[User] qmt-spgr number of pre-pulses

[tanguyduval]

Email received

We have been working the qMR software and have found it very helpful for the qMT; thanks to all of you!
We are trying to obtain qMT maps of the calf muscle.
I have a question on the input:
For the number of MT pulses, I believe it is the number of pre-pulses used to achieve steady state? The sequence we are using does not have pre-pulses but we are using a 3D SPGR sequence (128x128x16 partitions): As the centre of k-space defines the contrast, would it be equivalent to having =128x8=1024 MT pre-pulses (we start . I am in the process of modifying the sequence to have dummy repeats but wanted to clarify before hand what the number of MT pulses stood for in the qMR GUI.

Yes, that is correct, # of MT pulses is the number of pre-pulses used to achieve steady state. Usually a dummy scan is performed prior to the scanning.
Without dummy scans, you have an unstable image while filling your k-space --> blurring, signal dispersion.
See, for instance, this paper for some illustrations:

I see your point about the center of the kspace being after 1024 pulses. Some clarifications about the # of MT pulses:

• The analytical solution DOES NOT handle this parameter. Basically in the fitting; the transient phase is not handle and we assume that you are in steady state for the entire readout.
• This parameter is used only for block equations used in the simulations. Since the simulation only support a single voxel (we do not simulate kspace filling), there is no readout. Note that this simulation can be used to test how many dummy scans you need 😃

Some simulation with kspace filling are required to investigate the impact of the transient phase. If you do not see any artefacts and signal in the background, and if the fitting looks good (especially the first volume), you might be right that dummy scans are not necessary. But I cannot advise you to do so...
I would recommend to skip the first volume (assuming that all volumes have been acquired in a row). Or, as you said... redo the experiments.

Best,
Tanguy

[tanguyduval]

New question:

We are trying to derive qMT parameters for lower leg (calf muscle) : we have still not implemented the prep pulses that we discussed before but think that may offer some improvement but not too much. We are acquiring 14 data points (flip angles and offset frequencies in enclosed screenshot).

we fixed both R1f to acquired R1map and T2f through fixing R1f*T2f (constraining these two parameters makes the fit better and we get less noisy fits and parametric images). However, the value of F is 2.86% vs the value of 8% found in other studies: we are not using a B0 map: this may affect F as well but dont expect that to be too much.

There might be many reasons why you obtain such small values for F. I would say that field maps errors and poor spoiling are the most probable. Their effects on the parameter F could be simulated. @mathieuboudreau is certainly more used to qmt spgr dataset than I am and could help you find the issue in your dataset. @mathieuboudreau What are your thoughts??

Couple of other questions:

1. Do you have a B1 smoothing software in your qMR ?

The preprocessing is not included in qMRLab. However, you can run the following command in your Matlab Command Window to create the file B1_smooth.nii.gz:

B1 = 'B1.nii.gz';
save_nii_v2(smooth3(load_nii_data(B1)),'B1_smooth.nii.gz',B1);

2. We are now deriving the T1 map for MP2RAGE from the direct source: I saw on the GitHub discussions that you may have integrated the T1 map calculations from MP2RAGE

Sorry the MP2rage module is not ready yet... see #255 (still open issue).

Best,
Tanguy

[mathieuboudreau]

Thanks @tanguyduval !

A few quick thoughts:

1 - To my knowledge, fixing R1f*T2f = constant has only been done in studies using the Yarnykh model, I'm not sure how it will behave with other ones.
2 - The constant that Yarnykh found for that ratio may be tissue dependent, I'm not sure. I would ask the authors to look at the muscle literature to see if this constant is appropriate for them (as they mention other papers that have qMT values, simply multiply their R1f to their T2f from those papers); I'm assuming they did this already though, since their values are different from our default (0.055).. Even then, Yarnykh uses a range of different values for this "constant" in their studies (0.055 in 2004 at 1.5T, 0.022 in 2012 at 3.0T), so it's likely field dependent amongst other things, which is partly why I've never been keen on using this option myself. P.S. We should probably change the default value to the 3.0T Yarnykh one.
3 - Most of my experience has been with the Sled model. However, in that case, I've often seen F being severely biased by T1obs/R1obs (e.g. see Figure 2 of my B1 sensitivity paper of qMT paper). I would recommend the authors to check that their T1obs/R1obs are close to values others have seen in that tissue in the literature, and if not, to check how the F values changes if the T1/R1 is recalibrated.
4- At first glance, my gut also tells me that B0 won't affect F too much, but I haven't looked at this before. They could test by creating a fake B0 map that's "flat" (all same values), and adjusting the value to see how it affect F.
5- I think my main advice, since they seem to have access to literature values, would be to use those values and the qMRLab single voxel fit simulation tool to simulate what the signal curves should look like, as there could be pulse sequence issues as well. I've been told by other people that qMT can be sensitive to the filtering of the Gaussian pulse, which is typically Hamming-filtered I believe.

Last notes, MP2RAGE is going to get working on in the coming months, and a B1 filtering module is currently being worked on by @ileppe in #135

[mathieuboudreau]

I can't see what their R1obs value is from their second image, but assuming that it's very close to R1f, it might be a bit low (1.28 s vs ~1.5 for Sinclair et al 2010), but I don't know how fixing R1f*T2f and/or using the Ramani model impacts the sensitivity of F to R1obs (for me, not fixing it and using Sled, underestimating T1 resulted in overestimating F, which is the opposite that the authors might be experiencing).

[spinicist]

I agree with @mathieuboudreau's points. The ratio/product of R1f/T2f that Yarnykh has used was derived for brain, I see no reason for it to be the same in muscle. B0 maps should have only a small impact on F, if it was having an effect you would see variations in F that resembled the B0 map.

However I have an additional theory that MP2RAGE is likely not appropriate to use to measure R1obs for qMT. I have not seen a comparison of MP2RAGE and VFA, but every T1 method disagrees so I strongly suspect that MP2RAGE disagrees with VFA. I have also not seen the MT effect of MP2RAGE investigated or quantified.

Hence I'd recommend sticking with VFA, with similar parameters to the qMT experiment itself, to get the R1obs measurement.

[usinha2]

git-hub-comment1.docx

[mathieuboudreau]

Thanks @usinha2 !

For everyone's convenience, here is what the document says:

Thanks for all your comments. I just registered on GitHub and saw these comments.

To give a little background on our muscle qMT work:

1. Initially we used the same 3DSPGR sequence for T1 mapping as we do for the qMT data acquisitions (a standard 3DFLASH Siemens sequence---the screen capture from my original query provides the offsets and FAs of the MT pulse); the VFA method used TR= 25 ms, and three FAs= 4, 10, 25. With the VFA method, T1 values were constantly underestimated from reported values using either VFA or FSE-IR methods either with B1 correction and this underestimation increased after applying the B1 correction (e.g. typical values using VFA reported for muscle are ~1350 to 1500 ms: for us the values were in the range of 900- 1100 ms, which with B1 correction went down to ~700 ms). We tried several combinations of TR and several flip angles for VFA, but the T1 values continued to be underestimated. Our values of ‘f’ were small at the underestimated T1 values from VFA (2-3% compared to the 8% reported).
2. Then we switched to MP2RAGE as it gave us T1 values closer to that reported for muscle. It was disappointing that VFA with B1 correction gave us values that were so much underestimated (if anything literature tells that VFA overestimates T1). I am now evaluating a T1 Look Locker sequence for T1 (but having read your paper, it appears that B1 dependent methods like VFA of T1 mapping provides more robust estimates of ‘f’ in the presence B1 variations than B1 independent T1 mapping methods.
3. The 3D SPGR sequences (for qMT or for VFA) used here have both RF phase cycling as well as gradient spoiling (spoilers applied after the data acquisition at the end of each TR) to spoil the transverse magnetization. These are the standard spoiling methods provided by Siemens. We don’t have spoiler gradients after the MT pulse (between the MT pulse and the readout RF pulse—is this necessary?). Currently we don’t have any dummy RF cycles before the actual start of the acquisition, but we are working on including these prep pulses.
4. Our method for B1 mapping is a custom sequence provided by Siemens which is a saturation prepared turboFLASH: it is slice selective but they have the saturation pulse at twice the slice thickness so that slice profile effects are not an issue in the B1 calibration. I was thinking that the AFI method which uses the same 3D SPGR as the VFA/ qMT sequence may generate a more accurate B1 map for VFA correction (I see though that you consider the DA method as a reference standard and I am thinking of trying that as well for B1 mapping).
5. In the last fit shown, we constrained R1f to the R1 map (from MP2RAGE) and the product of R1f*T2f to 0.0174 (latter product from the muscle literature: we took the average of all the subjects reported in the Sinclair study). The reason to fix the product was that T2f was not close to the values for muscle and the fits were noisy—since this constraint is specific to the Yaraynkh model, we will be dropping this from future fits.
6. From your paper on the T1 mapping methods and B1 insensitivity, your conclusion is that B1 mapping may not be required for those protocols using VFA T1 mapping method. Does that imply that B1 maps are not even required for the MT pulse FA correction? As you mentioned, this dependency will be model dependent (we were fitting to the Ramani model thus far) but will try the Sled-Pike model as well.

1. DA method for generating the B1 map—how good is that compared to sat prepared TFL from Siemens. Use that for VFA correction and MT pulse correction.
2. Smooth B1 maps with a 10 mm3 filter
3. Don’t include a B1 map at all with VFA mapping
4. Look Locker—T1 mapping
5. AFI method for B1 mapping
6. Sled-pike model

[mathieuboudreau]

Then we switched to MP2RAGE as it gave us T1 values closer to that reported for muscle. It was disappointing that VFA with B1 correction gave us values that were so much underestimated (if anything literature tells that VFA overestimates T1). I am now evaluating a T1 Look Locker sequence for T1 (but having read your paper, it appears that B1 dependent methods like VFA of T1 mapping provides more robust estimates of ‘f’ in the presence B1 variations than B1 independent T1 mapping methods.

Just to clarify, my paper only investigated the Sled and Pike model, and didn't restrict any of the parameters (besides R1r). So, changing some of these options could potentially lead to different results, so take my conclusions with a grain of salt. I saw paper more as a framework on how to investigate the B1 sensitivity for a particular fitting model, and the results I concluded can only be applied to my settings unless as of now.

[mathieuboudreau]

From your paper on the T1 mapping methods and B1 insensitivity, your conclusion is that B1 mapping may not be required for those protocols using VFA T1 mapping method. Does that imply that B1 maps are not even required for the MT pulse FA correction? As you mentioned, this dependency will be model dependent (we were fitting to the Ramani model thus far) but will try the Sled-Pike model as well.

Another note, for my paper, I observed that this was the case if you are only interested in F - the noise or error is mostly propagated to other parameters (e.g. kf, T1f). If these parameters might be of interest to you (I think kf may be for MSK studies), then I would certainly recommend that you acquire a B1+T1 map, just be aware that different setup (e.g. B1-dependent or B1-indepent T1 technique) may propagate errors differently to different parameters. At this stage, I don't think you should be too concerned with this, as I think you have a much larger parameter bias to correct (so odd that your VFA T1 maps were underestimating by 50%! I've heard of severe overestimations on GE scanners, but not under and not on Siemens)

[mathieuboudreau]

Don’t include a B1 map at all with VFA mapping

Again, at this stage, I would recommend that you do B1 correction with VFA. I don't think this is the major source of your bias at the moment, you can explore this type of hyper error-propagation optimisation later on, once you've bridged the larger gap in your parameter estimates.

Sorry that my conclusions for that paper might have gotten you slightly off-track! When I first mentioned it to Tanguy to be forwarded to you, I intended to mean that generating my Figure 2 from that paper with simulations using your setting could give you some additional insight on if T1 errors could be a major contributor in the underestimation you were observing in "F".

[mathieuboudreau]

We tried several combinations of TR and several flip angles for VFA, but the T1 values continued to be underestimated. Our values of ‘f’ were small at the underestimated T1 values from VFA (2-3% compared to the 8% reported).
Then we switched to MP2RAGE as it gave us T1 values closer to that reported for muscle.

@usinha2 What range of values did you get for F when you switched to MP2RAGE? Did you F increase closer to the 8% reported, or decrease further?

[usinha2]

Hi Mathieu Thanks for all your suggestions. I will get back on the T1 and f with VFA/MP2RAGE T1 mapping soon after I look at all our data and analysis. I see your point that the discrepancies we are getting for the f values are large and that has to be solved before going onto exploring what T1 mapping method is better. I was wondering if the 3D MT SPGR sequence needs to have spoiler gradients between the MT pulse and the read Rf pulse? Tanguy had indicated that inadequate spoiling maybe a problem: we are using the default Siemens RF phase cycling (of read RF pulse) and spoiler gradients after readout; should we include spoiler gradients after the MT pulse as well? One other point: when we normalize the MT signal intensity, we are using the signal intensity of the largest offset image (100 Khz) at the given flip angle of the MT pulse : should we normalize all to an image without a MT pulse Usha

…

On Thu, Jan 10, 2019 at 1:11 PM Mathieu Boudreau ***@***.***> wrote: We tried several combinations of TR and several flip angles for VFA, but the T1 values continued to be underestimated. Our values of ‘f’ were small at the underestimated T1 values from VFA (2-3% compared to the 8% reported). Then we switched to MP2RAGE as it gave us T1 values closer to that reported for muscle. @usinha2 <https://github.com/usinha2> What range of values did you get for F when you switched to MP2RAGE? Did you F increase closer to the 8% reported, or decrease further? — You are receiving this because you were mentioned. Reply to this email directly, view it on GitHub <#292 (comment)>, or mute the thread <https://github.com/notifications/unsubscribe-auth/AsTb6SmothHcPQPWtfZjjnR9IoGyAdhwks5vB6yPgaJpZM4ZPKRc> .

-- ******************************************************************************* Usha Sinha, Ph.D. Director, Medical Physics Professor and Chair, Physics San Diego State University San Diego, CA 92182

[spinicist]

I was wondering if the 3D MT SPGR sequence needs to have spoiler gradients between the MT pulse and the read Rf pulse?

Yes. For small MT offset frequencies, your MT pulse can start to excite the water pool (from the tails/sidebands of the MT pulse). This can leave some water magnetization in the transverse plane before your imaging pulse, leading to image artefacts. Spoiling this transverse magnetization prevents the artefacts. The longitudinal water magnetization will still be reduced, but the Sled & Pike model accounts for this. The Ramani/Yarnykh models do not, which is why they have a limit to the lowest offset frequency that can be used (1 kHz and 3 kHz respectively IIRC).

If your lowest offset frequency is above 1 kHz, then artefacts should be minimal depending on the sidebands of your pulse. However, I would still recommend having the spoiler in the sequence.

we are using the signal intensity of the largest offset image (100 Khz) at the given flip angle of the MT pulse : should we normalize all to an image without a MT pulse

What you absolutely must avoid is introducing different scaling between the different flip-angles. Hence the way I would do it is to ensure that all receive gains etc. are the same for the two flip-angles, and then normalize to one image only.

Your method ought to work, because the 100 kHz offset images should be essentially identical between the different MT flip-angles. This is worth checking.

Good luck!

[mathieuboudreau]

@usinha2 I agree with everything that @spinicist said.

Acquiring an image with no MT RF pulse is typically recommended; I believe this is done in most qMT studies that I've read. If you do this, please note that I would recommend you still have the spoiler gradient that as you suspected should be used and that @spinicist recommended (between the MT pulse and the readout excitation pulse). Now this is an anectodal tip and don't have a reference for it, but the idea is that if the gradient impacts the image in any way (e.g. through eddy currents), then this the "imaging environment" will be the same for the MT on/off iamges if you keep the gradient on (which should be desirable, because the MT off image is supposed to be used for normalisation).

If you're really tight for time, and want to omit acquiring an MT-off, then I second that it may be best if you only use one of the high offset images to normalize all your data; I think the lowest flip angle image should be the best choice, to avoid any on-resonance effects as Toby mentioned. But feel free to compare both high-offsets to verify if they have any bias between them for all the tissue parameters in your region of interest.

[usinha2]

Thanks, Mathieu and Spinicist: will update after implementing your suggestions.
One point: @spinicist wrote: The Ramani/Yarnykh models do not, which is why they have a limit to the lowest offset frequency that can be used (1 kHz and 3 kHz respectively IIRC).

So is it 1 kHz the minimum lower offset frequency for the Ramani model and 3 kHz for the Yarnykh model? What is IIRC?

[mathieuboudreau]

IIRC = if I remember correctly.

For the limits, those sounds about right. See the discussion in Yarnykh's 2002 paper for that number; I'm not sure if it might be field dependent though. See below for a screenshot of the relevant section.

[usinha2]

Thanks--will look at Yarnykh's paper as well

…

On Fri, Jan 11, 2019 at 7:41 AM Mathieu Boudreau ***@***.***> wrote: IIRC = if I remember correctly. For the limits, those sounds about right. See the discussion in Yarnykh's 2002 paper <https://onlinelibrary.wiley.com/doi/full/10.1002/mrm.10120> for that number; I'm not sure if it might be field dependent though. See below for a screenshot of the relevant section. [image: capture d ecran 2019-01-11 a 12 10 41] <https://user-images.githubusercontent.com/1421029/51043727-f9677f80-1599-11e9-9494-438a1b726729.png> — You are receiving this because you were mentioned. Reply to this email directly, view it on GitHub <#292 (comment)>, or mute the thread <https://github.com/notifications/unsubscribe-auth/AsTb6RUSYjEObHxgJWCR17psF3b9WKtNks5vCLCpgaJpZM4ZPKRc> .

-- ******************************************************************************* Usha Sinha, Ph.D. Director, Medical Physics Professor and Chair, Physics San Diego State University San Diego, CA 92182

[usinha2]

Hi Mathieu One point that has been bothering me: When I acquire the images (Siemens PRISMA 3T) for qMT, I have no way of skipping the tuning between scans (though Siemens support tells me that the receive gain does not typically change between these acquisitions; strangely the actual value of receive setting is not given but it is only set to Low or High gain but we also checked the image header scaling factor that did not change between scans). initially I had planned to tune the protocol for the largest offset frequency (having the highest signal intensity) and using that setting of receive gain for all the other qMT scans: I did play around with the manual tune and I believe I can open that window for each scan (after the first tuning for the highest offset MT pulse) and prevent an auto-tuning: is it how you recommend doing it. Thanks Usha

…

On Fri, Jan 11, 2019 at 7:41 AM Mathieu Boudreau ***@***.***> wrote: IIRC = if I remember correctly. For the limits, those sounds about right. See the discussion in Yarnykh's 2002 paper <https://onlinelibrary.wiley.com/doi/full/10.1002/mrm.10120> for that number; I'm not sure if it might be field dependent though. See below for a screenshot of the relevant section. [image: capture d ecran 2019-01-11 a 12 10 41] <https://user-images.githubusercontent.com/1421029/51043727-f9677f80-1599-11e9-9494-438a1b726729.png> — You are receiving this because you were mentioned. Reply to this email directly, view it on GitHub <#292 (comment)>, or mute the thread <https://github.com/notifications/unsubscribe-auth/AsTb6RUSYjEObHxgJWCR17psF3b9WKtNks5vCLCpgaJpZM4ZPKRc> .

-- ******************************************************************************* Usha Sinha, Ph.D. Director, Medical Physics Professor and Chair, Physics San Diego State University San Diego, CA 92182

[mathieuboudreau]

Hi Usha,

That is a troubleshooting problem that I don't know how to fix. When I was acquiring qMT data during my PhD, we used a custom sequence and each qMT protocol parameter were different "measurements" within the same sequence, so no tuning was being done between images (to the best of my knowledge). I was working on a Trio, so I don't know what challenges you might be facing on a Prisma or how to tackle that. Maybe @ileppe encountered something like this and could chime in?

[ileppe]

re: adjustments. I have asked Siemens this question as well. It seems you can't easily 'copy' the receiver gain but that it would be very obvious in the image if it had changed from one acquisition to the next (i.e. very quantized). To avoid re-shimming, we were told to 'Copy Adjustment Volume' from the previous sequence, this should force at least the shim parameters to be the same (we do this when we manually shim). I hope that helps.

[usinha2]

Hi ileppe
Since you have worked on a Siemens system, I was wondering if you had any experience with T1 mapping with the VFA sequence: I am using it to map T1 of calf muscle and it is severely underestimated, becomes worse with B1 correction (using the Siemens sat prepared turoFLASH for B1 mapping).

[agahkarakuzu]

@usinha2 I have an example for B1 (acquired using the Siemens B1_map_for_T1_mapping) corrected VFA T1 map of the lower leg at 3T here:

values are around 1.4-1.5s, in decent agreement with those reported in the literature. For VFA, I used two FLASH volumes with 5 and 25deg flip angles. 2.86ms TE and 35ms TR.

As @ileppe suggested, I divided the B1 map given by B1_map_for_T1_mapping sequence by 800, upsampled to the T1 domain, then smoothed.

How off your values are from what you expect?

[mathieuboudreau]

@agahkarakuzu did you save the protocol file for that VFA acquisition? Maybe you could contact @usinha2 by email and exchange some Siemens-specific details, and see where both your protocols might differ to help resolve their very low T1 values.

[mathieuboudreau]

How off your values are from what you expect?

They said in a comment above.

1. Initially we used the same 3DSPGR sequence for T1 mapping as we do for the qMT data acquisitions (a standard 3DFLASH Siemens sequence---the screen capture from my original query provides the offsets and FAs of the MT pulse); the VFA method used TR= 25 ms, and three FAs= 4, 10, 25. With the VFA method, T1 values were constantly underestimated from reported values using either VFA or FSE-IR methods either with B1 correction and this underestimation increased after applying the B1 correction (e.g. typical values using VFA reported for muscle are ~1350 to 1500 ms: for us the values were in the range of 900- 1100 ms, which with B1 correction went down to ~700 ms). We tried several combinations of TR and several flip angles for VFA, but the T1 values continued to be underestimated. Our values of ‘f’ were small at the underestimated T1 values from VFA (2-3% compared to the 8% reported).

[agahkarakuzu]

Sorry @usinha2 I did not notice that you've already mentioned the T1 values you get, thanks @mathieuboudreau for bringing that to my attention.

I will share the protocol and sequence params as soon as I have access to my workstation.

[agahkarakuzu]

@usinha2 I am sharing the protocol and my sample data with you:

vfa_t1_calf.zip

The following contains 3 DICOM images of fa5, fa25 and b1, so that you can see all the sequence params:

info.zip

[usinha2]

Hi Agah Thanks so much. Your calf muscle T1 values looks pretty much what is reported in the literature. We also use the 3D FLASH sequence on the Siemens (FLASH is the equivalent of SPGR --acronym on GE scanners): with TE=3 ms, TR= 25 ms, FA= 5, 15, 25 degrees. The B1 mapping sequence we are using is I believe the same sequence as you refer to since we also get a map scaled by 10 (as the nominal 80 degree saturation pulse is scaled by 10), then we divide by 800 to get the relative map and then smoothed : we acquired with the same in plane resolution as the T1 map (what was your in-plane resolution for the B1 map?). Now I am thinking that maybe, it is best to keep it low resolution as this is a tuboFLASH acquisition even though it is centric reordered. I have your data and am going to look at it and process it: will also set the protocol on our scanner to match yours. Our T1 values after correction were as low as 800 ms. Thanks, will get back to you soon Usha

…

On Mon, Jan 14, 2019 at 6:17 AM Agah ***@***.***> wrote: @usinha2 <https://github.com/usinha2> I have an example for B1 (acquired using the Siemens B1_map_for_T1_mapping) corrected VFA T1 map of the lower leg at 3T here: [image: screenshot from 2019-01-14 09-00-19] <https://user-images.githubusercontent.com/9632322/51117238-2e164980-17db-11e9-8949-6b6a18d5328a.png> values are around 1.4-1.5, in decent agreement with those reported in the literature. For VFA, I used two SPGR volumes with 5 and 25deg flip angles. 2.86ms TE and 35ms TR. As @ileppe <https://github.com/ileppe> suggested, I divided the B1 map given by B1_map_for_T1_mapping sequence by 800, upsampled to the T1 domain, then smoothed. How off your values are from what you expect? — You are receiving this because you were mentioned. Reply to this email directly, view it on GitHub <#292 (comment)>, or mute the thread <https://github.com/notifications/unsubscribe-auth/AsTb6U5d7ghSTUKrYAmLJGlRKfPdnMrRks5vDJF_gaJpZM4ZPKRc> .

-- ******************************************************************************* Usha Sinha, Ph.D. Director, Medical Physics Professor and Chair, Physics San Diego State University San Diego, CA 92182

[usinha2]

Hi All
It has been a while before I started to look at the problem of underestimated T1 values.
I followed Agah's protocol faithfully and it did give T1 values similar to what he got. I identified the problem to be in the fat suppression (fatsat by a chemsat pulse). Keeping everything identical to Agah, but including the fatsat pulse brought the T1 to values in the range of ~800 ms. In muscle it is important to suppress fat since intramuscular fat infiltration can cause lower MT values (of course lower T1 values as well). I was testing on young subject where we don't have much fat, so in theory the fat suppression should not have made a big difference to either T1 or MT-- but it lowered T1. I tried with water excitation (normal) and (fast) and T1 values were closest to no fat sat was water excitation (normal). I list the values for the different sequences below (all follow VFA and are measurements in calf muscle)

3DFLASH | Fat Suppression | T1 (Seconds)
  | No fatsat | 1.3863
  | fatsat . | 0.8397
  | water exc. (fast) | 1.1945
  | water exc. (normal) | 1.3582

It is not clear why fatsat should decrease T1s-- it may saturate water signal but this should be the same for all the flip angles used in the VFA maps-- it will reduce SNR but not clear why T1 is underestimated.

[spinicist]

Hello,

The problem here is that a FatSat pulse is also an MT saturation pulse - both are off-resonance saturation pulses. The only difference is the specific offset frequency used - but the MT resonances will overlap with fat. Hence by adding FatSat you are adding extra saturation of the MT pool, which will change your apparent T1.

If you were on a GE scanner I would suggest using Spectral-Spatial pulses, which avoid exciting the fat resonance at the cost of a slightly increased imaging TR. I've used this trick to avoid exciting scalp fat when doing CEST in the brain. However, I do not think these are available on Siemens?

EDIT: Hang on, I guess "Water Exc." might be the Siemens name for Spectral-Spatial? In which case I would go with that option.

[mathieuboudreau]

@usinha2 First off, I'm happy you were able to reproduce Agah's VFA T1 mapping sequence/results! 🎉

I second what @spinicist said; adding fat sat pulses to your VFA and MT measurements will add an additional MT-weighting to your signal, which will ultimately impact your parameter quantification efforts. I've never tried avoiding fat in my own measurements, so I would try @spinicist's suggestions if you are concerned with fat in your image. I don't recall this being discussed a lot in brain qMT research, but I'd have to check again to be sure. Maybe you could contact the authors from the Sinclair 2010 paper and ask if this was a consideration for them; I know that the senior author Prof. Xavier Golay is very active on Twitter.

It is not clear why fatsat should decrease T1s-- it may saturate water signal but this should be the same for all the flip angles used in the VFA maps

This would need to be checked, but my first instinct if that no it won't be the same for all VFA maps. The MT signal equations are non-linear, and the signal saturation from MT is not simply "added on" to the steady-state SPGR signal. The MT pulse (i.e. in your case, fat sat) and excitation pulse do affect the steady-state signal independently. It is likely that if anything, as @spinicist hinted, it would change the apparent T1, meaning that the entire SPGR might be shifted for a constant MT pulse but varying excitation flip angles (i.e. shifted relative to the no MT saturation case). This could be checked with simulations; if I find some time this week or the next, I might explore that in a Jupyter Notebook and share it.

[mathieuboudreau]

Also, this recent open-access paper on how MT impacts VFA measurements might contain some relevant information, if you want to inform yourself a bit further: https://onlinelibrary.wiley.com/doi/full/10.1002/mrm.27442 . Note that this isn't completely what is happening to you though.

[spinicist]

@mathieuboudreau I was going to recommend that exact paper. Rui works at the other KCL campus 👍

[spinicist]

PS - As to why FatSat isn't discussed in the brain qMT literature, that's because the brain itself does not contain much "fat". Yes, it contains lipids in myelin, but fat as a separate tissue is not found in the parenchyma (to my knowledge). There is the layer of scalp fat, which most of the time is not an issue but was causing recon artefacts for a collaborator of mine.

[usinha2]

Hi all
I was thinking that the fat sat pulse may cause some free water saturation-- which I thought would be the same for all the FA images used for the VFA mapping. I now agree with you all that it is the MT effect, though my initial thoughts was that the fatsat pulse would not result in significant MT (e.g, the large flip angles like 300-500 used in the MT pulses vs. the 90 flip for the fatsat.)
Further, when I measured MTsat and tried nofatsuppression, fatsat, and water excitation, I found no difference in MTsat (of muscle in a young subject) between nofatsuppression and fatsat (as you would expect as a young subject muscle has a low fat fraction in muscle). On the other hand, the MTsat water excitation (spectral spatial composite pulses) was actually higher than the other two (if anything, I had anticipated that the fatsat may result in adding to MT contrast due to the off-resonance pulse, but that was not the case). So when doing qMT, I chose fatsat as my fat suppression method: our T1 data show that water excitation results in better accuracy in T1.
There is a paper by Li et al (Magn Reson Imaging. 2015 Jul;33(6):709-17. ) that discusses the qMT in muscle in the presence of fat: since fat does not exhibit MT, its presence will lower f, the macromolecular fraction. Li et al recommend a spectral-spatial pulse (water excitation) to minimize incidental MT effects: I chose the fatsat primarily based on MTsat experience. Sinclair et al did not use any fat suppression: their cohort was a young age group and hence the results are not likely to be affected by fat. Out intention is to study the aging muscle when infiltrating fat is an issue.

[usinha2]

Hi All
Now that the T1 mapping is solved by a water excitation pulse, we still have problems with getting the correct macromolecular fraction for muscle (reported values at 8%). We are getting around 4.5 %
The MTpulse we are using is 7.7 ms, Gaussian with a BW of 375 Hz-- I calculated this to have a sigma of 6.28 ms which makes this a very truncated pulse. Can this truncated pulse be an issue when you calculate the CW equivalent pulse or does the algorithm calculate the right RF pulse area? I feel that our underestimation of 'f' may arise from the MT pulse profile. We are planning to increase the MT pulse duration but I wanted to check if we are on the right track

[spinicist]

Hi,
My instinct is that the CW equivalent will be calculated correctly, but that the wide bandwidth might be leading to unwanted water excitation with low offsets - either from the main lobe, or from a side-lobe. Can you load the pulse into MATLAB and do an FFT to check the spectrum?

(For brain qMT, here we use a 20ms Gaussian pulse that has a bandwidth < 100 Hz, and on a different project with a Hanning pulse I once had very odd off-resonance problems above the sinus due to bad sidelobes on the pulse)

[mathieuboudreau]

Hi @usinha2 ,

I second what @spinicist said, however it looks like you were already aware that this might have been an issue with your sentence quoted below:

We are planning to increase the MT pulse duration but I wanted to check if we are on the right track

Yes, I think pulse truncation can be an issue (I've heard so from experience of others). The sharp drop from the truncation on both sides of the Gaussian will cause some high frequency components to the MT saturation, and I don't know if these are modelled well in the methods. That's why an apodized Gaussian pulse is often reported in papers; it attenuates these components. If you have access to modify your MT pulse shape on your scanner, you could re-write the pulse shape to contain some appodization (see here for a Gaussian-Hanning example; all our pulse shapes are here). It may be possible for you to simulate these and see the difference between fitting with data simulated with Gaussian and Gaussian-Hann for your parameters (e.g. maybe simulate using the Sled and Pike model, then fit with the Ramani which I think you are using?), however nothing will ever beat the real experiment (particularly in MT). If you have MT phantoms at your disposal, these might help for this debugging too (since those parameters are theoretically calculable).

Hope some of this info help!

[agahkarakuzu]

This issue was moved to https://forum.qmrlab.org/t/user-qmt-spgr-number-of-pre-pulses/34