Видео

Thank you!!

Thank you!

@@MRIPhysicsEXPLAINED I had no idea this channel is for physics specifically in the context of MRIs lol! Goes to show how important fourier is, i'm here for totally different reasons!

@@JR-uc3nk Haha glad you stuck with it, I was afraid the MRI Physics part would scare the people off who were just wanting to learn about Fourier Transforms when this really is more of a math lecture first and foremost that just happens to be essential to understanding MRI physics.

Danke schön!

❤️

I think we can make this happen my friend, just need a short break from the pulse sequences 🙂

Thanks for watching and commenting!

🤣 Thank you! I'm very excited to bring these next lectures to you all, hope they will be worth the wait and meet your expectations!

These are great questions! Is this coming from after seeing the newest lecture? If not, check it out now as we talk about this very issue! ruclips.net/video/ANUDUGg4F1c/видео.html

@ no I haven’t gotten that far yet. I guess something to look forward to! Thanks

Thank you my friend!

Hello, thanks for commenting and so glad to hear the video helped! A little hint since you're in 3rd year of residency... the topics in yellow in Lectures 1-7 are CORE testable facts so you can both study for CORE and learn some MRI physics along the way. Cheers and best of luck to the rest of training!

Thank you!

Awesome happy to hear it helped make it somewhat understandable! Good luck with residency and the core, actually started making these lectures R4 year when I realized I still knew nothing about MRI physics despite all that training 😂

Thank you 😂 Working on the next lecture as we speak!

@ cool I’ve got idea once I teach my junior colleagues about mri lol

@ which country are u from

@@grace_fitness USA

Thank you!!

Too kind, thanks for watching and commenting!

Wow thank you so much!! This lecture was one of the hardest to put together but so glad that we found a way to make it a little more digestible than textbooks. If you liked the technicality of this one then hold tight because the next set of lectures are going to be groundbreaking, stay tuned!

Thanks for watching and commenting!

Wow thanks so much! Stay tuned as the next lecture series we're really going to get into the math and show how the image building process truly works!

Glad they are helpful and thanks for commenting!

Ah too kind, thanks for the comment and happy the lecture helped!

Timestamp??

@ 32:15​ the ADC image shows the movement of Hydrogen atoms? Or ? @@MRIPhysicsEXPLAINED

​@@mars4free the ADC map is a purely mathematically calculated value of the average diffusion coefficient of water within each image voxel that we then assign a color scheme to, the higher the value the closer to white and the lower the closer to black. If you haven't already, check out the lecture on Diffusion Tensor Imaging which is a continuation of this lecture and where we create images that show both the magnitude of diffusing water molecules AND direction. ruclips.net/video/mbpUalR_Z3E/видео.html

Glad it helped!

Thank you!!

Thank you! My brain hurts equally as much making them haha, if you liked this one then definitely check out the latest on Diffusion Tensor Imaging which is a continuation of this one: ruclips.net/video/mbpUalR_Z3E/видео.html

Haha thanks a lot, too kind! Please feel free to share with any friends and check out the other lectures if you liked this one!

Always love hearing about the relativistic and quantum mechanics underpinning these simplified classical explanations! If you like getting into the weeds, the latest lecture is probably the most technical yet. Check it out here if you're interested! ruclips.net/video/mbpUalR_Z3E/видео.html

@kemchobhenchod You throw around a lot of jargon about QM but your explanation makes zero sense. In QM, the electrons do not "move" around the protons, they blip around according to a probability distribution given by Schrodinger equation (classical quantum) or Dirac equation (relativistic quantum). Guys, please don't trust anything you read on the internet without checking the facts, my comment included. Maxwell's equation is the classical description for FID, if you want to know why changing an electric field produces a magnetic field you have to use Quantum Electrodynamics. I am a physics PhD student working with MRI, here is some resource on Maxwell's equation if you care enough to read: engineering.purdue.edu/wcchew/ece604f19/Lecture%20Notes/Lect1.pdf

Lol thank you!

Thanks for commenting! Best of luck with your studies and please let me know if there is any way I can further help!

​@@MRIPhysicsEXPLAINEDparallel imaging lecture please

@@karthikraju6286 Will look into it!

So think of the RF pulse as the initial step to energize the system. This is what gets all those protons precessing together around the B0/Z axis. The slice select gradient is applied at the same time as the RF pulse so that we only energize the slice that we want to produce an image of (explained in ttps://ruclips.net/video/v8jW8K1y-KE/видео.html). It is only AFTER our RF pulse where the protons are precessing together that we can apply our frequency and phase encoding gradients (further explained in the frequency encoding lecture ttps://ruclips.net/video/DYj1SLNppQM/видео.html and phase encoding lecture ruclips.net/video/nFDzXvjF7gg/видео.html). Hope this helps!

Thanks for watching! New video coming this weekend!

These are good questions, and as always with MRI physics there are layers of ever finer details you will find the more specific you try to understand it. When we talk about precession in MRI physics, what we're really referring to is the precession of the net magnetic vector. Yes, the individual protons precess about their axis when put into a magnetic field. You don't even need an MRI machine, we're in a magnetic field right now on Earth. But does this generate signal in our MRI machine? So we need to get these individual precessing protons aligned, and with enough energy from our magnetic field, we can cause more to align with the field than the random orientations caused by the background kinetic energy. This is what builds our net magnetic vector, and it doesn't necessarily mean all the individual protons are in phase with each other, but there is a net magnetic vector due to their alignment. The RF pulse then tips all of these aligned protons off the B0 axis, and they precess together at the larmor frequency, generating our signal. It is the dephasing of the orientation that causes our signal loss. Check out this article for more information: www.mriquestions.com/how-does-b1-tip-m.html

Thanks for watching and commenting!

I’m just saying this as a fan so that you can improve your channel

Ha I will be the first to admit my animations are rudimentary and not the best, and this is a very difficult subject so not quite sure how to present this in a different way but the key point is if we're able to invert the spins with a strong enough RF pulse, the receiver coil will see a reversal of the spins that causes rephasing of our spins and signal. The screw example at the end is the most tangible example of this I could think of. This is just to get a grasp on what's going on before proceeding to the pulse sequences, starting with the spin-echo sequence ruclips.net/video/vK6PeCPpOLY/видео.html where we flush out this idea out a little more so maybe check out that video and see if it helps further clear up these concepts.

This is because we have changed the magnetic field across the x-axis so that the CSF voxel see a different field strength than the fat voxel, and remember that the Larmor frequency is directly related to the magnetic field strength for each voxel. Hope this helps!

Glad it was helpful!

Thanks for watching and commenting!

Hope it helped and thanks for commenting!

Thank you! Cheers!

Thanks for watching and commenting!

Thank you!

Thanks for commenting!

Such a nice comment, thanks so much and glad you are finding the videos helpful!

Thanks for watching!

The frequency localizing gradient is not a shift but an encoding of different frequencies along the x-axis. Since we create this gradient magnetic field, we know which frequencies should be where spatially along the frequency encoding axis, so we when we break our raw signal down into frequencies via the Fourier Transform, we get this information. Check out the dedicated frequency encoding lecture here for further info if you haven't already: ruclips.net/video/DYj1SLNppQM/видео.html