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To date, cerebral perfusion characteristics in DSA are primarily assessed visually by interventionists, which is time-consuming, error-prone, and subjective. To facilitate fast and reproducible assessment of cerebral perfusion, this work aims to develop and validate a fully automatic and quantitative framework for perfusion DSA.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Methods<\/jats:title>\n            <jats:p>We put forward a framework, perfDSA, that automatically generates deconvolution-based perfusion parametric images from cerebral DSA. It automatically extracts the arterial input function from the supraclinoid internal carotid artery (ICA) and computes deconvolution-based perfusion parametric images including cerebral blood volume (CBV), cerebral blood flow (CBF), mean transit time (MTT), and Tmax.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Results<\/jats:title>\n            <jats:p>On a DSA dataset with 1006 patients from the multicenter MR CLEAN registry, the proposed perfDSA achieves a Dice of 0.73(\u00b10.21) in segmenting the supraclinoid ICA, resulting in high accuracy of arterial input function (AIF) curves similar to manual extraction. Moreover, some extracted perfusion images show statistically significant associations (<jats:italic>P<\/jats:italic>=2.62e<jats:inline-formula>\n                <jats:alternatives>\n                  <jats:tex-math>$$-$$<\/jats:tex-math>\n                  <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                    <mml:mo>-<\/mml:mo>\n                  <\/mml:math>\n                <\/jats:alternatives>\n              <\/jats:inline-formula>5) with favorable functional outcomes in stroke patients.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Conclusion<\/jats:title>\n            <jats:p>The proposed perfDSA framework promises to aid therapeutic decision-making in cerebrovascular interventions and facilitate discoveries of novel quantitative biomarkers in clinical practice. The code is available at <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/github.com\/RuishengSu\/perfDSA\" ext-link-type=\"uri\">https:\/\/github.com\/RuishengSu\/perfDSA<\/jats:ext-link>.<\/jats:p>\n          <\/jats:sec>","DOI":"10.1007\/s11548-025-03359-4","type":"journal-article","created":{"date-parts":[[2025,4,24]],"date-time":"2025-04-24T12:27:18Z","timestamp":1745497638000},"page":"1195-1203","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["perfDSA: Automatic Perfusion Imaging in Cerebral Digital Subtraction Angiography"],"prefix":"10.1007","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5013-1370","authenticated-orcid":false,"given":"Ruisheng","family":"Su","sequence":"first","affiliation":[]},{"given":"P. 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Aad van der Lugt received research grants from Siemens Healthineers, GE Healthcare, and Philips Healthcare, all paid to the institution. Danny Ruijters is an employee of Philips. Theo van Walsum received research grants from Philips Healthcare, paid to the institution. The rest of the authors have no competing financial or nonfinancial interests to disclose beyond the funding sources listed above.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"The MR CLEAN Registry was approved by the ethics committee of the Erasmus University MC, Rotterdam, The Netherlands (MEC-2014-235). The need for individual patient consent has been waived.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Patient consent for publication"}}]}}