Qualitative and Quantitative Assessment of Metal Artifacts Arising from Implantable Cardiac Pacing Devices in Oncological PET/CT Studies: A Phantom Study

Purpose: We evaluate the magnitude of metallic artifacts caused by various implantable cardiac pacing devices (without leads) on both attenuation maps (μ-maps) and positron emission tomography (PET) images using experimental phantom studies. We also assess the efficacy of a metal artifact reduction (MAR) algorithm along with the severity of artifacts in the presence of misalignment between μ-maps and PET images. Methods: Four pacing devices including two pacemakers (pacemakers 1 and 2) and two cardiac resynchronization therapy (CRT) devices of pacemaker (CRT-P) and defibrillator (CRT-D) type were placed in three phantoms including a cylindrical Ge-68 phantom, a water-bath phantom and an anthropomorphic heart/thorax phantom. The μ-maps were derived from computed tomography (CT) images reconstructed using the standard method supplied by the manufacturer and those reconstructed using the MAR algorithm. In addition, the standard reconstructed CT images of the last two phantoms were manually misaligned by 10mm along the patient's axis to simulate misalignment between CT and PET images. Results: The least and severest artifacts produced on both μ-maps and PET images of the Ge-68 phantom were induced by CRT-P and pacemaker 1 devices, respectively. In the water-bath phantom, CRT-P induced 17.5% over- and 9.2% underestimation of tracer uptake whereas pacemaker 1 induced 69.6% over- and 65.7% underestimation. In the heart/thorax phantom representing a pacemaker-bearing patient, pacemaker 1 induced 41.8% increase and 36.6% decrease in tracer uptake and attenuation coefficients on average in regions corresponding to bright and dark streak artifacts, respectively. Statistical analysis revealed that the MAR algorithm was successful in reducing bright streak artifacts, yet unsuccessful for dark ones. In the heart/thorax phantom, the MAR algorithm reduced the overestimations to 4.4% and the underestimations to 35.5% on average. Misalignment between μ-maps and PET images increased the peak of pseudo-uptake by approximately 20%. Conclusions: This study demonstrated that, depending on their elemental composition, different implantable cardiac pacing devices result in varying magnitudes of metal artifacts and thus pseudo-uptake on PET images. The MAR algorithm was not successful in compensating for underestimations which calls for a more efficient algorithm. The results showed that misalignments between PET and CT images render metal-related pseudo-uptake more sever

Development of image reconstruction and correction techniques in PET/CT and PET/MR imaging

Positron emission tomography (PET) is one of the leading molecular imaging techniques for the non-invasive detection and quantitative assessment of a variety of biochemical processes associated with neoplasms and physiopathological conditions. Following injection of a positron-emitting radiotracer to the patient, the PET scanner measures the biodistribution and kinetics of the administered radiopharmaceutical. However, this modality does not provide the anatomical information necessary for localization of metabolically active lesions. This has motivated the combination of PET with anatomical imaging modalities such as x-ray computed tomography (CT) and magnetic resonance imaging (MRI). The advent of hybrid PET/CT and PET/MRI systems and their introduction in clinical practice has tremendously improved the diagnostic confidence of PET findings. Specifically, the additional information provided by CT and MRI can be exploited for PET image reconstruction and data correction. In PET/CT, CT images can be directly converted to 511-keV attenuation maps for attenuation correction of PET data,..

Research supporting data

<p>The file contains all simulation and clinical results presented in </p> <p>Abolfazl Mehranian, Martin A. Belzunce, Claudia Prieto, Alexander Hammers and Andrew J. Reader "Synergistic PET and SENSE MR image reconstruction using joint sparsity regularization", IEEE Transactions on Medical Imaging, 2017, in press</p> <p>This work is supported by Engineering and Physical Sciences Research Council (EPSRC) under grant EP/M020142/1. </p

Emission-based estimation of lung attenuation coefficients for attenuation correction in time-of-flight PET/MR.

In standard segmentation-based MRI-guided attenuation correction (MRAC) of PET data on hybrid PET/MRI systems, the inter/intra-patient variability of linear attenuation coefficients (LACs) is ignored owing to the assignment of a constant LAC to each tissue class. This can lead to PET quantification errors, especially in the lung regions. In this work, we aim to derive continuous and patient-specific lung LACs from time-of-flight (TOF) PET emission data using the maximum likelihood reconstruction of activity and attenuation (MLAA) algorithm. The MLAA algorithm was constrained for estimation of lung LACs only in the standard 4-class MR attenuation map using Gaussian lung tissue preference and Markov random field smoothness priors. MRAC maps were derived from segmentation of CT images of 19 TOF-PET/CT clinical studies into background air, lung, soft tissue and fat tissue classes, followed by assignment of predefined LACs of 0, 0.0224, 0.0864 and 0.0975 cm(-1), respectively. The lung LACs of the resulting attenuation maps were then estimated from emission data using the proposed MLAA algorithm. PET quantification accuracy of MRAC and MLAA methods was evaluated against the reference CT-based AC method in the lungs, lesions located in/near the lungs and neighbouring tissues. The results show that the proposed MLAA algorithm is capable of retrieving lung density gradients and compensate fairly for respiratory-phase mismatch between PET and corresponding attenuation maps. It was found that the mean of the estimated lung LACs generally follow the trend of the reference CT-based attenuation correction (CTAC) method. Quantitative analysis revealed that the MRAC method resulted in average relative errors of -5.2 ± 7.1% and -6.1 ± 6.7% in the lungs and lesions, respectively. These were reduced by the MLAA algorithm to -0.8 ± 6.3% and -3.3 ± 4.7%, respectively. In conclusion, we demonstrated the potential and capability of emission-based methods in deriving patient-specific lung LACs to improve the accuracy of attenuation correction in TOF PET/MR imaging, thus paving the way for their adaptation in the clinic

MR-guided joint reconstruction of activity and attenuation in brain PET-MR

With the advent of time-of-flight (TOF) PET scanners, joint maximum-likelihood reconstruction of activity and attenuation (MLAA) maps has recently regained attention for the estimation of PET attenuation maps from emission data. However, the estimated attenuation and activity maps are scaled by unknown scaling factors. We recently demonstrated that in hybrid PET-MR, the scaling issue of this algorithm can be effectively addressed by imposing MR spatial constraints on the estimation of attenuation maps using a penalized MLAA (P-MLAA(+)) algorithm. With the advent of simultaneous PET-MR systems, MRI-guided PET image reconstruction has also gained attention for improving the quantitative accuracy of PET images, usually degraded by noise and partial volume effects. The aim of this study is therefore to increase the benefits of MRI information for improving the quantitative accuracy of PET images by exploiting MRI-based anatomical penalty functions to guide the reconstruction of both activity and attenuation maps during their joint estimation. We employed an anato-functional joint entropy penalty function for the reconstruction of activity and an anatomical quadratic penalty function for the reconstruction of attenuation. The resulting algorithm was referred to as P-MLAA(++) since it exploits both activity and attenuation penalty functions. The performance of the P-MLAA algorithms were compared with MLAA and the widely used activity reconstruction algorithms such as maximum likelihood expectation maximization (MLEM) and penalized MLEM (P-MLEM) both corrected for attenuation using a conventional MRI segmentation-based attenuation correction (MRAC) method. The studied methods were evaluated using simulations and clinical studies taking the PET image reconstructed using reference CT-based attenuation maps as a reference. The simulation results showed that the proposed method can notably improve the visual quality of the PET images by reducing noise while preserving structural boundaries and at the same time improving the quantitative accuracy of the PET images. Our clinical reconstruction results showed that the MLEM-MRAC, P-MLEM-MRAC, MLAA, PMLAA(+) and P-MLAA(++) algorithms result in, on average, quantification errors of -13.5 +/- 3.1%, -13.4 +/- 3.1%, -2.0 +/- 6.5%, -3.0 +/- 3.5% and -4.2 +/- 3.6%, respectively, in different regions of the brain. In conclusion, whilst the P-MLAA(+) algorithm showed the best overall quantification performance, the proposed P-MLAA(++) algorithm provided simultaneous partial volume and attenuation corrections with only a minor compromise of PET quantification