Advanced Medical Imaging Analysis

Medical image analysis, including classifying and reconstructing medical images (e.g., CT, MRI, X-ray), image registration (aligning scans) and segmentation (identifying structures) require solving complex optimisation problems. These tasks strain classical algorithms, especially in data-intensive contexts like population-scale studies or detecting subtle anomalies in images, especially in neuroimaging, where brain structures vary greatly between individuals and dynamic functional imaging (fMRI) adds complexity.

Quantum optimisation methods (e.g., quantum annealing, quantum solvers for energy minimization) can accelerate and improve core imaging workflows. Neuroimaging-specific models (e.g. brain surface registration, functional alignment) are especially promising targets for quantum speedups. On the other hand, quantum machine learning algorithms, like quantum neural networks (QNNs) and quantum Fourier transforms (QFT), can accelerate the training of image classification models and enhance image reconstruction.

Faster, more accurate image analysis leads to quicker diagnostics (e.g., Alzheimer's, cancer), and more accurate diagnosis and treatment. In neuroimaging, quantum optimisation could enable precise, large-scale studies of brain structure and function, advance understanding of brain-behaviour relationships, and support breakthroughs in neurology, psychiatry, and personalised brain health.