Long-axial field-of -view (FOV) positron emission tomography (PET) scanners have gained a lot of interest in the recent years.Such scanners provide increased sensitivity and enable unique imaging opportunities that were not previously feasible. Benefiting from the high sensitivity of a long-axial FOV PET scanner,we studied a computed tomography (CT)-less reconstruction algorithm for the Siemens Biograph Vision Quadra with an axial FOV of 106 cm. Methods: In this work, the background radiation from radioisotope lutetium-176 in the scintillators was used to create an initial estimate of the attenuation maps. Then, joint activity and attenuation reconstruction algorithms were used to create an improved attenuation map of the object. The final attenuation maps were then used to reconstruct quantitative PET images, which were compared against CT-based PET images. The proposed method was evaluated on data from three patients who underwent a flurodeoxyglucouse PET scan. Results: Segmentation of the PET images of the three studied patients showed an average quantitative error of 6.5%-8.3% across all studied organs when using attenuation maps from maximum likelihood estimation of attenuation and activity and 5.3%-6.6% when using attenuation maps from maximum likelihood estimation of activity and attenuation correction coefficients. Conclusions: Benefiting from the background radiation of lutetium-based scintillators, a quantitative CT-less PET imaging technique was evaluated in this work.

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 construct attenuation maps (µ-maps) at 511 keV. In this work, the increased sensitivity of long axial field-of-view (LAFOV) PET scanners was exploited to develop and evaluate a deep learning (DL) and joint reconstruction-based method to generate µ-maps utilizing background radiation from lutetium-based (LSO) scintillators. Methods Data from 18 subjects were used to train convolutional neural networks to enhance initial µ-maps generated using joint activity and attenuation reconstruction algorithm (MLACF) with transmission data from LSO background radiation acquired before and after the administration of 18F-fluorodeoxyglucose (18F-FDG) (µ-mapMLACF-PRE and µ-mapMLACF-POST respectively). The deep learning-enhanced µ-maps (µ-mapDL-MLACF-PRE and µ-mapDL-MLACF-POST) were compared against MLACF-derived and CT-based maps (µ-mapCT). The performance of the method was also evaluated by assessing PET images reconstructed using each µ-map and computing volume-of-interest based standard uptake value measurements and percentage relative mean error (rME) and relative mean absolute error (rMAE) relative to CT-based method. Results No statistically significant difference was observed in rME values for µ-mapDL-MLACF-PRE and µ-mapDL-MLACF-POST both in fat-based and water-based soft tissue as well as bones, suggesting that presence of the radiopharmaceutical activity in the body had negligible effects on the resulting µ-maps. The rMAE values µ-mapDL-MLACF-POST were reduced by a factor of 3.3 in average compared to the rMAE of µ-mapMLACF-POST. Similarly, the average rMAE values of PET images reconstructed using µ-mapDL-MLACF-POST (PETDL-MLACF-POST) were 2.6 times smaller than the average rMAE values of PET images reconstructed using µ-mapMLACF-POST. The mean absolute errors in SUV values of PETDL-MLACF-POST compared to PETCT were less than 5% in healthy organs, less than 7% in brain grey matter and 4.3% for all tumours combined. Conclusion We describe a deep learning-based method to accurately generate µ-maps from PET emission data and LSO background radiation, enabling CT-free attenuation and scatter correction in LAFOV PET scanners.

No abstract

No abstract

A PET insert, mounted in a MR scanner, provides virtually all the advantages of a fully integrated PET/MR scanner. Various PET inserts have been proposed and built for small animal (Goertzen et al 2016, Ko et al 2016) and brain (Kolb et al 2012, Nishikido et al 2014, Jung et al 2015) hybrid PET/MR imaging. The limited diameter of an animal or organ-dedicated PET insert enhances the parallax error at large radial offsets which leads to a resolution loss in the scanner. Depth-of-interaction (DOI) detectors have been used to reduce the parallax error and maintain the resolution uniformity in small diameter PET scanners (