Monte Carlo simulations for system modeling in emission tomography

Non-invasive diagnostic imaging can be performed with different technologies:X-ray radiography, computed radiography, direct radiography, mammography,Computed Tomography (CT), UltraSound (US), and Magnetic Resonance Imaging (MRI), which all give anatomical information, and also with functional MRI (fMRI), optical imaging, thermography, planar isotope imaging,Single Photon Emission Tomography (SPECT), Positron Emission Tomography (PET), and gamma camera PET which return functional information.Recent devices combine two modalities on the same gantry in order to achieve hardware fusion of anatomical and functional images. Given the demographic aging in Western Europe, there exists a large interest in what is popularly referred to as a GPS-tool for cancer, i.e. a diagnostic tool for oncology that detects small malignant lesions in a very early stadium and that can be used for disease staging. Therefore research in nuclear medicine has a social support and bearing. In nuclear medicine examinations, a radiopharmaceutical is injected in the patient, marked with a radionuclide emitting one single photon with an energy of 100-200 keV in SPECT and a positron emitting radionuclide in PET. The emission of a positron finally results in two annihilation photons of 511 keV. Those photons are detected, mostly using a scintillation crystal that generates optical photons which travel through a light guide before reaching the PhotoMultiplierTubes (PMTs). Those PMTs convert the optical photons to electrons, which are in their turn used to generate a position and energy encoding signal. In PET there is an electronic collimation to acquire directional information while this information is obtained by applying a lead collimator in SPECT. The acquired data is afterwards reconstructed to result in a threedimensional radioactive tracer distribution within the patient. Optimization,evaluation and (re)design of all elements in this detection chain is mostly done using simulations. Given the possibility of modeling different physical processes, the Monte Carlo method has also been applied in nuclear medicine to a wide range of problems that could not be addressed by experimental or analytical approaches

Tomographic image quality of rotating slat versus parallel hole-collimated SPECT

Parallel and converging hole collimators are most frequently used in nuclear medicine. Less common is the use of rotating slat collimators for single photon emission computed tomography (SPECT). The higher photon collection efficiency, inherent to the geometry of rotating slat collimators, results in much lower noise in the data. However, plane integrals contain spatial information in only one direction, whereas line integrals provide two-dimensional information. It is not a trivial question whether the initial gain in efficiency will compensate for the lower information content in the plane integrals. Therefore, a comparison of the performance of parallel hole and rotating slat collimation is needed. This study compares SPECT with rotating slat and parallel hole collimation in combination with MLEM reconstruction with accurate system modeling and correction for scatter and attenuation. A contrast-to-noise study revealed an improvement of a factor 2-3 for hot lesions and more than a factor of 4 for cold lesion. Furthermore, a clinically relevant case of heart lesion detection is simulated for rotating slat and parallel hole collimators. In this case, rotating slat collimators outperform the traditional parallel hole collimators. We conclude that rotating slat collimators are a valuable alternative for parallel hole collimators

Influence of skull inhomogeneities on EEG source localization

We investigated the influence of using simplified models of the skull on electroencephalogram (EEG) source localization. An accurately segmented skull from computed tomography (CT) images, including spongy and compact bones as well as some air–filled cavities, was used as a reference model. The simplified models approximated the skull as a homogeneous compartment with: (1) isotropic, and (2) anisotropic conductivity. The results showed that these approximations could lead to errors of more than 2 cm in dipole estimation. We recommend the use of anisotropy but considering a different ratio for each region of the skull, according to the amount of spongy bone

Characterizing the parallax error in multi-pinhole micro-SPECT reconstruction

The usage of pinholes is very important in preclinical micro-SPECT. Pinholes can magnify the object onto the detector, resulting in better system resolutions than the detector resolution. The loss in sensitivity is usually countered by adding more pinholes, each projecting onto a specific part of the detector. As a result, gamma rays have an oblique incidence to the detector. This causes displacement and increased uncertainty in the position of the interaction of the gamma ray in the detector, also known as parallax errors or depth-of-interaction (DOI) errors. This in turn has a large influence on image reconstruction algorithms using ray tracers as a forward projector model, as the end-point of each ray on the detector has to be accurately known. In this work, we used GATE to simulate the FLEX Triumph-I system (Gamma Medica-Ideas, Northridge, CA), a CZT-based multi-pinhole micro-SPECT system. This system uses 5 mm thick CZT pixels, with 1.5 mm pixel pitch. The simulated information was then used to enhance the image resolution by accurately modeling the DOI. Two hundred point sources were simulated and rebinned to use the DOI information. This data was then used in a GPU-based iterative reconstruction algorithm taking the simulated DOI into account. The average displacement was then determined for all point sources, and the FWHM was calculated in three dimensions, by fitting the point sources with 3D Gaussians. We show that the displacement is reduced by 83% on average. We also show a 15% resolution gain when only 5 DOI levels are used

Iterative CT reconstruction using shearlet-based regularization

In computerized tomography, it is important to reduce the image noise without increasing the acquisition dose. Extensive research has been done into total variation minimization for image denoising and sparse-view reconstruction. However, TV minimization methods show superior denoising performance for simple images (with little texture), but result in texture information loss when applied to more complex images. Since in medical imaging, we are often confronted with textured images, it might not be beneficial to use TV. Our objective is to find a regularization term outperforming TV for sparse-view reconstruction and image denoising in general. A recent efficient solver was developed for convex problems, based on a split-Bregman approach, able to incorporate regularization terms different from TV. In this work, a proof-of-concept study demonstrates the usage of the discrete shearlet transform as a sparsifying transform within this solver for CT reconstructions. In particular, the regularization term is the 1-norm of the shearlet coefficients. We compared our newly developed shearlet approach to traditional TV on both sparse-view and on low-count simulated and measured preclinical data. Shearlet-based regularization does not outperform TV-based regularization for all datasets. Reconstructed images exhibit small aliasing artifacts in sparse-view reconstruction problems, but show no staircasing effect. This results in a slightly higher resolution than with TV-based regularization