{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T05:10:51Z","timestamp":1779340251271,"version":"3.51.4"},"reference-count":79,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T00:00:00Z","timestamp":1779321600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100022308","name":"Karl Landsteiner Privatuniversit\u00e4t f\u00fcr Gesundheitswissenschaften","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100022308","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Physiol."],"abstract":"<jats:sec>\n                    <jats:title>Background and purpose<\/jats:title>\n                    <jats:p>Quantitative perfusion measurement using magnetic resonance imaging (P-MRI) is an important method to detect cerebral pathologies. An accurate measurement of quantitative perfusion parameters in correctly placed regions of interest (ROIs) is mandatory to distinguish between regular and pathological tissue perfusion. Especially in large cohorts, the current gold-standard of manually drawn ROIs is time-consuming, and comparisons of different examinations require an additional transformation of perfusion parameter maps from individual native to the so-called standard space, which additionally confounds the quantitative measurements due to necessary spatial interpolations, which potentially introduces substantial error into the measurements. Therefore, we propose an automatic reverse transformation (arT) method that projects generic ROIs from standard to native space, which enables direct assessment of the original quantitative data in native space.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and methods<\/jats:title>\n                    <jats:p>P-MRI data from 36 subjects without detectable lesions were manually segmented in native space using seven predefined ROIs. The same ROIs were manually drawn in standard space using a high-resolution MNI template, and after applying arT, spatial overlap with manual ROIs was assessed using the S\u00f8rensen-Dice index (SDI). Furthermore, the impact from spatial overlap error on quantitative perfusion parameters (standardized Time-to-Peak [stdTTP], cerebral blood volume [CBV]) was evaluated.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Depending on ROI size, the SDI ranged from 0.766 (right basal ganglia) to 0.948 (right cerebral hemisphere). For evaluation of stdTTP, the bias was -0.002 s, with limits of agreement from -0.160 s to 0.138 s and Pearson correlation coefficient between 0.946 and 0.999. Similar correlations were observed for CBV.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Given the acceptably small relative variability (automatic vs manual) and low error of absolute measurements, arT could become a promising tool for the automatic assessment of quantitative cerebral P-MRI parameters.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fphys.2026.1829907","type":"journal-article","created":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T04:37:54Z","timestamp":1779338274000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Precision of automatically generated generic regions of interest for direct assessment of perfusion-MRI"],"prefix":"10.3389","volume":"17","author":[{"given":"Stephan","family":"Dehen","sequence":"first","affiliation":[{"name":"Department of Diagnostic and Interventional Radiology, University Hospital Tulln, Karl Landsteiner University","place":["Tulln, Austria"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Christian","family":"Nasel","sequence":"additional","affiliation":[{"name":"Department of Diagnostic and Interventional Radiology, University Hospital Tulln, Karl Landsteiner University","place":["Tulln, Austria"]},{"name":"Department of Medical Imaging and Image-guided Therapy, Medical University of Vienna","place":["Vienna, Austria"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2026,5,21]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"726","DOI":"10.1016\/j.neuroimage.2009.02.018","article-title":"Multi-atlas based segmentation of brain images: atlas selection and its effect on accuracy","volume":"46","author":"Aljabar","year":"2009","journal-title":"NeuroImage"},{"key":"B2","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1016\/j.neuroimage.2010.06.003","article-title":"Cost function masking during normalization of brains with focal lesions: Still a necessity","volume":"53","author":"Andersen","year":"2010","journal-title":"NeuroImage"},{"key":"B3","doi-asserted-by":"publisher","DOI":"10.1016\/j.jocn.2025.111073","article-title":"Artificial intelligence for brain neuroanatomical segmentation in magnetic resonance imaging: A literature review","volume":"134","author":"Andrews","year":"2025","journal-title":"J. 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