Fluoroscopy Magnification and Pulsed Fluoroscopy

Pulsed Fluoroscopy and Magnification on Fluoroscopy systems are covered and aspects of both flat panel imagers and image intensifiers are covered.

[@abdovitamins6331]

Detailed and focused manner in simplifying the pulsed Fluroscopy system. Thanks a lot senior Brian

[@HowRadiologyWorks]

Glad it was helpful! Thanks Abdo vitamins for the comment 👍

[@natalieleal7777]

I am in need of some clarification. For pulsed fluoroscopy, I understand that a grid-controlled xray tube is utilized. However, I am having trouble narrowing down the specifics of this "grid". One textbook says it's the charged focusing cup, another source mentions an extra hardware component (charged wire mesh), placed between the cathode and anode. Is it one or the other, or both? Thanks

[@HowRadiologyWorks]

ajronline.org/doi/pdf/10.2214/ajr.175.5.1751453?download=true , Figure 2 shows a picture of the grid

[@MichaelSalaA]

I like the video, but just want to question you about the eye not seeing beyond 30 frames per second? Have you compared a 120 hz monitor to a 30 hz monitor? Dont need an image update really more than 24 hz in medical applications under most circumstances... though one might argue 60 hz mights be useful in angiographic applications.

[@HowRadiologyWorks]

Thanks for the feedback. From the literature I have seen the just noticeable difference for the human perception system is somewhere under 30 FPS especially if the task is not a complete flicker where the screen goes blank completely. s3.cad.rit.edu/cadgallery_production/storage/media/uploads/faculty-s-projects/1918/documents/177/framerate-visibility-thesis.pdf While it maybe possible for some extreme tasks to perceive a difference there is also a dose penalty associated with the higher dose rates so there are more sites moving from 15fps for 7.5fps for many protocols. Thanks for the comment though because in the future also we may want higher framerates when we have AI reading the frames and that can potentially benefit from framerates even higher than our visual system can reliably differentiate. I have not compared monitors for these type of signal so can't comment on that.

[@MichaelSalaA]

@@HowRadiologyWorks This article suggets observers can identify differences in frame rate at both 24 and 48 fps? I've not specifically looked at literature which tests the hypothesis that our perception is limited to under 30 fps.... I just know that I am driven mad when I look at 30 hz monitors. Even under normal desktop conditions I'll select out a 120 hz monitor over a 60 hz monitor. Under clinical circumstances 7.5 fps if fine for fluoro though... I've not done any angio, just observed, but it just made me wonder if say the pt has a high HR it might help to have a higher FPS... aha but Im not a cardiologist! The AI can help with dose reduction using a variety of techniques whilst increasing image quality in both spatial and temporal domains :P

[@abdovitamins6331]

Hi Mr Brian I have got 2 questions : 1- Brightness gain is increased when the exposure to the input phosphor is INCREASED? IS it true or false? 2- what is the effect of increased tube voltage on the brightness gain? I am a little bit confused about the first one, so I will be grateful to you if you could clarify it. Thanks in advance.

[@HowRadiologyWorks]

Brightness gain is typically the flux gain times minification gain and doesn’t depend directly on exposure parameters like kVp

[@abdovitamins6331]

@@HowRadiologyWorks thanks a lot for you response, i deeply appreciate you help 🙏

[@abdullah7472]

Have you FRCR part 1 exam ? ..

[@abdovitamins6331]

@@abdullah7472 do you mean if I passed ?!

[@SuperSooner]

I think you need to educate the audience on machine learning and deep learning in radiology

[@HowRadiologyWorks]

Thanks Liberal, can you be a little more specific? What types of topics are you thinking? I have also been considering some of this content and would be interesting in your feedback

[@SuperSooner]

@@HowRadiologyWorks A lot of people are using deep learning for image denoising. However, before diving into deep learning, viewers of this channel should become familiar with image denoising using traditional spatial and frequency domain filtering. Matlab offers many traditional denoisers that are worth exploring. Additionally, Matlab has a deep learning toolbox that allows you to train a model using both noiseless and noisy images, which can be externally validated. This can help viewers gain a deeper understanding of both traditional and modern AI based approaches to image denoising.