Deep learning MR imaging–based attenuation correction for PET/MR imaging F Liu, H Jang, R Kijowski, T Bradshaw, AB McMillan Radiology 286 (2), 676-684, 2018 | 418 | 2018 |
Current methods to define metabolic tumor volume in positron emission tomography: which one is better? HJ Im, T Bradshaw, M Solaiyappan, SY Cho Nuclear medicine and molecular imaging 52, 5-15, 2018 | 235 | 2018 |
A deep learning approach for 18F-FDG PET attenuation correction F Liu, H Jang, R Kijowski, G Zhao, T Bradshaw, AB McMillan EJNMMI physics 5, 1-15, 2018 | 113 | 2018 |
Repeatability of quantitative 18F-NaF PET: a multicenter study C Lin, T Bradshaw, T Perk, S Harmon, J Eickhoff, N Jallow, PL Choyke, ... Journal of nuclear medicine 57 (12), 1872-1879, 2016 | 76 | 2016 |
Molecular imaging to plan radiotherapy and evaluate its efficacy R Jeraj, T Bradshaw, U Simončič Journal of Nuclear Medicine 56 (11), 1752-1765, 2015 | 70 | 2015 |
Nuclear medicine and artificial intelligence: best practices for algorithm development TJ Bradshaw, R Boellaard, J Dutta, AK Jha, P Jacobs, Q Li, C Liu, A Sitek, ... Journal of Nuclear Medicine 63 (4), 500-510, 2022 | 59 | 2022 |
Deep learning based MRAC using rapid ultrashort echo time imaging H Jang, F Liu, G Zhao, T Bradshaw, AB McMillan Medical physics 45 (8), 3697-3704, 2018 | 59 | 2018 |
Feasibility of deep learning–based PET/MR attenuation correction in the pelvis using only diagnostic MR images TJ Bradshaw, G Zhao, H Jang, F Liu, AB McMillan Tomography 4 (3), 138-147, 2018 | 54 | 2018 |
Automated classification of benign and malignant lesions in 18F-NaF PET/CT images using machine learning T Perk, T Bradshaw, S Chen, H Im, S Cho, S Perlman, G Liu, R Jeraj Physics in Medicine & Biology 63 (22), 225019, 2018 | 53 | 2018 |
Joint EANM/SNMMI guideline on radiomics in nuclear medicine: Jointly supported by the EANM Physics Committee and the SNMMI Physics, Instrumentation and Data Sciences Council M Hatt, AK Krizsan, A Rahmim, TJ Bradshaw, PF Costa, A Forgacs, ... European Journal of Nuclear Medicine and Molecular Imaging 50 (2), 352-375, 2023 | 52 | 2023 |
Nuclear medicine and artificial intelligence: best practices for evaluation (the RELAINCE guidelines) AK Jha, TJ Bradshaw, I Buvat, M Hatt, KC Prabhat, C Liu, NF Obuchowski, ... Journal of Nuclear Medicine 63 (9), 1288-1299, 2022 | 52 | 2022 |
Convolutional neural networks for automated PET/CT detection of diseased lymph node burden in patients with lymphoma AJ Weisman, MW Kieler, SB Perlman, M Hutchings, R Jeraj, L Kostakoglu, ... Radiology: Artificial Intelligence 2 (5), e200016, 2020 | 45 | 2020 |
Heterogeneity in intratumor correlations of 18F-FDG, 18F-FLT, and 61Cu-ATSM PET in canine sinonasal tumors TJ Bradshaw, SR Bowen, N Jallow, LJ Forrest, R Jeraj Journal of Nuclear Medicine 54 (11), 1931-1937, 2013 | 37 | 2013 |
Automated quantification of baseline imaging PET metrics on FDG PET/CT images of pediatric Hodgkin lymphoma patients AJ Weisman, J Kim, I Lee, KM McCarten, S Kessel, CL Schwartz, KM Kelly, ... EJNMMI physics 7, 1-12, 2020 | 34 | 2020 |
Comparison of 11 automated PET segmentation methods in lymphoma AJ Weisman, MW Kieler, S Perlman, M Hutchings, R Jeraj, L Kostakoglu, ... Physics in Medicine & Biology 65 (23), 235019, 2020 | 29 | 2020 |
Molecular imaging biomarkers of resistance to radiation therapy for spontaneous nasal tumors in canines TJ Bradshaw, SR Bowen, MA Deveau, L Kubicek, P White, SM Bentzen, ... International Journal of Radiation Oncology* Biology* Physics 91 (4), 787-795, 2015 | 29 | 2015 |
Rapid dual‐echo ramped hybrid encoding MR‐based attenuation correction (d RHE‐MRAC) for PET/MR H Jang, F Liu, T Bradshaw, AB McMillan Magnetic resonance in medicine 79 (6), 2912-2922, 2018 | 28 | 2018 |
Deep learning for classification of benign and malignant bone lesions in [F-18] NaF PET/CT images. T Bradshaw, T Perk, S Chen, HJ Im, S Cho, S Perlman, R Jeraj Journal of Nuclear Medicine 59 (supplement 1), 327-327, 2018 | 25 | 2018 |
Spatiotemporal stability of Cu-ATSM and FLT positron emission tomography distributions during radiation therapy TJ Bradshaw, S Yip, N Jallow, LJ Forrest, R Jeraj International Journal of Radiation Oncology* Biology* Physics 89 (2), 399-405, 2014 | 24 | 2014 |
Factors affecting the harmonization of disease-related metabolic brain pattern expression quantification in [18F] FDG-PET (PETMETPAT) RV Kogan, BA de Jong, RJ Renken, SK Meles, PJH van Snick, S Golla, ... Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring 11, 472-482, 2019 | 20 | 2019 |