Genome-wide DNA methylation profiling of stomach cancer in the ethnic population of Mizoram, North East India.

Stomach cancer is the fifth most common cancer in terms of prevalence and incidence and the fourth leading cause of mortality in men and women worldwide. It is well-established that aberrant DNA methylation in cells can lead to carcinogenesis. The primary objective of our study was to investigate the aberrant DNA methylation status of genes associated with stomach cancer with a particular reference to the ethnic population of Mizoram, North East India. The site-level analysis identified 2883 CpG sites differentially methylated, representing ∼922 genes. Out of which 476 Differentially Methylated Positions (DMPs) were promoter-associated, 452 DMPs were hypermethylated, and 24 were hypomethylated. The region-level analysis identified 462 Differentially Methylated Regions (DMRs) corresponding to ∼320 genes, of which ∼281 genes were hypermethylated and âˆ¼40 genes were hypomethylated. TCGA analysis showed that some of the genes had been previously implicated in other cancers including stomach cancer. Five hypermethylated genes were selected as candidate genes for further investigations and they have shown to be novel and could serve as candidate hypermethylation biomarkers for stomach cancer in this particular ethnic group.

Hypoxia imaging with [18F]-FMISO-PET for guided dose escalation with intensity-modulated radiotherapy in head-and-neck cancers.

BACKGROUND AND PURPOSE: Positron emission tomography (PET) with [(18)F]-fluoromisonidazole ([(18)F]-FMISO) provides a non-invasive assessment of hypoxia. The aim of this study is to assess the feasibility of a dose escalation with volumetric modulated arc therapy (VMAT) guided by [(18)F]-FMISO-PET for head-and-neck cancers (HNC). PATIENTS AND METHODS: Ten patients with inoperable stages III-IV HNC underwent [(18)F]-FMISO-PET before radiotherapy. Hypoxic target volumes (HTV) were segmented automatically by using the fuzzy locally adaptive Bayesian method. Retrospectively, two VMAT plans were generated delivering 70 Gy to the gross tumour volume (GTV) defined on computed tomography simulation or 79.8 Gy to the HTV. A dosimetric comparison was performed, based on calculations of tumour control probability (TCP), normal tissue complication probability (NTCP) for the parotid glands and uncomplicated tumour control probability (UTCP). RESULTS: The mean hypoxic fraction, defined as the ratio between the HTV and the GTV, was 0.18. The mean average dose for both parotids was 22.7 Gy and 25.5 Gy without and with dose escalation respectively. FMISO-guided dose escalation led to a mean increase of TCP, NTCP for both parotids and UTCP by 18.1, 4.6 and 8% respectively. CONCLUSION: A dose escalation up to 79.8 Gy guided by [(18)F]-FMISO-PET with VMAT seems feasible with improvement of TCP and without excessive increase of NTCP for parotids.

Variability of average SUV from several hottest voxels is lower than that of SUVmax and SUVpeak.

OBJECTIVES: To assess variability of the average standard uptake value (SUV) computed by varying the number of hottest voxels within an (18)F-fluorodeoxyglucose ((18)F-FDG)-positive lesion. This SUV metric was compared with the maximal SUV (SUV(max): the hottest voxel) and peak SUV (SUV(peak): SUV(max) and its 26 neighbouring voxels). METHODS: Twelve lung cancer patients (20 lesions) were analysed using PET dynamic acquisition involving ten successive 2.5-min frames. In each frame and lesion, average SUV obtained from the N = 5, 10, 15, 20, 25 or 30 hottest voxels (SUV(max-N)), SUV(max) and SUV(peak) were assessed. The relative standard deviations (SDrs) from ten frames were calculated for each SUV metric and lesion, yielding the mean relative SD from 20 lesions for each SUV metric (SDr(N), SDr(max) and SDr(peak)), and hence relative measurement error and repeatability (MEr-R). RESULTS: For each N, SDr(N) was significantly lower than SDr(max) and SDr(peak). SDr(N) correlated strongly with N: 6.471 × N(-0.103) (r = 0.994; P < 0.01). MEr-R of SUV(max-30) was 8.94-12.63% (95% CL), versus 13.86-19.59% and 13.41-18.95% for SUV(max) and SUV(peak) respectively. CONCLUSIONS: Variability of SUV(max-N) is significantly lower than for SUV(max) and SUV(peak). Further prospective studies should be performed to determine the optimal total hottest volume, as voxel volume may depend on the PET system. KEY POINTS: • PET imaging provides functional parameters of (18) F-FDG-positive lesions, such as SUVmax and SUVpeak. • Averaging SUV from several hottest voxels (SUVmax-N) is a further SUV metric. • Variability of SUVmax-N is significantly lower than SUVmax and SUVpeak variability. • SUVmax-N should improve SUV accuracy for predicting outcome or assessing treatment response. • An optimal total hottest volume should be determined through further prospective studies.

Potential of [18F]-fluoromisonidazole positron-emission tomography for radiotherapy planning in head and neck squamous cell carcinomas.

BACKGROUND AND PURPOSE: Positron-emission tomography (PET) with [(18)F]-fluoromisonidazole (FMISO) permits consideration of radiotherapy dose escalation to hypoxic volumes in head and neck cancers (HNC). However, the definition of FMISO volumes remains problematic. The aims of this study are to confirm that delayed acquisition at 4 h is most appropriate for FMISO-PET imaging and to assess different methods of volume segmentation. PATIENTS AND METHODS: A total of 15 HNC patients underwent several FMISO-PET/computed tomography (CT) acquisitions 2, 3 and 4 h after FMISO injection. Three automatic methods of PET image segmentation were tested: fixed threshold, adaptive threshold based on the ratio between tumour-derived and background activities (R(T/B)) and the fuzzy locally adaptive Bayesian (FLAB) method. The hypoxic fraction (HF), which is defined as the ratio between the FMISO and CT volumes, was also calculated. RESULTS: The R(T/B) for images acquired at 2, 3 and 4 h differed significantly, with mean values of 2.5 (1.7-2.9), 3 (2-4.5) and 3.4 (2.3-6.1), respectively. The mean tumour volume, as defined manually using CT images, was 39.1 ml (1.2-116 ml). After 4 h, the mean FMISO volumes were 18.9 (0.1-81), 9.5 (0.9-33.1) and 12.5 ml (0.9-38.4 ml) with fixed threshold, adaptive threshold and the FLAB method, respectively; median HF values were 0.47 (0.1-1.93), 0.25 (0.11-0.75) and 0.35 (0.14-1.05), respectively. FMISO volumes were significantly different. CONCLUSION: The best contrast is obtained at the 4-hour acquisition time. Large discrepancies were found between the three tested methods of volume segmentation.

Influence of partial volume correction in staging of head and neck squamous cell carcinoma using PET/CT.

Aim: PET/CT is widely used for the detection of lymph node involvement in head and neck squamous cell carcinoma (HNSCC). However, PET qualitative and quantitative capabilities are hindered by partial volume effects (PVE). Therefore, a retrospective study on 32 patients (57 lymph nodes) was carried out to evaluate the potential improvement of PVE correction (PVEC) in FDG PET/CT imaging for the diagnosis of HNSCC. Histopathological analysis of lymph nodes following neck dissection was used as the gold standard. Methods: A previously proposed deconvolution based PVEC approach was used to derive improved quantitative accuracy PET images, while the anatomical lymph node volumes were determined on the CT images. Different parameters including SUVmax and SUVmean were derived from both original and PVEC PET images for each patient. Results: Histopathology confirmed that SUVmax and SUVmean after PVEC allows a statistically significant differentiation of malignant and benign lymph nodes (p<0.05). The sensitivity of SUVmax and SUVmean was 64% and 57% respectively with or without PVEC. PVEC increased specificity from 71% to 76% for SUVmax and 57% to 66% for SUVmean. Corresponding accuracy increased from 66% to 71% for SUVmax and from 59% to 66% for SUVmean. However, the most accurate differentiation between benign and malignant nodes was obtained while using the magnitude of SUVmax increase after PVEC with a corresponding sensitivity, specificity and accuracy of 77%, 82% and 80% respectively. Conclusion: Our work shows that the use of partial volume effects correction allows a more accurate nodal staging using FDG PET imaging in HNSCC.

PET respiratory motion correction: quo vadis?

Positron emission tomography (PET) respiratory motion correction has been a subject of great interest for the last twenty years, prompted mainly by the development of multimodality imaging devices such as PET/computed tomography (CT) and PET/magnetic resonance imaging (MRI). PET respiratory motion correction involves a number of steps including acquisition synchronization, motion estimation and finally motion correction. The synchronization steps include the use of different external device systems or data driven approaches which have been gaining ground over the last few years. Patient specific or generic motion models using the respiratory synchronized datasets can be subsequently derived and used for correction either in the image space or within the image reconstruction process. Similar overall approaches can be considered and have been proposed for both PET/CT and PET/MRI devices. Certain variations in the case of PET/MRI include the use of MRI specific sequences for the registration of respiratory motion information. The proposed review includes a comprehensive coverage of all these areas of development in field of PET respiratory motion for different multimodality imaging devices and approaches in terms of synchronization, estimation and subsequent motion correction. Finally, a section on perspectives including the potential clinical usage of these approaches is included.

Contrast enhancement in emission tomography by way of synergistic PET/CT image combination.

UNLABELLED: The display of image fusion is well accepted as a powerful tool in visual image analysis and comparison. In clinical practice, this is a mandatory step when studying images from a dual PET/CT scanner. However, the display methods that are implemented on most workstations simply show both images side by side, in separate and synchronized windows. Sometimes images are presented superimposed in a single window, preventing the user from doing quantitative analysis. In this article a new image fusion scheme is presented, allowing performing quantitative analysis directly on the fused images. METHODS: The objective is to preserve the functional information provided by PET while incorporating details of higher resolution from the CT image. The process relies on a discrete wavelet-based image merging: both images are decomposed into successive details layers by using the "à trous" transform. This algorithm performs wavelet decomposition of images and provides coarser and coarser spatial resolution versions of them. The high-spatial frequencies of the CT, or details, can be easily obtained at any level of resolution. A simple model is then inferred to compute the lacking details of the PET scan from the high frequency detail layers of the CT. These details are then incorporated in the PET image on a voxel-to-voxel basis, giving the fused PET/CT image. RESULTS: Aside from the expected visual enhancement, quantitative comparison of initial PET and CT images with fused images was performed in 12 patients. The obtained results were in accordance with the objectives of the study, in the sense that the organs' mean intensity in PET was preserved in the fused image. CONCLUSION: This alternative approach to PET/CT fusion display should be of interest for people interested in a more quantitative aspect of image fusion. The proposed method is actually complementary to more classical visualization tools.

Respiratory motion correction for PET oncology applications using affine transformation of list mode data.

Respiratory motion is a source of artefacts and reduced image quality in PET. Proposed methodology for correction of respiratory effects involves the use of gated frames, which are however of low signal-to-noise ratio. Therefore a method accounting for respiratory motion effects without affecting the statistical quality of the reconstructed images is necessary. We have implemented an affine transformation of list mode data for the correction of respiratory motion over the thorax. The study was performed using datasets of the NCAT phantom at different points throughout the respiratory cycle. List mode data based PET simulated frames were produced by combining the NCAT datasets with a Monte Carlo simulation. Transformation parameters accounting for respiratory motion were estimated according to an affine registration and were subsequently applied on the original list mode data. The corrected and uncorrected list mode datasets were subsequently reconstructed using the one-pass list mode EM (OPL-EM) algorithm. Comparison of corrected and uncorrected respiratory motion average frames suggests that an affine transformation in the list mode data prior to reconstruction can produce significant improvements in accounting for respiratory motion artefacts in the lungs and heart. However, the application of a common set of transformation parameters across the imaging field of view does not significantly correct the respiratory effects on organs such as the stomach, liver or spleen.

Fuzzy hidden Markov chains segmentation for volume determination and quantitation in PET.

Accurate volume of interest (VOI) estimation in PET is crucial in different oncology applications such as response to therapy evaluation and radiotherapy treatment planning. The objective of our study was to evaluate the performance of the proposed algorithm for automatic lesion volume delineation; namely the fuzzy hidden Markov chains (FHMC), with that of current state of the art in clinical practice threshold based techniques. As the classical hidden Markov chain (HMC) algorithm, FHMC takes into account noise, voxel intensity and spatial correlation, in order to classify a voxel as background or functional VOI. However the novelty of the fuzzy model consists of the inclusion of an estimation of imprecision, which should subsequently lead to a better modelling of the 'fuzzy' nature of the object of interest boundaries in emission tomography data. The performance of the algorithms has been assessed on both simulated and acquired datasets of the IEC phantom, covering a large range of spherical lesion sizes (from 10 to 37 mm), contrast ratios (4:1 and 8:1) and image noise levels. Both lesion activity recovery and VOI determination tasks were assessed in reconstructed images using two different voxel sizes (8 mm3 and 64 mm3). In order to account for both the functional volume location and its size, the concept of % classification errors was introduced in the evaluation of volume segmentation using the simulated datasets. Results reveal that FHMC performs substantially better than the threshold based methodology for functional volume determination or activity concentration recovery considering a contrast ratio of 4:1 and lesion sizes of <28 mm. Furthermore differences between classification and volume estimation errors evaluated were smaller for the segmented volumes provided by the FHMC algorithm. Finally, the performance of the automatic algorithms was less susceptible to image noise levels in comparison to the threshold based techniques. The analysis of both simulated and acquired datasets led to similar results and conclusions as far as the performance of segmentation algorithms under evaluation is concerned.

List-mode-based reconstruction for respiratory motion correction in PET using non-rigid body transformations.

Respiratory motion in emission tomography leads to reduced image quality. Developed correction methodology has been concentrating on the use of respiratory synchronized acquisitions leading to gated frames. Such frames, however, are of low signal-to-noise ratio as a result of containing reduced statistics. In this work, we describe the implementation of an elastic transformation within a list-mode-based reconstruction for the correction of respiratory motion over the thorax, allowing the use of all data available throughout a respiratory motion average acquisition. The developed algorithm was evaluated using datasets of the NCAT phantom generated at different points throughout the respiratory cycle. List-mode-data-based PET-simulated frames were subsequently produced by combining the NCAT datasets with Monte Carlo simulation. A non-rigid registration algorithm based on B-spline basis functions was employed to derive transformation parameters accounting for the respiratory motion using the NCAT dynamic CT images. The displacement matrices derived were subsequently applied during the image reconstruction of the original emission list mode data. Two different implementations for the incorporation of the elastic transformations within the one-pass list mode EM (OPL-EM) algorithm were developed and evaluated. The corrected images were compared with those produced using an affine transformation of list mode data prior to reconstruction, as well as with uncorrected respiratory motion average images. Results demonstrate that although both correction techniques considered lead to significant improvements in accounting for respiratory motion artefacts in the lung fields, the elastic-transformation-based correction leads to a more uniform improvement across the lungs for different lesion sizes and locations.

Validation of a Monte Carlo simulation of the Philips Allegro/GEMINI PET systems using GATE.

A newly developed simulation toolkit, GATE (Geant4 Application for Tomographic Emission), was used to develop a Monte Carlo simulation of a fully three-dimensional (3D) clinical PET scanner. The Philips Allegro/GEMINI PET systems were simulated in order to (a) allow a detailed study of the parameters affecting the system's performance under various imaging conditions, (b) study the optimization and quantitative accuracy of emission acquisition protocols for dynamic and static imaging, and (c) further validate the potential of GATE for the simulation of clinical PET systems. A model of the detection system and its geometry was developed. The accuracy of the developed detection model was tested through the comparison of simulated and measured results obtained with the Allegro/GEMINI systems for a number of NEMA NU2-2001 performance protocols including spatial resolution, sensitivity and scatter fraction. In addition, an approximate model of the system's dead time at the level of detected single events and coincidences was developed in an attempt to simulate the count rate related performance characteristics of the scanner. The developed dead-time model was assessed under different imaging conditions using the count rate loss and noise equivalent count rates performance protocols of standard and modified NEMA NU2-2001 (whole body imaging conditions) and NEMA NU2-1994 (brain imaging conditions) comparing simulated with experimental measurements obtained with the Allegro/GEMINI PET systems. Finally, a reconstructed image quality protocol was used to assess the overall performance of the developed model. An agreement of <3% was obtained in scatter fraction, with a difference between 4% and 10% in the true and random coincidence count rates respectively, throughout a range of activity concentrations and under various imaging conditions, resulting in <8% differences between simulated and measured noise equivalent count rates performance. Finally, the image quality validation study revealed a good agreement in signal-to-noise ratio and contrast recovery coefficients for a number of different volume spheres and two different (clinical level based) tumour-to-background ratios. In conclusion, these results support the accurate modelling of the Philips Allegro/GEMINI PET systems using GATE in combination with a dead-time model for the signal flow description, which leads to an agreement of <10% in coincidence count rates under different imaging conditions and clinically relevant activity concentration levels.

A multiresolution image based approach for correction of partial volume effects in emission tomography.

Partial volume effects (PVEs) are consequences of the limited spatial resolution in emission tomography. They lead to a loss of signal in tissues of size similar to the point spread function and induce activity spillover between regions. Although PVE can be corrected for by using algorithms that provide the correct radioactivity concentration in a series of regions of interest (ROIs), so far little attention has been given to the possibility of creating improved images as a result of PVE correction. Potential advantages of PVE-corrected images include the ability to accurately delineate functional volumes as well as improving tumour-to-background ratio, resulting in an associated improvement in the analysis of response to therapy studies and diagnostic examinations, respectively. The objective of our study was therefore to develop a methodology for PVE correction not only to enable the accurate recuperation of activity concentrations, but also to generate PVE-corrected images. In the multiresolution analysis that we define here, details of a high-resolution image H (MRI or CT) are extracted, transformed and integrated in a low-resolution image L (PET or SPECT). A discrete wavelet transform of both H and L images is performed by using the "à trous" algorithm, which allows the spatial frequencies (details, edges, textures) to be obtained easily at a level of resolution common to H and L. A model is then inferred to build the lacking details of L from the high-frequency details in H. The process was successfully tested on synthetic and simulated data, proving the ability to obtain accurately corrected images. Quantitative PVE correction was found to be comparable with a method considered as a reference but limited to ROI analyses. Visual improvement and quantitative correction were also obtained in two examples of clinical images, the first using a combined PET/CT scanner with a lymphoma patient and the second using a FDG brain PET and corresponding T1-weighted MRI in an epileptic patient.

Local respiratory motion correction for PET/CT imaging: Application to lung cancer.

PURPOSE: Despite multiple methodologies already proposed to correct respiratory motion in the whole PET imaging field of view (FOV), such approaches have not found wide acceptance in clinical routine. An alternative can be the local respiratory motion correction (LRMC) of data corresponding to a given volume of interest (VOI: organ or tumor). Advantages of LRMC include the use of a simple motion model, faster execution times, and organ specific motion correction. The purpose of this study was to evaluate the performance of LMRC using various motion models for oncology (lung lesion) applications. METHODS: Both simulated (NURBS based 4D cardiac-torso phantom) and clinical studies (six patients) were used in the evaluation of the proposed LRMC approach. PET data were acquired in list-mode and synchronized with respiration. The implemented approach consists first in defining a VOI on the reconstructed motion average image. Gated PET images of the VOI are subsequently reconstructed using only lines of response passing through the selected VOI and are used in combination with a center of gravity or an affine/elastic registration algorithm to derive the transformation maps corresponding to the respiration effects. Those are finally integrated in the reconstruction process to produce a motion free image over the lesion regions. RESULTS: Although the center of gravity or affine algorithm achieved similar performance for individual lesion motion correction, the elastic model, applied either locally or to the whole FOV, led to an overall superior performance. The spatial tumor location was altered by 89% and 81% for the elastic model applied locally or to the whole FOV, respectively (compared to 44% and 39% for the center of gravity and affine models, respectively). This resulted in similar associated overall tumor volume changes of 84% and 80%, respectively (compared to 75% and 71% for the center of gravity and affine models, respectively). The application of the nonrigid deformation model in LRMC led to over an order of magnitude gain in computational efficiency of the correction relative to the application of the deformable model to the whole FOV. CONCLUSIONS: The results of this study support the use of LMRC as a flexible and efficient correction approach for respiratory motion effects for single lesions in the thoracic area.

GATE: a simulation toolkit for PET and SPECT.

Monte Carlo simulation is an essential tool in emission tomography that can assist in the design of new medical imaging devices, the optimization of acquisition protocols and the development or assessment of image reconstruction algorithms and correction techniques. GATE, the Geant4 Application for Tomographic Emission, encapsulates the Geant4 libraries to achieve a modular, versatile, scripted simulation toolkit adapted to the field of nuclear medicine. In particular, GATE allows the description of time-dependent phenomena such as source or detector movement, and source decay kinetics. This feature makes it possible to simulate time curves under realistic acquisition conditions and to test dynamic reconstruction algorithms. This paper gives a detailed description of the design and development of GATE by the OpenGATE collaboration, whose continuing objective is to improve, document and validate GATE by simulating commercially available imaging systems for PET and SPECT. Large effort is also invested in the ability and the flexibility to model novel detection systems or systems still under design. A public release of GATE licensed under the GNU Lesser General Public License can be downloaded at http:/www-lphe.epfl.ch/GATE/. Two benchmarks developed for PET and SPECT to test the installation of GATE and to serve as a tutorial for the users are presented. Extensive validation of the GATE simulation platform has been started, comparing simulations and measurements on commercially available acquisition systems. References to those results are listed. The future prospects towards the gridification of GATE and its extension to other domains such as dosimetry are also discussed.

Estimating the amount of FDG uptake in physiological tissues.

INTRODUCTION: It is known that for a fixed amount of injected tracer, the amount available for a tissue of interest will be less if other tissues show intense uptake. The aim of this study was to estimate the magnitude of 2-deoxy-2-[(18)F]fluoro-D-glucose (18FDG) uptake amount in physiological tissues that may show an intense uptake in current clinical practice. METHODS: A formula was established providing an estimate of the percentage of injected 18FDG molecules (P; in %) that are irreversibly trapped in an 18FDG-positive tissue during a PET examination. RESULTS: P ≅ 0.17*exp(-λt(acq))*TLG/W, where λ is the (18)F physical decay constant, t(acq) is the injection-acquisition time delay, TLG is total lesion glycolysis (g) and W is the patient weight (kg). The magnitude of P was calculated in two patients showing an intense uptake in brown fat, myocardium and bowels: 0.5, 3.5, and 4.2% respectively. CONCLUSIONS: A formula is available to quickly estimate the amount of 18FDG uptake in tissues. We suggest that the accumulation of different physiological uptakes may actually affect SUV quantification in a tissue of interest.

Influence of partial volume correction in staging of head and neck squamous cell carcinoma using PET/CT.

AIM: PET/CT is widely used for the detection of lymph node involvement in head and neck squamous cell carcinoma (HNSCC). However, PET qualitative and quantitative capabilities are hindered by partial volume effects (PVE). Therefore, a retrospective study on 32 patients (57 lymph nodes) was carried out to evaluate the potential improvement of PVE correction (PVEC) in FDG PET/CT imaging for the diagnosis of HNSCC. Histopathological analysis of lymph nodes following neck dissection was used as the gold standard. METHODS: A previously proposed deconvolution based PVEC approach was used to derive improved quantitative accuracy PET images, while the anatomical lymph node volumes were determined on the CT images. Different parameters including SUVmax and SUVmean were derived from both original and PVEC PET images for each patient. RESULTS: Histopathology confirmed that SUVmax and SUVmean after PVEC allows a statistically significant differentiation of malignant and benign lymph nodes (P<0.05). The sensitivity of SUVmax and SUVmean was 64% and 57% respectively with or without PVEC. PVEC increased specificity from 71% to 76% for SUVmax and 57% to 66% for SUVmean. Corresponding accuracy increased from 66% to 71% for SUVmax and from 59% to 66% for SUVmean. However, the most accurate differentiation between benign and malignant nodes was obtained while using the magnitude of SUVmax increase after PVEC with a corresponding sensitivity, specificity and accuracy of 77%, 82% and 80% respectively. CONCLUSION: Our work shows that the use of partial volume effects correction allows a more accurate nodal staging using FDG PET imaging in HNSCC.

Evaluation of respiratory and cardiac motion correction schemes in dual gated PET/CT cardiac imaging.

PURPOSE: Cardiac imaging suffers from both respiratory and cardiac motion. One of the proposed solutions involves double gated acquisitions. Although such an approach may lead to both respiratory and cardiac motion compensation there are issues associated with (a) the combination of data from cardiac and respiratory motion bins, and (b) poor statistical quality images as a result of using only part of the acquired data. The main objective of this work was to evaluate different schemes of combining binned data in order to identify the best strategy to reconstruct motion free cardiac images from dual gated positron emission tomography (PET) acquisitions. METHODS: A digital phantom study as well as seven human studies were used in this evaluation. PET data were acquired in list mode (LM). A real-time position management system and an electrocardiogram device were used to provide the respiratory and cardiac motion triggers registered within the LM file. Acquired data were subsequently binned considering four and six cardiac gates, or the diastole only in combination with eight respiratory amplitude gates. PET images were corrected for attenuation, but no randoms nor scatter corrections were included. Reconstructed images from each of the bins considered above were subsequently used in combination with an affine or an elastic registration algorithm to derive transformation parameters allowing the combination of all acquired data in a particular position in the cardiac and respiratory cycles. Images were assessed in terms of signal-to-noise ratio (SNR), contrast, image profile, coefficient-of-variation (COV), and relative difference of the recovered activity concentration. RESULTS: Regardless of the considered motion compensation strategy, the nonrigid motion model performed better than the affine model, leading to higher SNR and contrast combined with a lower COV. Nevertheless, when compensating for respiration only, no statistically significant differences were observed in the performance of the two motion models considered. Superior image SNR and contrast were seen using the affine respiratory motion model in combination with the diastole cardiac bin in comparison to the use of the whole cardiac cycle. In contrast, when simultaneously correcting for cardiac beating and respiration, the elastic respiratory motion model outperformed the affine model. In this context, four cardiac bins associated with eight respiratory amplitude bins seemed to be adequate. CONCLUSIONS: Considering the compensation of respiratory motion effects only, both affine and elastic based approaches led to an accurate resizing and positioning of the myocardium. The use of the diastolic phase combined with an affine model based respiratory motion correction may therefore be a simple approach leading to significant quality improvements in cardiac PET imaging. However, the best performance was obtained with the combined correction for both cardiac and respiratory movements considering all the dual-gated bins independently through the use of an elastic model based motion compensation.