Aliasing (Wraparound) Artifact and Parallel Imaging in MRI | MRI Physics Course #13
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- เผยแพร่เมื่อ 9 ส.ค. 2023
- High yield radiology physics past paper questions with video answers
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We've seen how selecting a specific bandwidth and field of view size determines the gradient field strength (in the frequency encoding direction) and delta k in the phase encoding direction. These field strengths determine the frequency changes in both the x and y axes.
Let's look at what happens if tissue extends beyond the predetermined FOV and we are unable to accurately sample these signals. This misrepresentation of the sampled signals will lead to an artifact known as aliasing or wraparound artifact.
Here we review what aliasing is, why it happens and what we can do to reduce or prevent it occurring.
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If you're here, you're likely studying for a radiology physics exam. I've spent the last few months collating past papers from multiple different countries selecting the most commonly asked questions. You'll be surprised how often questions repeat themselves!
The types of questions asked in FRCR, RANZCR AIT, ARRT, FC Rad Diag (SA), ABR qualifying Core Physics and MICR part 1 are surprisingly similar and the key concepts remain the same throughout. I've taken the most high-yield questions and answered them in video format so that I can take you through why certain answers are correct and others are not.
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Michael
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Just a note: Adding time with multiple phase encodings for oversampling is more of a problem for introducing more patient motion in the longer scan than it is the department being busy. Motion artifact is the #1 artifact in MRI.
Your videos are very good, and I'm sharing them with MRI tech students as I teach them in department. Your animations are what really take a step over previously existing content. Comes through in a way a textbook just can't.
Thank you so much. Really appreciate your input. Whilst editing this I said to my wife I was so bleak I forgot to mention motion 😅 Difficult when filming in one take! Will pin this comment so that those watching can see your point. Thank you for sharing the videos with the MRI techs 😊
@@radiologytutorials You do a nice job for not having an audience that might ask a question that reminds you of a topic or point you were prepared for but just didn't touch on. But that's also what makes your content short enough for the average viewer to digest, and maybe even multiple views.
If you get into time of flight imaging in the future it could be an easy transition to discuss motion as a whole since that is desirable motion? Or in pulse sequence sections discussing single shot imaging, since the purpose of that is to overcome inherent patient motion? Either way, looking forward to more content when you get it out!
Cheers!
Thanks a lot sir Michael,,,, we were waiting for so long time, stay blessed 🎉
Pleasure Fazal 👍🏼
Hats off to you 👏
I do appreciate your effort and the way you explain to a degree not maths complicated but enough to be really satisfied 🙏
Thank you. I've tried to steer clear of math and focus on the concepts. I'm glad you are finding them helpful!
Excellent content ! Thank you for the efforts to make it this clear !
thank you for this!!!! Hope the series is complete. prior to Sep exams.
My pleasure! I’ll try my best 😣 but definitely won’t be getting anywhere near CT/nuc med by then 😅
Amazing video, thank you so much, I’m in love with your channel
Thank you! I really appreciate it. So glad its helpful!
Thank you this amazing introduction
@17:30
i just wanted to highlight that from what I know (please correct me if I am wrong) , the signal adc receive from the excited slice is not different sinosoids along the encoding direction as depicted in the image but rather a sum of all of those signals that's why we apply inverse Fourier transform to extract the sinosoids of different frequencies and phases. and based on the different frequencies and phases we are able to know where along x-y axis the signal is coming from (localization). In case of frequency encoding aliasing accurs because the number of sinosoids with distinct frequencies we can extract form the singal dependends on the sampling rate of the original signal. any sinosoid of the frequency higher than the nyquist border will be thought of as a sinusoid signal of lower frequency and will be miss represented on along the frequency encoding axis.
so for a more accurate depiction it would be great to demonstrate the concepts of aliasing by showing the complex signal sampled by the adc and the extracted harmonics and how those higher harmonics will be aliased due to the low sampling rate
This is absolutely correct. In this course I have steered clear of the concepts of complex signal (ie the real and imaginary components of MRI signal) as it is meant to be a basic introduction to MRI. I will in future do a more detailed series focussing on the mathematics - especially regarding k space and Fourier transform as the explanations in this course have been purposefully kept basic 🙂
Thankyou sir wait for it so long ❤❤
Sorry for the wait!
Amazing🎉
Thank you Sahista 🙏🏻
Great sir🎉🎉
Thank you!
Michael sir love from India..❤
Abishek! Love from South Africa 🇿🇦
Thankyou micheal
🤗
Great sir
Thank you!
excellent
Thank you!
Thankyou sir❤
Thanks for watching Gajala ☺️
¡Gracias!
Thank you! That's very kind of you 😊
Thankyou sir
It’s my pleasure 🙂
Thanks
Thank you ☺️ much appreciated 🙏🏻
thankyou sir
My pleasure!
Great conceptual video! I am not sure if you still reply but in case, do you have recommendations for sources that do go into the mathematics of parallel imaging?
Thank you!!! I am reading a textbook that mentions saturation (pre-saturation) could also help in reducing aliasing, in order to eliminate those "outside" artifacts. What are your thoughts? Thank you again for all these vids, they are life-saving as I prepare for boards.
Hi, Thanks for your great tutorial. I think there is one mistake about "oversampling" at the Y axis (phase encoding) in your slides. For this case, we need to make "delta Gradient finer and finer" (the height should be the same and more dense) instead of "keep the same delta Gradient and add more 'black' Phase Gradient" (same density and higher). Check the top timeline of PEG signal at "23:58".
For your drawing, only space resolution is improved but Aliasing/FOV is still the same.
What is the difference between Phase oversampling vs Slice oversampling?
Hello sir. Sir I have a Satish bhargav MCQ book and there is a question that says . "During the acquisition of axial images of the body with the frequency direction L/R, phase encoding gradient is performed by which physical gradient. The answer which is given in this book is Z. But according to what you taught it should happen in Y axis. So could you please clear my doubt.
Hurry up and do pulse sequences before I take my boards in a month 😂😂
Are you understand previous lectures are you kidding me😂😂
@@sohailkohri7269 are you actually kidding me? There's more than T1 and T2. There is inversion recovery, gradient echoes, diffusion, and many more. I'd like to see more of these.
@@bee.x3 okk👍
Pulse sequences start next week. Can't wait to share them with you! Releasing chemical shift video now. Then sequences, I promise!
@radiologytutorials you're a blissful man. Love your content keep it up!!!
What is the difference between how many boxes we have and how many frequencies we have? Doesnt the bandwith determine the resolution?
The number of times we sample the signal during the frequency encoding gradient will determine the maximum resolution (number of boxes/pixels/matrix size). Bandwidth determines the range of frequencies along the slice. The number of samples we take determines how accurately we can split those frequencies up to locate signal on the x axis (using a 1D Fourier transformation). Hope that makes some sense 🙂
Thanks! Do you know maybe how sampling every second row or column in k space would effect aliasing?*
@@radiologytutorials
The aliasing artefact, is it similar to truncation artefact?
Not quite. The truncation artifact occurs as a result of mathematical errors in the inverse Fourier transform of k-space and results in bands showing up in the final image. This occurs when there are abrupt changes in signal close together in the image (ie brain and skull interface). Too complex to explain fully here. Perhaps in a future video 🙂
when is next video plz
Thankyou micheal
Cheers!
Thankyou micheal
My pleasure Sayra ☺️