[BIC-announce] Fwd: BME Special Seminar Tomorrow - Wednesday, May 12 at 1 P.M., Duff, Seminar Rom 321

[G. Bruce Pike, Prof.]

Begin forwarded message:

From: "Pina Sorrini, Ms." <pina.sorrini at MCGILL.CA<mailto:pina.sorrini at MCGILL.CA>>
Date: May 11, 2010 3:30:02 PM EDT
To: "217L-SEMINAR_NOTICE at LISTS.MCGILL.CA<mailto:217L-SEMINAR_NOTICE at LISTS.MCGILL.CA>" <217L-SEMINAR_NOTICE at LISTS.MCGILL.CA<mailto:217L-SEMINAR_NOTICE at LISTS.MCGILL.CA>>
Subject: FW: BME Special Seminar Tomorrow - Wednesday, May 12 at 1 P.M., Duff, Seminar Rom 321
Reply-To: "Pina Sorrini, Ms." <pina.sorrini at mcgill.ca<mailto:pina.sorrini at mcgill.ca>>

PLEASE NOTE LOCATION OF SEMINAR IS DUFF, ROOOM 321 AS INDICATED ON SUBJECT LINE

From: Pina Sorrini, Ms.
Sent: Tuesday, May 11, 2010 3:25 PM
Subject: BME Special Seminar Tomorrow - Wednesday, May 12 at 1 P.M., Duff, Seminar Rom 321
Importance: High

The speaker is a candidate for associate membership in BME Dept.; the title and abstract appear below.


Title: Advances in image reconstruction for positron emission tomography (PET)

Speaker:  Dr. Andrew Reader , Associate Professor, Neurology & Neurosurgery


Abstract:

Image reconstruction in positron emission tomography (PET) is conventionally regarded

as the algorithm applied to the acquired data to produce images used for estimation

of physiological parameters, or to determine the presence of disease. There are numerous approaches

to image reconstruction, and the method chosen has a significant impact on the utility

of PET. The use of iterative image reconstruction algorithms, the use of resolution

modelling (‘‘resolution recovery’’) and fully 4D methods are three specific advances from recent years which have

demonstrated marked improvements in image quality. This talk touches upon five main aspects

of PET image reconstruction in which there are still promising possibilities for further advance:

(i) full consideration of the raw acquired PET data (with minimal pre-processing), (ii) careful

selection of the parameters to estimate, (iii) accurate definition of the system matrix, (iv) the objective function,

and (v) the algorithm.

Some example advances in these areas will be presented.

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