KEYWORDS: Monte Carlo methods, 3D modeling, Databases, Blood, Tumors, Single photon emission computed tomography, Cameras, Computer simulations, Image processing, 3D image processing
Patient-specific dosimetry in nuclear medicine relies on activity quantification in volumes of interest from scintigraphic imaging. Clinical dosimetry protocols have to be benchmarked against results computed from test phantoms. The design of an adequate model is a crucial step for the validation of image-based activ ity quantification. We propose a computing platform to automatically generate simulated SPECT images from a dynamic phantom for arbitrary scintigraphic image protocols. As regards the image generation, we first use the open-source NCAT phantom code to generate an anatomical model and 3D activity maps for different source compartments. This information is used as input for an image simulator and each source is modelled separately. Then, a compartmental model is designed, which describes interactions between dif ferent functional compartments. As a result, we can derive time-activity curves for each compartment with sampling time determined from real image acquisition protocols. Finally, to get an image at a given time after radionuclide injection, the resulting projections are aggregated by scaling the compartment contribution using the specific pharmacokinetics and corrupted by Poisson noise. Our platform consists of many software packages, either in-house developments or open-source codes. In particular, an important part of our work has been to integrate the GATE simulator in our platform, in order to generate automatically the command files needed to run a simulation. Furthermore, some developments were added in the GATE code, to optimize the generation of projections with multiple energy windows in a minimum computation time.
This paper deals with registering 3D PET images in order to monitor lung tumor evolution. Registering directly
two PET images, taken at different stages of a cancer therapy, leads to deforming the tumor of the moving image
to take the shape of the fixed image, loosing the tumor evolution information. This results in aberrant medical
diagnosis. In order to solve this problem, we propose an indirect registration method that consists of processing
pairs of CT-PET images. The CT images acquired at each stage are first registered to estimate anatomical
transformations. The free-form deformation obtained is then applied to the corresponding PET images. The
reconstructed PET images can be compared and used to monitor the tumor. The volume ratio and radiation
density are calculated to assess the evolution of the tumor and evaluate the effectiveness of a therapy. The
proposed iconic registration method is based on a B-Spline deformable model and mutual information. Two
approaches have been used to validate the proposed method. First, we used phantoms to simulate the evolution
of a tumor. The second approach consisted of simulating a tumor within real images. Quantitative measures
show that our registration method keeps invariant volume and density distribution ratios of the tumor within
PET images. This leads to improved tumor localisation and better evaluation of the efficiency of therapies.
This paper deals with enhancing the formation of PET images. Physiological motion, such as breathing, may cause significant alteration of image quality. Correction methods include gated acquisitions that significantly increase the acquisition time. In this paper we propose an original method for reducing respiratory motion artefacts in PET images. It is based on synchronous acquisition of PET and CT data with a spirometer. CT images are acquired at each step of a subdivided respiratory cycle, and registered to estimate the body transformations. Then PET data is indirectly registered and corrected for attenuation before reconstructing a PET image with enhanced quality. This method has been validated using a specific phantom experimentation. Results show that the method brings improved accuracy in tumour volume representation. In addition, the PET imaging clinical protocol is unchanged: our method does not increase the acquisition time nor constrain the patient breathing.
The class of geometric deformable models, also known as level sets, has brought tremendous impact on medical imagery due to its capability of topology preservation and fast shape recovery. Ultrasound images are often characterized by a high level of speckle causing erroneous detection of contours. This work proposes a new stopping term for level sets, based on the coefficient of variation and a multilayer perceptron, in order to robustly detect the contours in ultrasound images. Successful applications of the MLP-Level Sets to detection of contours on synthetics and real images are presented.
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