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Fiducial markers appear as small signal voids in MRI images and are often difficult to discern. Existing clinical methods for fiducial marker localization require multiple MRI sequences and\/or manual interaction and specialized expertise. In this study, we develop a robust method for automatic fiducial marker detection in prostate MRI simulation images and quantify the pretreatment alignment accuracy using automatically detected fiducial markers in MRI.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods and Materials<\/jats:title><jats:p>In this study, a deep learning\u2010based algorithm was used to convert MRI images into labeled fiducial marker volumes. Seventy\u2010seven prostate cancer patients who received marker implantation prior to MRI and CT simulation imaging were selected for this study. Multiple\u2010Echo T<jats:sub>1<\/jats:sub>\u2010VIBE MRI images were acquired, and images were stratified (at the patient level) based on the presence of intraprostatic calcifications. Ground truth (GT) contours were defined by an expert on MRI using CT images. Training was done using the pix2pix generative adversarial network (GAN) image\u2010to\u2010image translation package and model testing was done using fivefold cross validation. For performance comparison, an experienced medical dosimetrist and a medical physicist each manually contoured fiducial markers in MRI images. The percent of correct detections and F<jats:sub>1<\/jats:sub> classification scores are reported for markers detected using the automatic detection algorithm and human observers. The patient positioning errors were quantified by calculating the target registration errors (TREs) from fiducial marker driven rigid registration between MRI and CBCT images. Target registration errors were quantified for fiducial marker contours defined on MRI by the automatic detection algorithm and the two expert human observers.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Ninety\u2010six percent of implanted fiducial markers were correctly identified using the automatic detection algorithm. Two expert raters correctly identified 97% and 96% of fiducial markers, respectively. The F<jats:sub>1<\/jats:sub> classification score was 0.68, 0.75, and 0.72 for the automatic detection algorithm and two human raters, respectively. The main source of false discoveries was intraprostatic calcifications. The mean TRE differences between alignments from automatic detection algorithm and human detected markers and GT were &lt;1\u00a0mm.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>We have developed a deep learning\u2010based approach to automatically detect fiducial markers in MRI\u2010only simulation images in a clinically representative patient cohort. The automatic detection algorithm\u2010predicted markers can allow for patient setup with similar accuracy to independent human observers.<\/jats:p><\/jats:sec>","DOI":"10.1002\/mp.14498","type":"journal-article","created":{"date-parts":[[2020,9,29]],"date-time":"2020-09-29T05:24:14Z","timestamp":1601357054000},"page":"6405-6413","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["A generative adversarial network\u2010based (GAN\u2010based) architecture for automatic fiducial marker detection in prostate MRI\u2010only radiotherapy simulation images"],"prefix":"10.1002","volume":"47","author":[{"given":"Kamal","family":"Singhrao","sequence":"first","affiliation":[{"name":"Department of Radiation Oncology University of California, Los Angeles Los Angeles CA 90095 USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jie","family":"Fu","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology University of California, Los Angeles Los Angeles CA 90095 USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Neil R.","family":"Parikh","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology University of California, Los Angeles Los Angeles CA 90095 USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Argin G.","family":"Mikaeilian","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology University of California, Los Angeles Los Angeles CA 90095 USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dan","family":"Ruan","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology University of California, Los Angeles Los Angeles CA 90095 USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amar U.","family":"Kishan","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology University of California, Los Angeles Los Angeles CA 90095 USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"John H.","family":"Lewis","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology Cedars\u2010Sinai Medical Center Los Angeles CA 90048 USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2020,10,19]]},"reference":[{"key":"e_1_2_7_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0360-3016(98)00351-4"},{"key":"e_1_2_7_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/0360-3016(96)00232-5"},{"key":"e_1_2_7_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2009.02.019"},{"key":"e_1_2_7_5_1","doi-asserted-by":"publisher","DOI":"10.1088\/0031-9155\/60\/22\/R323"},{"key":"e_1_2_7_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0167-8140(02)00440-1"},{"key":"e_1_2_7_7_1","doi-asserted-by":"publisher","DOI":"10.7785\/tcrt.2012.500342"},{"key":"e_1_2_7_8_1","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2019.00826"},{"key":"e_1_2_7_9_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.12516"},{"key":"e_1_2_7_10_1","doi-asserted-by":"publisher","DOI":"10.1002\/jmri.24590"},{"key":"e_1_2_7_11_1","unstructured":"MR\u2010PROTECT: Clinical feasibility of a prostate MRI\u2010only radiotherapy treatment workflow and investigation of acceptance criteria | Radiation Oncology | Full Text. 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