[Mristudio-users] Relationship between acquisition and reconstructed voxel size.

Mon Aug 19 10:59:24 EDT 2013

Thank you Dr. Mori,

On another side, I tried to reduce TE today and was able to go down to 65ms
(from 90ms). This changed "Relative signal level" (in Philips) to 32% more
signal. This maybe great. Is there any downside of having shorter TE? Is it
ok to choose the shortest TE possible?

Thank you
Dorian
TJU

2013/8/17 Susumu Mori <smoriw at gmail.com>

>
>
>
> On Fri, Aug 16, 2013 at 11:54 PM, Dorian P. <alb.net at gmail.com> wrote:
>
>> Dr. Mori, thank you for the exhaustive explanation.
>>
>> I have two follow-up questions.
>>
>> First, you wrote:
>> "Because field-of-view, which is determined by the brain size, is about
>> 200 - 250 mm, DTI spatial resolution is limited to 2-2.5mm."
>> It appears that the number of acquired points depends on the FOV. Does
>> this mean that a smaller FOV have more SNR than a bigger FOV for the same
>> voxel resolution?
>>
>
> Suppose your FOV is 256mm. If the matrix is 96, the voxel size is 2.5mm.
> If you make FOV 192mm, the voxel size becomes 2mm. The smaller voxel size
> means higher resolution (even though the matrix size is the same) and lower
> SNR (less water molecules in each voxel and thus weaker signal). If you
> keep the FOV the same (256mm) and increase the matrix to 128, you can also
> get 2mm resolution. Again, higher resolution and lower SNR. The former
> (192mm/96) is better than the latter (256mm/128) because the data
> acquisition time is shorter (acquisition is completed while there remain a
> plenty of signal left). However, FOV = 192 mm may be too small for some
> subjects. There is a good chance that 192/96 has higher SNR than 256/128.
> Also, smaller matrix size allows shorter TE, which contributes to better
> SNR. Image distortion is smaller too.
>
>
>>
>> Second, I usually acquire with FOV=230mm. If smaller FOV gets more SNR
>> (depending on answer of 1st question), I can reduce FOV to match the
>> patients brain. Question is, will variable FOV be problematic in a
>> scientific publication?
>> I think that a fixed FOV keeps SNR standard between subjects, but brain
>> size affects DTI anyway. For example, within the same 2mm voxel size
>> chances are that a small brain will have more heterogenous fibers for each
>> voxel than a bigger brain. Therefore having more SNR for a smaller brain
>> may (kind of) balance the disadvantage. My plain thoughts anyway.
>>
>>
>>
> Higher SNR is not due to smaller FOV, but due to smaller matrix size.
> 192/96 gives noticeably better image quality (SNR, distortion) than
> 256/128. However, this also means, you should not mix data with different
> FOV/Matrix size within one study. I want to use 192/96, but can't because
> 192mm is too small for some subjects. That's dilemma.
>
> When we do pediatric MRI, there is a big issue; should we keep geometrical
> resolution the same or anatomical resolution the same. If we use 2mm
> resolution for all subjects, we have the same geometrical resolution. If we
> always put 96 voxels from the left edge to the right edge of the brains for
> all subjects, we keep the same anatomical resolution. If the subject brain
> is 256 mm large, it is 2.5mm resolution and 192mm brain would be 2mm
> resolution. We can argue that the latter approach makes more sense from a
> biological point of view. However, from a physics point of view, the former
> makes more sense because the SNR is kept constant. We know that SNR has a
> large impact on FA (lower SNR increases FA).
>
> The conclusion is, there is no perfect study design.
>
>
>>  Thank you for your comments.
>>
>> Dorian
>> TJU
>>
>>
>> 2013/8/16 Susumu Mori <smoriw at gmail.com>
>>
>>> MRI raw data is so-called time-domain data. This means, within about
>>> 10-100ms of time, signals are acquired and recorded. For example, if your
>>> image matrix is 128x128, there are 16,384 data points to acquire. For DTI,
>>> to freeze the motion effect, all 16,384 points are acquired at once (8,192
>>> points if you use a parallel imaging with factor = 2). Actually there are
>>> real and imaginary data points and therefore there are 16,384x2 points. By
>>> the way, because all 16,384 points are acquired at once, the data
>>> acquisition time becomes very long and there is not much signal left by the
>>> time all 16,384 points are read from the signal. Therefore, for DTI, there
>>> is no point to acquire 256x256 (=65,536) points because after about 20,000
>>> point-read, all the remaining points are reading just noise. This is why
>>> all DTI studies have been done using 96x96 or 128x128. Because
>>> field-of-view, which is determined by the brain size, is about 200 - 250
>>> mm, DTI spatial resolution is limited to 2-2.5mm.
>>>
>>> Now, when we do interpolation by the scanner, the scanner simply add "0"
>>> and extend the 128 points to 256 points. This is called zerofilling.
>>> After the fourier transform, the time-domain data is converted to the
>>> frequency-domain or image-domain data (the same thing with different
>>> names).
>>>
>>> If you have 128x128, after the fourier transformation, you get a
>>> 128x128-pixel image.
>>>
>>> Now, you have two options, do the interpolation by the scanner
>>> (time-domain interpolation), convert the 128x128 time-domain matrix to
>>> 256x256 time-domain matrix and FT it to the 256x256 image-domain matrix.
>>>
>>> Alternatively, you can FT first, get a 128x128 image-domain matrix and
>>> then digitally interpolate it to 256x256.
>>>
>>> A big question is, are they different? Signal processing people say,
>>> "sinc-interpolation of 128x128 image to 256x256 image is the same as
>>> time-domain zerofilling". However, things are not that easy because
>>> time-domain data has real and imaginary parts. If you have 10 physicists,
>>> I'm sure that you get two camps; one say they are the same and the other
>>> say, time-domain interpolation is better and you can never get the same
>>> quality after the time-domain data are converted to an image.
>>>
>>> There is a famous paper by a novel laureate, Dr. Ernst, proving the
>>> latter is the case, but the problem is, ordinary people like us can't
>>> understand the paper.
>>>
>>> Anyway, answering your question, you can always interpolate the data
>>> into higher resolution, but many people believe that it is just cosmetic,
>>> especially in the image-domain. We can argue that you are wasting the hard
>>> drive space. I always do twice time-domain zerofilling (128 becomes 256) by
>>> the scanner. In the imaging domain, we further digitally interpolate to
>>> 1x1x1mm because that is the voxel size of many atlases.
>>>
>>> I don't think there is a large impact on SNR by the interpolation.
>>>
>>>
>>> On Mon, Aug 12, 2013 at 12:40 PM, Dorian P. <alb.net at gmail.com> wrote:
>>>
>>>> Hi all,
>>>>
>>>> Is there any relationship between acquired and reconstructed voxelsize.
>>>> Is there any downside of reconstructing to much smaller voxels, for example
>>>> acquire at 3mm and reconstruct at 2mm or 1mm? Is SNR going to be the same?
>>>>
>>>>
>>>> Thank you.
>>>> Dorian
>>>> TJU
>>>>
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