{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,10]],"date-time":"2026-10-10T13:54:56Z","timestamp":1791640496584,"version":"4.3.4"},"reference-count":45,"publisher":"IOP Publishing","issue":"8","license":[{"start":{"date-parts":[[2015,3,24]],"date-time":"2015-03-24T00:00:00Z","timestamp":1427155200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/publishingsupport.iopscience.iop.org\/iop-standard\/v1"},{"start":{"date-parts":[[2015,3,24]],"date-time":"2015-03-24T00:00:00Z","timestamp":1427155200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["Phys. Med. Biol."],"published-print":{"date-parts":[[2015,4,21]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    This work introduces a task-driven imaging framework that incorporates a mathematical definition of the imaging task, a model of the imaging system, and a patient-specific anatomical model to prospectively design image acquisition and reconstruction techniques to optimize task performance. The framework is applied to joint optimization of tube current modulation, view-dependent reconstruction kernel, and orbital tilt in cone-beam CT. The system model considers a cone-beam CT system incorporating a flat-panel detector and 3D filtered backprojection and accurately describes the spatially varying noise and resolution over a wide range of imaging parameters in the presence of a realistic anatomical model. Task-based detectability index (\n                    <jats:italic>d<\/jats:italic>\n                    \u2032) is incorporated as the objective function in a task-driven optimization of image acquisition and reconstruction techniques. The orbital tilt was optimized through an exhaustive search across tilt angles ranging\u00a0\u00b130\u00b0. For each tilt angle, the view-dependent tube current and reconstruction kernel (i.e. the modulation profiles) that maximized detectability were identified via an alternating optimization. The task-driven approach was compared with conventional unmodulated and automatic exposure control (AEC) strategies for a variety of imaging tasks and anthropomorphic phantoms. The task-driven strategy outperformed the unmodulated and AEC cases for all tasks. For example,\n                    <jats:italic>d<\/jats:italic>\n                    \u2032 for a sphere detection task in a head phantom was improved by 30% compared to the unmodulated case by using smoother kernels for noisy views and distributing mAs across less noisy views (at fixed total mAs) in a manner that was beneficial to task performance. Similarly for detection of a line-pair pattern, the task-driven approach increased\n                    <jats:italic>d<\/jats:italic>\n                    \u2032 by 80% compared to no modulation by means of view-dependent mA and kernel selection that yields modulation transfer function and noise-power spectrum optimal to the task. Optimization of orbital tilt identified the tilt angle that reduced quantum noise in the region of the stimulus by avoiding highly attenuating anatomical structures. The task-driven imaging framework offers a potentially valuable paradigm for prospective definition of acquisition and reconstruction protocols that improve task performance without increase in dose.\n                  <\/jats:p>","DOI":"10.1088\/0031-9155\/60\/8\/3129","type":"journal-article","created":{"date-parts":[[2015,3,24]],"date-time":"2015-03-24T13:24:49Z","timestamp":1427203489000},"page":"3129-3150","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":28,"title":["Task-driven image acquisition and reconstruction in cone-beam CT"],"prefix":"10.1088","volume":"60","author":[{"given":"Grace J","family":"Gang","sequence":"first","affiliation":[{"name":"Johns Hopkins University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"J Webster","family":"Stayman","sequence":"additional","affiliation":[{"name":"Johns Hopkins University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tina","family":"Ehtiati","sequence":"additional","affiliation":[{"name":"Siemens Healthcare AX Division"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jeffrey H","family":"Siewerdsen","sequence":"additional","affiliation":[{"name":"Johns Hopkins University"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"266","published-online":{"date-parts":[[2015,3,24]]},"reference":[{"key":"pmb509716bib001","doi-asserted-by":"publisher","first-page":"4878","DOI":"10.1118\/1.3231824","type":"journal-article","article-title":"Accelerating Monte Carlo simulations of photon transport in a voxelized geometry using a massively parallel graphics processing unit","volume":"36","author":"Badal","year":"2009","journal-title":"Med. 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All rights, including for text and data mining, AI training, and similar technologies, are reserved.","name":"copyright_information","label":"Copyright Information"},{"value":"2014-11-10","name":"date_received","label":"Date Received","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2015-02-06","name":"date_accepted","label":"Date Accepted","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2015-03-24","name":"date_epub","label":"Online publication date","group":{"name":"publication_dates","label":"Publication dates"}}]}}