Journal

High Spatial-Resolution Skull Base Imaging With Photon-Counting Computed Tomography and Energy-Integrating Computed Tomography: A Comparative Phantom Study

Rajagopal J, Schwartz FR, Solomon J, Enterline DS, Samei E.
High Spatial-Resolution Skull Base Imaging With Photon-Counting Computed Tomography and Energy-Integrating Computed Tomography: A Comparative Phantom Study.
Journal of Computer Assisted Tomography. 2023;47(4):p613-620,7/8. Epub 2023/07/01. [Foundation]
https://doi.org/10.1097/RCT.0000000000001464

High Spatial-Resolution Skull Base Imaging With Photon-Counting Computed Tomography and Energy-Integrating Computed Tomography: A Comparative Phantom Study Read More »

Quantitative performance of photon-counting CT at low dose: Virtual monochromatic imaging and iodine quantification

Vrbaski S, Bache S, Rajagopal J, Samei E.
Quantitative performance of photon-counting CT at low dose: Virtual monochromatic imaging and iodine quantification.
Medical Physics. 2023;50:5421–5433. Epub 2023/06/12. [Foundation]
https://doi.org/10.1002/mp.16583

Quantitative performance of photon-counting CT at low dose: Virtual monochromatic imaging and iodine quantification Read More »

Emphysema quantifications with CT: Assessing the effects of acquisition protocols and imaging parameters using virtual imaging trials

Abadi E, Jadick G, Lynch DA, Segars WP, Samei E.
Emphysema quantifications with CT: Assessing the effects of acquisition protocols and imaging parameters using virtual imaging trials.
Chest. 2023;163(5):1084-1100. Epub 2023/05/17. [Foundation]
https://doi.org/10.1016/j.chest.2022.11.033

Emphysema quantifications with CT: Assessing the effects of acquisition protocols and imaging parameters using virtual imaging trials Read More »

Multi-label annotation of text reports from computed tomography of the chest, abdomen, and pelvis using deep learning.

D’Anniballe VM, Tushar FI, Faryna K, Han S, Mazurowski MA, Rubin GD, Lo JY.
Multi-label annotation of text reports from computed tomography of the chest, abdomen, and pelvis using deep learning.
BMC Medical Informatics and Decision Making. 2022;22(1):102. 2022.
https://doi.org/10.1186/s12911-022-01843-4

Multi-label annotation of text reports from computed tomography of the chest, abdomen, and pelvis using deep learning. Read More »

Development of scalable lymphatic system in the 4D XCAT phantom: application to quantitative evaluation of lymphoma PET segmentations

Fedrigo R, Segars WP, Martineau P, Gowdy C, Bloise I, Uribe CF, Rahmim A.
Development of scalable lymphatic system in the 4D XCAT phantom: application to quantitative evaluation of lymphoma PET segmentations.
Medical Physics. 2022.
https://doi.org/10.1002/mp.15963

Development of scalable lymphatic system in the 4D XCAT phantom: application to quantitative evaluation of lymphoma PET segmentations Read More »