Using domain knowledge for robust and generalizable deep learning-based CT-free PET attenuation and scatter correction

Autor: Rui Guo, Song Xue, Jiaxi Hu, Hasan Sari, Clemens Mingels, Konstantinos Zeimpekis, George Prenosil, Yue Wang, Yu Zhang, Marco Viscione, Raphael Sznitman, Axel Rominger, Biao Li, Kuangyu Shi

Publikováno v: Nature Communications, Vol 13, Iss 1, Pp 1-9 (2022)

Deep learning-based methods have been proposed to substitute CT-based PET attenuation and scatter correction to achieve CT-free PET imaging. Here, the authors present a simple way to integrate domain knowledge in deep learning for CT-free PET imaging

Externí odkaz: https://doaj.org/article/2669a4cebe2441658deeaa6f5c04e23d

An encoder-decoder network for direct image reconstruction on sinograms of a long axial field of view PET

Autor: Ruiyao Ma, Jiaxi Hu, Hasan Sari, Song Xue, Clemens Mingels, Marco Viscione, Venkata Sai Sundar Kandarpa, Wei Bo Li, Dimitris Visvikis, Rui Qiu, Axel Rominger, Junli Li, Kuangyu Shi

Publikováno v: Eur. J. Nucl. Med. Mol. Imaging 49, 4464-4477 (2022)

PURPOSE Deep learning is an emerging reconstruction method for positron emission tomography (PET), which can tackle complex PET corrections in an integrated procedure. This paper optimizes the direct PET reconstruction from sinogram on a long axial f

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6c18ff69d75487a1e7709a297d5de280
https://doi.org/10.1007/s00259-022-05861-2

Feasibility of using abbreviated scan protocols with population-based input functions for accurate kinetic modeling of [18F]-FDG datasets from a long axial FOV PET scanner

Autor: Hasan Sari, Lars Eriksson, Clemens Mingels, Ian Alberts, Michael E. Casey, Ali Afshar-Oromieh, Maurizio Conti, Paul Cumming, Kuangyu Shi, Axel Rominger

Publikováno v: Sari, Hasan; Eriksson, Lars; Mingels, Clemens; Alberts, Ian; Casey, Michael E; Afshar-Oromieh, Ali; Conti, Maurizio; Cumming, Paul; Shi, Kuangyu; Rominger, Axel (2023). Feasibility of using abbreviated scan protocols with population-based input functions for accurate kinetic modeling of [18F]-FDG datasets from a long axial FOV PET scanner. European journal of nuclear medicine and molecular imaging, 50(2), pp. 257-265. Springer 10.1007/s00259-022-05983-7

Background Accurate kinetic modeling of 18F-fluorodeoxyglucose ([18F]-FDG) positron emission tomography (PET) data requires accurate knowledge of the available tracer concentration in the plasma during the scan time, known as the arterial input funct

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e0a66eccde827e19d268cb4fdab302d2
https://boris.unibe.ch/173499/1/s00259-022-05983-7.pdf

Feasibility of late acquisition [68Ga]Ga-PSMA-11 PET/CT using a long axial field-of-view PET/CT scanner for the diagnosis of recurrent prostate cancer—first clinical experiences

Autor: Marco Viscione, Axel Rominger, Ali Afshar-Oromieh, George Prenosil, Ian Alberts, Karl Peter Bohn, Clemens Mingels, Hasan Sari

Publikováno v: European Journal of Nuclear Medicine and Molecular Imaging
Alberts, Ian; Prenosil, George; Mingels, Clemens; Bohn, Karl Peter; Viscione, Marco; Sari, Hasan; Rominger, Axel; Afshar-Oromieh, Ali (2021). Feasibility of late acquisition [68Ga]Ga-PSMA-11 PET/CT using a long axial field-of-view PET/CT scanner for the diagnosis of recurrent prostate cancer—first clinical experiences. European journal of nuclear medicine and molecular imaging, 48(13), pp. 4456-4462. Springer-Verlag 10.1007/s00259-021-05438-5

Purpose While acquisition of images in [68 Ga]Ga-PSMA-11 following longer uptake times can improve lesion uptake and contrast, resultant imaging quality and count statistics are limited by the isotope’s half-life (68 min). Here, we present a series

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::76f7d0a0b7877f4cff2c61e1c785aa0c
http://europepmc.org/articles/PMC8566391

Quantitative evaluation of a deep learning-based framework to generate whole-body attenuation maps using LSO background radiation in long axial FOV PET scanners

Autor: Hasan Sari, Mohammadreza Teimoorisichani, Clemens Mingels, Ian Alberts, Vladimir Panin, Deepak Bharkhada, Song Xue, George Prenosil, Kuangyu Shi, Maurizio Conti, Axel Rominger

Publikováno v: Sari, Hasan; Teimoorisichani, Mohammadreza; Mingels, Clemens; Alberts, Ian; Panin, Vladimir; Bharkhada, Deepak; Xue, Song; Prenosil, George; Shi, Kuangyu; Conti, Maurizio; Rominger, Axel (2022). Quantitative evaluation of a deep learning-based framework to generate whole-body attenuation maps using LSO background radiation in long axial FOV PET scanners. European journal of nuclear medicine and molecular imaging, 49(13), pp. 4490-4502. Springer 10.1007/s00259-022-05909-3

Purpose Attenuation correction is a critically important step in data correction in positron emission tomography (PET) image formation. The current standard method involves conversion of Hounsfield units from a computed tomography (CT) image to const

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::51126b81801375b3c702d9e73b9fa204
https://pubmed.ncbi.nlm.nih.gov/35852557