{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:12:04Z","timestamp":1790813524253,"version":"4.1.0"},"reference-count":43,"publisher":"Wiley","issue":"2","license":[{"start":{"date-parts":[[2020,10,31]],"date-time":"2020-10-31T00:00:00Z","timestamp":1604102400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/100000070","name":"National Institute of Biomedical Imaging and Bioengineering","doi-asserted-by":"publisher","award":["K08 EB026503"],"award-info":[{"award-number":["K08 EB026503"]}],"id":[{"id":"10.13039\/100000070","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000892","name":"Prostate Cancer Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000892","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Magnetic Resonance Imaging"],"published-print":{"date-parts":[[2021,2]]},"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Multicompartmental modeling outperforms conventional diffusion\u2010weighted imaging (DWI) in the assessment of prostate cancer. Optimized multicompartmental models could further improve the detection and characterization of prostate cancer.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>To optimize multicompartmental signal models and apply them to study diffusion in normal and cancerous prostate tissue in vivo.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Study Type<\/jats:title>\n                    <jats:p>Retrospective.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Subjects<\/jats:title>\n                    <jats:p>Forty\u2010six patients who underwent MRI examination for suspected prostate cancer; 23 had prostate cancer and 23 had no detectable cancer.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Field Strength\/Sequence<\/jats:title>\n                    <jats:p>3T multishell diffusion\u2010weighted sequence.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Assessment<\/jats:title>\n                    <jats:p>Multicompartmental models with 2\u20135 tissue compartments were fit to DWI data from the prostate to determine optimal compartmental apparent diffusion coefficients (ADCs). These ADCs were used to compute signal contributions from the different compartments. The Bayesian Information Criterion (BIC) and model\u2010fitting residuals were calculated to quantify model complexity and goodness\u2010of\u2010fit. Tumor contrast\u2010to\u2010noise ratio (CNR) and tumor\u2010to\u2010background signal intensity ratio (SIR) were computed for conventional DWI and multicompartmental signal\u2010contribution maps.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Statistical Tests<\/jats:title>\n                    <jats:p>\n                      Analysis of variance (ANOVA) and two\u2010sample\n                      <jats:italic>t<\/jats:italic>\n                      \u2010tests (\u03b1 = 0.05) were used to compare fitting residuals between prostate regions and between multicompartmental models.\n                      <jats:italic>T<\/jats:italic>\n                      \u2010tests (\u03b1 = 0.05) were also used to assess differences in compartmental signal\u2010fraction between tissue types and CNR\/SIR between conventional DWI and multicompartmental models.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The lowest BIC was observed from the 4\u2010compartment model, with optimal ADCs of 5.2e\u20104, 1.9e\u20103, 3.0e\u20103, and\u2009&gt;3.0e\u20102\u2009mm\n                      <jats:sup>2<\/jats:sup>\n                      \/sec. Fitting residuals from multicompartmental models were significantly lower than from conventional ADC mapping (\n                      <jats:italic>P<\/jats:italic>\n                      \u2009&lt;\u20090.05). Residuals were lowest in the peripheral zone and highest in tumors. Tumor tissue showed the largest reduction in fitting residual by increasing model order. Tumors had a greater proportion of signal from compartment 1 than normal tissue (\n                      <jats:italic>P<\/jats:italic>\n                      \u2009&lt;\u20090.05). Tumor CNR and SIR were greater on compartment\u20101 signal maps than conventional DWI (\n                      <jats:italic>P<\/jats:italic>\n                      \u2009&lt;\u20090.05) and increased with model order.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Data Conclusion<\/jats:title>\n                    <jats:p>The 4\u2010compartment signal model best described diffusion in the prostate. Compartmental signal contributions revealed by this model may improve assessment of prostate cancer.<\/jats:p>\n                    <jats:p>\n                      <jats:bold>Level of Evidence<\/jats:bold>\n                      3\n                    <\/jats:p>\n                    <jats:p>\n                      <jats:bold>Technical Efficacy Stage<\/jats:bold>\n                      3\n                    <\/jats:p>\n                    <jats:p>J. MAGN. RESON. IMAGING 2021;53:628\u2013639.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/jmri.27393","type":"journal-article","created":{"date-parts":[[2020,10,31]],"date-time":"2020-10-31T21:24:36Z","timestamp":1604179476000},"page":"628-639","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":48,"title":["Improved Characterization of Diffusion in Normal and Cancerous Prostate Tissue Through Optimization of Multicompartmental Signal Models"],"prefix":"10.1002","volume":"53","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4509-8702","authenticated-orcid":false,"given":"Christopher C.","family":"Conlin","sequence":"first","affiliation":[{"name":"Department of Radiology UC San Diego School of Medicine  La Jolla California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christine H.","family":"Feng","sequence":"additional","affiliation":[{"name":"Department of Radiation Medicine and Applied Sciences UC San 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