BEGIN:VCALENDAR VERSION:2.0 PRODID:-//Sabre//Sabre VObject 4.5.8//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:Europe/Zurich X-LIC-LOCATION:Europe/Zurich TZURL:http://tzurl.org/zoneinfo/Europe/Zurich BEGIN:DAYLIGHT TZOFFSETFROM:+0100 TZOFFSETTO:+0200 TZNAME:CEST DTSTART:19810329T020000 RRULE:FREQ=YEARLY;BYMONTH=3;BYDAY=-1SU END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:+0200 TZOFFSETTO:+0100 TZNAME:CET DTSTART:19961027T030000 RRULE:FREQ=YEARLY;BYMONTH=10;BYDAY=-1SU END:STANDARD END:VTIMEZONE BEGIN:VEVENT UID:news287@dmi.unibas.ch DTSTAMP;TZID=Europe/Zurich:20180716T234452 DTSTART;TZID=Europe/Zurich:20120525T090000 SUMMARY:Seminar in Numerical Analysis: Wolfgang Bangerth (Texas A&M Univers ity) DESCRIPTION:In many of the modern biomedical imaging modalities\, the measu rable signal can be described as the solution of a partial differential equation that depends nonlinearly on the tissue properties (the "paramete rs") one would like to image. Consequently\, there are typically no expli cit solution formulas for these so-called "inverse problems" that can rec over the parameters from the measurements\, and the only way to generate body images from measurements is through numerical approximation.\\r\\nTh e resulting parameter estimation schemes have the underlying partial diff erential equations as side-constraints\, and the solution of these optimi zation problems often requires solving the partial differential equation thousands or hundred of thousands of times. The development of efficient schemes is therefore of great interest for the practical use of such imag ing modalities in clinical settings. In this talk\, the formulation and e fficient solution strategies for such inverse problems will be discussed\ , and we will demonstrate its efficacy using examples from our work on Op tical Tomography\, a novel way of imaging tumors in humans and animals. T he talk will conclude with an outlook to even more complex problems that attempt to automatically optimize experimental setups to obtain better i mages. X-ALT-DESC:In many of the modern biomedical imaging modalities\, the measur able signal can be described as the solution of a partial differential e quation that depends nonlinearly on the tissue properties (the "\;par ameters"\;) one would like to image. Consequently\, there are typicall y no explicit solution formulas for these so-called "\;inverse proble ms"\; that can recover the parameters from the measurements\, and the only way to generate body images from measurements is through numerical approximation.\nThe resulting parameter estimation schemes have the unde rlying partial differential equations as side-constraints\, and the solut ion of these optimization problems often requires solving the partial dif ferential equation thousands or hundred of thousands of times. The develo pment of efficient schemes is therefore of great interest for the practic al use of such imaging modalities in clinical settings. In this talk\, th e formulation and efficient solution strategies for such inverse problems will be discussed\, and we will demonstrate its efficacy using examples from our work on Optical Tomography\, a novel way of imaging tumors in hu mans and animals. The talk will conclude with an outlook to even more com plex problems that attempt to automatically optimize experimental setups to obtain better images. DTEND;TZID=Europe/Zurich:20120525T100000 END:VEVENT END:VCALENDAR