Master Thesis AI Based Prediction for Anatomical Motion Modelling on Unity

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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.
• Choose a suitable AI model to predict centroid motion at clinically relevant prediction horizons.
• Adapt the model and train on the data collected.

Responsibilities

• 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.
• 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.

Requirements

• 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.
• Programming experience and the ability to work independently on an exploratory research problem.
• A strong scientific or engineering background, including confidence with quantitative analysis.

Preferred

• 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