Li Yang
University of California, Davis
StatisticsImage restorationAlgorithmParametric statisticsPatlak plotIterative reconstructionArtificial intelligenceTomographyPattern recognitionMonte Carlo methodPositron emission tomographyDirect methodsCoincidenceObserver (special relativity)LesionParametric ImageComputer visionMathematicsComputer scienceMaximum likelihoodImage qualityPenalty methodMedicineSignal-to-noise ratioObserver (quantum physics)Regularization (mathematics)Image processingDirect method
6Publications
4H-index
36Citations
Publications 5
Newest
#1Li Yang (UC Davis: University of California, Davis)H-Index: 4
#2Guobao Wang (UC Davis: University of California, Davis)H-Index: 17
Last. Jinyi Qi (UC Davis: University of California, Davis)H-Index: 52
view all 3 authors...
Detecting cancerous lesions is a major clinical application of emission tomography. In a previous work, we 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 first reconstruct...
4 CitationsSource
Apr 16, 2015 in ISBI (International Symposium on Biomedical Imaging)
#1Li Yang (UC Davis: University of California, Davis)H-Index: 4
#2Guobao Wang (UC Davis: University of California, Davis)H-Index: 17
Last. Jinyi Qi (UC Davis: University of California, Davis)H-Index: 52
view all 3 authors...
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 ...
Source
#1Li Yang (UC Davis: University of California, Davis)H-Index: 4
#2Andrea Ferrero (UC Davis: University of California, Davis)H-Index: 8
Last. Jinyi Qi (UC Davis: University of California, Davis)H-Index: 52
view all 5 authors...
Detecting cancerous lesions is a major clinical application in emission tomography. Previously, we developed a method to design a shift-variant quadratic penalty function in penalized maximum-likelihood (PML) image reconstruction to improve the lesion detectability. We used a multiview channelized Hotelling observer (mvCHO) to assess the lesion detectability in three-dimensional images and validated the penalty design using computer simulations. In this study, we evaluate the benefit of the prop...
4 CitationsSource
#1Li Yang (UC Davis: University of California, Davis)H-Index: 4
#2Jian Zhou (UC Davis: University of California, Davis)H-Index: 12
Last. Jinyi Qi (UC Davis: University of California, Davis)H-Index: 52
view all 5 authors...
Detecting cancerous lesions is a major clinical application in emission tomography. In previous work, we have studied penalized maximum-likelihood (PML) image reconstruction for the detection task and proposed a method to design a shift-invariant quadratic penalty function to maximize detectability of a lesion at a known location in a two dimensional image. Here we extend the regularization design to maximize detectability of lesions at unknown locations in fully 3D PET. We used a multiview chan...
24 CitationsSource
May 2, 2012 in ISBI (International Symposium on Biomedical Imaging)
#1Li Yang (UC Davis: University of California, Davis)H-Index: 4
#2Jian Zhou (UC Davis: University of California, Davis)H-Index: 140
Last. Jinyi Qi (UC Davis: University of California, Davis)H-Index: 52
view all 3 authors...
Detecting cancerous lesion is a major clinical application in emission tomography. In a previous work, we have shown that penalized maximum likelihood image reconstruction can improve lesion detection at a fixed location by designing a shift-invariant quadratic penalty function. Here we extend this work to detection of tumors at unknown positions. We present a method to design a shift-variant quadratic penalty function that maximizes the detectability of lesions at all possible locations. We con...
5 CitationsSource