Quantitative PET image reconstruction employing nested expectation-maximization deconvolution for motion compensation

Bulk body motion may randomly occur during PET acquisitions introducing blurring, attenuation-emission mismatches and, in dynamic PET, discontinuities in the measured time activity curves between consecutive frames. Meanwhile, dynamic PET scans are longer, thus increasing the probability of bulk motion. In this study, we propose a streamlined 3D PET motion-compensated image reconstruction (3D-MCIR) framework, capable of robustly deconvolving intra-frame motion from a static or dynamic 3D sinogram. The presented 3D-MCIR methods need not partition the data into multiple gates, such as 4D MCIR algorithms, or access list-mode (LM) data, such as LM MCIR methods, both associated with increased computation or memory resources. The proposed algorithms can support compensation for any periodic and non-periodic motion, such as cardio-respiratory or bulk motion, the latter including rolling, twisting or drifting. Inspired from the widely adopted point-spread function (PSF) deconvolution 3D PET reconstruction techniques, here we introduce an image-based 3D generalized motion deconvolution method within the standard 3D maximum-likelihood expectation-maximization (ML-EM) reconstruction framework. In particular, we initially integrate a motion blurring kernel, accounting for every tracked motion within a frame, as an additional MLEM modeling component in the image space (integrated 3D-MCIR). Subsequently, we replaced the integrated model component with a nested iterative Richardson-Lucy (RL) image-based deconvolution method to accelerate the MLEM algorithm convergence rate (RL-3D-MCIR). The final method was evaluated with realistic simulations of whole-body dynamic PET data employing the XCAT phantom and real human bulk motion profiles, the latter estimated from volunteer dynamic MRI scans. In addition, metabolic uptake rate Ki parametric images were generated with the standard Patlak method. Our results demonstrate significant improvement in contrast-to-noise ratio (CNR) and noise-bias performance in both dynamic and parametric images. The proposed nested RL-3D-MCIR method is implemented on the Software for Tomographic Image Reconstruction (STIR) open-source platform and is scheduled for public release

Artificial Intelligence in PET: An Industry Perspective

Artificial intelligence (AI) has significant potential to positively impact and advance medical imaging, including positron emission tomography (PET) imaging applications. AI has the ability to enhance and optimize all aspects of the PET imaging chain from patient scheduling, patient setup, protocoling, data acquisition, detector signal processing, reconstruction, image processing, and interpretation. AI poses industry-specific challenges which will need to be addressed and overcome to maximize the future potentials of AI in PET. This article provides an overview of these industry-specific challenges for the development, standardization, commercialization, and clinical adoption of AI and explores the potential enhancements to PET imaging brought on by AI in the near future. In particular, the combination of on-demand image reconstruction, AI, and custom-designed data-processing workflows may open new possibilities for innovation which would positively impact the industry and ultimately patients

Study of a convergent subsetized list-mode EM reconstruction algorithm

Abstract-We have implemented a convergent subsetized (CS) list-mode reconstruction algorithm, based on previous work [1]- [3] on complete-data OS-EM reconstruction. The first step of the convergent algorithm is exactly equivalent (unlike the histogrammode case) to the regular subsetized list-mode EM algorithm, while the second and final step takes the form of additive updates in image space. A hybrid algorithm based on the ordinary and the convergent algorithms is also proposed, and is shown to combine the advantages of the two algorithms: it is able to reach a higher image quality in fewer iterations while maintaining the convergent behavior, making the hybrid approach a good alternative to the ordinary subsetized list-mode EM algorithm. Reconstructions using various LOR-driven projection techniques (Siddon method, trilinear and bilinear interpolation) were considered and it was demonstrated that in terms of FWHM, the Siddon technique is inferior to the other two algorithms, with the bilinear interpolation technique performing nearly similarly as the trilinear while being considerably faster

Analytic System Matrix Resolution Modeling in PET: An Application to Rb-82 Cardiac Imaging

Abstract-An area of growing interest in PET imaging has been that of incorporating increasingly more accurate system matrix elements into the reconstruction task, thus arriving at images of higher quality. This work explores application of an analytic approach which individually models and combines the various resolution degrading phenomenon in PET (inter-crystal scattering, inter-crystal penetration, photon non-collinearity and positron range), and does not require extensive experimental measurements and/or simulations. The approach is able to produce considerable enhancements in image quality. The reconstructed resolution is seen to improve from 5.1mm-7.7mm across the field-of-view (FoV) to ≈3.5mm nearly uniformly across the FoV. Furthermore, phantom studies indicate clearly improved images, while similar significant improvements are seen for the particular task of Rb-82 cardiac imaging. Keywords: Positron emission tomography, Image reconstruction, Image enhancement, Positrons, Compton scattering. I. OVERVIEW AND MOTIVATION In PET imaging, four processes are responsible for degrading image resolution: positron range, photon non-collinearity, intercrystal scattering as well as penetration. Aside from improvements to PET detection (hardware), different reconstruction approaches have been proposed in the literature to model the aforementioned factors, with the aim of improving image resolution. First, let us consider an image with J basis functions (usually voxels) and a histogrammed dataset with I projection bins. We then denote the system matrix as P=(p ij ) I×J , where each element p ij models the probability that an event generated in voxel j is detected along line-of-response (LOR) i. Next, one may decompose [1] the system matrix into three components Here, the matrix B=(b ij ) J×J is used to account for imagebased blurring effects, while the matrix G=(g ij ) I×J contains the geometric probability terms relating each voxel j to an LOR i. In addition, the matrix W=(w ij ) I×I can be used to account for sensitivity variations (i.e. due to attenuation and normalization) as well inter-crystal blurring effects. An approach [2], [3] has been to model overall resolution blurring entirely into the image-space component B of the system matrix. This approach is very straight-forward to implement, and produces images of higher quality. However, the method is somewhat ad hoc and in particular does not model the varying degrees of inter-crystal blurring in the projection space. The method is thus not suited to model the parallax effect. An approach developed in A more accurate approach An alternative approach [5]- A new approach has been to make very accurate noncollimated A new approach is investigated in this work, which takes the approach of analytically modeling each of the resolution degrading phenomenon, followed by their combination in the overall system matrix, thus not requiring extensive simulations or experimental measurements, and producing significantly improved image qualities. We describe each of these next. II. DESCRIPTION OF METHOD A. Positron Range In the seminal work of Palmer and Brownel

Comparative assessment of different energy mapping methods for generation of 511-KEV attenuation map from ct images in pet/ct systems: a phantom study

The use of X-ray CT images for CT-based attenuation correction (CTAC) of PET data results in the decrease of overall scanning time and creates a noise-free attenuation map (μmap). The linear attenuation coefficient (LAC) measured with CT is calculated at the x-ray energy rather than at the 511 keV. It is therefore necessary to convert the linear attenuation coefficients obtained from the CT scan to those corresponding to the 511 keV. Several conversion strategies have been developed including scaling, segmentation, hybrid, bilinear and dual-energy decomposition methods. The aim of this study is to compare the accuracy of different energy mapping methods for generation of attenuation map form CT images. An in-house made polyethylene phantom with different concentrations of K2HPO4 was used in order to quantitatively measure the accuracy of the nominated methods, using quantitative analysis of created μmaps. The generated μmaps using different methods compared with theoretical values calculated using XCOM cross section library. Accurate quantitative analysis showed that for low concentrations of K2HPO4 all these methods produce acceptable attenuation maps at 511 keV, but for high concentration of K2HPO4 the last three methods produced the lowest errors (10.1 in hybrid, 9.8 in bilinear, and 4.7 in dual energy method). The results also showed that in dual energy method, combination of 80 and 140 kVps produces the least error (4.2) compared to other combinations of kVps. ©2008 IEEE

Is correction for metallic artefacts mandatory in cardiac SPECT/CT imaging in the presence of pacemaker and implantable cardioverter defibrillator leads?

Introduction: Metallic artifacts due to pacemaker/ implantable cardioverter defibrillator (ICD) leads in CT images can produce artifactual uptake in cardiac SPECT/CT images. The aim of this study was to determine the influence of the metallic artifacts due to pacemaker and ICD leads on myocardial SPECT/CT imaging. Methods: The study included 9 patients who underwent myocardial perfusion imaging (MPI). A cardiac phantom with an inserted solid defect was used. The SPECT images were corrected for attenuation using both artifactual CT and CT corrected using metal artifact reduction (MAR). VOI-based analysis was performed in artifactual regions. Results: In phantom studies, mean-of-relative-difference in white-region, between artifact-free attenuation-map without/with MAR were changed from 9.2 and 2.1 to 3.7 and 1.2 for ICD and pacemaker lead, respectively. However, these values for typical patient were 9.7±7.0 and 3.8±2.4 for ICD and pacemaker leads respectively, in white-region. MAR effectively reduces the artifacts in white-regions while this reduction is not significant in black-regions. Conclusion: Following application of MAR, visual and quantification analyses revealed that while quality of CT images were significantly improved, the improvements in the SPECT/CT images were not as pronounced or significant. Therefore cardiac SPECT images corrected for attenuation using CT in the presence of metallic-leads can be interpreted without correction for metal artefacts. © 2018 Tehran University of Medical Sciences. All rights reserved

Spatiotemporal PET reconstruction using ML-EM with learned diffeomorphic deformation

Patient movement in emission tomography deteriorates reconstruction quality because of motion blur. Gating the data improves the situation somewhat: each gate contains a movement phase which is approximately stationary. A standard method is to use only the data from a few gates, with little movement between them. However, the corresponding loss of data entails an increase of noise. Motion correction algorithms have been implemented to take into account all the gated data, but they do not scale well, especially not in 3D. We propose a novel motion correction algorithm which addresses the scalability issue. Our approach is to combine an enhanced ML-EM algorithm with deep learning based movement registration. The training is unsupervised, and with artificial data. We expect this approach to scale very well to higher resolutions and to 3D, as the overall cost of our algorithm is only marginally greater than that of a standard ML-EM algorithm. We show that we can significantly decrease the noise corresponding to a limited number of gates

Deep-JASC: joint attenuation and scatter correction in whole-body 18F-FDG PET using a deep residual network

Objective: We demonstrate the feasibility of direct generation of attenuation and scatter-corrected images from uncorrected images (PET-nonASC) using deep residual networks in whole-body 18F-FDG PET imaging. Methods: Two- and three-dimensional deep residual networks using 2D successive slices (DL-2DS), 3D slices (DL-3DS) and 3D patches (DL-3DP) as input were constructed to perform joint attenuation and scatter correction on uncorrected whole-body images in an end-to-end fashion. We included 1150 clinical whole-body 18F-FDG PET/CT studies, among which 900, 100 and 150 patients were randomly partitioned into training, validation and independent validation sets, respectively. The images generated by the proposed approach were assessed using various evaluation metrics, including the root-mean-squared-error (RMSE) and absolute relative error (ARE ) using CT-based attenuation and scatter-corrected (CTAC) PET images as reference. PET image quantification variability was also assessed through voxel-wise standardized uptake value (SUV) bias calculation in different regions of the body (head, neck, chest, liver-lung, abdomen and pelvis). Results: Our proposed attenuation and scatter correction (Deep-JASC) algorithm provided good image quality, comparable with those produced by CTAC. Across the 150 patients of the independent external validation set, the voxel-wise REs () were � 1.72 ± 4.22, 3.75 ± 6.91 and � 3.08 ± 5.64 for DL-2DS, DL-3DS and DL-3DP, respectively. Overall, the DL-2DS approach led to superior performance compared with the other two 3D approaches. The brain and neck regions had the highest and lowest RMSE values between Deep-JASC and CTAC images, respectively. However, the largest ARE was observed in the chest (15.16 ± 3.96) and liver/lung (11.18 ± 3.23) regions for DL-2DS. DL-3DS and DL-3DP performed slightly better in the chest region, leading to AREs of 11.16 ± 3.42 and 11.69 ± 2.71, respectively (p value < 0.05). The joint histogram analysis resulted in correlation coefficients of 0.985, 0.980 and 0.981 for DL-2DS, DL-3DS and DL-3DP approaches, respectively. Conclusion: This work demonstrated the feasibility of direct attenuation and scatter correction of whole-body 18F-FDG PET images using emission-only data via a deep residual network. The proposed approach achieved accurate attenuation and scatter correction without the need for anatomical images, such as CT and MRI. The technique is applicable in a clinical setting on standalone PET or PET/MRI systems. Nevertheless, Deep-JASC showing promising quantitative accuracy, vulnerability to noise was observed, leading to pseudo hot/cold spots and/or poor organ boundary definition in the resulting PET images. © 2020, Springer-Verlag GmbH Germany, part of Springer Nature