Laboratory-based ROTEM(®) analysis: implementing pneumatic tube transport and real-time graphic transmission

ROTEM(®) is considered a helpful point-of-care device to monitor blood coagulation. Centrally performed analysis is desirable but rapid transport of blood samples and real-time transmission of graphic results are an important prerequisite. The effect of sample transport through a pneumatic tube system on ROTEM(®) results is unknown. The aims of the present work were (i) to determine the influence of blood sample transport through a pneumatic tube system on ROTEM(®) parameters compared to manual transportation, and (ii) to verify whether graphic results can be transmitted on line via virtual network computing using local area network to the physician in charge of the patient.

Hardware accelerated ray cast of volume data and volume gradient for an optimized splines-based multi-resolution 2D-3D registration

This paper describes a method for DRR generation as well as for volume gradients projection using hardware accelerated 2D texture mapping and accumulation buffering and demonstrates its application in 2D-3D registration of X-ray fluoroscopy to CT images. The robustness of the present registration scheme are guaranteed by taking advantage of a coarse-to-fine processing of the volume/image pyramids based on cubic B-splines. A human cadaveric spine specimen together with its ground truth was used to compare the present scheme with a purely software-based scheme in three aspects: accuracy, speed, and capture ranges. Our experiments revealed an equivalent accuracy and capture ranges but with much shorter registration time with the present scheme. More specifically, the results showed 0.8 mm average target registration error, 55 second average execution time per registration, and 10 mm and 10° capture ranges for the present scheme when tested on a 3.0 GHz Pentium 4 computer.

Hierarchical Markov Random Fields Applied to Model Soft Tissue Deformations on Graphics Hardware

Many methodologies dealing with prediction or simulation of soft tissue deformations on medical image data require preprocessing of the data in order to produce a different shape representation that complies with standard methodologies, such as mass–spring networks, finite element method s (FEM). On the other hand, methodologies working directly on the image space normally do not take into account mechanical behavior of tissues and tend to lack physics foundations driving soft tissue deformations. This chapter presents a method to simulate soft tissue deformations based on coupled concepts from image analysis and mechanics theory. The proposed methodology is based on a robust stochastic approach that takes into account material properties retrieved directly from the image, concepts from continuum mechanics and FEM. The optimization framework is solved within a hierarchical Markov random field (HMRF) which is implemented on the graphics processor unit (GPU See Graphics processing unit ).