Accessing image resolution and frame rate effects in radiology from a human and a machine point of view

In teleradiology a vast amount of medical images is sent from one location to another location. If the network infrastructure between the locations is poor, users experience long download times or, if a client application is used, application lags. To solve this issue lossless compression algorithms can be used as a first option. Unfortunately these algorithms can only compress the data to a certain degree which is most of the time not enough for the heavy requirements in teleradiology. As a second option the image data can be compressed lossily by reducing the image quality. This however can have an impact on the work of the user and also on image processing tools, when the images are post-processed. In this contribution we give a first impression of frame rate and resolution effects on the work of both, humans and machines, using the example of tumor diagnosis

GPU-based RFA simulation for minimally invasive cancer treatment of liver tumours

PURPOSE: Radiofrequency ablation (RFA) is one of the most popular and well-standardized minimally invasive cancer treatments (MICT) for liver tumours, employed where surgical resection has been contraindicated. Less-experienced interventional radiologists (IRs) require an appropriate planning tool for the treatment to help avoid incomplete treatment and so reduce the tumour recurrence risk. Although a few tools are available to predict the ablation lesion geometry, the process is computationally expensive. Also, in our implementation, a few patient-specific parameters are used to improve the accuracy of the lesion prediction. METHODS: Advanced heterogeneous computing using personal computers, incorporating the graphics processing unit (GPU) and the central processing unit (CPU), is proposed to predict the ablation lesion geometry. The most recent GPU technology is used to accelerate the finite element approximation of Penne's bioheat equation and a three state cell model. Patient-specific input parameters are used in the bioheat model to improve accuracy of the predicted lesion. RESULTS: A fast GPU-based RFA solver is developed to predict the lesion by doing most of the computational tasks in the GPU, while reserving the CPU for concurrent tasks such as lesion extraction based on the heat deposition at each finite element node. The solver takes less than 3 min for a treatment duration of 26 min. When the model receives patient-specific input parameters, the deviation between real and predicted lesion is below 3 mm. CONCLUSION: A multi-centre retrospective study indicates that the fast RFA solver is capable of providing the IR with the predicted lesion in the short time period before the intervention begins when the patient has been clinically prepared for the treatment

Diagnostics et modelisation d'un plasma d'argon-hydrogene cree par decharge H.F. inductive utilisee pour la gravure de silicium

SIGLECNRS T 58179 / INIST-CNRS - Institut de l'Information Scientifique et TechniqueFRFranc

High-resolution contrast enhanced multi-phase hepatic computed tomography data fromaporcine Radio-Frequency Ablation study

Data below 1 mm voxel size is getting more and more common in the clinical practice but it is still hard to obtain a consistent collection of such datasets for medical image processing research. With this paper we provide a large collection of Contrast Enhanced (CE) Computed Tomography (CT) data from porcine animal experiments and describe their acquisition procedure and peculiarities. We have acquired three CE-CT phases at the highest available scanner resolution of 57 porcine livers during induced respiratory arrest. These phases capture contrast enhanced hepatic arteries, portal venous veins and hepatic veins. Therefore, we provide scan data that allows for a highly accurate reconstruction of hepatic vessel trees. Several datasets have been acquired during Radio-Frequency Ablation (RFA) experiments. Hence, many datasets show also artificially induced hepatic lesions, which can be used for the evaluation of structure detection methods

Distributed Agency in Food Waste : A Focus on Non-Human Actors in Retail Setting

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