3D PET Research Articles

Joint cardiac and respiratory motion estimation for motion-corrected cardiac PET-MR

Respiratory and cardiac motion can strongly impair cardiac PET image quality and tracer uptake quantification. Standard gating techniques can minimize these motion artefacts but suffer from low signal-to-noise ratio because only a small percentage of the total data is utilized. Motion correction approaches have been proposed to overcome this problem but require accurate knowledge of such physiological motion. Here we present a joint PET-MR motion estimation approach which combines complimentary dynamic image information from simultaneously acquired MR and PET to ensure improved cardiac and respiratory motion estimation for motion-corrected image reconstruction (MCIR) of PET images. A 3D triple-echo Dixon MR scan is used both for calculation of MR-based attenuation correction (AC) maps and estimation of physiological motion. PET listmode data is obtained simultaneously to the MR acquisition which is used for a joint motion estimation and reconstruction of the final MCIR PET. In a first step, dynamic cardiac and respiratory motion resolved 4D MR and PET images are reconstructed. These image series are used in a joint image registration to estimate non-rigid cardiac and respiratory motion fields. In a second step, the motion fields are utilized in a MR MCIR to obtain cardiac and respiratory resolved dynamic MR-based AC maps. In the last step, the non-rigid motion fields and the dynamic AC maps are applied in a PET MCIR to obtain the final motion-corrected PET images. PET-MR data has been obtained in six patients without any known heart disease. Motion amplitudes were between 5.6 and 16 mm, with higher values in the basal compared to the mid-ventricular and apical segments. The proposed joint PET-MR motion estimation provided more accurate motion estimation than using either modality separately. The underestimation of PET uptake due to respiratory and cardiac motion artefacts in the AC maps was up to 17%. The average increase in uptake values using MCIR was 23% ± 10% (p < 0.0001), with values of 28% ± 11% (p < 0.0001) for basal, 21% ± 8% (p < 0.0001) for mid-cavity and 17% ± 7% (p < 0.0001) for apical segments. With the proposed scheme we could ensure high PET image quality and improve local PET uptake quantification by up to 30%. Attenuation correction and motion information was obtained from the same PET-MR raw data, which was obtained during free-breathing to minimize scan times and to increase patient comfort.

A new methodology to derive 3D kinetic parametric FDG PET images based on a mathematical approach integrating an error model of measurement

BackgroundIn FDG-PET, SUV images are hampered by large potential biases. Our aim was to develop an alternative method (ParaPET) to generate 3D kinetic parametric FDG-PET images easy to perform in clinical oncology.MethodsThe key points of our method are the use of a new error model of PET measurement extracted from a late dynamic PET acquisition of 15 min, centered over the lesion and an image-derived input function (IDIF). The 15-min acquisition is reconstructed to obtain five images of FDG mean activity concentration and images of its variance to model errors of PET measurement. Our approach is carried out on each voxel to derive 3D kinetic parameter images. ParaPET was evaluated and compared to Patlak analysis as a reference. Hunter and Barbolosi methods (Barbolosi-Bl: with blood samples or Barbolosi-Im: with IDIF) were also investigated and compared to Patlak. Our evaluation was carried on Ki index, the net influx rate and its maximum value in the lesion (Ki,max).ResultsThis parameter was obtained from 41 non-small cell lung cancer lesions associated with 4 to 5 blood samples per patient, required for the Patlak analysis. Compare to Patlak, the median relative difference and associated range (median; [min;max]) in Ki,max estimates were not statistically significant (Wilcoxon test) for ParaPET (− 3.0%; [− 31.9%; 47.3%]; p = 0.08) but statistically significant for Barbolosi-Bl (− 8.0%; [− 30.8%; 53.7%]; p = 0.001), Barbolosi-Im (− 7.9%; [− 38.4%; 30.6%]; p = 0.007) or Hunter (32.8%; [− 14.6%; 132.2%]; p < 10− 5). In the Bland-Altman plots, the ratios between the four methods and Patlak are not dependent of the Ki magnitude, except for Hunter. The 95% limits of agreement are comparable for ParaPET (34.7%), Barbolosi-Bl (30.1%) and Barbolosi-Im (30.8%), lower to Hunter (81.1%). In the 25 lesions imaged before and during the radio-chemotherapy, the decrease in the FDG uptake (ΔSUVmax or ΔKi,max) is statistically more important (p < 0.02, Wilcoxon one-tailed test) when estimated from the Ki images than from the SUV images (additional median variation of − 2.3% [− 52.6%; + 19.1%] for ΔKi,max compared to ΔSUVmax).ConclusionNone of the four methodologies is yet ready to replace the Patlak approach, and further improvements are still required. Nevertheless, ParaPET remains a promising approach, offering a non-invasive alternative to methods based on multiple blood samples and only requiring a late PET acquisition. It allows deriving Ki values, highly correlated and presenting the lowest relative bias with Patlak estimates, in comparison to the other methods we evaluated. Moreover, ParaPET gives access to quantitative information at the pixel level, which needs to be evaluated in the perspective of radiomic and tumour response.Trial registrationNCT 02821936; May 2016.

Investigation of the quantitative accuracy of low-dose amyloid and tau PET imaging.

With the increasing incidence of dementia worldwide, the frequent use of amyloid and tau positron emission tomography imaging requires low-dose protocols for the differential diagnoses of various neurodegenerative diseases and the monitoring of disease progression. In this study, we investigated the feasibility to reduce the PET dose without a significant loss of quantitative accuracy in 3D dynamic row action maximum likelihood algorithm-reconstructed PET images using [11C]PIB and [18F]THK5351. Eighteen cognitively normal young controls, cognitively normal elderly controls, and patients with probable Alzheimer's disease (n = 6 each), were included. Reduced doses were simulated by randomly sampling half and quarter of the full counts in list mode data for one independent realization at each simulated dose. Bias was evaluated between the reduced dose from the full dose of standardized uptake value ratio (SUVR), distribution volume ratio (DVR) from reference Logan, and non-displaceable binding potential (BPND) from simplified reference tissue model (SRTM). DVR yielded the least bias at low dose compared to SUVR and BPND, and thus, is highly recommended. The dose of [18F]THK5351 and [11C]PIB can be reduced to a quarter of the full dose using DVR for evaluation, whereas the dose can only be reduced to half and a quarter of the full dose for [18F]THK5351 and [11C]PIB using SUVR. BPND showed inconsistent trend and large bias at low dose. The feasibility of dose reduction was dependent on the selected parameters of interest, reconstruction algorithms, reference regions, and to a lesser degree by motion effects.

Automatic localization of normal active organs in 3D PET scans

PET imaging captures the metabolic activity of tissues and is commonly visually interpreted by clinicians for detecting cancer, assessing tumor progression, and evaluating response to treatment. To automate accomplishing these tasks, it is important to distinguish between normal active organs and activity due to abnormal tumor growth. In this paper, we propose a deep learning method to localize and detect normal active organs visible in a 3D PET scan field-of-view. Our method adapts the deep network architecture of YOLO to detect multiple organs in 2D slices and aggregates the results to produce semantically labeled 3D bounding boxes. We evaluate our method on 479 18F-FDG PET scans of 156 patients achieving an average organ detection precision of 75–98%, recall of 94–100%, average bounding box centroid localization error of less than 14 mm, wall localization error of less than 24 mm and a mean IOU of up to 72%.

SMART (SiMulAtion and ReconsTruction) PET: an efficient PET simulation-reconstruction tool

BackgroundPositron-emission tomography (PET) simulators are frequently used for development and performance evaluation of segmentation methods or quantitative uptake metrics. To date, most PET simulation tools are based on Monte Carlo simulations, which are computationally demanding. Other analytical simulation tools lack the implementation of time of flight (TOF) or resolution modelling (RM). In this study, a fast and easy-to-use PET simulation-reconstruction package, SiMulAtion and ReconsTruction (SMART)-PET, is developed and validated, which includes both TOF and RM. SMART-PET, its documentation and instructions to calibrate the tool to a specific PET/CT system are available on Zenodo.SMART-PET allows the fast generation of 3D PET images. As input, it requires one image representing the activity distribution and one representing the corresponding CT image/attenuation map. It allows the user to adjust different parameters, such as reconstruction settings (TOF/RM), noise level or scan duration. Furthermore, a random spatial shift can be included, representing patient repositioning. To evaluate the tool, simulated images were compared with real scan data of the NEMA NU 2 image quality phantom. The scan was acquired as a 60-min list-mode scan and reconstructed with and without TOF and/or RM. For every reconstruction setting, ten statistically equivalent images, representing 30, 60, 120 and 300 s scan duration, were generated. Simulated and real-scan data were compared regarding coefficient of variation in the phantom background and activity recovery coefficients (RCs) of the spheres. Furthermore, standard deviation images of each of the ten statistically equivalent images were compared.ResultsSMART-PET produces images comparable to actual phantom data. The image characteristics of simulated and real PET images varied in similar ways as function of reconstruction protocols and noise levels. The change in image noise with variation of simulated TOF settings followed the theoretically expected behaviour. RC as function of sphere size agreed within 0.3–11% between simulated and actual phantom data.ConclusionsSMART-PET allows for rapid and easy simulation of PET data. The user can change various acquisition and reconstruction settings (including RM and TOF) and noise levels. The images obtained show similar image characteristics as those seen in actual phantom data.

Accelerated PET kinetic maps estimation by analytic fitting method

In this work, we propose and test a new approach for non-linear kinetic parameters' estimation from dynamic PET data. A technique is discussed, to derive an analytical closed-form expression of the compartmental model used for kinetic parameters' evaluation, using an auxiliary parameter set, with the aim of reducing the computational burden and speeding up the fitting of these complex mathematical expressions to noisy TACs.Two alternative algorithms based on numeric calculations are considered and compared to the new proposal. We perform a simulation study aimed at (i) assessing agreement between the proposed method and other conventional ways of implementing compartmental model fitting, and (ii) quantifying the reduction in computational time required for convergence. It results in a speed-up factor of ∼120 when compared to a fully numeric version, or ∼38, with respect to a more conventional implementation, while converging to very similar values for the estimated model parameters.The proposed method is also tested on dynamic 3D PET clinical data of four control subjects. The results obtained supported those of the simulation study, and provided input and promising perspectives for the application of the proposed technique in clinical practice.

Classification of Alzheimer's Disease by Combination of Convolutional and Recurrent Neural Networks Using FDG-PET Images.

Alzheimer’s disease (AD) is an irreversible brain degenerative disorder affecting people aged older than 65 years. Currently, there is no effective cure for AD, but its progression can be delayed with some treatments. Accurate and early diagnosis of AD is vital for the patient care and development of future treatment. Fluorodeoxyglucose positrons emission tomography (FDG-PET) is a functional molecular imaging modality, which proves to be powerful to help understand the anatomical and neural changes of brain related to AD. Most existing methods extract the handcrafted features from images, and then design a classifier to distinguish AD from other groups. These methods highly depends on the preprocessing of brain images, including image rigid registration and segmentation. Motivated by the success of deep learning in image classification, this paper proposes a new classification framework based on combination of 2D convolutional neural networks (CNN) and recurrent neural networks (RNNs), which learns the intra-slice and inter-slice features for classification after decomposition of the 3D PET image into a sequence of 2D slices. The 2D CNNs are built to capture the features of image slices while the gated recurrent unit (GRU) of RNN is cascaded to learn and integrate the inter-slice features for image classification. No rigid registration and segmentation are required for PET images. Our method is evaluated on the baseline FDG-PET images acquired from 339 subjects including 93 AD patients, 146 mild cognitive impairments (MCI) and 100 normal controls (NC) from Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. Experimental results show that the proposed method achieves an area under receiver operating characteristic curve (AUC) of 95.3% for AD vs. NC classification and 83.9% for MCI vs. NC classification, demonstrating the promising classification performance.

Significance of the impact of motion compensation on the variability of PET image features

In lung cancer, quantification by positron emission tomography/computed tomography (PET/CT) imaging presents challenges due to respiratory movement. Our primary aim was to study the impact of motion compensation implied by retrospectively gated (4D)-PET/CT on the variability of PET quantitative parameters. Its significance was evaluated by comparison with the variability due to (i) the voxel size in image reconstruction and (ii) the voxel size in image post-resampling. The method employed for feature extraction was chosen based on the analysis of (i) the effect of discretization of the standardized uptake value (SUV) on complementarity between texture features (TF) and conventional indices, (ii) the impact of the segmentation method on the variability of image features, and (iii) the variability of image features across the time-frame of 4D-PET. Thirty-one PET-features were involved. Three SUV discretization methods were applied: a constant width (SUV resolution) of the resampling bin (method RW), a constant number of bins (method RN) and RN on the image obtained after histogram equalization (method EqRN). The segmentation approaches evaluated were 40 of SUVmax and the contrast oriented algorithm (COA). Parameters derived from 4D-PET images were compared with values derived from the PET image obtained for (i) the static protocol used in our clinical routine (3D) and (ii) the 3D image post-resampled to the voxel size of the 4D image and PET image derived after modifying the reconstruction of the 3D image to comprise the voxel size of the 4D image. Results showed that TF complementarity with conventional indices was sensitive to the SUV discretization method. In the comparison of COA and 40 contours, despite the values not being interchangeable, all image features showed strong linear correlations (r > 0.91, ). Across the time-frames of 4D-PET, all image features followed a normal distribution in most patients. For our patient cohort, the compensation of tumor motion did not have a significant impact on the quantitative PET parameters. The variability of PET parameters due to voxel size in image reconstruction was more significant than variability due to voxel size in image post-resampling. In conclusion, most of the parameters (apart from the contrast of neighborhood matrix) were robust to the motion compensation implied by 4D-PET/CT. The impact on parameter variability due to the voxel size in image reconstruction and in image post-resampling could not be assumed to be equivalent.

Dynamic PET image reconstruction integrating temporal regularization associated with respiratory motion correction for applications in oncology

Respiratory motion reduces both the qualitative and quantitative accuracy of PET images in oncology. This impact is more significant for quantitative applications based on kinetic modeling, where dynamic acquisitions are associated with limited statistics due to the necessity of enhanced temporal resolution. The aim of this study is to address these drawbacks, by combining a respiratory motion correction approach with temporal regularization in a unique reconstruction algorithm for dynamic PET imaging.Elastic transformation parameters for the motion correction are estimated from the non-attenuation-corrected PET images. The derived displacement matrices are subsequently used in a list-mode based OSEM reconstruction algorithm integrating a temporal regularization between the 3D dynamic PET frames, based on temporal basis functions. These functions are simultaneously estimated at each iteration, along with their relative coefficients for each image voxel. Quantitative evaluation has been performed using dynamic FDG PET/CT acquisitions of lung cancer patients acquired on a GE DRX system. The performance of the proposed method is compared with that of a standard multi-frame OSEM reconstruction algorithm.The proposed method achieved substantial improvements in terms of noise reduction while accounting for loss of contrast due to respiratory motion. Results on simulated data showed that the proposed 4D algorithms led to bias reduction values up to 40% in both tumor and blood regions for similar standard deviation levels, in comparison with a standard 3D reconstruction. Patlak parameter estimations on reconstructed images with the proposed reconstruction methods resulted in 30% and 40% bias reduction in the tumor and lung region respectively for the Patlak slope, and a 30% bias reduction for the intercept in the tumor region (a similar Patlak intercept was achieved in the lung area).Incorporation of the respiratory motion correction using an elastic model along with a temporal regularization in the reconstruction process of the PET dynamic series led to substantial quantitative improvements and motion artifact reduction. Future work will include the integration of a linear FDG kinetic model, in order to directly reconstruct parametric images.

Characterization and simulation of noise in PET images reconstructed with OSEM: Development of a method for the generation of synthetic images

IntroductionThe goals of the study are to characterize imaging properties in 2D PET images reconstructed with the iterative algorithm Ordered-Subset Expectation Maximization (OSEM) and to propose a new method for the generation of synthetic images. Material and methodsThe noise is analyzed in terms of its magnitude, spatial correlation, and spectral distribution through standard deviation, Autocorrelation Function, and Noise Power Spectrum (NPS), respectively. Their variations with position and activity level are also analyzed. This noise analysis is based on phantom images acquired from 18F uniform distributions. Experimental Recovery Coefficients of hot spheres in different backgrounds are employed to study the spatial resolution of the system through Point Spread Function (PSF). The NPS and PSF functions provide the baseline for the proposed simulation method: convolution with PSF as kernel and noise addition from NPS. ResultsThe noise spectral analysis shows that the main contribution is of random nature. It is also proven that attenuation correction does not alter noise texture but it modifies its magnitude. Finally, synthetic images of two phantoms, one of them an anatomical brain, are quantitatively compared with experimental images showing a good agreement in terms of pixel values and pixel correlations. Thus, the Contrast to Noise Ratio for the biggest sphere in the NEMA IEC phantom is 10.7 for the synthetic image and 8.8 for the experimental image. ConclusionsThe properties of the analyzed OSEM-PET images can be described by NPS and PSF functions. Synthetic images, even anatomical ones, are successfully generated by the proposed method based on the NPS and PSF.

Novel transparent high-performance AgNWs/ZnO electrodes prepared on unconventional substrates with 3D structured surfaces

Abstract With the development of optoelectronic devices with three-dimensional (3D) structured surfaces, transparent electrodes that can be deposited on non-plane substrates have become increasingly important. In this paper, novel transparent silver nanowire (AgNWs)/ZnO film electrodes were uniformly prepared on treated 3D glass and PET substrates with a combination of spin-coating and heat-welding. The AgNWs/ZnO films show a transmittance of ∼88% and a sheet resistance of ∼10 Ω/sq. They are comparable with commercial ITO films. Furthermore, only a small in-plane resistance variation of ∼1 Ω/sq was measured using four-point probe mapping in films with a 10 cm × 10 cm area. These results confirm that these novel film electrodes are very uniform. Both electrical resistance and optical transmittance of the films remain mostly intact after 1000 bending cycles and tape peeling-tests with 10 cycles. The films show high thermal stability for more than one month at 80 °C. The strategy provides a new route for the design and fabrication of optoelectronic devices with 3D structured surfaces.

Respiratory-gated (4D) contrast-enhanced FDG PET-CT for radiotherapy planning of lower oesophageal carcinoma: feasibility and impact on planning target volume

BackgroundTo assess the feasibility and potential impact on target delineation of respiratory-gated (4D) contrast-enhanced 18Fluorine fluorodeoxyglucose (FDG) positron emission tomography - computed tomography (PET-CT), in the treatment planning position, for a prospective cohort of patients with lower third oesophageal cancer.MethodsFifteen patients were recruited into the study. Imaging included 4D PET-CT, 3D PET-CT, endoscopic ultrasound and planning 4D CT. Target volume delineation was performed on 4D CT, 4D CT with co-registered 3D PET and 4D PET-CT. Planning target volumes (PTV) generated with 4D CT (PTV4DCT), 4D CT co-registered with 3D PET-CT (PTV3DPET4DCT) and 4D PET-CT (PTV4DPETCT) were compared with multiple positional metrics.ResultsMean PTV4DCT, PTV3DPET4DCT and PTV4DPETCT were 582.4 ± 275.1 cm3, 472.5 ± 193.1 cm3 and 480.6 ± 236.9 cm3 respectively (no significant difference). Median DICE similarity coefficients comparing PTV4DCT with PTV3DPET4DCT, PTV4DCT with PTV4DPETCT and PTV3DPET4DCT with PTV4DPETCT were 0.85 (range 0.65–0.9), 0.85 (range 0.69–0.9) and 0.88 (range 0.79–0.9) respectively. The median sensitivity index for overlap comparing PTV4DCT with PTV3DPET4DCT, PTV4DCT with PTV4DPETCT and PTV3DPET4DCT with PTV4DPETCT were 0.78 (range 0.65–0.9), 0.79 (range 0.65–0.9) and 0.89 (range 0.68–0.94) respectively.ConclusionsPlanning 4D PET-CT is feasible with careful patient selection. PTV generated using 4D CT, 3D PET-CT and 4D PET-CT were of similar volume, however, overlap analysis demonstrated that approximately 20% of PTV3DPETCT and PTV4DPETCT are not included in PTV4DCT, leading to under-coverage of target volume and a potential geometric miss. Additionally, differences between PTV3DPET4DCT and PTV4DPETCT suggest a potential benefit for 4D PET-CT.Trial registrationClinicalTrials.gov Identifier – NCT02285660 (Registered 21/10/2014).

Optimization of temporal sampling for 82rubidium PET myocardial blood flow quantification

BackgroundSuboptimal temporal sampling of left ventricular (LV) blood pool and tissue time-activity curves (TACs) may introduce bias and increased variability in estimates of myocardial blood flow (MBF) and flow reserve (MFR) from dynamic PET myocardial perfusion images. We aimed to optimize temporal sampling for estimation of MBF and MFR. MethodsTwenty-four normal volunteers and 32 patients underwent dynamic stress/rest rubidium-82 chloride (82Rb) PET imaging. Fine temporal sampling was used to estimate the full width at half maximum (FWHM) of the LV blood pool TAC. Fourier analysis was used to determine the longest sampling interval, TS, as a function of FWHM, which preserved the information content of the blood phase. Dynamic datasets were reconstructed with frame durations varying from 2 to 20 seconds over the first 2 minutes for the blood phase and 30 to 120 seconds for the tissue phase. The LV blood pool and tissue TACs were sampled using regions of interest (ROI) and fit to a compartment model for quantification of MBF and MFR. The effects of temporal sampling on MBF and MFR were evaluated using clinical data and simulations. ResultsTS increased linearly with input function FWHM (R = 0.93). Increasing the blood phase frame duration from 5 to 15 seconds resulted in MBF and MFR biases of 6-12% and increased variability of 14-24%. Frame durations <5 seconds had biases of less than 5% for both MBF and MFR values. Increasing the tissue phase frame durations from 30 to 120 seconds resulted in <5% biases. ConclusionsA two-phase framing of dynamic 82Rb PET images with frame durations of 5 seconds (blood phase) and 120 seconds (tissue phase) optimally samples the blood pool TAC for modern 3D PET systems.

Assessment of the impact of modeling axial compression on PET image reconstruction.

To comprehensively evaluate both the acceleration and image-quality impacts of axial compression and its degree of modeling in fully 3D PET image reconstruction. Despite being used since the very dawn of 3D PET reconstruction, there are still no extensive studies on the impact of axial compression and its degree of modeling during reconstruction on the end-point reconstructed image quality. In this work, an evaluation of the impact of axial compression on the image quality is performed by extensively simulating data with span values from 1 to 121. In addition, two methods for modeling the axial compression in the reconstruction were evaluated. The first method models the axial compression in the system matrix, while the second method uses an unmatched projector/backprojector, where the axial compression is modeled only in the forward projector. The different system matrices were analyzed by computing their singular values and the point response functions for small subregions of the FOV. The two methods were evaluated with simulated and real data for the Biograph mMR scanner. For the simulated data, the axial compression with span values lower than 7 did not show a decrease in the contrast of the reconstructed images. For span 11, the standard sinogram size of the mMR scanner, losses of contrast in the range of 5-10 percentage points were observed when measured for a hot lesion. For higher span values, the spatial resolution was degraded considerably. However, impressively, for all span values of 21 and lower, modeling the axial compression in the system matrix compensated for the spatial resolution degradation and obtained similar contrast values as the span 1 reconstructions. Such approaches have the same processing times as span 1 reconstructions, but they permit significant reduction in storage requirements for the fully 3D sinograms. For higher span values, the system has a large condition number and it is therefore difficult to recover accurately the higher frequencies. Modeling the axial compression also achieved a lower coefficient of variation but with an increase of intervoxel correlations. The unmatched projector/backprojector achieved similar contrast values to the matched version at considerably lower reconstruction times, but at the cost of noisier images. For a line source scan, the reconstructions with modeling of the axial compression achieved similar resolution to the span 1 reconstructions. Axial compression applied to PET sinograms was found to have a negligible impact for span values lower than 7. For span values up to 21, the spatial resolution degradation due to the axial compression can be almost completely compensated for by modeling this effect in the system matrix at the expense of considerably larger processing times and higher intervoxel correlations, while retaining the storage benefit of compressed data. For even higher span values, the resolution loss cannot be completely compensated possibly due to an effective null space in the system. The use of an unmatched projector/backprojector proved to be a practical solution to compensate for the spatial resolution degradation at a reasonable computational cost but can lead to noisier images.

Directional analysis of cardiac motion field from gated fluorodeoxyglucose PET images using the Discrete Helmholtz Hodge Decomposition

ObjectivesExtract directional information related to left ventricular (LV) rotation and torsion from a 4D PET motion field using the Discrete Helmholtz Hodge Decomposition (DHHD). Materials and methodsSynthetic motion fields were created using superposition of rotational and radial field components and cardiac fields produced using optical flow from a control and patient image. These were decomposed into curl-free (CF) and divergence-free (DF) components using the DHHD. ResultsSynthetic radial components were present in the CF field and synthetic rotational components in the DF field, with each retaining its center position, direction of motion and diameter after decomposition. Direction of rotation at apex and base for the control field were in opposite directions during systole, reversing during diastole. The patient DF field had little overall rotation with several small rotators. ConclusionsThe decomposition of the LV motion field into directional components could assist quantification of LV torsion, but further processing stages seem necessary.

Enhancing ejection fraction measurement through 4D respiratory motion compensation in cardiac PET imaging

ECG gated cardiac PET imaging measures functional parameters such as left ventricle (LV) ejection fraction (EF), providing diagnostic and prognostic information for management of patients with coronary artery disease (CAD). Respiratory motion degrades spatial resolution and affects the accuracy in measuring the LV volumes for EF calculation. The goal of this study is to systematically investigate the effect of respiratory motion correction on the estimation of end-diastolic volume (EDV), end-systolic volume (ESV), and EF, especially on the separation of normal and abnormal EFs. We developed a respiratory motion incorporated 4D PET image reconstruction technique which uses all gated-frame data to acquire a motion-suppressed image. Using the standard XCAT phantom and two individual-specific volunteer XCAT phantoms, we simulated dual-gated myocardial perfusion imaging data for normally and abnormally beating hearts. With and without respiratory motion correction, we measured the EDV, ESV, and EF from the cardiac-gated reconstructed images. For all the phantoms, the estimated volumes increased and the biases significantly reduced with motion correction compared with those without. Furthermore, the improvement of ESV measurement in the abnormally beating heart led to better separation of normal and abnormal EFs. The simulation study demonstrated the significant effect of respiratory motion correction on cardiac imaging data with motion amplitude as small as 0.7 cm. The larger the motion amplitude the more improvement respiratory motion correction brought about on the EF measurement. Using data-driven respiratory gating, we also demonstrated the effect of respiratory motion correction on estimating the above functional parameters from list mode patient data. Respiratory motion correction has been shown to improve the accuracy of EF measurement in clinical cardiac PET imaging.

Influences of 3D PET scanner components on increased scatter evaluated by a Monte Carlo simulation

Monte Carlo simulation is widely applied to evaluate the performance of three-dimensional positron emission tomography (3D-PET). For accurate scatter simulations, all components that generate scatter need to be taken into account. The aim of this work was to identify the components that influence scatter. The simulated geometries of a PET scanner were: a precisely reproduced configuration including all of the components; a configuration with the bed, the tunnel and shields; a configuration with the bed and shields; and the simplest geometry with only the bed. We measured and simulated the scatter fraction using two different set-ups: (1) as prescribed by NEMA-NU 2007 and (2) a similar set-up but with a shorter line source, so that all activity was contained only inside the field-of-view (FOV), in order to reduce influences of components outside the FOV. The scatter fractions for the two experimental set-ups were, respectively, 45% and 38%. Regarding the geometrical configurations, the former two configurations gave simulation results in good agreement with the experimental results, but simulation results of the simplest geometry were significantly different at the edge of the FOV. From the simulation of the precise configuration, the object (scatter phantom) was the source of more than 90% of the scatter. This was also confirmed by visualization of photon trajectories. Then, the bed and the tunnel were mainly the sources of the rest of the scatter. From the simulation results, we concluded that the precise construction was not needed; the shields, the tunnel, the bed and the object were sufficient for accurate scatter simulations.

Association of PET index quantifying skeletal uptake in NaF PET/CT images with overall survival in prostate cancer patients.

178 Background: Bone Scan Index (BSI) derived from 2D whole-body bone scans is considered an imaging biomarker of bone metastases burden carrying prognostic information. Sodium fluoride (NaF) PET/CT is more sensitive than bone scan in detecting bone changes due to metastases. We aimed to develop a semi-quantitative PET index similar to the BSI for NaF PET/CT imaging and to study its relationship to BSI and overall survival in patients with prostate cancer. Methods: NaF PET/CT and bone scans were analyzed in 48 patients (aged 53-92 years) with prostate cancer. Thoracic and lumbar spines, sacrum, pelvis, ribs, scapulae, clavicles, and sternum were automatically segmented from the CT images, representing approximately 1/3 of the total skeletal volume. Hotspots in the PET images, within the segmented parts in the CT images, were visually classified and hotspots interpreted as metastases were included in the analysis. The PET index was defined as the quotient obtained as the hotspot volume from the PET images divided by the segmented bone tissue volume from the CT images. BSI was automatically calculated using EXINIboneBSI. Results: The correlation between the PET index and BSI was r2= 0.54. The median BSI was 0.39 (IQR 0.08-2.05). The patients with a BSI ≥ 0.39 had a significantly shorter median survival time than patients with a BSI &lt; 0.39 (2.3 years vs. not reached after 5 years). BSI was significantly associated with overall survival (HR 1.13, 95% CI 1.13 to 1.41; p &lt; 0.001), and the C-index was 0.68. The median PET index was 0.53 (IQR 0.02-2.62). The patients with a PET index ≥ 0.53 had a significantly shorter median survival time than patients with a PET index &lt; 0.53 (2.5 years vs. not reached after 5 years). The PET index was significantly associated with overall survival (HR 1.18, 95% CI 1.01 to 1.30; p &lt; 0.001) and C-index was 0.68. Conclusions: PET index based on NaF PET/CT images was correlated to BSI and significantly associated with overall survival in patients with prostate cancer. Further studies are needed to evaluate the clinical value of this novel 3D PET index as a possible future imaging biomarker.

呼吸同期PET/CT を用いた横隔膜境界における肉眼的腫瘍体積の算出能の評価

The present study aimed to clarify gross tumor volume (GTV) contouring accuracy at the diaphragm boundary using respiratory-gated PET/CT. The lung/diaphragm boundary was simulated using a phantom containing 18F solution (10.6 kBq/mL). Tumors were simulated using spheres (diameter, 11-38 mm) containing 18F and located at the positions of the lungs and liver. The tumor background ratios (TBR) were 2, 4, and 8. The phantom was moved from the superior to inferior direction with a 20-mm motion displacement at 3.6 s intervals. The recovery coefficient (RC), volume RC (VRC), and standardized uptake value (SUV) threshold were calculated using stationary, non-gated (3D), and gated (4D) PET/CT. In lung cancer simulation, RC and VRC in 3D PET images were, respectively, underestimated and overestimated in smaller tumors, whereas both improved in 4D PET images regardless of tumor size and TBR. The optimal SUV threshold was about 30% in 4D PET images. In liver cancer simulation, RC and VRC were, respectively, underestimated and overestimated in smaller tumors, and when the TBR was lower, but both improved in 4D PET images when tumors were >17 mm and the TBR was >4. The optimal SUV threshold tended to depend on the TBR. The contouring accuracy of GTV was improved by considering TBR and using an optimal SUV threshold acquired from 4D PET images.

OpenPET: a novel open-type PET system for 3D dose verification in particle therapy

The OpenPET is the world’s first open-type 3D PET scanner for PET image-guided particle therapy such as in situ dose verification and direct tumour tracking. Even with a full-ring geometry, the OpenPET has an open gap between its two detector rings through which the treatment beam passes. Following the initial proposal of the dual-ring OpenPET (DROP), the single-ring OpenPET (SROP) was also proposed as a more efficient geometry than DROP in terms of manufacturing cost and sensitivity. A small SROP prototype was developed and feasibility of visualizing a 3D distribution of beam stopping positions inside a phantom was shown with the help of radioisotope particle beams, used as primary beams. Following these results, a full-size whole-body SROP prototype was developed.