Dual-energy computed tomography (DECT) enables material decomposition for tissues and produces additional information for PET/CT imaging to potentially improve the characterization of diseases. PET-enabled DECT (PDECT) allows the generation of PET and DECT images simultaneously with a conventional PET/CT scanner without the need for a second x-ray CT scan. In PDECT, high-energy γ-ray CT (GCT) images at 511 keV are obtained from time-of-flight (TOF) PET data and are combined with the existing x-ray CT images to form DECT imaging. We have developed a kernel-based maximum-likelihood attenuation and activity (MLAA) method that uses x-ray CT images as a priori information for noise suppression. However, our previous studies focused on GCT image reconstruction at the PET image resolution which is coarser than the image resolution of the x-ray CT. In this work, we explored the feasibility of generating super-resolution GCT images at the corresponding CT resolution. The study was conducted using both phantom and patient scans acquired with the uEXPLORER total-body PET/CT system. GCT images at the PET resolution with a pixel size of 4.0 mm × 4.0 mm and at the CT resolution with a pixel size of 1.2 mm × 1.2 mm were reconstructed using both the standard MLAA and kernel MLAA methods. The results indicated that the GCT images at the CT resolution had sharper edges and revealed more structural details compared to the images reconstructed at the PET resolution. Furthermore, images from the kernel MLAA method showed substantially improved image quality compared to those obtained with the standard MLAA method.
Developing reconstruction methods for diffuse optical imaging requires accurate modeling of photon propagation, including boundary conditions arising due to refractive index mismatch as photons propagate from the tissue to air. For this purpose, we developed an analytical Neumann-series radiative transport equation (RTE)-based approach. Each Neumann series term models different scattering, absorption, and boundary-reflection events. The reflection is modeled using the Fresnel equation. We use this approach to design a gradient-descent-based analytical reconstruction algorithm for a three-dimensional (3D) setup of a diffuse optical imaging (DOI) system. The algorithm was implemented for a three-dimensional DOI system consisting of a laser source, cuboidal scattering medium (refractive index > 1), and a pixelated detector at one cuboid face. In simulation experiments, the refractive index of the scattering medium was varied to test the robustness of the reconstruction algorithm over a wide range of refractive index mismatches. The experiments were repeated over multiple noise realizations. Results showed that by using the proposed algorithm, the photon propagation was modeled more accurately. These results demonstrated the importance of modeling boundary conditions in the photon-propagation model.
Fluorescence molecular tomography (FMT) is a promising tool for real time in vivo quantification of neurotransmission (NT) as we pursue in our BRAIN initiative effort. However, the acquired image data are noisy and the reconstruction problem is ill-posed. Further, while spatial sparsity of the NT effects could be exploited, traditional compressive-sensing methods cannot be directly applied as the system matrix in FMT is highly coherent. To overcome these issues, we propose and assess a three-step reconstruction method. First, truncated singular value decomposition is applied on the data to reduce matrix coherence. The resultant image data are input to a homotopy-based reconstruction strategy that exploits sparsity via ℓ1 regularization. The reconstructed image is then input to a maximum-likelihood expectation maximization (MLEM) algorithm that retains the sparseness of the input estimate and improves upon the quantitation by accurate Poisson noise modeling. The proposed reconstruction method was evaluated in a three-dimensional simulated setup with fluorescent sources in a cuboidal scattering medium with optical properties simulating human brain cortex (reduced scattering coefficient: 9.2 cm−1, absorption coefficient: 0.1 cm−1 and tomographic measurements made using pixelated detectors. In different experiments, fluorescent sources of varying size and intensity were simulated. The proposed reconstruction method provided accurate estimates of the fluorescent source intensity, with a 20% lower root mean square error on average compared to the pure-homotopy method for all considered source intensities and sizes. Further, compared with conventional ℓ2 regularized algorithm, overall, the proposed method reconstructed substantially more accurate fluorescence distribution. The proposed method shows considerable promise and will be tested using more realistic simulations and experimental setups.
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