How MRI Works - Part 4 - The Gradient Recalled Echo (GRE)

How MRI Works - Part 4 - The Gradient Recalled Echo (GRE) MRI Sequence
Part 1 - NMR Basics: • How MRI Works - Part 1...
Part 2 - The Spin Echo: • How MRI Works - Part 2...
Part 3 - Fourier Theory and k-Space: • How MRI Works - Part 3...
Powerpoint slides from this lecture can be purchased here:
www.patreon.com/thePIRL769/sh...
0:00 - Intro
1:07 - NMR Review
2:14 - Laboratory/Rotating Reference Frames
4:18 - The Gradient Echo
7:28 - GRE Overview
10:52 - Scanner: B0 Magnet
12:57 - Scanner: Gradient Coils
16:45 - Scanner: RF Coil
19:23 - Slice Selection
26:46 - The Signal Equation
29:17 - Frequency Encoding
32:56 - Phase Encoding
36:20 - k-Space and Gradients
44:19 - k-Space and Signal
48:30 - The Gradient Recalled Echo Sequence
52:06 - Phase vs Frequency Encoding
55:42 - Echo Planar Imaging
56:23 - GRE Exercise and Outro
[1] mri-q.com/uploads/3/4/5/7/345...

[@thepirl903]

Error Correction: 20:19 - Δω is not 42.578 kHz, it's 4.2578 kHz (thanks to @johnclarky4375 for catching this!) 36:06 - My labels for 'phase encoding' and 'frequency encoding' are swapped. (thanks to @timangoes for this one!)

[@brucevanbeek3133]

I am here because my son is in MRI ! At a university. So, to understand his passion . I'm learning from these videos. The language and understanding of MRI. Thank you . 🎉

[@CollinKeegan]

Genuinely amazing how much commitment you have to finishing such an insanely high quality, detailed, easy to understand explanation on a relatively obscure topic. This is, across all sorts of topics, really one of the best KZbin channels I've ever seen for explaining complex topics in an understandable way.

[@lordtopkek2995]

You Sir, are a Scholar and a Gentleman!

[@someonesomeone529]

omg dude you are a legend. My journey with your channel started as I was a med student interested in spin dynamics, now I am a noob radiologist and just recently starting to work with MRI, you can not imagine how happy it made to see this video in notifications. Your videos are best explanation to MRI physics anywhere on internet, I just love it!

[@Frofelfel]

Thanks ! It's fantastic you are still completing this series after years...looking forward to more from you

[@Blueyzachary]

Yooo I immediately thought of string art being like CT scanners

[@user-xy7fw5qv6z]

this video is a fantastic for anyone interested in learning about MRI technology. Great work!

[@valentirodonfont2107]

Arguably, one of the best (if not THE best) Educational Series I have ever seen in my entire + 50 life and BSc Industrial Engineering background. Its painstaking attention to detail, the beauty and virtuosity in all the curated visuals, its deep-dive yet easy-to-understand explanations... a top-level Educational Masterpiece, in a word. My sincerest congratulations and admiration thus to the generous and talented Artist behind this Series. Just can't wait for the next one!

[@paramgoyal2860]

Best videos. Have watched this video 6 times by now. I am starting to get it. The maths part between 30-45 minutes needs some work from me to fully get.

[@TEMMIE-hu4uz]

Your series on MRI is absolutely brilliant, and this episode in particular is especially amazing considering the depth you go into whilst maintaining a clear and coherent train of thought. So happy to see you still uploading :D

[@user-mo1oe7le1r]

This is so far the best MRI lecture I have ever found. I am a first year resident in radiology and have been struggling to understand the complicated concept in MRI for a long time. These fantastic videos really helps me out! Thanks a lot dude!

[@matejnastran9586]

Thank you for this wonderful series. I hope to see more from you. I loved how you didn't simply neglect the physical coil design.

[@thelosc2]

I am really looking forward to MRI part 5. All the possible topics you mentioned at the end sound fascinating!

[@thelosc2]

Stimulated echos and EPGs would be nice

[@carson7928]

As a graduate student in medical physics, I just have to say this is one of the best lecture series I've come across (on par with 3Blue1Brown). Thank you for enhancing our learning experience. Hope to see more videos in the future!

[@MRIPhysicsEXPLAINED]

Top notch lecture, one of the best out there on K-Space and image formation!

[@je_tai6276]

You are an absolute genius in vulgarization and on top of it you are extremely talented for making animations which help tremendously in the visualization and understanding of such advanced subjects. Thank you so much for continuing this series of video after the years.

[@OllieAndLuna-me7js]

Your lecture series so far is just outstanding! Please do not stop creating this content! This is a true gift to the MRI community!

[@lopzag]

Incredible video, really impressed by the clarity of explanation.

[@zainraza6389]

I will hate who ever will push dislike on these videos, they might be my greatest enemy.

[@vma011]

Quality and detail are INSANE! Thanks so much for sharing

[@MrSylthas]

I build a company in the ultra low field MRI space and your videos are an invaluable source of educational content for me and my teammates! Thank you so much, can't wait to see what's next. Diffusion please :)

[@seekingtruth1315]

Amazing! The best video for MRI introduction on KZbin.

[@rodolfoassereto7626]

Please, keep this up. This is one of the best channels I've ever seen (I would go as far as saying that you should explain more general stuff: for example, your explanations of the Fourier transform are one of the best I have seen on the internet)

[@TAHA-TOUABA-RADIOGRAPHER]

Please make more videos like this

[@tictactoe101]

Super fantastic mri series! Keep going ❤❤

[@gabrielpetre3569]

Best mri stuff on yt.

[@zenmark42]

This is really wonderful stuff! I really appreciate how much work you're putting into these. Thank you!!!!!!

[@YZhang-ms8hy]

Thanks for the series, very well made and extremely useful.

[@Violet-fe8cy]

Thank you for these videos - much clearer explanation than I was able to find elsewhere! One suggestion: while the background music makes things entertaining for our neurons, there were times when I genuinely found it distracting as I tried hard to focus on the details - sometimes a less shiny video is easier to understand!

[@emersonchaves567]

This is the best series on MRI and I'm thankful for you keeping this series going even after years. I have recommended your videos to many radiologists and technicians. Keep going, you have so much potential with educational content.

[@0siris85]

I have 1 year left to obtain my MRI certification. If you keep explaining MRI the way you have been in this series.... I think I have a shot!

[@ghchd159]

Hey there! I am eagerly awaiting your next installment, particularly in the topic of different imaging modalities, as I've been looking for a comprehensive, intuitive approach to understanding them. This way I can internalize the modalities/sequences by knowing the underlying mechanism, making it much more satisfying and memorable. Thank you for everything you've been doing. You found an excellent method to explain what actually happens when someone undergoes a scan.

[@danielvanderveen1523]

Awesome job! This has helped my studying significantly. I am following a slightly broader course on MRI but this video provides much of the intuitive understanding you need to grasp a lot of related concepts. Wonderful animations as well!

[@rodas4yt137]

Thanks for your beautiful videos.

[@francismanno3822]

The best!!!! Great content and visual dialogue. Use to study MRSE exam.

[@tobiascampos5629]

Best video series!

[@xeramainali9270]

Yes, please make videos on CT and reconstruction.

[@PMaldeev]

Great work!

[@tanchienhao]

Thank you for this masterpiece!

[@akekalukardjnaphai464]

You’re the best for MRI lecture ❤🎉

[@jianzhangni4361]

Amazing stuff!🎉

[@777seven777]

Incredible videos and series! You're the 3B1B of MRI! If you can do it, the video about CT imaging would be great, also.😅

[@wesslesyt3304]

Someone give this man a raise

[@wesslesyt3304]

Yes!! He’s back!

[@stephendrury7692]

Bravo sir! Its like 3blue1brown for MRI. Great work!

[@TAHA-TOUABA-RADIOGRAPHER]

Thank you so much pro

[@mihaimoldovan7291]

oh boy! Christmas in August.

[@nccastro653]

PLEASE MOREEEE!

[@luc7478]

Thank you so much,

[@TheOneAndOnlyOuuo]

Diffusion tensor imaging and fMRI next for sure!

[@davidvela2383]

This is beautiful

[@sanawarhussain]

nice, i will do my phD thesis on your video....

[@giuseppefusco1189]

I. Was. Waiting! 🎉🎉🎉

[@The.life-long.learner]

So does MRI recieves the signal from each say cm2 of the 2nd slice, and then it produces the image, or it just recieves the overall sum and use FFT to differentiate. If it does how does it know the exact location from which the signal is coming? Please answer this. Btw I dont normally comment on videos, but man this is exceptional content. Its very sad that useless channels get 10 or more million subs and views but people like you get less. It shows the reality and useless aims of people's mind. I just dont know how to show gratitude. Hands down one of the BEST youtube channels i have seen. I have a doubt i dont know if you see this but here it goes.

[@thepirl903]

The MRI detects the vector sum signal from all excited spins which the gradients encode, then the FFT transforms to an image. In the gradient echo sequence, the process is done one slice at a time. The scanner does not 'know' where the signal is coming from - but the different frequencies contained in the detected signal are known to have originated from specific locations. Hope that helps? Cheers

[@The.life-long.learner]

@@thepirl903 Ok, but it still did not answer my question (or that im dumb!) But let me clarify my question first. Lets keep it simple and say from different tissues you get signals of different frequencies. Now, suppose we are imaging the kidney. From the renal medulla, a short frequency signal is coming, and from the cortex a long frequency signal is coming. Using FFT you can seperate the frequencies and know that you are scanning two different tissues (lets juts say medulla and cortex is the only parts of kidney). For eg how does it know than renal medulla beneath or below the renal cortex?. How does it correctly 'position' the different tissues or parts in an organ so complex as brain? I understand you will get the information of how many different tissues are present, but how will you correctly postion that different tissues. For another example, im saying to you, suppose you are an artist, that there are 4 different types of tissues in an organ, apart from you use different colours for different tissues, how will you correctly draw the organ with correct position. Hope that is not lengthy!.

[@thepirl903]

Hmm, ok I think I understand better your question, but we'll see. So the location of the separate tissues signals are indeed teased apart by the FFT in order to localize them in the image. The positions of tissues in space are directly mapped to frequencies by the larmor equation Δω=γGΔx. So the individual tissue signals can be precisely mapped in space. The MRI of course doesn't see 'tissue', it only sees protons (hydrogen nuclei). The way tissues are differentiated comes from their different nuclear relaxation properties (T1,T2, etc.), thus the brightness of a voxel reflects these contrast mechanisms, which in turn reflect the different properties of the tissue. Not sure if that helps at all?..

[@The.life-long.learner]

@@thepirl903 Thanks, that helps, so in short, larmor equation maps it. Btw I really appreciate the long detailed videos, exactly what i love. I dont know how to help or say thanks. Im just 15 years old, and you are really an inspiration! May I know what is your proffession?

[@saragh1266]

Thank you very much for the insightful lecture! Could you kindly share the solutions to the exercise presented at the end of the video? I'd like to cross-reference my responses with the correct ones. Thanks again!

[@DontDisturbMyCircles]

Amazing content! Are there any plans to make the code used to generate these graphics and animations available on github? Would love to be able to play with the simulations to get a hands-on feel for MRI signal acquisition.

[@timangoes]

@36:12 , it seems that the labeled exponentials for phase encoding and frequency encoding are flipped? based on the preceding sections, frequency encoding should be the one with time dependence and the phase encoding term should be the one where the gradient is only turned on for some time tau

[@thepirl903]

Arg, you are correct. I did swap those. Thanks for catching!

[@josephorr1078]

I use a Siemens Prisma (with 80/200 gradients) and we recently acquired a next gen Siemens Cima which has 200/200 gradients. Does the benefit of the higher gradient strength come from better separation of the phases at each end of FoV, thus yielding greater contrast?

[@thepirl903]

There's a few benefits to higher maximum gradient amplitudes including faster traversal of kSpace (good for collecting signal of short T2* samples, and rapid imaging of the heart) and ability to get to the kSpace edges faster (better resolution), better diffusion weighting (can acheive higher b-values in same time), fewer susceptability artifacts. Though higher gradients strengths can be louder and are more likely to cause peripheral nerve stimulation in some sequences. Cheers!

[@impossiblephysix2633]

How did you animate this? It is absolutely Amazing

[@YZhang-ms8hy]

see you people in another one year

[@johnclarky4375]

Minute 21:15, when I calculate delta omega i get 4.2578 kHz. Where am I missing the faktor 10?

[@thepirl903]

Oh wow. You're absolutlely right, the bandwidth is 4.258 kHz, not 42.58 MHz. Good catch! A bit embarassing that not once in making the slide, writing the script, and recording it did the abnormally high bandwidth seem odd, ha.Thanks!

[@georgychistov2103]

On the 28:47 you tell about explicit Fourier transform. I tried to understand it and it seems you are not mentioning something. Fourier transform (and the same is true for RFT) of function with two variables should be a function with two variables - F(u,v) = fft(F(x,y)) or f(x,y) = rfft(F(u,v)). In your example, you have S(t) = rfft(M(x,y)). How is it possible? Where the second variable in S(t)?

[@georgychistov2103]

And THANK YOU FOR THE LECTURES!!! With videos and animation mathematics starts becoming less complicated. Really helpful

[@georgychistov2103]

I found the answer in one book. They say "let's remember that our gradient in y-direction is a function of repetition Gy=Ginit*n. After substitution we get that our signal is function of time and repetition S=f(t,n) and n*tau=pseudo time, where tau is a time of application of Gy"

[@thepirl903]

Yes, exactly, the signal recorded is a function of both time S(t) and k-Space location S(kx,ky). The connection between the two come from the gradients which determine our kx, ky locations and are themselves functions of time. Hope that helps!

[@AimonQazi123456789]

Can we get a physics video fully explaining the flip angle in part 1 of this series around 14min please?

[@thepirl903]

Yes, I will do this at some point. That explanation isn't my favorite of the series, and I think it confuses people more than anything. Probably the best resource for QM treatment of flip angle (imho) is Slichter's Principles of Magnetic Resonance section 2.6 (www.google.com/books/edition/Principles_of_Magnetic_Resonance/pWzrCAAAQBAJ?hl=en&gbpv=0)

[@AimonQazi123456789]

@@thepirl903 Thank you so much! I appreciate it (:

[@mihaimoldo]

Hell no. Keep making these MRI videos because the subject is vast and there's much to learn. CT and X-rays had their eras...in the 90s. Now its MR Time!

[@GeoffryGifari]

dare to delve deep!

[@snowron7497]

[@Jacob-ye7gu]

as someone who already knew the basics of mri that magnetic field strength and phase are varied along the axes to encode location, I can say that this video did nothing but obfuscate the concepts. it seems you are more interested in making fancy looking equations than explaining the concept, clearly exemplified by using an integral to denote a simple linear equation for phase as a function of a linear dimension. i will have to come back to see if this video is useful as a way to solidify knowledge one already has, because it certainly is a terrible way to learn this stuff