|Radiomic signature of 18F fluorodeoxyglucose PET/CT for prediction of gastric cancer survival and chemotherapeutic benefits|
Y Jiang, Q Yuan, W Lv, S Xi, W Huang, Z Sun, H Chen, L Zhao, W Liu, ...
Theranostics 8 (21), 5915, 2018
|Robustness of Radiomic Features in [11C]Choline and [18F]FDG PET/CT Imaging of Nasopharyngeal Carcinoma: Impact of Segmentation and Discretization|
L Lu, W Lv, J Jiang, J Ma, Q Feng, A Rahmim, W Chen
Molecular Imaging and Biology 18, 935-945, 2016
|Improved prediction of outcome in Parkinson's disease using radiomics analysis of longitudinal DAT SPECT images|
A Rahmim, P Huang, N Shenkov, S Fotouhi, E Davoodi-Bojd, L Lu, Z Mari, ...
NeuroImage: Clinical 16, 539-544, 2017
|Radiomics analysis of PET and CT components of PET/CT imaging integrated with clinical parameters: application to prognosis for nasopharyngeal carcinoma|
W Lv, Q Yuan, Q Wang, J Ma, Q Feng, W Chen, A Rahmim, L Lu
Molecular imaging and biology 21, 954-964, 2019
|Multi-level multi-modality fusion radiomics: application to PET and CT imaging for prognostication of head and neck cancer|
W Lv, S Ashrafinia, J Ma, L Lu, A Rahmim
IEEE journal of biomedical and health informatics 24 (8), 2268-2277, 2019
|Robustness versus disease differentiation when varying parameter settings in radiomics features: application to nasopharyngeal PET/CT|
W Lv, Q Yuan, Q Wang, J Ma, J Jiang, W Yang, Q Feng, W Chen, ...
European radiology 28, 3245-3254, 2018
|Machine learning methods for optimal radiomics-based differentiation between recurrence and inflammation: application to nasopharyngeal carcinoma post-therapy PET/CT images|
D Du, H Feng, W Lv, S Ashrafinia, Q Yuan, Q Wang, W Yang, Q Feng, ...
Molecular imaging and biology 22, 730-738, 2020
|Subregional radiomics analysis of PET/CT imaging with intratumor partitioning: application to prognosis for nasopharyngeal carcinoma|
H Xu, W Lv, H Feng, D Du, Q Yuan, Q Wang, Z Dai, W Yang, Q Feng, J Ma, ...
Molecular Imaging and Biology 22, 1414-1426, 2020
|Prognostic modeling for patients with colorectal liver metastases incorporating FDG PET radiomic features|
A Rahmim, KP Bak-Fredslund, S Ashrafinia, L Lu, CR Schmidtlein, ...
European journal of radiology 113, 101-109, 2019
|Integration of PET/CT radiomics and semantic features for differentiation between active pulmonary tuberculosis and lung cancer|
D Du, J Gu, X Chen, W Lv, Q Feng, A Rahmim, H Wu, L Lu
Molecular Imaging and Biology 23, 287-298, 2021
|Penalized weighted least-squares approach for multienergy computed tomography image reconstruction via structure tensor total variation regularization|
D Zeng, Y Gao, J Huang, Z Bian, H Zhang, L Lu, J Ma
Computerized Medical Imaging and Graphics 53, 19-29, 2016
|3.5 D dynamic PET image reconstruction incorporating kinetics-based clusters|
L Lu, NA Karakatsanis, J Tang, W Chen, A Rahmim
Physics in Medicine & Biology 57 (15), 5035, 2012
|3.5D dynamic PET image reconstruction incorporating kinetics-based clusters|
L Lu, N Karakatsanis, J Tang, W Chen, A Rahmim
Society of Nuclear Medicine Annual Meeting Abstracts 53 (Supplement 1), 318, 2012
|Direct 4D parametric imaging for linearized models of reversibly binding PET tracers using generalized AB-EM reconstruction|
A Rahmim, Y Zhou, J Tang, L Lu, V Sossi, DF Wong
Physics in Medicine & Biology 57 (3), 733, 2012
|The effects of volume of interest delineation on MRI-based radiomics analysis: evaluation with two disease groups|
X Zhang, L Zhong, B Zhang, L Zhang, H Du, L Lu, S Zhang, W Yang, ...
Cancer Imaging 19, 1-12, 2019
|Artificial neural network–based prediction of outcome in Parkinson’s disease patients using DaTscan SPECT imaging features|
J Tang, B Yang, MP Adams, NN Shenkov, IS Klyuzhin, S Fotouhi, ...
Molecular imaging and biology 21, 1165-1173, 2019
|Multi-view secondary input collaborative deep learning for lung nodule 3D segmentation|
X Dong, S Xu, Y Liu, A Wang, MI Saripan, L Li, X Zhang, L Lu
Cancer imaging 20, 1-13, 2020
|Prediction of local recurrence and distant metastasis using radiomics analysis of pretreatment nasopharyngeal [18F] FDG PET/CT images|
L Peng, X Hong, Q Yuan, L Lu, Q Wang, W Chen
Annals of Nuclear Medicine 35, 458-468, 2021
|Cerebral perfusion computed tomography deconvolution via structure tensor total variation regularization|
D Zeng, X Zhang, Z Bian, J Huang, H Zhang, L Lu, W Lyu, J Zhang, ...
Medical physics 43 (5), 2091-2107, 2016
|Dynamic positron emission tomography image restoration via a kinetics-induced bilateral filter|
Z Bian, J Huang, J Ma, L Lu, S Niu, D Zeng, Q Feng, W Chen
PloS one 9 (2), e89282, 2014