PET Attenuation Map Generation for Brain Imaging from Virtual Non-Contrast and Virtual Non-Iodine Images Obtained with Photon Counting CT Scanners
Abstract
This study presents a transformation scheme for converting measured Hounsfield units (HUs) into linear attenuation coefficients (LACs) for 511 keV gamma photons in iodine contrast-enhanced angiographic CT images. The method is based on spectral CT data acquired from tissue-equivalent inserts of the Sun Nuclear electron density phantom. The resulting transformation will be used to develop an attenuation map template for our in-house developed BrainPET 7T system. Three approaches were used to obtain the attenuation maps. The first approach is based on angiographic spectral CT scans with iodinated contrast agent administration, and the computation of virtual non contrast (VNC) and virtual non-iodine (VNI) images. In this case, two images are combined: bone regions are segmented from the VNI images, and the corresponding voxel values in the VNC images are replaced, yielding a single quantitative monochromatic CT image. The second approach relies exclusively on VNC images, whereas the third approach uses only VNI images. The transformation schemes from HUs to LACs were established for different tube voltages (120 kV and 140 kV) and virtual monochromatic energies (64 keV, 67 keV, 70 keV, 76 keV, 80 keV, and 100 keV), showing the HUs dependency on the energy setting of the reconstructed virtual monochromatic energies. The accuracy and precision of the proposed transformation scheme were further evaluated using previously acquired angiographic CT datasets. Spectral CT can be expected to minimize the systematic error when converting HUs to LACs for 511 keV gamma rays using virtual monochromatic images (VMIs).
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BibTeX
@misc{alrakh2026attenuation,
title = {{PET Attenuation Map Generation for Brain Imaging from Virtual Non-Contrast and Virtual Non-Iodine Images Obtained with Photon Counting CT Scanners}},
author = {Heba Alrakh and Lutz Tellmann and Omid Nikoubashman and Sadaf Shokrgozar and Claudia Regio Brambilla and Ahlam Said Mohamad Issa and Keith George Ciantar and Sebastian Faby and Ali Abu arra and Martin Wiesmann and Charlotte Weyland and N. Jon Shah and Christoph Lerche},
howpublished = {arXiv},
year = {2026},
month = nov,
doi = {10.48550/arxiv.2611.03425},
eprint = {2611.03425},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2611.03425},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - PET Attenuation Map Generation for Brain Imaging from Virtual Non-Contrast and Virtual Non-Iodine Images Obtained with Photon Counting CT Scanners
AU - Heba Alrakh
AU - Lutz Tellmann
AU - Omid Nikoubashman
AU - Sadaf Shokrgozar
AU - Claudia Regio Brambilla
AU - Ahlam Said Mohamad Issa
AU - Keith George Ciantar
AU - Sebastian Faby
AU - Ali Abu arra
AU - Martin Wiesmann
AU - Charlotte Weyland
AU - N. Jon Shah
AU - Christoph Lerche
PY - 2026
DA - 2026/11/02
PB - arXiv
DO - 10.48550/arxiv.2611.03425
UR - https://arxiv.org/abs/2611.03425
AB - This study presents a transformation scheme for converting measured Hounsfield units (HUs) into linear attenuation coefficients (LACs) for 511 keV gamma photons in iodine contrast-enhanced angiographic CT images. The method is based on spectral CT data acquired from tissue-equivalent inserts of the Sun Nuclear electron density phantom. The resulting transformation will be used to develop an attenuation map template for our in-house developed BrainPET 7T system. Three approaches were used to obtain the attenuation maps. The first approach is based on angiographic spectral CT scans with iodinated contrast agent administration, and the computation of virtual non contrast (VNC) and virtual non-iodine (VNI) images. In this case, two images are combined: bone regions are segmented from the VNI images, and the corresponding voxel values in the VNC images are replaced, yielding a single quantitative monochromatic CT image. The second approach relies exclusively on VNC images, whereas the third approach uses only VNI images. The transformation schemes from HUs to LACs were established for different tube voltages (120 kV and 140 kV) and virtual monochromatic energies (64 keV, 67 keV, 70 keV, 76 keV, 80 keV, and 100 keV), showing the HUs dependency on the energy setting of the reconstructed virtual monochromatic energies. The accuracy and precision of the proposed transformation scheme were further evaluated using previously acquired angiographic CT datasets. Spectral CT can be expected to minimize the systematic error when converting HUs to LACs for 511 keV gamma rays using virtual monochromatic images (VMIs).
ER -