{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T16:06:04Z","timestamp":1790697964449,"version":"4.1.0"},"reference-count":27,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T00:00:00Z","timestamp":1776902400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T00:00:00Z","timestamp":1776902400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J Applied Clin Med Phys"],"published-print":{"date-parts":[[2026,5]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Deep learning reconstruction (DLR) algorithms have begun replacing iterative reconstruction (IR) in CT. Besides the potential to reduce noise, the application of artificial intelligence can also allow for \u201csuper resolution,\u201d allowing traditional CT systems to produce images with improved spatial resolution by training against higher\u2010resolution images. This study aims to characterize the image quality produced by a DLR \u201csuper resolution\u201d algorithm.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>This study compares Canon's Precise IQ Engine (PIQE) Cardiac, a DLR algorithm, with the AIDR Enhanced 3D iterative reconstruction (IR) algorithm using quantitative phantom\u2010based image quality measurements.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and methods<\/jats:title>\n                    <jats:p>\n                      Scans of the American College of Radiology (ACR) Gammex 464 image quality phantom with and without a torso\u2010sized elliptical ring were acquired using CTDI\n                      <jats:sub>vol<\/jats:sub>\n                      of 2.6, 15.7, and 32.9 mGy. Each experimental condition was scanned 20 times. Reconstructions used AIDR with a cardiac kernel and PIQE Cardiac with both 512\u00a0\u00d7\u00a0512 and 1024\u00a0\u00d7\u00a01024 matrices. The task\u2010based modulation transfer function (MTF\n                      <jats:sub>task<\/jats:sub>\n                      ) was calculated for Teflon, acrylic, polyethylene, and air inserts. Contrast\u2010to\u2010noise ratio (CNR) and low\u2010contrast object specific CNR (CNR\n                      <jats:sub>LO<\/jats:sub>\n                      ) were calculated for the 25\u2010mm and 6\u2010mm low\u2010contrast disks in module 2 of the phantom. NPS and noise magnitude were calculated from the module's background.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Both versions of PIQE showed improvement in MTF\n                      <jats:sub>task<\/jats:sub>\n                      over AIDR for most frequencies, with PIQE 1024 outperforming PIQE 512. Larger differences were noted at higher contrast levels and with the smaller phantom. NPS curves for PIQE 512 and PIQE 1024 were nearly identical at all doses. AIDR showed a noise magnitude at least 44% higher than PIQE for all scenarios. The NPS shapes differed between AIDR and PIQE, producing noticeably different noise textures that were dose\u2010 and phantom size\u2010dependent. CNR\n                      <jats:sub>LO<\/jats:sub>\n                      values were similar between PIQE 512 and PIQE 1024 and were higher than those of AIDR for most scenarios; however, AIDR outperformed PIQE for the 6\u2010mm objects at the medium and high doses. Use of PIQE on the 25\u2010mm disk permits dose reductions in the small phantom without loss of CNR\n                      <jats:sub>LO<\/jats:sub>\n                      .\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>PIQE provides substantial image quality improvements over AIDR in phantom\u2010based quantitative measurements. The potential for dose reduction is also noted. PIQE 1024 can be used to improve the spatial resolution over PIQE 512, with no apparent trade\u2010offs in image quality.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/acm2.70597","type":"journal-article","created":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T06:25:51Z","timestamp":1777011951000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Deep learning \u201csuper resolution\u201d versus iterative reconstruction: Phantom\u2010based image quality assessment"],"prefix":"10.1002","volume":"27","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9936-5664","authenticated-orcid":false,"given":"Stephanie M.","family":"Leon","sequence":"first","affiliation":[{"name":"Department of Radiology University of Florida  Gainesville Florida USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-7599-1812","authenticated-orcid":false,"given":"Daniella","family":"Fabri","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery University of Florida  Gainesville Florida USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Colin J.","family":"Schaeffer","sequence":"additional","affiliation":[{"name":"Department of Diagnostic Radiology Oregon Health &amp; Science University  Portland Oregon USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2719-2634","authenticated-orcid":false,"given":"Edmond A.","family":"Olguin","sequence":"additional","affiliation":[{"name":"Department of Radiology Beth Israel Deaconess Medical Center  Boston Massachusetts USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,4,23]]},"reference":[{"key":"e_1_2_11_2_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00330\u2010012\u20102765\u2010y"},{"key":"e_1_2_11_3_1","doi-asserted-by":"publisher","DOI":"10.1120\/jacmp.v17i2.5709"},{"key":"e_1_2_11_4_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.14319"},{"key":"e_1_2_11_5_1","unstructured":"Toshiba White Paper:AIDR 3D\u2014Integrated Automated and Adaptive Dose Reduction.Toshiba America Medial Systems;2012. 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