Fiducial tracking is a widely used method in image guided procedures such as image guided radiosurgery and
radiotherapy. Our group has developed a new fiducial identification algorithm, concurrent Viterbi with association
(CVA) algorithm, based on a modified Hidden Markov Model (HMM), and reported our initial results previously. In this
paper, we present an extensive performance evaluation of this novel algorithm using phantom testing and clinical images
acquired during patient treatment. For a common three-fiducial case, the algorithm execution time is less than two
seconds. Testing with a collection of images from more than 35 patient treatments, with a total of more than 10000
image pairs, we find that the success rate of the new algorithm is better than 99%. In the tracking test using a phantom,
the phantom is moved to a variety of positions with translations up to 8 mm and rotations up to 4 degree. The new
algorithm correctly tracks the phantom motion, with an average translation error of less than 0.5 mm and rotation error
less than 0.5 degrees. These results demonstrate that the new algorithm is very efficient, robust, easy to use, and capable
of tracking fiducials in a large region of interest (ROI) at a very high success rate with high accuracy.
Near-field coded aperture imaging is known to have superior image resolution and count sensitivity over conventional parallel-hole collimated nuclear imaging. There have been several studies in image reconstruction for two-dimensional planar objects using the coded aperture imaging technology. However, coded aperture imaging for three-dimensional (3D) objects has not been extensively investigated. In this paper, a 3D reconstruction method for near-field coded aperture imaging is presented. We first introduce the "out-of-focus" correction factor into the generic expectation maximization (EM) algorithm for 3D near-field coded aperture images with the assumption that the photon emissions of coded aperture projections follow the Poisson statistics. The ordered subset expectation maximization (OSEM) method is then adapted for full 3D coded aperture image reconstruction. A 3D capillary tube phantom filled with 99mTc radioactive solution was used to evaluate the performance of our methods. A dual-head SPECT camera, one head quipped with a coded aperture module and the other with a parallel-hole collimator, was utilized for image acquisitions. Images were reconstructed using the modified EM and OSEM methods associated with the depth-dependent out-of-focus correction. The preliminary phantom results showed that our methods may have potential of reconstructing 3D near-field coded aperture images and also providing superior image resolution as compared to conventional parallel-hole collimated images.
Radiation monitoring systems are desired in many places where radioactive materials are utilized. In this paper, a color gamma camera system developed in Tsinghua University (P.C. China) is reported. The system consist of a compact X - (gamma) ray detector system, a single hole collimator, the scanning mechanism and computer system. The MLEM method is implemented for image reconstruction, which enables one to generate images of high resolution with relatively big aperture. With the associated software, several scanning modes, which work with different speeds and resolutions, are provided and can be selected in the operations. In addition, the system can detect radioactive sources emitting rays of different energies and display them with color images. Experiments were made using Am-241 (59.5 KeV) and Na-22 (511 KeV) to test the performance of the system. The results are presented which show that the resolution of this system can be as high as 1.5 degrees. Furthermore, simulations using Matlab were made to examine the capability of imaging point sources with a small number of counts and imaging distributed sources. Promising results were obtained and are reported. Discussions about camera design and further improvements are given at the end.
Image restoration is a procedure which is characterized by ill- poseness, ill-conditioning and non-uniqueness of the solution in the presence of noise. Iterative numerical methods have gained much attention for solving these inverse problems. Among the methods, minimal variance or least squares approaches are widely used and often generate good results at a reasonable cost in computing time using iterative optimization of the associated cost functional. In this paper, a new regularization method obtained by minimizing the autocorrelation function of residuals is proposed. Several numerical tests using the BFGS nonlinear optimization method are reported and comparisons to the classical Tikhonov regularization method are given. The results show that this method gives competitive restoration and is not sensitive to the regularization weighting parameter. Furthermore, a comprehensive procedure of image restoration is proposed by introducing a modified version of the Mumford-Shah model, which is often used in image segmentation. This approach shows promising improvement in restoration quality.
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