Practical implementation of tetrahedral mesh reconstruction in emission tomography.

This paper presents a practical implementation of image reconstruction on tetrahedral meshes optimized for emission computed tomography with parallel beam geometry. Tetrahedral mesh built on a point cloud is a convenient image representation method, intrinsically three-dimensional and with a multi-level resolution property. Image intensities are defined at the mesh nodes and linearly interpolated inside each tetrahedron. For the given mesh geometry, the intensities can be computed directly from tomographic projections using iterative reconstruction algorithms with a system matrix calculated using an exact analytical formula. The mesh geometry is optimized for a specific patient using a two stage process. First, a noisy image is reconstructed on a finely-spaced uniform cloud. Then, the geometry of the representation is adaptively transformed through boundary-preserving node motion and elimination. Nodes are removed in constant intensity regions, merged along the boundaries, and moved in the direction of the mean local intensity gradient in order to provide higher node density in the boundary regions. Attenuation correction and detector geometric response are included in the system matrix. Once the mesh geometry is optimized, it is used to generate the final system matrix for ML-EM reconstruction of node intensities and for visualization of the reconstructed images. In dynamic PET or SPECT imaging, the system matrix generation procedure is performed using a quasi-static sinogram, generated by summing projection data from multiple time frames. This system matrix is then used to reconstruct the individual time frame projections. Performance of the new method is evaluated by reconstructing simulated projections of the NCAT phantom and the method is then applied to dynamic SPECT phantom and patient studies and to a dynamic microPET rat study. Tetrahedral mesh-based images are compared to the standard voxel-based reconstruction for both high and low signal-to-noise ratio projection datasets. The results demonstrate that the reconstructed images represented as tetrahedral meshes based on point clouds offer image quality comparable to that achievable using a standard voxel grid while allowing substantial reduction in the number of unknown intensities to be reconstructed and reducing the noise.

A detector response function design in pinhole SPECT including geometrical calibration.

Clinical single photon emission computed tomography (SPECT) equipped with pinhole collimators have a magnification factor that results in high spatial resolution images for small animal imaging. Using Monte Carlo simulations to model the acquisition process and the propagation of the photons from their point of emission to their detection point then integrating the model into an iterative reconstruction algorithm improves the signal-to-noise ratio, the contrast and the spatial resolution in the reconstructed images. However, pinhole SPECT systems are known to be very sensitive to geometrical misalignments. Geometrical misalignments are defined as the radial or axial shift of the collimator pinhole and/or twist and tilt of the detector heads and are introduced in the system each time the collimation device is changed (pinhole to parallel holes or vice versa). In this work, we present a flexible detector response function table (DRFT) design that takes into account the geometrical misalignments and avoids performing new Monte Carlo simulations for each exam in order to calculate a geometrical study-dependent system matrix. The utilization of the DRFT for the calculation of the system matrix speeds up its computation time by two orders of magnitude making it acceptable for preclinical and clinical applications.

Medical applications of shortwave FM radar: remote monitoring of cardiac and respiratory motion.

PURPOSE: This article introduces the use of low power continuous wave frequency modulated radar for medical applications, specifically for remote monitoring of vital signs in patients. METHODS: Gigahertz frequency radar measures the electromagnetic wave signal reflected from the surface of a human body and from tissue boundaries. Time series analysis of the measured signal provides simultaneous information on range, size, and reflective properties of multiple targets in the field of view of the radar. This information is used to extract the respiratory and cardiac rates of the patient in real time. RESULTS: The results from several preliminary human subject experiments are provided. The heart and respiration rate frequencies extracted from the radar signal match those measured independently for all the experiments, including a case when additional targets are simultaneously resolved in the field of view and a case when only the patient's extremity is visible to the radar antennas. CONCLUSIONS: Micropower continuous wave FM radar is a reliable, robust, inexpensive, and harmless tool for real-time monitoring of the cardiac and respiratory rates. Additionally, it opens a range of new and exciting opportunities in diagnostic and critical care medicine. Differences between the presented approach and other types of radars used for biomedical applications are discussed.