BEGIN:VCALENDAR
VERSION:2.0
PRODID:IEEE vTools.Events//EN
CALSCALE:GREGORIAN
BEGIN:VTIMEZONE
TZID:America/New_York
BEGIN:DAYLIGHT
DTSTART:20250309T030000
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
RRULE:FREQ=YEARLY;BYDAY=2SU;BYMONTH=3
TZNAME:EDT
END:DAYLIGHT
BEGIN:STANDARD
DTSTART:20251102T010000
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=11
TZNAME:EST
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTAMP:20250605T011716Z
UID:295C50A1-16F0-4A29-954E-349F0C749F2F
DTSTART;TZID=America/New_York:20250603T190000
DTEND;TZID=America/New_York:20250603T203000
DESCRIPTION:Magnetic Resonance Imaging (MRI) is undergoing a paradigm shift
  driven by advances in computational techniques and artificial intelligenc
 e. This talk will introduce the challenges and opportunities in the area o
 f medical imaging. It will then explore the evolving landscape of computat
 ional MRI\, where physics-based modeling\, signal processing\, and machine
  learning converge to accelerate acquisition\, enhance image quality\, and
  enable new diagnostic capabilities. It will start with supervised machine
  learning approaches\, followed by new advances in generative artificial i
 ntelligence tools.\n\nSpeaker(s): \, Dr. Mathews Jacob\n\nAgenda: \n7:00 P
 M - Welcome and Introductions\n\n7:10 PM - Presentation Begins\n\n8:10 PM 
 - Q&A\n\n8:30 PM - Finish\n\nVirtual: https://events.vtools.ieee.org/m/486
 020
LOCATION:Virtual: https://events.vtools.ieee.org/m/486020
ORGANIZER:schulman@ieee.org
SEQUENCE:15
SUMMARY:Computational MR Imaging in the AI Era
URL;VALUE=URI:https://events.vtools.ieee.org/m/486020
X-ALT-DESC:Description: <br /><p><span style="font-size: 13.0pt\;">Magnetic
  Resonance Imaging (MRI) is undergoing a paradigm shift driven by advances
  in computational techniques and artificial intelligence. This talk will i
 ntroduce the challenges and opportunities in the area of medical imaging. 
 It will then explore the evolving landscape of computational MRI\, where p
 hysics-based modeling\, signal processing\, and machine learning converge 
 to accelerate acquisition\, enhance image quality\, and enable new diagnos
 tic capabilities. It will start with supervised machine learning approache
 s\, followed by new advances in generative artificial intelligence tools. 
 </span></p><br /><br />Agenda: <br /><p>7:00 PM - Welcome and Introduction
 s</p>\n<p>7:10 PM - Presentation Begins</p>\n<p>8:10 PM - Q&amp\;A</p>\n<p
 >8:30 PM - Finish</p>
END:VEVENT
END:VCALENDAR