Year: 2026
Type: article
Abstract: This work introduces a new efficient iterative solver for the reconstruction of real-time cone-beam computed tomography, which is based on the prior image constrained compressed sensing (PICCS) regularization and leverages the efficiency of Krylov subspace methods. In particular, we focus on the setting where a sequence of under-sampled computed tomography (CT) scans are taken on the same object with only local changes (e.g. changes in a tumour size or the introduction of a surgical tool). This is very common, for example, in image-guided surgery, where the amount of measurements is limited to ensure the safety of the patient. In this case, we can also typically assume that a (good) initial reconstruction for the solution exists, coming from a previously over-sampled scan, so we can use this information to aid the subsequent reconstructions. The effectiveness of this method is demonstrated in both a synthetic scan and using real CT data, where it can be observed that the PICCS framework is very effective for the reduction of artefacts, and that the new method is faster than other common alternatives used in the same setting.
Source: Physics in Medicine and Biology
Authors: Fred Hastings, S M Ragib Shahriar Islam, Malena Sabaté Landman, Sepideh Hatamikia, Carola-Bibiane Schönlieb, Ander Biguri
Language: English
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
Estimated APC paid: $3,325
Funders: National Science Foundation, Wellcome Trust, National Institute for Health and Care Research, Leverhulme Trust, Department of Health and Social Care, European Commission, Innovation and Technology Commission, Engineering and Physical Sciences Research Council, NIHR Cambridge Biomedical Research Centre
Awards: All in one cancer imaging optimisation using an integrated mathematical and deep learning approach, G1680483594, G2493601830, G2537968379, Nonlocal Methods for Arbitrary Data Sources, Robust and Efficient Analysis Approaches of Remote Imagery for Assessing Population and Forest Health in India, The Mathematics of Deep Learning, G3243167903, Revolutionizing Medical Imaging (ReImagine) through Ubiquitous, Low-Dose, Automated Computed Tomography Diagnostic Systems, G5022494042, G5838368081, Cambridge Mathematics of Information in Healthcare (CMIH), G6153437118, EPSRC Centre for Mathematical and Statistical Analysis of Multimodal Clinical Imaging, G7293733238, G7531310420, Combining Knowledge And Data Driven Approaches to Inverse Imaging Problems, PET++: Improving Localisation, Diagnosis and Quantification in Clinical and Medical PET Imaging with Randomised Optimisation, Predicting Dementia: Optimising and translating AI to improve prognosis and clinical pathways
OpenAlex ID: W4417070753