Abstract
A Positron Emission Mammography (PEM) scanner dedicated to breast imaging is being developed at our laboratory. We have developed a list mode likelihood reconstruction algorithm for this scanner. Here we theoretically study the lesion detection and quantitation. The lesion detectability is studied theoretically using computer observers. We found that for the zero-order quadratic prior, the region of interest observer can achieve the performance of the prewhitening observer with a properly selected smoothing parameter. We also study the lesion quantitation using the test statistic of the region of interest observer. The theoretical expressions for the bias, variance, and ensemble mean squared error of the quantitation are derived. Computer simulations show that the theoretical predictions are in good agreement with the Monte Carlo results for both lesion detection and quantitation.
| Original language | English (US) |
|---|---|
| Title of host publication | IEEE Nuclear Science Symposium and Medical Imaging Conference |
| Pages | 2248-2252 |
| Number of pages | 5 |
| Volume | 4 |
| State | Published - 2002 |
| Externally published | Yes |
| Event | 2001 IEEE Nuclear Science Symposium Conference Record - San Diego, CA, United States Duration: Nov 4 2001 → Nov 10 2001 |
Other
| Other | 2001 IEEE Nuclear Science Symposium Conference Record |
|---|---|
| Country | United States |
| City | San Diego, CA |
| Period | 11/4/01 → 11/10/01 |
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ASJC Scopus subject areas
- Computer Vision and Pattern Recognition
- Industrial and Manufacturing Engineering
Cite this
Lesion detection and quantitation of positron emission mammography. / Qi, Jinyi; Huesman, Ronald H.
IEEE Nuclear Science Symposium and Medical Imaging Conference. Vol. 4 2002. p. 2248-2252.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Lesion detection and quantitation of positron emission mammography
AU - Qi, Jinyi
AU - Huesman, Ronald H.
PY - 2002
Y1 - 2002
N2 - A Positron Emission Mammography (PEM) scanner dedicated to breast imaging is being developed at our laboratory. We have developed a list mode likelihood reconstruction algorithm for this scanner. Here we theoretically study the lesion detection and quantitation. The lesion detectability is studied theoretically using computer observers. We found that for the zero-order quadratic prior, the region of interest observer can achieve the performance of the prewhitening observer with a properly selected smoothing parameter. We also study the lesion quantitation using the test statistic of the region of interest observer. The theoretical expressions for the bias, variance, and ensemble mean squared error of the quantitation are derived. Computer simulations show that the theoretical predictions are in good agreement with the Monte Carlo results for both lesion detection and quantitation.
AB - A Positron Emission Mammography (PEM) scanner dedicated to breast imaging is being developed at our laboratory. We have developed a list mode likelihood reconstruction algorithm for this scanner. Here we theoretically study the lesion detection and quantitation. The lesion detectability is studied theoretically using computer observers. We found that for the zero-order quadratic prior, the region of interest observer can achieve the performance of the prewhitening observer with a properly selected smoothing parameter. We also study the lesion quantitation using the test statistic of the region of interest observer. The theoretical expressions for the bias, variance, and ensemble mean squared error of the quantitation are derived. Computer simulations show that the theoretical predictions are in good agreement with the Monte Carlo results for both lesion detection and quantitation.
UR - http://www.scopus.com/inward/record.url?scp=0035553516&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=0035553516&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:0035553516
VL - 4
SP - 2248
EP - 2252
BT - IEEE Nuclear Science Symposium and Medical Imaging Conference
ER -