Diffusion Tensor Imaging (DTI)

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  • เผยแพร่เมื่อ 11 ก.ค. 2024
  • Describes and demonstrates the MR technique of Diffusion Tensor Imaging and reviews some of the basic mathematics of Tensors including matrix multiplication, eigenvalues and eigenvectors.

ความคิดเห็น • 49

  • @AryaPhoenix
    @AryaPhoenix 3 ปีที่แล้ว +21

    Never heard a more clear and down to the point explanation of these matrix algebra concepts!

  • @alfonsotoriz7563
    @alfonsotoriz7563 3 ปีที่แล้ว +1

    Marvellous explanation, Dr. Klioze. Thanks to you, I've finally been able to grasp DWI and DTI key concepts.

  • @dimitheodoro
    @dimitheodoro 4 ปีที่แล้ว +8

    Excellent!!! Finally, Tensors seem friendly to me thanks to your inspirational video!

  • @treywest268
    @treywest268 ปีที่แล้ว +2

    I am not a doctor, nor am I very strong at higher levels of mathematics, I must say though that this video has been very informative to to me and easy to follow and understand a complicated subject almost down to layman's terms.
    I have been very interested in DTI since I have been on a journey of discovery about myself.
    I am 60 years old and only in the past 1y 6m have realized that I am very likely to be on the ASD. I have found out that DTI's can be used to reveal autism in children with a high precision of accuracy. I am hoping that those same stats will hold true for an older adult.
    I have been in the process of finding a doctor and a facility that performs such testing.
    Thank you for this video as it has given me a bit more hope that other medical professionals will see it and find out that it is an extremely safe, non-invasive way to study the inner workings of one individuals brain.
    Thank you again!!!

  • @drpowerdoppler4620
    @drpowerdoppler4620 2 ปีที่แล้ว

    This mini class.... just awesome. THANK YOU!

  • @LD-vi5ls
    @LD-vi5ls 2 ปีที่แล้ว

    Thank you so much! I was struggling so much with understanding of DTI. Your video saved me! 👏

  • @SF-fb6lv
    @SF-fb6lv 3 ปีที่แล้ว

    You are the best! Great tensor explanation!

  • @zzzeina3566
    @zzzeina3566 2 ปีที่แล้ว

    This video was incredible, thank you so much!

  • @PoetryVistas
    @PoetryVistas 5 ปีที่แล้ว +1

    Bravo! Fantastic explanation!

  • @asifhaiderelhan
    @asifhaiderelhan 3 ปีที่แล้ว

    Excellent detailed presentation!

  • @yeahzisue
    @yeahzisue 4 ปีที่แล้ว +1

    the most underrated youtube video !

  • @iosuegea9607
    @iosuegea9607 4 ปีที่แล้ว +1

    You explained it perfectly. Thank you

  • @bhuvaneshs.k638
    @bhuvaneshs.k638 5 ปีที่แล้ว +2

    U just nailed it... Awesome video thqs 👍🏻

  • @gabrielaraleva2900
    @gabrielaraleva2900 4 ปีที่แล้ว

    This is extremely helpful, thanks a lot :)

  • @brettruss3359
    @brettruss3359 4 ปีที่แล้ว +1

    amazing content, thank you!

  • @AnilKumar-iy6hr
    @AnilKumar-iy6hr ปีที่แล้ว

    Nice explanation! thanks for the video!

  • @maxwellchen5952
    @maxwellchen5952 4 ปีที่แล้ว +1

    amazing video!

  • @federicoortega5664
    @federicoortega5664 3 ปีที่แล้ว

    This is just... so awesome.

  • @alirezaei447
    @alirezaei447 4 ปีที่แล้ว

    very informative, good job!

  • @volverearound6445
    @volverearound6445 3 ปีที่แล้ว

    Thanks! looking forward to the explanation of HARDI!

  • @Charlotamy
    @Charlotamy 5 ปีที่แล้ว +1

    Amazing, thank you!

  • @dr.monikasati1427
    @dr.monikasati1427 4 ปีที่แล้ว +1

    Thank you so much it's very helpful for me 😊

  • @rostammolaee7885
    @rostammolaee7885 3 ปีที่แล้ว

    So amazing Doctor

  • @lakshikasewwandi6151
    @lakshikasewwandi6151 3 ปีที่แล้ว +1

    I understood fundamental things and it's very useful video 👍👍

  • @hchattaway
    @hchattaway ปีที่แล้ว

    ok, where were you 40 years ago when i took discrete mathematics in college? Where!? Matrix Algebra was wicked confusing and you made it simple along with an application that is totally remarkable and bordering on magic... Thanks!

  • @manonlevayer6035
    @manonlevayer6035 2 ปีที่แล้ว +1

    Hi Dr Klioze! Thank you so much for your videos, they are so helpful! I was wondering if you had plans to make one for Quantitative Susceptibility Mapping? :)

  • @MrJemoeder1990
    @MrJemoeder1990 3 ปีที่แล้ว

    Amazing video

  • @austincooper5132
    @austincooper5132 3 ปีที่แล้ว

    Absolutely fantastic content. Where'd you learn all of these computational illustration skills Dr. Klioze?

  • @user-ee2jl6uu1d
    @user-ee2jl6uu1d 3 ปีที่แล้ว

    BEST. CHANNEL. EVER

  • @evberger-wolf6219
    @evberger-wolf6219 4 ปีที่แล้ว +2

    Thank you for helping me to understand concepts and for saving my grade in my first year seminar :)

  • @drallisimo34
    @drallisimo34 2 ปีที่แล้ว

    very explanatory tutorial on DTI

  • @melanygomez5028
    @melanygomez5028 ปีที่แล้ว

    Hi, thank you for this video. Do you think this method could be used to study the neural origin and mechanism of phantom limp pain as it could possible show us the flow of information between different neural networks?

  • @isacahasverus7434
    @isacahasverus7434 ปีที่แล้ว

    Thanks for the great class. A question: are the eigenvalues of matrix 5,7,3 correct? Redoing the calculations I found 5, -2 and -1.

  • @deyumai
    @deyumai 3 ปีที่แล้ว

    I wish I could have seen this earlier.

  • @ruthven78
    @ruthven78 5 ปีที่แล้ว +2

    just wish we could get insurances to cover these better for TBI survivors like myself. injury decades where MRI and CT are normal but QEEG showing deficit

  • @tamamalenazi4682
    @tamamalenazi4682 5 ปีที่แล้ว +1

    YOU ARE THE BEST>>>>>>>>>>>3

  • @faprakaisomwan8843
    @faprakaisomwan8843 2 ปีที่แล้ว

    thank you

  • @Devan1191
    @Devan1191 5 ปีที่แล้ว +2

    Great video! Does DTI only show white matter tracts or can it show grey matter direction as well?

    • @DoctorKlioze
      @DoctorKlioze  5 ปีที่แล้ว +7

      The grey matter is basically the axon cell bodies which, if normal would demonstrate isotropic diffusion. Since the flow is symmetric in all directions, tensor analysis would basically give you a spherical ellipsoid which really would be meaningless in a tractography reconstruction. Besides, we can see the pathology of the grey matter very well with standard MR techniques. Remember, with tensor analysis we're demonstrating the anisotropic diffusion of hydrogen protons through the brain which would infer the path of the axon bundles. Does that make sense?

    • @Devan1191
      @Devan1191 5 ปีที่แล้ว

      Yes thank you.

  • @oliver_siegel
    @oliver_siegel 4 ปีที่แล้ว +1

    Bruh... 🤯 Needs a link or annotation to the first video that's referenced tho

  • @tomislavzg742
    @tomislavzg742 11 หลายเดือนก่อน

    Can water diffusion explain how reflexology work? For example, if someone massage my foot, I feel it in my brain. Maybe dynamic of water in the brain is affected when we do a pressure on water diffusion fibers in foot.

  • @bluelionpuza206
    @bluelionpuza206 2 ปีที่แล้ว

    omg, i understood eigenvector.

  • @tomislavzg742
    @tomislavzg742 11 หลายเดือนก่อน

    Is there any disease in which water starts to recede from some body structures?

    • @DoctorKlioze
      @DoctorKlioze  11 หลายเดือนก่อน +1

      There are many factors that affect regional tissue fluid content including your state of hydration, the integrity of the arteries and veins carrying blood to and from the regional tissues, a functional lymphatic system and abnormal oncotic pressures (various solutes dissolved in the blood like proteins and minerals that draw fluid into the vessels for recirculation and eventual elimination from the body by the kidneys).
      So, the short answer would be no. Not a specific disease but a combination of factors could definitely affect regional tissue fluid content. Does that answer your question?

    • @tomislavzg742
      @tomislavzg742 11 หลายเดือนก่อน

      @@DoctorKlioze Yes, thank you.

  • @mattis1307
    @mattis1307 3 ปีที่แล้ว

    4:58

  • @Luosofthesoul
    @Luosofthesoul 3 ปีที่แล้ว +1

    M for Monster truck took me out

  • @tomislavzg742
    @tomislavzg742 ปีที่แล้ว

    I bet whole body is structured from fibers like in picture on 2:10