{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T03:28:39Z","timestamp":1788406119817,"version":"build-2803163510"},"reference-count":35,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2003,11,21]],"date-time":"2003-11-21T00:00:00Z","timestamp":1069372800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Magnetic Resonance in Med"],"published-print":{"date-parts":[[2003,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    The accurate assessment of the presence and extent of vascular disease, and planning of vascular interventions based on MRA requires the determination of vessel dimensions. The current standard is based on measuring vessel diameters on maximum intensity projections (MIPs) using calipers. In order to increase the accuracy and reproducibility of the method, automated analysis of the 3D MR data is required. A novel method for automatically determining the trajectory of the vessel of interest, the luminal boundaries, and subsequent the vessel dimensions is presented. The automated segmentation in 3D uses deformable models, combined with knowledge of the acquisition protocol. The trajectory determination was tested on 20 in vivo studies of the abdomen and legs. In 93% the detected trajectory followed the vessel. The luminal boundary detection was validated on contrast\u2010enhanced (CE) MRA images of five stenotic phantoms. The results from the automated analysis correlated very well with the true diameters of the phantoms used in the in vitro study (\n                    <jats:italic>r<\/jats:italic>\n                    = 0.999,\n                    <jats:italic>P<\/jats:italic>\n                    &lt; 0.001). MRA and x\u2010ray angiography (XA) of the phantoms also correlated well (\n                    <jats:italic>r<\/jats:italic>\n                    = 0.895,\n                    <jats:italic>P<\/jats:italic>\n                    &lt; 0.001). The average unsigned difference between the MRA and XA measurements was 0.08 \u00b1 0.05 mm. In conclusion, the automated approach allows the accurate assessment of vessel dimensions in MRA images. Magn Reson Med 50:1189\u20131198, 2003. \u00a9 2003 Wiley\u2010Liss, Inc.\n                  <\/jats:p>","DOI":"10.1002\/mrm.10617","type":"journal-article","created":{"date-parts":[[2003,12,2]],"date-time":"2003-12-02T09:18:19Z","timestamp":1070356699000},"page":"1189-1198","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":43,"title":["Automated segmentation and analysis of vascular structures in magnetic resonance angiographic images"],"prefix":"10.1002","volume":"50","author":[{"given":"P.J.H.","family":"de Koning","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"J.A.","family":"Schaap","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"J.P.","family":"Janssen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"J.J.M.","family":"Westenberg","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"R.J.","family":"van der Geest","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"J.H.C.","family":"Reiber","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2003,11,21]]},"reference":[{"key":"e_1_2_6_2_2","first-page":"1087","article-title":"Contrast\u2010enhanced magnetic resonance angiography","volume":"143","author":"Leiner T","year":"1999","journal-title":"Ned Tijdschr Geneeskd"},{"key":"e_1_2_6_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0730-725X(99)00099-5"},{"key":"e_1_2_6_4_2","doi-asserted-by":"publisher","DOI":"10.2214\/ajr.169.1.9207499"},{"key":"e_1_2_6_5_2","doi-asserted-by":"publisher","DOI":"10.1148\/radiology.206.3.9494486"},{"key":"e_1_2_6_6_2","doi-asserted-by":"publisher","DOI":"10.1148\/radiology.206.3.9494485"},{"key":"e_1_2_6_7_2","doi-asserted-by":"publisher","DOI":"10.1002\/1522-2586(200012)12:6<924::AID-JMRI16>3.0.CO;2-0"},{"key":"e_1_2_6_8_2","doi-asserted-by":"publisher","DOI":"10.1148\/radiographics.20.4.g00jl461113"},{"key":"e_1_2_6_9_2","doi-asserted-by":"crossref","unstructured":"AylwrdS BullitE PizerS EberlyD.Intensity ridge and widths for tubular object segmentation and description. 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