Master Thesis AI Based Prediction for Anatomical Motion Modelling on Unity
Confirmed on the employer's own hiring board on Nov 5, 2026. First seen by Alion on Oct 22, 2026. Elekta scores A on the Alion truth index.
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Introduction
Radiotherapy targets and surrounding anatomy can move during treatment. The Elekta Unity combines an MR scanner with a linear accelerator, enabling anatomy to be visualised during radiation delivery and allowing treatment adaptation based on real-time information. However, there are inherent latencies associated with image acquisition, image processing, data transfer, and motion estimation. To compensate for these latencies, future anatomical motion must be predicted. This project will investigate whether an AI-based approach can improve prediction accuracy at extended prediction horizons.
Background
Radiotherapy treatments are designed with millimeter precision to ensure that high doses of radiation can be delivered to the target while sparing surrounding healthy tissues and critical anatomical structures. However, both the target and surrounding anatomy may move during treatment due to physiological processes such as respiration, peristalsis, and organ drift. The Elekta Unity can visualise and respond to this motion in real time, improving dosimetric accuracy and therefore improving patient outcomes.
The current Automatic Position Management (APM) framework acquires an orthogonal pair of 2D cine images at a rate of 5 Hz. Each image is compared with a reference image using rigid registration to estimate target motion. A prediction engine then extrapolates the observed motion to estimate the target position once all processing steps have been completed.
In the future, longer prediction horizons may be required to support more advanced image acquisition strategies, target-tracking approaches, or dose-based interventions. To enable these developments, a prediction algorithm must maintain accuracy while predicting further into the future. AI methods such as recurrent neural networks have shown promise for time-series prediction tasks both within and beyond radiation oncology and will therefore form the primary focus of this investigation.
Problem Statement
The project will investigate the suitability of AI approaches for motion prediction on the Unity platform through the following objectives:
- Curate a dataset of target centroid motion from a range of anatomies influenced by respiratory motion using a combination of publicly available datasets and historical MR-Linac treatment data.
- Establish a benchmark by evaluating the curated dataset using the currently implemented prediction algorithm.
- Choose a suitable AI model to predict centroid motion at clinically relevant prediction horizons. Adapt the model and train on the data collected.
- Compare the performance of the proposed approach against the existing prediction algorithm using appropriate quantitative metrics.
Output
- An assessment of the potential benefits of incorporating AI-based motion prediction within the APM framework
- A curated motion dataset that can support future research and development activities.
- Prototype software, reproducible processing pipelines, and documentation.
- A written report and final presentation
Who are we looking for?
We are looking for a master’s student in medical physics, computer science, applied mathematics, engineering physics, or a related scientific discipline.
Required profile:
- Programming experience and the ability to work independently on an exploratory research problem.
- A strong scientific or engineering background, including confidence with quantitative analysis.
Desirable experience:
- Experience in medical image processing, ideally in Python
- Experience with AI / deep learning methods
- Familiarity with radiotherapy, especially the MR-Linac
Practicalities
Location: Elekta office, Stockholm
Supervisor: Ned Goodwin
Start date: Flexible