Improved bolus shaping accuracy using the surface segmentation and spectral clustering

ABSTRACT

The bolus is used to cover tumors in the shallow skin of cancer patients for a desired dose distribution in the high-energy radiotherapy. This research develops methods to improve the accuracy of bolus shaping by unfolding the human surface model for refolding. A surface segmentation method is proposed to separate a non-developable surface into a few pieces based on a clustering method to increase flattenability. In the segmentation process, a 3D surface model is embedded into a spectral space using a Laplacian matrix to combine the surface segmentation saliency and geometric characteristics. A smooth common boundary is designed based on clustering results to simplify the shape of each part. The 3D surface model is unfolded into 2D patches by the coordinate transformation and optimized using a mass-spring model. Case studies are conducted to verify the effectiveness of the proposed method in the bolus shaping.

KEYWORDS:

• Surface segmentation
• clustering
• surface unfolding
• bolus

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Notes on contributors

Rui Li

Rui Li is a Ph.D. student in the Department of Mechanical Engineering at the University of Manitoba, Canada. She gained her Bachelor’s Degree in 2014 and Master’s Degree in 2017 from Mechanical Engineering of Northeastern University, China. She joined to the University of Manitoba in 2017 and her research interests include geometric modeling, point cloud data processing, and machine learning.

Qingjin Peng

Qingjin Peng is a full professor in the Department of Mechanical Engineering, University of Manitoba, Canada. He received his Bachelor and Master Degrees in Mechanical Engineering from Xi'an Jiaotong University, China and Ph.D. degree from the University of Birmingham, UK. His research interests are digital manufacturing, system modeling and simulation, and design for product life cycle.

Harry Ingleby

Harry Ingleby is an Imaging Physicist with CancerCare Manitoba, with appointments as a Lecturer in Radiology and Adjunct Professor in Physics & Astronomy at the University of Manitoba. He received his Ph.D. in Electrical Engineering in 2006 from the Royal Military College of Canada and completed his residency in Imaging Physics at CancerCare Manitoba in 2009.  His research interests are in the development of systems and algorithms for patient scheduling in radiotherapy and capital equipment replacement in diagnostic imaging.

David Sasaki

David Sasaki is Medical Physicist at CancerCare Manitoba with appointments as a Lecturer in the Department of Radiology at the University of Manitoba. His research interests are advanced design and manufacturing of patient treatment devices, optimization of patient scheduling, and use of respiratory gating for treatment simulation and delivery.