Year: 2026
Type: data-paper
Abstract: Image quality assessment (IQA) is crucial in the evaluation stage of novel algorithms operating on images, including traditional and machine learning based methods. Due to the lack of available quality-rated medical images, most commonly used full-reference IQA measures have been developed and tested for natural images. Reported pitfalls and inconsistencies arising when applying such measures for medical images are not surprising, as they rely on different properties than natural images. In photoacoustic imaging (PAI), especially, standard benchmarking approaches for assessing the quality of image reconstructions are lacking. PAI is a multi-physics imaging modality, in which two inverse problems have to be solved, which makes the application of IQA measures uniquely challenging due to both, acoustic and optical, artifacts. To support the development and testing of IQA measures we assembled PhotIQA, a data set consisting of 1134 photoacoustic images. The images were rated by five experts across five quality properties in a full-reference setting, where the detailed rating enables usage beyond PAI. The data set with the images and corresponding ratings is publicly available on Zenodo.
Source: Scientific Data
Authors: Anna Breger, Janek Gröhl, Clemens Karner, Thomas R. Else, Ian Selby, Tom Rix, Lara-Sophie Witt, Merle Duchêne, Jonathan Weir-McCall, Carola-Bibiane Schönlieb
Institutions: University of Cambridge, Medical University of Vienna, Cancer Research UK Cambridge Center, Universitätsmedizin Göttingen, European Neuroscience Institute Göttingen, Max Planck Institute for Multidisciplinary Sciences, University of Göttingen, Cambridge University Hospitals NHS Foundation Trust, German Cancer Research Center, Heidelberg University, King's College London
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,490
Funders: Wellcome Trust, European Federation of Pharmaceutical Industries and Associations, Cancer Research UK, National Institute for Health and Care Research, Leverhulme Trust, Department of Health and Social Care, European Commission, Deutsche Forschungsgemeinschaft, Austrian Science Fund, 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, G139696651, G1521853241, G1560739324, Streudepolarisierung im gyrencephalen Gehirn: Multispektrale optische und photoakustische Analyse, Unterdrückung und Modulation, G1680483594, G1891881142, G2493601830, G2537968379, Robust and Efficient Analysis Approaches of Remote Imagery for Assessing Population and Forest Health in India, Image based Data Evaluation Analyses and Medical Application, G299057155, G3004532622, ProbAI: A Hub for the Mathematical and Computational Foundations of Probabilistic AI, The Mathematics of Deep Learning, G3243167903, G347399793, G3584577881, Ubiquitous Optical Healthcare Technologies (ubOHT) Programme Grant, G370598735, G4655236649, G5022494042, The RapiD and SecuRe AI enhAnced DiaGnosis, Precision Medicine and Patient EmpOwerment Centered Decision Support System for Coronavirus PaNdemics, G515689015, G6077662935, G6084690111, Cambridge Mathematics of Information in Healthcare (CMIH), G6153437118, G63068527, G63586632, G6543108417, EPSRC Centre for Mathematical and Statistical Analysis of Multimodal Clinical Imaging, G6779181853, 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, G7669185616, Predicting Dementia: Optimising and translating AI to improve prognosis and clinical pathways, G8854708280, G970520811
OpenAlex ID: W4415344580