Regularized image reconstruction algorithms for dual-isotope myocardial perfusion SPECT (MPS) imaging using a cross-tracer prior

(An overview of) Synergistic reconstruction for multimodality/multichannel imaging methods

Imaging is omnipresent in modern society with imaging devices based on a zoo of physical principles, probing a specimen across different wavelengths, energies and time. Recent years have seen a change in the imaging landscape with more and more imaging devices combining that which previously was used separately. Motivated by these hardware developments, an ever increasing set of mathematical ideas is appearing regarding how data from different imaging modalities or channels can be synergistically combined in the image reconstruction process, exploiting structural and/or functional correlations between the multiple images. Here we review these developments, give pointers to important challenges and provide an outlook as to how the field may develop in the forthcoming years. This article is part of the theme issue 'Synergistic tomographic image reconstruction: part 1'.

Improved myocardial perfusion PET imaging using artificial neural networks

Myocardial perfusion (MP) PET imaging plays a key role in risk assessment and stratification of patients with coronary artery disease. In this work, we proposed a patch-based artificial neural network (ANN) fusion approach that integrates information from the ML and the post-smoothed ML reconstruction to improve MP PET imaging. The proposed method was applied to images reconstructed from different noise levels to enhance quantification and task-based MP defect detection. Using the XCAT phantom, we simulated three MP PET imaging cases, one with normal perfusion and the other two with non-transmural and transmural regionally reduced perfusion of the left ventricular (LV) myocardium. The proposed ANN fusion technique was quantitatively evaluated in terms of the noise versus bias and noise versus contrast tradeoff, and compared with the post-smoothed ML reconstruction. Using the channelized Hotelling observer, we evaluated the detectability of the non-transmural and transmural defects through the receiver operating characteristic analysis. The quantitative results demonstrated that the ANN enhancement method reduced bias and improved contrast while reaching comparable noise to what the post-smoothed ML reconstruction achieved. Moreover, the ANN fusion technique significantly improved the defect detectability of both the non-transmural and transmural defects. In addition to the simulation study, we further evaluated the proposed method using patient data. Compared with the post-smoothed ML reconstruction, the ANN fusion improved the tradeoff between noise and the mean value on the LV myocardium, indicating its potential clinical application in MP PET imaging.

Development and evaluation of convergent and accelerated penalized SPECT image reconstruction methods for improved dose-volume histogram estimation in radiopharmaceutical therapy

PURPOSE

Three-dimensional (3D) dosimetry has the potential to provide better prediction of response of normal tissues and tumors and is based on 3D estimates of the activity distribution in the patient obtained from emission tomography. Dose-volume histograms (DVHs) are an important summary measure of 3D dosimetry and a widely used tool for treatment planning in radiation therapy. Accurate estimates of the radioactivity distribution in space and time are desirable for accurate 3D dosimetry. The purpose of this work was to develop and demonstrate the potential of penalized SPECT image reconstruction methods to improve DVHs estimates obtained from 3D dosimetry methods.

METHODS

The authors developed penalized image reconstruction methods, using maximum a posteriori (MAP) formalism, which intrinsically incorporate regularization in order to control noise and, unlike linear filters, are designed to retain sharp edges. Two priors were studied: one is a 3D hyperbolic prior, termed single-time MAP (STMAP), and the second is a 4D hyperbolic prior, termed cross-time MAP (CTMAP), using both the spatial and temporal information to control noise. The CTMAP method assumed perfect registration between the estimated activity distributions and projection datasets from the different time points. Accelerated and convergent algorithms were derived and implemented. A modified NURBS-based cardiac-torso phantom with a multicompartment kidney model and organ activities and parameters derived from clinical studies were used in a Monte Carlo simulation study to evaluate the methods. Cumulative dose-rate volume histograms (CDRVHs) and cumulative DVHs (CDVHs) obtained from the phantom and from SPECT images reconstructed with both the penalized algorithms and OS-EM were calculated and compared both qualitatively and quantitatively. The STMAP method was applied to patient data and CDRVHs obtained with STMAP and OS-EM were compared qualitatively.

RESULTS

The results showed that the penalized algorithms substantially improved the CDRVH and CDVH estimates for large organs such as the liver compared to optimally postfiltered OS-EM. For example, the mean squared errors (MSEs) of the CDRVHs for the liver at 5 h postinjection obtained with CTMAP and STMAP were about 15% and 17%, respectively, of the MSEs obtained with optimally filtered OS-EM. For the CDVH estimates, the MSEs obtained with CTMAP and STMAP were about 16% and 19%, respectively, of the MSEs from OS-EM. For the kidneys and renal cortices, larger residual errors were observed for all algorithms, likely due to partial volume effects. The STMAP method showed promising qualitative results when applied to patient data.

CONCLUSIONS

Penalized image reconstruction methods were developed and evaluated through a simulation study. The study showed that the MAP algorithms substantially improved CDVH estimates for large organs such as the liver compared to optimally postfiltered OS-EM reconstructions. For small organs with fine structural detail such as the kidneys, a large residual error was observed for both MAP algorithms and OS-EM. While CTMAP provided marginally better MSEs than STMAP, given the extra effort needed to handle misregistration of images at different time points in the algorithm and the potential impact of residual misregistration, 3D regularization methods, such as that used in STMAP, appear to be a more practical choice.

Anatomy-guided brain PET imaging incorporating a joint prior model

We proposed a maximum a posterior (MAP) framework for incorporating information from co-registered anatomical images into PET image reconstruction through a novel anato-functional joint prior. The characteristic of the utilized hyperbolic potential function is determinate by the voxel intensity differences within the anatomical image, while the penalization is computed based on voxel intensity differences in reconstructed PET images. Using realistic simulated (18)FDG PET scan data, we optimized the performance of the proposed MAP reconstruction with the joint prior (JP-MAP) and compared its performance with conventional 3D MLEM and 3D MAP reconstructions. The proposed JP-MAP reconstruction algorithm resulted in quantitatively enhanced reconstructed images, as demonstrated in extensive FDG PET simulation study. The proposed method was also tested on a 20 min Florbetapir patient study performed on the high-resolution research tomograph. It was shown to outperform conventional methods in visual as well as quantitative accuracy assessment (in terms of regional noise versus activity value performance). The JP-MAP method was also compared with another MR-guided MAP reconstruction method, utilizing the Bowsher prior and was seen to result in some quantitative enhancements, especially in the case of MR-PET mis-registrations, and a definitive improvement in computational performance.

Multi-material decomposition using statistical image reconstruction for spectral CT

Spectral computed tomography (CT) provides information on material characterization and quantification because of its ability to separate different basis materials. Dual-energy (DE) CT provides two sets of measurements at two different source energies. In principle, two materials can be accurately decomposed from DECT measurements. However, many clinical and industrial applications require three or more material images. For triple-material decomposition, a third constraint, such as volume conservation, mass conservation or both, is required to solve three sets of unknowns from two sets of measurements. The recently proposed flexible image-domain (ID) multi-material decomposition) method assumes each pixel contains at most three materials out of several possible materials and decomposes a mixture pixel by pixel. We propose a penalized-likelihood (PL) method with edge-preserving regularizers for each material to reconstruct multi-material images using a similar constraint from sinogram data. We develop an optimization transfer method with a series of pixel-wise separable quadratic surrogate (PWSQS) functions to monotonically decrease the complicated PL cost function. The PWSQS algorithm separates pixels to allow simultaneous update of all pixels, but keeps the basis materials coupled to allow faster convergence rate than our previous proposed material- and pixel-wise SQS algorithms. Comparing with the ID method using 2-D fan-beam simulations, the PL method greatly reduced noise, streak and cross-talk artifacts in the reconstructed basis component images, and achieved much smaller root mean square errors.

Evaluation of simultaneous 201Tl/99mTc dual-isotope cardiac SPECT imaging with model-based crosstalk compensation using canine studies

BACKGROUND

Simultaneous (201)Tl/(99m)Tc-sestamibi dual-isotope myocardial perfusion SPECT imaging can reduce imaging time and produce perfectly registered rest/stress images. However, crosstalk from (99m)Tc into (201)Tl images can significantly reduce (201)Tl image quality. We have developed a model-based compensation (MBC) method to compensate for this crosstalk. The method has previously been validated with phantom and simulation studies. In this study, we evaluated the MBC method using a canine model.

METHODS

Left anterior descending or left circumflex coronary artery stenoses were created in 50 adult mongrel dogs weighing 20-30 kg. The dogs were injected with 111 MBq (3 mCi) of (201)Tl at rest, and a SPECT study acquired. Stress was induced by administering adenosine to the dog, followed by injection of 740 MBq (20 mCi) of (99m)Tc-sestamibi at peak stress. A second SPECT study was performed with data acquired in both (201)Tl and (99m)Tc energy windows to provide simultaneous dual-isotope projection data. The images were reconstructed using the ordered-subsets expectation-maximization reconstruction algorithm with compensation for attenuation, scatter, and detector response. For simultaneously acquired (201)Tl data, we also applied the MBC method to compensate for crosstalk contamination from (99m)Tc.

RESULTS

Without compensation, (99m)Tc crosstalk increased the estimated (201)Tl activity concentration in the rest images and reduced defect contrast. After MBC, the (201)Tl images were in good agreement with the registered single-isotope images and ex vivo count data. The ischemic (IS) to non-ischemic (NIS) region (201)Tl activity concentration ratios were computed for single-isotope and dual-isotope studies. The correlation with ex vivo IS-NIS ratios was 0.815 after MBC, compared to the 0.495 from data without compensation. In addition, the regression line for the IS-NIS ratios with MBC was almost parallel to the line of identity with a slope of 0.93, compared to a slope of 0.45 without compensation.

CONCLUSIONS

These results demonstrate that model-based crosstalk compensation can provide substantial reduction of crosstalk effects in simultaneously acquired myocardial perfusion SPECT images in living biological systems.

A parametric level-set approach to simultaneous object identification and background reconstruction for dual-energy computed tomography

Dual-energy computerized tomography has gained great interest because of its ability to characterize the chemical composition of a material rather than simply providing relative attenuation images as in conventional tomography. The purpose of this paper is to introduce a novel polychromatic dual-energy processing algorithm, with an emphasis on detection and characterization of piecewise constant objects embedded in an unknown cluttered background. Physical properties of the objects, particularly the Compton scattering and photoelectric absorption coefficients, are assumed to be known with some level of uncertainty. Our approach is based on a level-set representation of the characteristic function of the object and encompasses a number of regularization techniques for addressing both the prior information we have concerning the physical properties of the object and the fundamental physics-based limitations associated with our ability to jointly recover the Compton scattering and photoelectric absorption properties of the scene. In the absence of an object with appropriate physical properties, our approach returns a null characteristic function and, thus, can be viewed as simultaneously solving the detection and characterization problems. Unlike the vast majority of methods that define the level-set function nonparametrically, i.e., as a dense set of pixel values, we define our level set parametrically via radial basis functions and employ a Gauss-Newton-type algorithm for cost minimization. Numerical results show that the algorithm successfully detects objects of interest, finds their shape and location, and gives an adequate reconstruction of the background.