TY - GEN

T1 - Linear and iterative reconstruction algorithms for a novel PET-insert scanner

AU - Pal, Debashish

AU - Tai, Yuan Chuan

AU - Janecek, Martin

AU - Wu, Heyu

AU - O'Sullivan, Joseph A.

PY - 2005

Y1 - 2005

N2 - Tal, et al. are developing insert devices for existing clinical PET scanners to improve the image resolution. Adding the insert inside the scanner leads to three types of coincidences: insert-insert, insert-scanner and scanner-scanner. The challenges in image reconstruction include development of a linear reconstruction algorithm for the insert-scanner coincidences, development of a linear reconstruction algorithm that incorporates all measurements, and development of an iterative (expectation-maximization) algorithm to form a maximum likelihood estimate of the image given all of the data. The data from the set of insert-insert coincidences and from the set of scanner-scanner coincidences each can be used in conventional linear reconstruction algorithms based on data from a ring of detectors. However, the geometry of an insert ring and a scanner ring together is analogous to the fan-beam geometry of fourth generation transmission tomography systems; this analogy leads to a new linear reconstruction algorithm for these data. The resulting algorithm was implemented on both simulated and experimental data, yielding promising results with few artifacts. Our development of an iterative algorithm is based on the standard expectation-maximization algorithm. The novelty of the iterative algorithm is in the incorporation of the details of the geometry, which is based in part on our characterization of the insert-scanner geometry.

AB - Tal, et al. are developing insert devices for existing clinical PET scanners to improve the image resolution. Adding the insert inside the scanner leads to three types of coincidences: insert-insert, insert-scanner and scanner-scanner. The challenges in image reconstruction include development of a linear reconstruction algorithm for the insert-scanner coincidences, development of a linear reconstruction algorithm that incorporates all measurements, and development of an iterative (expectation-maximization) algorithm to form a maximum likelihood estimate of the image given all of the data. The data from the set of insert-insert coincidences and from the set of scanner-scanner coincidences each can be used in conventional linear reconstruction algorithms based on data from a ring of detectors. However, the geometry of an insert ring and a scanner ring together is analogous to the fan-beam geometry of fourth generation transmission tomography systems; this analogy leads to a new linear reconstruction algorithm for these data. The resulting algorithm was implemented on both simulated and experimental data, yielding promising results with few artifacts. Our development of an iterative algorithm is based on the standard expectation-maximization algorithm. The novelty of the iterative algorithm is in the incorporation of the details of the geometry, which is based in part on our characterization of the insert-scanner geometry.

UR - http://www.scopus.com/inward/record.url?scp=33846591567&partnerID=8YFLogxK

U2 - 10.1109/NSSMIC.2005.1596711

DO - 10.1109/NSSMIC.2005.1596711

M3 - Conference contribution

AN - SCOPUS:33846591567

SN - 0780392213

SN - 9780780392212

T3 - IEEE Nuclear Science Symposium Conference Record

SP - 1938

EP - 1941

BT - 2005 IEEE Nuclear Science Symposium Conference Record -Nuclear Science Symposium and Medical Imaging Conference

T2 - Nuclear Science Symposium Conference Record, 2005 IEEE

Y2 - 23 October 2005 through 29 October 2005

ER -