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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8906337 | PMC |
| http://dx.doi.org/10.1148/radiol.2020202527 | DOI Listing |
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Investigating Simultaneity for Deep Learning-Enhanced Actual Ultra-Low-Dose Amyloid PET/MR Imaging.
AJNR Am J Neuroradiol
March 2022
From the Department of Radiology (K.T.C., M.Z., M.K., G.Z.), Stanford University, Stanford, California.
Background And Purpose: Diagnostic-quality amyloid PET images can be created with deep learning using actual ultra-low-dose PET images and simultaneous structural MR imaging. Here, we investigated whether simultaneity is required; if not, MR imaging-assisted ultra-low-dose PET imaging could be performed with separate PET/CT and MR imaging acquisitions.
Materials And Methods: We recruited 48 participants: Thirty-two (20 women; mean, 67.
True ultra-low-dose amyloid PET/MRI enhanced with deep learning for clinical interpretation.
Eur J Nucl Med Mol Imaging
July 2021
Department of Radiology, Stanford University, 1201 Welch Road, Stanford, CA, 94305, USA.
Purpose: While sampled or short-frame realizations have shown the potential power of deep learning to reduce radiation dose for PET images, evidence in true injected ultra-low-dose cases is lacking. Therefore, we evaluated deep learning enhancement using a significantly reduced injected radiotracer protocol for amyloid PET/MRI.
Methods: Eighteen participants underwent two separate F-florbetaben PET/MRI studies in which an ultra-low-dose (6.
Generalization of deep learning models for ultra-low-count amyloid PET/MRI using transfer learning.
Eur J Nucl Med Mol Imaging
December 2020
Department of Nuclear Medicine, University Hospital Leipzig, Leipzig, Germany.
Purpose: We aimed to evaluate the performance of deep learning-based generalization of ultra-low-count amyloid PET/MRI enhancement when applied to studies acquired with different scanning hardware and protocols.
Methods: Eighty simultaneous [F]florbetaben PET/MRI studies were acquired, split equally between two sites (site 1: Signa PET/MRI, GE Healthcare, 39 participants, 67 ± 8 years, 23 females; site 2: mMR, Siemens Healthineers, 64 ± 11 years, 23 females) with different MRI protocols. Twenty minutes of list-mode PET data (90-110 min post-injection) were reconstructed as ground-truth.
Ultra-Low-Dose F-Florbetaben Amyloid PET Imaging Using Deep Learning with Multi-Contrast MRI Inputs.
Radiology
March 2019
From the Departments of Radiology (K.T.C., F.B.d.C.M., S.S., G.Z.), Electrical Engineering (E.G., J.M.P.), and Neurology and Neurological Sciences (A.B., K.L.P., S.J.S., M.D.G., E.M.), Stanford University, 1201 Welch Rd, Stanford, CA 94305; Department of Engineering Physics, Tsinghua University, Bei
Purpose To reduce radiotracer requirements for amyloid PET/MRI without sacrificing diagnostic quality by using deep learning methods. Materials and Methods Forty data sets from 39 patients (mean age ± standard deviation [SD], 67 years ± 8), including 16 male patients and 23 female patients (mean age, 66 years ± 6 and 68 years ± 9, respectively), who underwent simultaneous amyloid (fluorine 18 [F]-florbetaben) PET/MRI examinations were acquired from March 2016 through October 2017 and retrospectively analyzed. One hundredth of the raw list-mode PET data were randomly chosen to simulate a low-dose (1%) acquisition.
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