{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T13:12:46Z","timestamp":1753881166002,"version":"3.41.2"},"reference-count":18,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2010,1,17]],"date-time":"2010-01-17T00:00:00Z","timestamp":1263686400000},"content-version":"vor","delay-in-days":381,"URL":"http:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"funder":[{"DOI":"10.13039\/501100004830","name":"Siemens","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100004830","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["International Journal of Biomedical Imaging"],"published-print":{"date-parts":[[2009,1]]},"abstract":"<jats:p>Functional imaging based on tomographic X\u2010ray imaging relies on the reconstruction of a temporal sequence of images which accurately reproduces the time attenuation curves of the tissue. The main constraints of these techniques are temporal resolution and dose. Using current techniques the data acquisition has to be performed fast so that the dynamic attenuation values can be regarded as static during the scan. Due to the relatively high number of repeated scans the dose per single scan has to be low yielding a poor signal\u2010to\u2010noise ratio (SNR) in the reconstructed images. In a previous publication a temporal interpolation scheme in the projection data space was relaxing the temporal resolution constraint. The aim of this contribution is the improvement of the SNR. A temporal smoothing term is introduced in the temporal interpolation scheme such that only the physiologic relevant bandwidth is considered. A significant increase of the SNR is achieved. The obtained level of noise only depends on the total dose applied and\nis independent of the number of scans and the SNR of a single reconstructed image. The\napproach might be the first step towards using slowly rotating CT systems for perfusion imaging like C\u2010arm or small animal CT scanners.<\/jats:p>","DOI":"10.1155\/2009\/108028","type":"journal-article","created":{"date-parts":[[2010,1,17]],"date-time":"2010-01-17T15:30:32Z","timestamp":1263742232000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Low\u2010Noise Dynamic Reconstruction for X\u2010Ray Tomographic Perfusion Studies Using Low Sampling Rates"],"prefix":"10.1155","volume":"2009","author":[{"given":"Pau","family":"Montes","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"G\u00fcnter","family":"Lauritsch","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2010,1,17]]},"reference":[{"volume-title":"Functional Computed Tomography","year":"1997","author":"Miles K.","key":"e_1_2_10_1_2"},{"volume-title":"Neuroradiologie","year":"2001","author":"Sartor K.","key":"e_1_2_10_2_2"},{"key":"e_1_2_10_3_2","doi-asserted-by":"publisher","DOI":"10.1161\/01.STR.32.2.431"},{"key":"e_1_2_10_4_2","doi-asserted-by":"publisher","DOI":"10.1161\/01.STR.0000023579.61630.AC"},{"key":"e_1_2_10_5_2","doi-asserted-by":"publisher","DOI":"10.1161\/01.STR.0000131271.54098.22"},{"key":"e_1_2_10_6_2","doi-asserted-by":"publisher","DOI":"10.1118\/1.2746486"},{"volume-title":"Computed Tomography: Fundamentals, System Technology, Image Quality, Applications","year":"2000","author":"Kalender W. 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Dynamic cone-beam reconstruction for perfusion computed tomography Ph.D. thesis 2006 Interdisciplinary Center for Scientific Computing Ruprecht-Karls-Universit\u00e4t Heidelberg Heidelberg Germany http:\/\/www.ub.uni-heidelberg.de\/archiv\/7020\/."},{"key":"e_1_2_10_10_2","doi-asserted-by":"publisher","DOI":"10.1109\/78.193220"},{"key":"e_1_2_10_11_2","doi-asserted-by":"publisher","DOI":"10.1109\/78.193221"},{"key":"e_1_2_10_12_2","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/0165-1684(92)90030-Z","article-title":"Cardinal spline filters: stability and convergence to the ideal sinc interpolator","volume":"28","author":"Aldroubi A.","year":"1992","journal-title":"Signal Processing"},{"key":"e_1_2_10_13_2","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2006.890843"},{"key":"e_1_2_10_14_2","doi-asserted-by":"publisher","DOI":"10.1118\/1.1677252"},{"key":"e_1_2_10_15_2","doi-asserted-by":"publisher","DOI":"10.1088\/0031-9155\/45\/2\/306"},{"key":"e_1_2_10_16_2","doi-asserted-by":"publisher","DOI":"10.1088\/0031-9155\/47\/15\/304"},{"key":"e_1_2_10_17_2","series-title":"Series in Systems Science","volume-title":"Probability, Random Variables and Stochastic Processes","author":"Papoulis A.","year":"1965"},{"volume-title":"Discrete-Time Signal Processing","year":"1998","author":"Oppenheim A. 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