Carotid surgery is a frequent act corresponding to 15 to 20 thousands operations per year in France. Cerebral perfusion has to be tracked before and after carotid surgery. In this paper, a diagnosis support using quality metrics is proposed to detect vascular lesions on MR images. Our key stake is to provide a detection tool mimicking the human visual system behavior during the visual inspection. Relevant Human Visual System (HVS) properties should be integrated in our lesion detection method, which must be robust to common distortions in medical images. Our goal is twofold: to help the neuroradiologist to perform its task better and faster but also to provide a way to reduce the risk of bias in image analysis. Objective quality metrics (OQM) are methods whose goal is to predict the perceived quality. In this work, we use Objective Quality Metrics to detect perceivable differences between pairs of images.
The Mojette transform is a discrete and exact Radon transform, based on the discrete geometry of the projection
and reconstruction lattice. The specific sampling scheme of the Mojette transform results in theoretical exact
image reconstruction. In this paper, we compare the reconstructions obtained with the Mojette transform to
the ones obtained with several usual projection/backprojection digitized Radon transform. These experiments
validate and demonstrate the performance of the Mojette transform sampling over classical implementations
based on continuous space.
Micro-CT represents a modality where the quality of CT reconstruction is very high thanks to the acquisition properties.
The goal of this paper is to challenge our proposed Mojette discrete reconstruction scheme from real micro-CT data. A
first study was done to analyze bone image degradations by lowering the number of projections. A second study analyzes
trabecular bone and vessels tree through an animal study. Small vessels are filling trabecular holes with almost the same
grey levels as the bone. Therefore vessel detectability that can be achieved from the reconstruction algorithm according
to the number of projections is a major issue.
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.
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