Realtime imaging of static and moving objects

[RT steven]

Often in the hospital setting, radiographers are asked to go up to theatre and perform real time imaging of patients for having tumours removed or drilling away some sort of object that may blocking a vessel. The mobile x-ray machine is an image intensifier (II) which is coupled to a TV which allows a surgeon to dynamically view internal anatomy. Vessel patency can be tested by injecting radiolucent contrast and observing its dynamic flow.

As a radiography student, in this research project, i aim to test the precision at which the image intensifier (II) may resolve moving objects.

My objective is to try and think up some very simple experiments which enable me to test the the machine and i will need to explain the precision of the machine in terms of contrast on the screen (distribution of black an white pixels over an edge), how fast an object can move during image acquisition before it becomes too blury.

There are 2 image acquisition modes i will use: single pulse, sequence pulse. In fact, a single pulse is not instantaneous and requires t time to generate an image on the screen. In sequence pulse, the machine make take 7 images per second.

Some ideas i have thought up

dyamic

simple pendulum experiemnt, using simple physics (and a piece of lead tied to a string), i will perform a single pulse, and time how long it takes for the image to appear (perhaps 1 second), then measure the blur over distance somehow on the image -any ideas? I will also do this with a sequence pulse.

my main questions are.. how am i to quantify these results? Can i graph the sharpness as a function of motion speed? how may i quantify sharpness. how can i determine the speed of the object if i know the period of the pendulum, and the blur distance it has moved for a given time for image aquisition. From this, can i use this as a calibration excercise to find out what is the max speed something can move for a moving object to be imaged correctly.

Basically, each pulse has an integration time, which is an average oer distance of the object moving for when the image is acquired.. how can i work this exposure time out? can i work out what this integration is?

My problems is, i can think up the experiments, but I am unsure how to quantify the results (or the physics calcultions involved) and to present them as statistical data.

My ultimate goal is to image flow of contrast in synthetic tubes (to simulate arteries) and see the effect of flow mixing with water and investigate the profile of the image as a function of tube diameter and other variables. I will however need calibration details of the II (obtained hopefully from the above experiments) in order to make calculations for this fluild dynamics model.

your help would be much appreciated!

steve

 

[RT steven]

hmm .. am in the correct forum for this topic? please help to redirect?

 

[wais]

First of all, it is great to see that you are thinking about ways to test and improve the precision of the mobile x-ray machine for real-time imaging. This is an important aspect of radiography in the hospital setting and your research project has the potential to contribute to the advancement of this technology.

In terms of quantifying your results, there are a few ways you can approach this. One way is to use a measurement tool on the imaging software to measure the blur distance of the pendulum in the image. You can then plot this distance against the motion speed of the pendulum to create a graph that shows the relationship between the two variables. This can help you determine the maximum speed at which an object can move before it becomes too blurry to be imaged accurately.

Another approach could be to use a high-speed camera to record the motion of the pendulum and then analyze the footage to determine the exact speed at which the pendulum is moving. You can then compare this speed to the integration time of the image acquisition to see if there is a correlation between the two.

As for the calibration exercise, you can use the results from your pendulum experiments to determine the maximum speed at which an object can move before it becomes too blurry. You can then use this information to calibrate the machine for your flow imaging experiments with synthetic tubes. This will allow you to accurately measure and analyze the flow of contrast in the tubes and determine the effect of different variables on the image profile.

In terms of presenting your results as statistical data, you can use graphs, tables, and descriptive statistics to summarize your findings. It may also be helpful to consult with a statistician or an experienced researcher for guidance on the best ways to present and analyze your data.

Overall, your research project has the potential to contribute valuable insights into the precision of real-time imaging in the hospital setting. Keep up the good work and good luck with your experiments!