High-resolution positron emission tomography (PET) scanners have brought many improvements to the nuclear medicine imaging field. However, the mechanical limitations in the construction of the scanners introduced gaps between the detectors, and accordingly, to the acquired projection data. When the methods requiring full-sinogram dataset, e.g., filtered backprojection (FBP) are applied, the missing parts degrade the reconstructed images. In this study, we aim to compensate the sinograms for the missing parts, i.e., gaps. For the gap filling, we propose an iterative discrete-cosine transform (DCT) domain method with two versions: (1) with basic DCT domain filter and (2) with dedicated and gap-dependent DCT domain filter. For the testing of the methods, 2-D FBP reconstructions were applied to the gap-filled sinograms. The proposed DCT domain gap-filling method with two different filters was compared to the constrained Fourier space (CFS) method. For the quantitative analysis, we used numerical phantoms at eight different Poisson noise levels with 100 realizations. Mean-square error, bias, and variance evaluations were performed over the selected regions of interest. Only the dedicated gap-dependent DCT domain filter showed quantitative improvement in all regions, at each noise level. We also assessed the methods visually with a [¹¹C] raclopride human brain study reconstructed by 2-D FBP after gap filling. The visual comparisons of the methods showed that the gap filling with both DCT domain filters performed better than the CFS method. The proposed technique can be used for the sinograms, not only with limited range of projections as in the high-resolution research tomograph (ECAT HRRT) PET scanner, but also with detector failure artifacts.

Electron tomography (ET) of biological samples is used to study the organization and the structure of the whole cell and subcellular complexes in great detail. However, projections cannot be acquired over full tilt angle range with biological samples in electron microscopy. ET image reconstruction can be considered an ill-posed problem because of this missing information. This results in artifacts, seen as the loss of three-dimensional (3D) resolution in the reconstructed images. The goal of this study was to achieve isotropic resolution with a statistical reconstruction method, sequential maximum a posteriori expectation maximization (sMAP-EM), using no prior morphological knowledge about the specimen. The missing wedge effects on sMAP-EM were examined with a synthetic cell phantom to assess the effects of noise. An experimental dataset of a multivesicular body was evaluated with a number of gold particles. An ellipsoid fitting based method was developed to realize the quantitative measures elongation and contrast in an automated, objective, and reliable way. The method statistically evaluates the sub-volumes containing gold particles randomly located in various parts of the whole volume, thus giving information about the robustness of the volume reconstruction. The quantitative results were also compared with reconstructions made with widely-used weighted backprojection and simultaneous iterative reconstruction technique methods. The results showed that the proposed sMAP-EM method significantly suppresses the effects of the missing information producing isotropic resolution. Furthermore, this method improves the contrast ratio, enhancing the applicability of further automatic and semi-automatic analysis. These improvements in ET reconstruction by sMAP-EM enable analysis of subcellular structures with higher three-dimensional resolution and contrast than conventional methods.

The ECAT High Resolution Research Tomograph (HRRT) (CTI PET Systems, Knoxville, TN, USA) is the state-of-the-art positron emission tomography (PET) scanner in the nuclear medicine imaging field. The gantry of the HRRT PET scanner has detector-free regions. These regions between the detector blocks introduce missing parts to the acquired PET data. Without the estimation of the missing parts of the sinogram data, the methods which require full sinogram dataset give undesired results. Previously, we proposed the iterative discrete cosine transform (DCT) domain gap-filling method which gave better quantitative and visual results than the previously published gap-filling methods. The gap-filling methods published so far estimate the missing parts only on the transaxial slices while they are ignoring the information from the neighboring transaxial slices. In this study, we propose a non-iterative gap-filling method for the compensation of the HRRT PET sinogram data by using the slices in the direction of radial samples. We compared the results of this new method with the results of the improved version of the DCT domain gap-filling approach. We used 3D numerical phantom sinogram at eight different Poisson noise levels for checking the regional quantitative results. For visual assessment, we employed a [11C]-raclopride human brain study. After the missing parts were estimated, we applied 2D filtered backprojection which obligates full sinogram dataset. The results showed that the non-iterative interpolation by using the slices in the direction of radial samples gave similar results as the DCT domain data estimation method. Its simplicity and the short processing time over the DCT domain gap-filling method are the notable advantageous properties of this non-iterative gap-filling approach.

We consider the denoising of noisy positron emission tomography (PET) sinograms by sparse 3D transform domain collaborative filtering. The filtering operation is realized by block-matching and 3D (BM3D) filtering algorithm that groups similar 2D sinogram fragments into 3D groups and filters each group by collaborative filtering. This filtering jointly filters the similar blocks in the transform domain and offers a way for efficient denoising while preserving essential information of each sinogram fragment. The obtained results are compared with conventional radial filtering and stackgram filtering that utilizes an intermediate stage between sinogram and image domains, called stackgram domain. Both quantitative and qualitative comparisons show that the proposed approach to filter the sinogram with BM3D filter outperforms the other filtering methods.

Visual technologies have an indispensable role in safety-critical applications, where tasks must often be performed through teleoperation. Due to the lack of stereoscopic and motion parallax depth cues in conventional images, alignment tasks pose a significant challenge to remote operation. In this context, machine vision can provide mission-critical information to augment the operator’s perception. In this paper, we propose a retro-reflector marker-based teleoperation aid to be used in hostile remote handling environments. The system computes the remote manipulator’s position with respect to the target using a set of one or two low-resolution cameras attached to its wrist. We develop an end-to-end pipeline of calibration, marker detection, and pose estimation, and extensively study the performance of the overall system. The results demonstrate that we have successfully engineered a retro-reflective marker from materials that can withstand the extreme temperature and radiation levels of the environment. Furthermore, we demonstrate that the proposed maker-based approach provides robust and reliable estimates and significantly outperforms a previous stereo-matching-based approach, even with a single camera.