BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:US/Eastern BEGIN:DAYLIGHT DTSTART:20220313T030000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 RRULE:FREQ=YEARLY;BYDAY=2SU;BYMONTH=3 TZNAME:EDT END:DAYLIGHT BEGIN:STANDARD DTSTART:20221106T010000 TZOFFSETFROM:-0400 TZOFFSETTO:-0500 RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=11 TZNAME:EST END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20221026T184858Z UID:D077D684-A721-4C1A-9765-69B2DD107326 DTSTART;TZID=US/Eastern:20221012T120000 DTEND;TZID=US/Eastern:20221012T130000 DESCRIPTION:Over the past two decades\, image reconstruction has tremendous ly gained in importance in MRI enabling reduced scan time\, improved image quality\, and extracting additional information from the measurements. In this time\, MRI has witnessed extensive developments in advanced computat ional algorithms for image reconstruction\, many of which have been fueled by signal processing advances in several areas\, including multi-channel sampling\, compressive sensing\, dictionary learning\, low-rank\, and stru ctured low-rank methods. Recently\, also neural networks have been employe d for image reconstruction achieving further improvements in scan time and image quality. Most importantly\, some of these techniques have found the ir way in the products of MRI vendors and show significant impact in the c linical practice. These developments\, together with the advancements in c omputational hardware have opened a new research field of MRI reconstructi on as a computational imaging problem. In this talk\, I will explain the f ramework of MRI reconstruction as a computational imaging problem and disc uss some of the advantages it gives in addressing important clinical needs in MRI.\n\nCo-sponsored by: Fairleigh Dickinson University\n\nSpeaker(s): Dr. Mariya Doneva\, \n\nAgenda: \nOver the past two decades\, image recon struction has tremendously gained in importance in MRI enabling reduced sc an time\, improved image quality\, and extracting additional information f rom the measurements. In this time\, MRI has witnessed extensive developme nts in advanced computational algorithms for image reconstruction\, many o f which have been fueled by signal processing advances in several areas\, including multi-channel sampling\, compressive sensing\, dictionary learni ng\, low-rank\, and structured low-rank methods. Recently\, also neural ne tworks have been employed for image reconstruction achieving further impro vements in scan time and image quality. Most importantly\, some of these t echniques have found their way in the products of MRI vendors and show sig nificant impact in the clinical practice. These developments\, together wi th the advancements in computational hardware have opened a new research f ield of MRI reconstruction as a computational imaging problem. In this tal k\, I will explain the framework of MRI reconstruction as a computational imaging problem and discuss some of the advantages it gives in addressing important clinical needs in MRI.\n\nRoom: M105\, Bldg: Muscarelle Center\ , M105\, \, 1000 River Road \, Teaneck \, New Jersey\, United States\, 076 66\, Virtual: https://events.vtools.ieee.org/m/320030 LOCATION:Room: M105\, Bldg: Muscarelle Center\, M105\, \, 1000 River Road \, Teaneck \, New Jersey\, United States\, 07666\, Virtual: https://events .vtools.ieee.org/m/320030 ORGANIZER:tan@fdu.edu SEQUENCE:3 SUMMARY:MR Image Reconstruction as a Computational Imaging Problem: From Mo del-Based Reconstruction and Sparsity to Machine Learning URL;VALUE=URI:https://events.vtools.ieee.org/m/320030 X-ALT-DESC:Description: <br /><p>Over the past two decades\, image reconstr uction has tremendously gained in importance in MRI enabling reduced scan time\, improved image quality\, and extracting additional information from the measurements. In this time\, MRI has witnessed extensive developments in advanced computational algorithms for image reconstruction\, many of w hich have been fueled by signal processing advances in several areas\, inc luding multi-channel sampling\, compressive sensing\, dictionary learning\ , low-rank\, and structured low-rank methods. Recently\, also neural netwo rks have been employed for image reconstruction achieving further improvem ents in scan time and image quality. Most importantly\, some of these tech niques have found their way in the products of MRI vendors and show signif icant impact in the clinical practice. These developments\, together with the advancements in computational hardware have opened a new research fiel d of MRI reconstruction as a computational imaging problem. In this talk\, I will explain the framework of MRI reconstruction as a computational ima ging problem and discuss some of the advantages it gives in addressing imp ortant clinical needs in MRI.</p>\n<p>&nbsp\;</p><br /><br />Agenda: <br / ><p>Over the past two decades\, image reconstruction has tremendously gain ed in importance in MRI enabling reduced scan time\, improved image qualit y\, and extracting additional information from the measurements. In this t ime\, MRI has witnessed extensive developments in advanced computational a lgorithms for image reconstruction\, many of which have been fueled by sig nal processing advances in several areas\, including multi-channel samplin g\, compressive sensing\, dictionary learning\, low-rank\, and structured low-rank methods. Recently\, also neural networks have been employed for i mage reconstruction achieving further improvements in scan time and image quality. Most importantly\, some of these techniques have found their way in the products of MRI vendors and show significant impact in the clinical practice. These developments\, together with the advancements in computat ional hardware have opened a new research field of MRI reconstruction as a computational imaging problem. In this talk\, I will explain the framewor k of MRI reconstruction as a computational imaging problem and discuss som e of the advantages it gives in addressing important clinical needs in MRI .</p>\n<p>&nbsp\;</p> END:VEVENT END:VCALENDAR