

The normal procedure for pre-assessment and planning prior to the interventional procedure relies on the clinicians looking a variety of patient image data. The subsequent procedure uses X-rays to provide the targeted radiation dose for the area of interest. There are currently few options for planning other than CT/MRI scans shown on a 2D screen. The 3D printed phantoms from 3D LifePrints provide an alternative option for better understanding patient anatomies.

From 3D Medical Segmentation to 3D Printing

3D LifePrints wanted to build a model to study accurate dosage measurement for radiation therapy of liver tumors. Patient-specific image data from an MRI scan was imported into Simpleware ScanIP for segmentation of anatomical regions of interest, in this case a realistic model of the liver. Following work on the image data, Simpleware software was used to generate a model of the liver with high accuracy for 3D printing using a PolyJet machine.

The stacked image DICOM data was imported directly into the specialized medical segmentation software and aligned appropriately in the axial and planar directions. Using the integrated thresholding tools, a general 3D render could be produced and visualized. Further refinement could then be achieved with the ‘paint with threshold’ and automatic global mask optimization tools.

The 3D mesh created from the segmentation process could then be exported into the 3D printer slicing software for pre-print processing. The 3D model contained three chambers suitable for holding radioisotope samples, with the chambers varying in size (4mm, 11m, and 40mm diameters) to mimic different-sized tumors. The 3D-printed phantom was then scanned (Phillips PET/CT) in the correct anatomical orientation and used as part of a surgical plan. By using the measured dosage and known cavity volume for the patient, the surrounding exposure on the liver could be estimated.

Conclusion

By using Simpleware software’s medical segmentation tools, 3D LifePrints were able to create a patient-specific 3D model suitable for printing and use by clinicians. The identification of the boundaries separating the liver and the tumor is particularly important, in this case, for identifying a more accurate and case-specific radiation dosage than with traditional 2D visualization of CT or MRI scans.

A 3D model helps the clinician understand the irregularities of a tumor before a treatment pathway is decided, reducing the risk of radiation exposure to surrounding tissues, as well as reduced damage to kidneys. Having the 3D model also means that clinicians have a better sense of the shape and size of the tumor, giving them more confidence in treatment planning.