Romain Grosjean, Benoît Sauer, Rui Guerra, Isabelle Kermarrec, Yannick Ponvianne, Daniel Winninger, Michel Daudon, Alain Blum, Jacques Felblinger, Jacques Hubert
KEYWORDS: Calcium, X-rays, Signal attenuation, Chemical analysis, In vitro testing, In vivo imaging, Kidney, X-ray computed tomography, Collimation, Scanners
The selection of the optimal treatment method for urinary stones diseases depends on the chemical composition of
the stone and its corresponding fragility. MDCT has become the most used modality to determine rapidly and
accurately the presence of stones when evaluating urinary lithiasis treatment. That is why several studies have
tempted to determine the chemical composition of the stones based on the stone X-ray attenuation in-vitro and invivo.
However, in-vitro studies did not reproduce the normal abdominal wall and fat, making uncertain the
standardization of the obtained values.
The aim of this study is to obtain X-ray attenuation values (in Hounsfield Units) of the six more frequent types of
human renal stones (n=217) and to analyze the influence of the surrounding media on these values. The stones were
first placed in a jelly, which X-ray attenuation is similar to that of the human kidney (30 HU at 120 kV). They were
then stuck on a grid, scanned in a water tank and finally scanned in the air.
Significant differences in CT-attenuation values were obtained with the three different surrounding media (jelly,
water, air). Furthermore there was an influence of the surrounding media and consequently discrepancies in
determination of the chemical composition of the renal stones.
Consequently, CT-attenuation values found in in-vitro studies cannot really be considered as a reference for the
determination of the chemical composition except if the used phantom is an anthropomorphic one.
A multimodality platform (CT, PET, Radiotherapy) has been developed in order to move phantoms (maximum weight: 70kg). This allows the study of the influence of motion on image quality. The translation system (160 mm in the z axis, maximal speed of 50 mm × s-1) was controlled by a computer via a NI Motion Controller PCI 7344 (National Instrument, TX, USA). As an initial experiment, an anthropomorphic cardiac CT phantom (QRM, Moehrendorf, Germany) was moved linearly with speeds of 5, 10 and 20 mm × s-1. Acquisitions were done on a Siemens Somatom Volume Zoom CT Scanner. To compare dynamic and static images, mutual information, correlation coefficient, standard deviation, volume computation and radiologist scoring were conducted. The mean position error of the platform was 0.1mm ± 0.04. Automatic evaluation of the image quality and/or the blurring is not easy. As predicted, we found an increase in artifacts with the speed of the phantom. The platform allows us to simulate physiological motions (respiratory and cardiac) in order to study their real influence on image quality and to correct them. We can already produce z axis physiological motion with the platform. More degrees of freedom (y and z rotations, x and y translations) will be added to improve the simulation of physiological motions.
Optimal time windowing in cardiac CT reconstruction would permit a reduction of motion artifacts. We used Doppler Tissue Imaging (DTI) in order to acquire the speed and displacement of region of interest in one direction with a high temporal resolution. Applied on different segments of coronary arteries, 3D information is recovered in three quasi orthogonal acquisitions (abdominal, parasternal and apical views). Their reproducibility was carried on five healthy subjects. This experiment will allow finding the best time window for reconstruction. A window of half the rotation time (250ms for our CT) was estimated based on minimization of the motion variation in 3D. Since we obtained a high reproducibility between the two sessions (generally < 2mm) DTI could be an interesting approach to evaluate motion in 3D. This yields more information as compared to 2D data. Future work will require additional acquisitions and experiments in order to analyze more in depth the variability and the influence of the heart rate. The collected data will be used also as input for our motion platform and CT simulation.
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