Asset Details
Do you wish to reserve the book?
Correction of partial volume effect in (18)F-FDG PET brain studies using coregistered MR volumes: voxel based analysis of tracer uptake in the white matter
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
Correction of partial volume effect in (18)F-FDG PET brain studies using coregistered MR volumes: voxel based analysis of tracer uptake in the white matter
Do you wish to request the book?
Correction of partial volume effect in (18)F-FDG PET brain studies using coregistered MR volumes: voxel based analysis of tracer uptake in the white matter
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Journal Article
Correction of partial volume effect in (18)F-FDG PET brain studies using coregistered MR volumes: voxel based analysis of tracer uptake in the white matter
2013
Overview
A voxel-based algorithm to correct for partial volume effect in PET brain volumes is presented. This method (named LoReAn) is based on MRI based segmentation of anatomical regions and accurate measurements of the effective point spread function of the PET imaging process. The objective is to correct for the spill-out of activity from high-uptake anatomical structures (e.g. grey matter) into low-uptake anatomical structures (e.g. white matter) in order to quantify physiological uptake in the white matter. The new algorithm is presented and validated against the state of the art region-based geometric transfer matrix (GTM) method with synthetic and clinical data. Using synthetic data, both bias and coefficient of variation were improved in the white matter region using LoReAn compared to GTM. An increased number of anatomical regions doesn't affect the bias (<5%) and misregistration affects equally LoReAn and GTM algorithms. The LoReAn algorithm appears to be a simple and promising voxel-based algorithm for studying metabolism in white matter regions.
