{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T20:49:51Z","timestamp":1791578991241,"version":"4.3.4"},"reference-count":54,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2027,1,1]],"date-time":"2027-01-01T00:00:00Z","timestamp":1798761600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2027,1,1]],"date-time":"2027-01-01T00:00:00Z","timestamp":1798761600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T00:00:00Z","timestamp":1790640000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100024370","name":"Ministero dell&apos;Istruzione dell&apos;Universit\u00e0 e della Ricerca","doi-asserted-by":"publisher","award":["FISA-2022-00770"],"award-info":[{"award-number":["FISA-2022-00770"]}],"id":[{"id":"10.13039\/501100024370","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100024370","name":"Ministero dell&apos;Istruzione dell&apos;Universit\u00e0 e della Ricerca","doi-asserted-by":"publisher","award":["2022342MEF"],"award-info":[{"award-number":["2022342MEF"]}],"id":[{"id":"10.13039\/501100024370","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003407","name":"Ministero dell\u2019Istruzione, dell\u2019Universit\u00e0 e della Ricerca","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003407","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Computer Methods and Programs in Biomedicine"],"published-print":{"date-parts":[[2027,1]]},"DOI":"10.1016\/j.cmpb.2026.109672","type":"journal-article","created":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T23:46:15Z","timestamp":1790725575000},"page":"109672","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["A radiomics-based machine learning framework for full-reference ultrasound image quality assessment across varying acquisition settings"],"prefix":"10.1016","volume":"288","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-2143-8406","authenticated-orcid":false,"given":"Alessandro","family":"Piccolo","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3525-177X","authenticated-orcid":false,"given":"Vito","family":"Papapicco","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrea","family":"Mariani","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6348-1081","authenticated-orcid":false,"given":"Arianna","family":"Menciassi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0626-8652","authenticated-orcid":false,"given":"Andrea","family":"Cafarelli","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.cmpb.2026.109672_bib0001","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1016\/j.clinimag.2022.09.010","article-title":"Inter-transducer variability of ultrasound image quality in obese adults: qualitative and quantitative comparisons","volume":"92","author":"Maar","year":"2022","journal-title":"Clin. Imaging"},{"issue":"12","key":"10.1016\/j.cmpb.2026.109672_bib0002","doi-asserted-by":"crossref","first-page":"1644","DOI":"10.1016\/j.jacr.2020.07.015","article-title":"Ultrasound practice redesign to improve image quality: implementation of a quality control sonographer","volume":"17","author":"Caserta","year":"2020","journal-title":"J. Am. Coll. Radiol."},{"key":"10.1016\/j.cmpb.2026.109672_bib0003","series-title":"2020 IEEE International Conference on Robotics and Automation (ICRA)","first-page":"9946","article-title":"Automated robotic breast ultrasound acquisition using ultrasound feedback","author":"Welleweerd","year":"2020"},{"key":"10.1016\/j.cmpb.2026.109672_bib0004","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1016\/j.media.2016.06.015","article-title":"Reflections on ultrasound image analysis","volume":"33","author":"Noble","year":"2016","journal-title":"Med. Image Anal."},{"issue":"8","key":"10.1016\/j.cmpb.2026.109672_bib0005","doi-asserted-by":"crossref","first-page":"1509","DOI":"10.1016\/j.ultrasmedbio.2022.03.015","article-title":"Optimizing lung ultrasound: the effect of depth, gain and focal position on sonographic B-lines","volume":"48","author":"Duggan","year":"2022","journal-title":"Ultrasound Med. Biol."},{"issue":"7","key":"10.1016\/j.cmpb.2026.109672_bib0006","doi-asserted-by":"crossref","first-page":"1198","DOI":"10.1109\/TUFFC.2019.2911267","article-title":"The effect of dynamic range alterations in the estimation of contrast","volume":"66","author":"Rindal","year":"2019","journal-title":"IEEE Trans. Ultrason. Ferroelectr. Freq. Control"},{"key":"10.1016\/j.cmpb.2026.109672_bib0007","series-title":"2023 IEEE International Conference on Robotics and Automation (ICRA)","first-page":"2717","article-title":"Expert-agnostic ultrasound image quality assessment using deep variational clustering","author":"Raina","year":"2023"},{"key":"10.1016\/j.cmpb.2026.109672_bib0008","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/j.bspc.2016.02.006","article-title":"Review of medical image quality assessment","volume":"27","author":"Chow","year":"2016","journal-title":"Biomed. Signal Process. Control"},{"key":"10.1016\/j.cmpb.2026.109672_bib0009","doi-asserted-by":"crossref","first-page":"140030","DOI":"10.1109\/ACCESS.2019.2943319","article-title":"A comprehensive performance evaluation of image quality assessment algorithms","volume":"7","author":"Athar","year":"2019","journal-title":"IEEE Access"},{"issue":"6","key":"10.1016\/j.cmpb.2026.109672_bib0010","doi-asserted-by":"crossref","first-page":"3444","DOI":"10.1007\/s10278-025-01462-1","article-title":"A study of why we need to reassess full reference image quality assessment with medical images","volume":"38","author":"Breger","year":"2025","journal-title":"J. Imaging Inform. Med."},{"key":"10.1016\/j.cmpb.2026.109672_bib0011","series-title":"International Conference on Medical Imaging and Computer-Aided Diagnosis","first-page":"451","article-title":"A study on the adequacy of common iqa measures for medical images","author":"Breger","year":"2024"},{"issue":"4","key":"10.1016\/j.cmpb.2026.109672_bib0012","doi-asserted-by":"crossref","first-page":"600","DOI":"10.1109\/TIP.2003.819861","article-title":"Image quality assessment: from error visibility to structural similarity","volume":"13","author":"Wang","year":"2004","journal-title":"IEEE Trans. Image Process."},{"issue":"11","key":"10.1016\/j.cmpb.2026.109672_bib0013","doi-asserted-by":"crossref","first-page":"3440","DOI":"10.1109\/TIP.2006.881959","article-title":"A statistical evaluation of recent full reference image quality assessment algorithms","volume":"15","author":"Sheikh","year":"2006","journal-title":"IEEE Trans. Image Process."},{"issue":"4","key":"10.1016\/j.cmpb.2026.109672_bib0014","doi-asserted-by":"crossref","first-page":"1064","DOI":"10.1109\/TMI.2019.2930338","article-title":"Comparison of objective image quality metrics to expert radiologists\u2019 scoring of diagnostic quality of MR images","volume":"39","author":"Mason","year":"2019","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.cmpb.2026.109672_bib0015","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2024.103343","article-title":"Low-dose computed tomography perceptual image quality assessment","volume":"99","author":"Lee","year":"2025","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.cmpb.2026.109672_bib0016","doi-asserted-by":"crossref","DOI":"10.1038\/s41597-026-07610-8","article-title":"PhotIQA: a photoacoustic image data set with image quality ratings","author":"Breger","year":"2026","journal-title":"Sci. Data"},{"issue":"1","key":"10.1016\/j.cmpb.2026.109672_bib0017","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1109\/TBC.2025.3609055","article-title":"Image quality assessment: exploring the similarity of deep features via covariance-constrained spectra","volume":"72","author":"Lang","year":"2026","journal-title":"IEEE Trans. Broadcast."},{"key":"10.1016\/j.cmpb.2026.109672_bib0018","series-title":"2025 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","first-page":"17990","article-title":"Image quality assessment: investigating causal perceptual effects with abductive counterfactual inference","author":"Shen","year":"2025"},{"key":"10.1016\/j.cmpb.2026.109672_bib0019","series-title":"Medical Imaging 2010: Ultrasonic Imaging, Tomography, and Therapy","first-page":"105","article-title":"Ultrasound image quality assessment: a framework for evaluation of clinical image quality","volume":"7629","author":"Hemmsen","year":"2010"},{"issue":"1","key":"10.1016\/j.cmpb.2026.109672_bib0020","article-title":"CNN-based medical ultrasound image quality assessment","volume":"2021","author":"Zhang","year":"2021","journal-title":"Complexity"},{"key":"10.1016\/j.cmpb.2026.109672_bib0021","series-title":"2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)","first-page":"4002","article-title":"Ultrasound image quality evaluation using a structural similarity based autoencoder","author":"Nesovic","year":"2021"},{"issue":"11","key":"10.1016\/j.cmpb.2026.109672_bib0022","doi-asserted-by":"crossref","first-page":"8233","DOI":"10.1109\/JBHI.2025.3572459","article-title":"DAM: degradation-aware model for ultrasound image quality assessment","volume":"29","author":"Liu","year":"2025","journal-title":"IEEE J. Biomed. Health Inf."},{"issue":"1","key":"10.1016\/j.cmpb.2026.109672_bib0023","doi-asserted-by":"crossref","first-page":"741","DOI":"10.1186\/s12884-025-07863-y","article-title":"Intelligent quality assessment of ultrasound images for fetal nuchal translucency measurement during the first trimester of pregnancy based on deep learning models","volume":"25","author":"Liu","year":"2025","journal-title":"BMC Pregnancy Childbirth"},{"issue":"1","key":"10.1016\/j.cmpb.2026.109672_bib0024","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1186\/s41747-019-0112-7","article-title":"Performance of machine learning software to classify breast lesions using BI-RADS radiomic features on ultrasound images","volume":"3","author":"Fleury","year":"2019","journal-title":"Eur. Radiol. Exp."},{"issue":"1","key":"10.1016\/j.cmpb.2026.109672_bib0025","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2022\/1847981","article-title":"Emerging feature extraction techniques for machine learning-based classification of carotid artery ultrasound images","volume":"2022","author":"Latha","year":"2022","journal-title":"Comput. Intell. Neurosci."},{"issue":"1","key":"10.1016\/j.cmpb.2026.109672_bib0026","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-019-46937-8","article-title":"Image quality evaluation in a modern PET system: impact of new reconstructions methods and a radiomics approach","volume":"9","author":"Reyn\u00e9s-Llompart","year":"2019","journal-title":"Sci. Rep."},{"issue":"1","key":"10.1016\/j.cmpb.2026.109672_bib0027","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1007\/s00330-024-10958-3","article-title":"Ultra-high b-value DWI in rectal cancer: image quality assessment and regional lymph node prediction based on radiomics","volume":"35","author":"Hao","year":"2025","journal-title":"Eur. Radiol."},{"key":"10.1016\/j.cmpb.2026.109672_bib0028","doi-asserted-by":"crossref","DOI":"10.1016\/j.ejrad.2023.110928","article-title":"Radiomics based automated quality assessment for T2W prostate MR images","volume":"165","author":"Thijssen","year":"2023","journal-title":"Eur. J. Radiol."},{"issue":"9","key":"10.1016\/j.cmpb.2026.109672_bib0029","doi-asserted-by":"crossref","first-page":"1234","DOI":"10.1016\/j.mri.2012.06.010","article-title":"Radiomics: the process and the challenges","volume":"30","author":"Kumar","year":"2012","journal-title":"Magn. Reson. Imaging"},{"issue":"2","key":"10.1016\/j.cmpb.2026.109672_bib0030","doi-asserted-by":"crossref","first-page":"328","DOI":"10.1148\/radiol.2020191145","article-title":"The image biomarker standardization initiative: standardized quantitative radiomics for high-throughput image-based phenotyping","volume":"295","author":"Zwanenburg","year":"2020","journal-title":"Radiology"},{"key":"10.1016\/j.cmpb.2026.109672_bib0031","unstructured":"Recommendation ITU-R BT. 500-14: methodologies for the subjective assessment of the quality of television images. 2019."},{"key":"10.1016\/j.cmpb.2026.109672_bib0032","first-page":"303","volume":"1","author":"Scott","year":"2009"},{"key":"10.1016\/j.cmpb.2026.109672_bib0033","doi-asserted-by":"crossref","first-page":"255","DOI":"10.2307\/2532051","article-title":"A concordance correlation coefficient to evaluate reproducibility","volume":"45","author":"Lin","year":"1989","journal-title":"Biometrics"},{"issue":"1","key":"10.1016\/j.cmpb.2026.109672_bib0034","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1037\/1082-989X.1.1.30","article-title":"Forming inferences about some intraclass correlation coefficients","volume":"1","author":"McGraw","year":"1996","journal-title":"Psychol. Methods"},{"issue":"2","key":"10.1016\/j.cmpb.2026.109672_bib0035","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1016\/j.jcm.2016.02.012","article-title":"A guideline of selecting and reporting intraclass correlation coefficients for reliability research","volume":"15","author":"Koo","year":"2016","journal-title":"J. Chiropr. Med."},{"issue":"1","key":"10.1016\/j.cmpb.2026.109672_bib0036","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1016\/j.ejrad.2004.09.007","article-title":"HVS-based medical image compression","volume":"55","author":"Kai","year":"2005","journal-title":"Eur. J. Radiol."},{"issue":"5","key":"10.1016\/j.cmpb.2026.109672_bib0037","doi-asserted-by":"crossref","first-page":"717","DOI":"10.1109\/83.760338","article-title":"A wavelet visible difference predictor","volume":"8","author":"Bradley","year":"1999","journal-title":"IEEE Trans. Image Process."},{"issue":"6","key":"10.1016\/j.cmpb.2026.109672_bib0038","first-page":"3055","article-title":"Texture based feature extraction methods for content based medical image retrieval systems","volume":"24","author":"Ergen","year":"2014","journal-title":"Biomed. Mater. Eng."},{"issue":"6","key":"10.1016\/j.cmpb.2026.109672_bib0039","doi-asserted-by":"crossref","first-page":"610","DOI":"10.1109\/TSMC.1973.4309314","article-title":"Textural features for image classification","volume":"SMC-3","author":"Haralick","year":"1973","journal-title":"IEEE Trans. Syst. Man Cybern."},{"issue":"6","key":"10.1016\/j.cmpb.2026.109672_bib0040","doi-asserted-by":"crossref","first-page":"524","DOI":"10.1016\/j.tranon.2015.11.013","article-title":"Variability of image features computed from conventional and respiratory-gated PET\/CT images of lung cancer","volume":"8","author":"Oliver","year":"2015","journal-title":"Transl. Oncol."},{"issue":"6","key":"10.1016\/j.cmpb.2026.109672_bib0041","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1016\/0167-8655(90)90112-F","article-title":"Use of gray value distribution of run lengths for texture analysis","volume":"11","author":"Chu","year":"1990","journal-title":"Pattern Recognit. Lett."},{"issue":"2","key":"10.1016\/j.cmpb.2026.109672_bib0042","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1016\/S0146-664X(75)80008-6","article-title":"Texture analysis using gray level run lengths","volume":"4","author":"Galloway","year":"1975","journal-title":"Comput. Graph. Image Process."},{"issue":"1","key":"10.1016\/j.cmpb.2026.109672_bib0043","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1111\/j.2517-6161.1996.tb02080.x","article-title":"Regression shrinkage and selection via the lasso","volume":"58","author":"Tibshirani","year":"1996","journal-title":"J. R. Stat. Soc. B: Stat. Methodol."},{"key":"10.1016\/j.cmpb.2026.109672_bib0044","first-page":"2825","article-title":"Scikit-learn: machine learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"10.1016\/j.cmpb.2026.109672_bib0045","series-title":"The Thirty-Seventh Asilomar Conference on Signals, Systems & Computers, 2003","first-page":"1398","article-title":"Multiscale structural similarity for image quality assessment","volume":"2","author":"Wang","year":"2003"},{"issue":"5","key":"10.1016\/j.cmpb.2026.109672_bib0046","doi-asserted-by":"crossref","first-page":"1185","DOI":"10.1109\/TIP.2010.2092435","article-title":"Information content weighting for perceptual image quality assessment","volume":"20","author":"Wang","year":"2010","journal-title":"IEEE Trans. Image Process."},{"issue":"8","key":"10.1016\/j.cmpb.2026.109672_bib0047","doi-asserted-by":"crossref","first-page":"2378","DOI":"10.1109\/TIP.2011.2109730","article-title":"FSIM: a feature similarity index for image quality assessment","volume":"20","author":"Zhang","year":"2011","journal-title":"IEEE Trans. Image Process."},{"issue":"9","key":"10.1016\/j.cmpb.2026.109672_bib0048","doi-asserted-by":"crossref","first-page":"2284","DOI":"10.1109\/TIP.2007.901820","article-title":"VSNR: a wavelet-based visual signal-to-noise ratio for natural images","volume":"16","author":"Chandler","year":"2007","journal-title":"IEEE Trans. Image Process."},{"issue":"12","key":"10.1016\/j.cmpb.2026.109672_bib0049","doi-asserted-by":"crossref","first-page":"2117","DOI":"10.1109\/TIP.2005.859389","article-title":"An information fidelity criterion for image quality assessment using natural scene statistics","volume":"14","author":"Sheikh","year":"2005","journal-title":"IEEE Trans. Image Process."},{"issue":"2","key":"10.1016\/j.cmpb.2026.109672_bib0050","doi-asserted-by":"crossref","first-page":"430","DOI":"10.1109\/TIP.2005.859378","article-title":"Image information and visual quality","volume":"15","author":"Sheikh","year":"2006","journal-title":"IEEE Trans. Image Process."},{"key":"10.1016\/j.cmpb.2026.109672_bib0051","series-title":"2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"586","article-title":"The unreasonable effectiveness of deep features as a perceptual metric","author":"Zhang","year":"2018"},{"issue":"5","key":"10.1016\/j.cmpb.2026.109672_bib0052","first-page":"2567","article-title":"Image quality assessment: unifying structure and texture similarity","volume":"44","author":"Ding","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"1","key":"10.1016\/j.cmpb.2026.109672_bib0053","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1016\/S0031-3203(98)00091-0","article-title":"An overlap invariant entropy measure of 3D medical image alignment","volume":"32","author":"Studholme","year":"1999","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.cmpb.2026.109672_bib0054","article-title":"A simple sequentially rejective multiple test procedure","volume":"65\u201370","author":"Holm","year":"1979","journal-title":"Scand. J. Stat."}],"container-title":["Computer Methods and Programs in Biomedicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0169260726004219?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0169260726004219?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T20:01:59Z","timestamp":1791576119000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0169260726004219"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2027,1]]},"references-count":54,"alternative-id":["S0169260726004219"],"URL":"https:\/\/doi.org\/10.1016\/j.cmpb.2026.109672","relation":{},"ISSN":["0169-2607"],"issn-type":[{"value":"0169-2607","type":"print"}],"subject":[],"published":{"date-parts":[[2027,1]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"A radiomics-based machine learning framework for full-reference ultrasound image quality assessment across varying acquisition settings","name":"articletitle","label":"Article Title"},{"value":"Computer Methods and Programs in Biomedicine","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.cmpb.2026.109672","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 The Author(s). Published by Elsevier B.V.","name":"copyright","label":"Copyright"}],"article-number":"109672"}}