Bradshaw T - Search Results

1

A deep learning approach for ¹⁸F-FDG PET attenuation correction.

Liu F, Jang H, Kijowski R, Zhao G, Bradshaw T, McMillan AB. Liu F, et al. Among authors: bradshaw t. EJNMMI Phys. 2018 Nov 12;5(1):24. doi: 10.1186/s40658-018-0225-8. EJNMMI Phys. 2018. PMID: 30417316 Free PMC article.

2

Predicting location of recurrence using FDG, FLT, and Cu-ATSM PET in canine sinonasal tumors treated with radiotherapy.

Bradshaw T, Fu R, Bowen S, Zhu J, Forrest L, Jeraj R. Bradshaw T, et al. Phys Med Biol. 2015 Jul 7;60(13):5211-24. doi: 10.1088/0031-9155/60/13/5211. Epub 2015 Jun 17. Phys Med Biol. 2015. PMID: 26083082 Free PMC article.

3

Deep Learning MR Imaging-based Attenuation Correction for PET/MR Imaging.

Liu F, Jang H, Kijowski R, Bradshaw T, McMillan AB. Liu F, et al. Among authors: bradshaw t. Radiology. 2018 Feb;286(2):676-684. doi: 10.1148/radiol.2017170700. Epub 2017 Sep 19. Radiology. 2018. PMID: 28925823 Free PMC article.

4

Rapid dual-echo ramped hybrid encoding MR-based attenuation correction (dRHE-MRAC) for PET/MR.

Jang H, Liu F, Bradshaw T, McMillan AB. Jang H, et al. Among authors: bradshaw t. Magn Reson Med. 2018 Jun;79(6):2912-2922. doi: 10.1002/mrm.26953. Epub 2017 Oct 2. Magn Reson Med. 2018. PMID: 28971513 Free PMC article.

5

Technical Note: Deep learning based MRAC using rapid ultrashort echo time imaging.

Jang H, Liu F, Zhao G, Bradshaw T, McMillan AB. Jang H, et al. Among authors: bradshaw t. Med Phys. 2018 May 15:10.1002/mp.12964. doi: 10.1002/mp.12964. Online ahead of print. Med Phys. 2018. PMID: 29763997 Free PMC article.

6

Feasibility of Deep Learning-Based PET/MR Attenuation Correction in the Pelvis Using Only Diagnostic MR Images.

Bradshaw TJ, Zhao G, Jang H, Liu F, McMillan AB. Bradshaw TJ, et al. Tomography. 2018 Sep;4(3):138-147. doi: 10.18383/j.tom.2018.00016. Tomography. 2018. PMID: 30320213 Free PMC article.

7

A statistically optimized regional thresholding method (SORT) for bone lesion detection in ¹⁸F-NaF PET/CT imaging.

Perk T, Chen S, Harmon S, Lin C, Bradshaw T, Perlman S, Liu G, Jeraj R. Perk T, et al. Among authors: bradshaw t. Phys Med Biol. 2018 Nov 20;63(22):225018. doi: 10.1088/1361-6560/aaebba. Phys Med Biol. 2018. PMID: 30457117

8

Automated classification of benign and malignant lesions in ¹⁸F-NaF PET/CT images using machine learning.

Perk T, Bradshaw T, Chen S, Im HJ, Cho S, Perlman S, Liu G, Jeraj R. Perk T, et al. Among authors: bradshaw t. Phys Med Biol. 2018 Nov 20;63(22):225019. doi: 10.1088/1361-6560/aaebd0. Phys Med Biol. 2018. PMID: 30457118

9

Comparison of 11 automated PET segmentation methods in lymphoma.

Weisman AJ, Kieler MW, Perlman S, Hutchings M, Jeraj R, Kostakoglu L, Bradshaw TJ. Weisman AJ, et al. Phys Med Biol. 2020 Nov 27;65(23):235019. doi: 10.1088/1361-6560/abb6bd. Phys Med Biol. 2020. PMID: 32906088

10

Automated quantification of baseline imaging PET metrics on FDG PET/CT images of pediatric Hodgkin lymphoma patients.

Weisman AJ, Kim J, Lee I, McCarten KM, Kessel S, Schwartz CL, Kelly KM, Jeraj R, Cho SY, Bradshaw TJ. Weisman AJ, et al. EJNMMI Phys. 2020 Dec 14;7(1):76. doi: 10.1186/s40658-020-00346-3. EJNMMI Phys. 2020. PMID: 33315178 Free PMC article.

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