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We currently have the following opportunity available - please contact us for more details!

We dont just build technology. We build hope for everyone dealing with cancer.

Master Thesis: Machine Learning Surrogates for Convolution-Based Dose Calculation in Leksell Gamma Knife Radiosurgery

What youll do at Elekta

Introduction
This project investigates machine learning surrogates for convolution-based dose calculation in Gamma Knife radiosurgery. The goal is to develop fast accurate models that reproduce heterogeneity-corrected dose distributions enabling real-time optimization without the computational cost of full convolutions.

Background
The Leksell Gamma Knife (LGK) is widely used for stereotactic radiosurgery of brain lesions. Treatment planning requires accurate calculation of the total dose delivered from 192 cobalt-60 sources. Clinically two dose models are used: the TMR10 water-based model which is fast but assumes water-equivalent tissue and the convolution algorithm which accounts for tissue heterogeneity (bone air) using CT data and Monte Carlo-derived kernels. Convolution is more accurate especially for targets near the skull or air cavities but it is slower and requires CT imaging whereas most LGK workflows are MRI-based.

The LGK Lightning inverse planner optimizes plans using the water model for speed then optionally recalculates dose with convolution for accuracy. However this post-hoc correction may reveal under- or over-dosage that cannot be easily fixed within the same workflow. A fast accurate surrogate for convolution would enable real-time heterogeneity-aware optimization improving both clinical accuracy and usability. Deep learning has shown promise in external beam radiotherapy dose prediction and recent work in LGK radiosurgery demonstrates feasibility of multiple network designs for dose distribution prediction as well as MRI-based heterogeneity correction without CT.

Problem Statement
The aim of this project is to investigate and prototype a machine learning model capable of predicting convolution-equivalent dose distributions for gamma knife treatments. The aims of the project are:

• To develop a machine-learning based model to fast and reliably approximate convolution-based dose distributions using training data generated from the treatment planning system (TPS).

• To assess the models potential to enable fast heterogeneity-aware dose prediction suitable for integration in optimization workflows.

Materials and Methods

• Data source:Simulated LGK dose distributions generated by exporting per-sector/per-collimator dose components from the treatment planning system. MR images of patient geometries (skull outlines targets) are available; CT images are not routinely available. However this is not an issue see the referenced material. The TPS convolution algorithm provides ground-truth labels.

• Data volume:Large amounts of synthetic training data can be generated (multiple shots per patient each with 24 sector/collimator combinations). Each 3D dose volume contains 4 million voxels requiring cropping to a bounding box around relevant dose regions (e.g. down to a dose cutoff) and possibly some level of compression.

• Evaluation metrics:Gamma index passing rates voxel-wise MAE/RMSE and clinical indices (coverage conformity index) compared to convolution TPS output.

Output

• Prototype codebase in Python (e.g. PyTorch) including data preprocessing baseline and extended ML models and evaluation scripts.

• Trained models and results with benchmark metrics and visualizations (2D/3D isodose comparisons).

• Masters thesis report including literature review methods experiments and critical evaluation.

• Possibly a simple demo script/notebook for dose prediction on new cases plus presentation materials (poster or slides).

The right stuff

• Curious and driven with a problem-solving mindset

• Structured independent and proactive in your approach to research

• Communicative and collaborative

• Excited about working in a multidisciplinary and international team

What you bring

We are looking for students from Physics Mathematics Computer Science or Engineering programs with similar orientation. Applicants should preferably have:

• A good understanding of deep learning preferably including computer vision. Basic radiation physics is an advantage.

• Solid Python programming skills

• Strong analytical skills

• The ability to work independently

• Clear communication skills both in writing and presentations

What youll get

In this role you will work for a higher purpose: hopefor everyone dealing with cancer and for everyone regardless of where in the world to have access to the best cancer care. Youll join a global leader in precision radiation therapy and contribute to real-world research with direct clinical application. Youll also benefit from mentorship by experts in medical physics machine learning and radiosurgery.

Hiring process

We are looking forward to hearing from you! Apply by submitting your CV academic transcript and a short cover letter in English via the Apply button. Please note that we do not accept applications by e-mail.

Your Elekta contact

For questions please contact the Global Talent Acquisition Partner responsible Gustaf Ericson at . Again we do not accept applications through e-mail.

We are an equal opportunity employer

We are an equal opportunity employer. We evaluate qualified applicants without regard to age race colour religion sex sexual orientation gender identity genetic information national origin disability veteran status or any other protected characteristic.

About Elekta

As a leader in precision radiation therapy Elekta is committed to ensuring every patient has access to the best cancer care possible. Elekta is a proud innovator and supplier of equipment and software used to improve prolong and save the lives of people with cancer and brain disorders.

More than 6000 hospitals worldwide rely on Elekta technology. We openly collaborate with customers to advance sustainable outcome-driven and cost-efficient solutions to meet evolving patient needs improve lives and bring hope to everyone dealing with cancer. To us its personal and our global team of 4700 employees combine passion science and imagination to profoundly change cancer care. We dont just build technology we build hope.

Elekta is headquartered in Stockholm Sweden with presence in more than 120 countries and listed on Nasdaq Stockholm. For more information visit or follow @Elekta on LinkedIn and Twitter.