TY - GEN
T1 - Evaluation of HYPR de-noising with MAP reconstruction in small animal PET imaging
AU - Cheng, Ju Chieh
AU - Laforest, Richard
PY - 2012
Y1 - 2012
N2 - We describe an evaluation of the HighlY constrained back-PRojection (HYPR) de-noising in conjunction with the maximum a posteriori (MAP) reconstruction for the microPET-Inveon scanner. The HYPR technique involves the creation of a composite image from the entire duration of the dynamic scan. This image provides high signal-to-noise ratio (SNR) as well as high spatial resolution, and then it is updated by the weighting images generated from each individual dynamic frame to form the HYPR images. Consequently, as long as the composite image has a good SNR, the image quality of each dynamic frame with poor SNR can be improved. Dynamic mouse as well as static tumor imaging with high and low doses was performed to evaluate the advantages and limitations of the HYPR method. Time-activity-curve (TAC) comparison, precision vs accuracy in tumor/muscle ratio and mean absolute error vs number of counts were evaluated. It was found that HYPR can not only improve SNR and produce smoother TACs (on both single-voxel and region-of-interest levels) with lower variance but also be able to reduce the amount of dose injected. Two limitations were found: (1) at very low counts the low activity regions cannot be restored completely, and a positive bias in the estimated tumor/muscle ratio was obtained as a result, and (2) motion contamination between the composite and each dynamic image can introduce motion-artifact in the HYPR image. Therefore, motion needs to be corrected before applying HYPR.
AB - We describe an evaluation of the HighlY constrained back-PRojection (HYPR) de-noising in conjunction with the maximum a posteriori (MAP) reconstruction for the microPET-Inveon scanner. The HYPR technique involves the creation of a composite image from the entire duration of the dynamic scan. This image provides high signal-to-noise ratio (SNR) as well as high spatial resolution, and then it is updated by the weighting images generated from each individual dynamic frame to form the HYPR images. Consequently, as long as the composite image has a good SNR, the image quality of each dynamic frame with poor SNR can be improved. Dynamic mouse as well as static tumor imaging with high and low doses was performed to evaluate the advantages and limitations of the HYPR method. Time-activity-curve (TAC) comparison, precision vs accuracy in tumor/muscle ratio and mean absolute error vs number of counts were evaluated. It was found that HYPR can not only improve SNR and produce smoother TACs (on both single-voxel and region-of-interest levels) with lower variance but also be able to reduce the amount of dose injected. Two limitations were found: (1) at very low counts the low activity regions cannot be restored completely, and a positive bias in the estimated tumor/muscle ratio was obtained as a result, and (2) motion contamination between the composite and each dynamic image can introduce motion-artifact in the HYPR image. Therefore, motion needs to be corrected before applying HYPR.
UR - http://www.scopus.com/inward/record.url?scp=84881606265&partnerID=8YFLogxK
U2 - 10.1109/NSSMIC.2012.6551531
DO - 10.1109/NSSMIC.2012.6551531
M3 - Conference contribution
AN - SCOPUS:84881606265
SN - 9781467320306
T3 - IEEE Nuclear Science Symposium Conference Record
SP - 2339
EP - 2342
BT - 2012 IEEE Nuclear Science Symposium and Medical Imaging Conference Record, NSS/MIC 2012
T2 - 2012 IEEE Nuclear Science Symposium and Medical Imaging Conference Record, NSS/MIC 2012
Y2 - 29 October 2012 through 3 November 2012
ER -