Abstract
Detecting cancerous lesion is a major clinical application in emission tomography. In previous work, we have studied penalized maximum-likelihood (PML) image reconstruction for lesion detection in static PET. Here we extend our theoretical analysis of static PET reconstruction to dynamic PET. We study both the conventional indirect reconstruction and direct reconstruction for Patlak parametric image estimation. In indirect reconstruction, Patlak parametric images are generated by reconstructing a sequence of dynamic PET images first and then performing Patlak analysis on the time activity curves (TACs) pixel-by-pixel. In direct reconstruction, Patlak parametric images are estimated directly from raw sinogram data by incorporating the Patlak model into the image reconstruction procedure. PML reconstruction is used in both the indirect and direct reconstruction methods. We use a channelized Hotelling observer (CHO) to assess lesion detectability in the Patlak slope image. Simplified expressions for evaluating the lesion detectability have been derived and applied to the selection of the regularization parameter value to maximize the lesion detectability. The proposed method is validated using computer-based Monte Carlo simulation. Good agreements between theoretical predictions and Monte Carlo results are observed. The theoretical formula also shows the benefit of the direct method in dynamic PET reconstruction for lesion detection.
| Original language | English (US) |
|---|---|
| Title of host publication | Proceedings - International Symposium on Biomedical Imaging |
| Publisher | IEEE Computer Society |
| Pages | 1188-1191 |
| Number of pages | 4 |
| Volume | 2015-July |
| ISBN (Print) | 9781479923748 |
| DOIs | |
| State | Published - Jul 21 2015 |
| Event | 12th IEEE International Symposium on Biomedical Imaging, ISBI 2015 - Brooklyn, United States Duration: Apr 16 2015 → Apr 19 2015 |
Other
| Other | 12th IEEE International Symposium on Biomedical Imaging, ISBI 2015 |
|---|---|
| Country | United States |
| City | Brooklyn |
| Period | 4/16/15 → 4/19/15 |
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Keywords
- dynamic PET
- lesion detection
- Patlak model
- PML reconstruction
ASJC Scopus subject areas
- Biomedical Engineering
- Radiology Nuclear Medicine and imaging
Cite this
Theoretical analysis of lesion detectability in penalized maximum-likelihood patlak parametric image reconstruction using dynamic PET. / Yang, Li; Wang, Guobao; Qi, Jinyi.
Proceedings - International Symposium on Biomedical Imaging. Vol. 2015-July IEEE Computer Society, 2015. p. 1188-1191 7164085.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Theoretical analysis of lesion detectability in penalized maximum-likelihood patlak parametric image reconstruction using dynamic PET
AU - Yang, Li
AU - Wang, Guobao
AU - Qi, Jinyi
PY - 2015/7/21
Y1 - 2015/7/21
N2 - Detecting cancerous lesion is a major clinical application in emission tomography. In previous work, we have studied penalized maximum-likelihood (PML) image reconstruction for lesion detection in static PET. Here we extend our theoretical analysis of static PET reconstruction to dynamic PET. We study both the conventional indirect reconstruction and direct reconstruction for Patlak parametric image estimation. In indirect reconstruction, Patlak parametric images are generated by reconstructing a sequence of dynamic PET images first and then performing Patlak analysis on the time activity curves (TACs) pixel-by-pixel. In direct reconstruction, Patlak parametric images are estimated directly from raw sinogram data by incorporating the Patlak model into the image reconstruction procedure. PML reconstruction is used in both the indirect and direct reconstruction methods. We use a channelized Hotelling observer (CHO) to assess lesion detectability in the Patlak slope image. Simplified expressions for evaluating the lesion detectability have been derived and applied to the selection of the regularization parameter value to maximize the lesion detectability. The proposed method is validated using computer-based Monte Carlo simulation. Good agreements between theoretical predictions and Monte Carlo results are observed. The theoretical formula also shows the benefit of the direct method in dynamic PET reconstruction for lesion detection.
AB - Detecting cancerous lesion is a major clinical application in emission tomography. In previous work, we have studied penalized maximum-likelihood (PML) image reconstruction for lesion detection in static PET. Here we extend our theoretical analysis of static PET reconstruction to dynamic PET. We study both the conventional indirect reconstruction and direct reconstruction for Patlak parametric image estimation. In indirect reconstruction, Patlak parametric images are generated by reconstructing a sequence of dynamic PET images first and then performing Patlak analysis on the time activity curves (TACs) pixel-by-pixel. In direct reconstruction, Patlak parametric images are estimated directly from raw sinogram data by incorporating the Patlak model into the image reconstruction procedure. PML reconstruction is used in both the indirect and direct reconstruction methods. We use a channelized Hotelling observer (CHO) to assess lesion detectability in the Patlak slope image. Simplified expressions for evaluating the lesion detectability have been derived and applied to the selection of the regularization parameter value to maximize the lesion detectability. The proposed method is validated using computer-based Monte Carlo simulation. Good agreements between theoretical predictions and Monte Carlo results are observed. The theoretical formula also shows the benefit of the direct method in dynamic PET reconstruction for lesion detection.
KW - dynamic PET
KW - lesion detection
KW - Patlak model
KW - PML reconstruction
UR - http://www.scopus.com/inward/record.url?scp=84944315599&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84944315599&partnerID=8YFLogxK
U2 - 10.1109/ISBI.2015.7164085
DO - 10.1109/ISBI.2015.7164085
M3 - Conference contribution
AN - SCOPUS:84944315599
SN - 9781479923748
VL - 2015-July
SP - 1188
EP - 1191
BT - Proceedings - International Symposium on Biomedical Imaging
PB - IEEE Computer Society
ER -