{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T13:59:11Z","timestamp":1791381551296,"version":"4.3.3"},"reference-count":28,"publisher":"Wiley","issue":"8","license":[{"start":{"date-parts":[[2025,8,13]],"date-time":"2025-08-13T00:00:00Z","timestamp":1755043200000},"content-version":"vor","delay-in-days":12,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2025,8,1]],"date-time":"2025-08-01T00:00:00Z","timestamp":1754006400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82001781"],"award-info":[{"award-number":["82001781"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["N2419003"],"award-info":[{"award-number":["N2419003"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Medical Physics"],"published-print":{"date-parts":[[2025,8]]},"abstract":"Abstract<\/jats:title>\n \n Background<\/jats:title>\n The standard Tofts model (STM) is an important pharmacokinetic model for analyzing dynamic contrast enhanced magnetic resonance imaging (DCE\u2010MRI) data. However, it is very time\u2010consuming using the STM to perform pixel\u2010by\u2010pixel analysis for 3D DCE\u2010MRI data.<\/jats:p>\n <\/jats:sec>\n \n Purpose<\/jats:title>\n We developed a decoupling iterative algorithm, prediction\u2010correction method (PCM), for rapid calculation of physiological parameters using the STM.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n \n The idea behind PCM is to eliminate the need to fit the entire contrast agent concentration (C(t)) as function of time (t) curve to calculate the volume transfer constant (\n \n K\n trans<\/jats:sup>\n <\/jats:italic>\n ) and the volume fraction of the extravascular extracellular space (\n \n v\n e<\/jats:sub>\n <\/jats:italic>\n ) using the STM. The early portion of C(t) was used to obtain\n \n K\n trans<\/jats:sup>\n <\/jats:italic>\n with prediced\n \n v\n e<\/jats:sub>\n <\/jats:italic>\n value, and the late portion of C(t) was used to obtain\n \n v\n e<\/jats:sub>\n <\/jats:italic>\n with predicted\n \n K\n trans<\/jats:sup>\n <\/jats:italic>\n . This procedure was iteratively performed until the changes of\n \n K\n trans<\/jats:sup>\n <\/jats:italic>\n and\n \n v\n e<\/jats:sub>\n <\/jats:italic>\n were less than the given tolerance errors. The method was first validated using the quantitative imaging biomarker alliance (QIBA) data. Then the public prostate DCE\u2010MRI dataset that was scanned twice and the breast DCE\u2010MRI dataset were used as applications of the PCM and compared with the conventional way of fitting the STM. The repeatability coefficients (RC) of the calculated parameters were also determined.\n <\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n \n For QIBA data, there was an excellent agreement for calculating physiological parameters between the PCM and the conventional STM. For clinical data, there was a small percentage error (<10%) in the calculations of\n \n K\n trans<\/jats:sup>\n <\/jats:italic>\n and\n \n v\n e<\/jats:sub>\n <\/jats:italic>\n between the two methods and between two scans. Overall, the PCM was about 10 times faster than the STM for each pixel. The repeatability of calculating\n \n K\n trans<\/jats:sup>\n <\/jats:italic>\n and\n \n v\n e<\/jats:sub>\n <\/jats:italic>\n was similar between the PCM and STM.\n <\/jats:p>\n <\/jats:sec>\n \n Conclusion<\/jats:title>\n \n The PCM significantly accelerated the calculations of\n \n K\n trans<\/jats:sup>\n <\/jats:italic>\n and\n \n v\n e<\/jats:sub>\n <\/jats:italic>\n with an accuracy close to the STM. By using the PCM, the physiological parameters can be calculated rapidly for 3D DCE\u2010MRI data to aid cancer diagnosis.\n <\/jats:p>\n <\/jats:sec>","DOI":"10.1002\/mp.18043","type":"journal-article","created":{"date-parts":[[2025,8,14]],"date-time":"2025-08-14T04:32:25Z","timestamp":1755145945000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Decoupling iterative algorithm for rapid calculation of physiological parameters from the standard Tofts model"],"prefix":"10.1002","volume":"52","author":[{"given":"Shu","family":"Chang","sequence":"first","affiliation":[{"name":"College of Medicine and Biological Information Engineering Northeastern University Shenyang China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaobing","family":"Fan","sequence":"additional","affiliation":[{"name":"Department of Radiology University of Chicago Chicago Illinois 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