A CT‐less approach to quantitative PET imaging using the LSO intrinsic radiation for long‐axial FOV PET scanners

Abstract

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.

Lutetium background radiation in total-body PET—A simulation study on opportunities and challenges in PET attenuation correction

“…While, in this study, MLAA-TX has shown no significant advantage over MLTR-based AC, it has to be noted that, at higher-count PET scans, the PET data can contribute more to estimation of the μ-map, as shown in the previous studies ( 34 , 40 ). However, as depicted in Figure 11 , the TOF-MLAA algorithm may require more iterations to converge to a solution that does not suffer from transmission-emission crosstalk artifacts.…”

“…To mitigate the effect of Compton scattering on quantification accuracy of the μ-maps and activity maps consequently, the MLTR-reconstructed μ-maps that included Compton-scattered events were scaled with a global scaling factor similar to the approach proposed by ( 40 ). This was done by finding the water peak in the histogram of the image voxel values of the μ-maps and computing a global scaling factor to shift the histogram peak to the attenuation coefficient of water.…”

“…Furthermore, a simulation study on the uEXPLORER scanner was presented in 2021, showing promising preliminary results in using the lutetium-based μ-maps for PET motion correction ( 39 ). The most recent work presented on utilizing the lutetium background for AC was by Teimoorisichani et al, which included 3 patient scans on the Biograph Vision Quadra scanner ( 40 ). However, these results were obtained by performing two 5-min scans before and after the PET acquisition, and the reconstructions were performed with a limited acceptance angle of 18°, similar to a conventional 24-cm-long scanner.…”

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

“…These µ-maps were mapped to 511 keV, and then averaged and smoothed using a Gaussian filter with a full width half maximum (FWHM) of 4 mm. The resulting LSO-TX derived µ-map and the TOF emission sinogram were used as inputs to MLACF algorithm to jointly reconstruct a PET image and an MLACF-derived attenuation map using 20 global iterations [ 30 ]. Two sets of MLACF-derived attenuation maps were generated by using the LSO-TX data acquired pre- and post- 18 F-FDG injection, referred as µ-map MLACF-PRE and µ-map MLACF-POST in the rest of the paper respectively.…”

“…The radioisotope lutetium-176 ( 176 Lu) found in LSO scintillators of PET detectors decays with a half-life of 38 billion years, emitting gamma rays with 307, 202, and 88 keV during the process [ 29 ]. We have previously demonstrated that this LSO background radiation can be detected using a high sensitivity LAFOV PET scanner and developed a method to generate µ-maps using MLACF algorithm with LSO transmission (LSO-TX) data (µ-map MLACF ) [ 30 ]. In this paper, we extend the previous method by incorporating a deep learning-based model to synthesize enhanced whole-body µ-maps (µ-map DL-MLACF ) based on µ-map MLACF images.…”