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

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.

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

PURPOSE: 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.

Count rate performance of brain-dedicated PET scanners: a Monte Carlo simulation study.

In recent years, there has been a renewed interest in brain-dedicated PET imaging systems, particularly in the context of combined PET/MR imaging. We are currently designing a brain-dedicated PET insert suitable for an ultra-high field brain-dedicated MR scanner, the Siemens Magnetom 7T MR scanner. In this paper, an investigation on the count rate performance of several possible detectors through a series of Monte Carlo simulations is reported. Brain-dedicated PET scanners with a lutetium oxyorthosilicate scintillator and a detector area of 0.04 (1 crystal per detector) to 101.37 (2500 crystals per detector) cm2, detector thickness of 10 to 20 mm and a fixed crystal pitch of ~2 mm were simulated. The count rate performance of each scanner was evaluated as a function of detector deadtime type and constant, coincidence timing window and lower level discriminator. Also, the effects of activity outside the field-of-view (FOV) on the count rate performance of each scanner were studied. For each detector geometry and performance metric, the scanner singles rate, scanner sensitivity and noise equivalent count rate as a function of activity in the FOV were measured. It was seen that scanners with detectors comprised a few crystal elements showed reduced scanner sensitivity due to a high number of inter-detector scattering. The count rate performance of scanners with large detectors, on the other hand, was mainly determined by the deadtime properties of the detectors. A model for the count rate performance of the scanner with each studied detector is presented in this work.

A study of inter-crystal scatter in dual-layer offset scintillator arrays for brain-dedicated PET scanners.

A dual-layer offset (DLO) detector enables depth-of-interaction (DOI) through light sharing between two layers of scintillation arrays with a single-ended readout (SER) scheme. However, the SER scheme in DLO detectors may lead to a layer misassignment when inter-crystal scattering occurs. The aim of this work is to study inter-crystal scattering and evaluate the effects of layer misidentifications in DLO detectors on the performance of scanners suitable for a brain-dedicated PET insert. The influence of layer misidentification on the coincidence response functions (CRFs) of 3 different DLO detectors with total/front/back layer thicknesses of 15/6/9 mm, 20/8/12 mm, and 25/7.5/17.5 mm and a crystal width of about 3 mm was studied through Monte Carlo simulations. To overcome layer misidentification, we studied a practical DLO detector design in which each layer can be read out independently through a discrete-layer readout (DLR) scheme where light sharing between the layers is avoided. The CRFs of the mentioned DLO detectors assuming SER and DLR were analyzed. To evaluate the effects of layer misidentification on image quality, images of a Derenzo-like phantom were also reconstructed for all DLO and their equivalent single layer PET scanners. Our analysis showed that layer misassignments due to inter-crystal scatter in DLO detectors mainly has effect on the full-width at tenth maximum of the CRFs. According to the reconstructed images of the phantom, no significant improvements in the quality of the images were seen when SER was replaced with DLR. The results suggest that layer misidentification in DLO detectors does not play an important role in the quality of the PET images.