Notes:

• The rules: the word has to be a real word (no proper nouns), and has to be a current product used in image acquisition.
• Some manufacturers seem more likely to use an acronym (acronyms are abbreviations which are also real dictionary words) than a simple abbreviation. Non-acronym abbreviations are not listed here, and so this word cloud should not be used to compare the quantities of manufacturers’ technique offerings.
• Beware of equating apparently equivalent techniques from different manufacturers; they may not be as similar as you first think. Of course the ultimate comparison is in image quality and sequence utility.

A one-page cheat sheet is at the end.

What is diffusion weighting?

We use magnetic field gradients to do useful things like encoding. But they also cause dephasing of signal, which we don’t want to happen (this is because when a gradient is on, there is a range of precessional frequencies along the gradient, even within a voxel). Diffusion weighting uses this dephasing effect to our advantage, to show where diffusion occurs.

How does diffusion weighting work?

Simple. A gradient is switched on – a big one to cause lots of dephasing. Then another gradient is used to completely undo all of the dephasing caused by the first gradient. We should end up with no effect, right? Right – but only if tissues and fluids are stationary. If there is motion – including microscopic diffusion of water molecules – then the dephasing caused by the first gradient is not “undone” by the second because the water molecules experienced different a gradient strength from the first gradient to the second, because they moved. As a result, the dephasing stays and we get signal loss on a diffusion weighted image.

So what are b values?

b values are actually a neat way of summing up into one parameter how much dephasing we are going to allow (size of diffusion weighting gradients etc). We need images with different b values to be able to work out diffusion-related parameters such as the ADC (see below). Note that we can’t say what diffusion is from the signal intensity alone, but from the signal intensity loss, which is why we usually acquire a b=0 (baseline) image to compare with.

So what is DTI?

We can only see that good diffusion exists (because of signal loss) along the direction in which a gradient is applied. So if we want to know the diffusion in all directions, we have to get many diffusion weighted images with diffusion weighting gradients in different directions. Ideally “all directions” would mean every possible direction on a sphere, but in practice we do, say, 12, 16, or 32 gradient directions (or more). The actual choice is up to the you. The minimum number of directions we can get away with is six – for example, one diffusion weighting anteriorly, one posteriorly, one superiorly, one inferiorly, one to the right and one to the left. That just about covers 3D space. Of course the diffusion in the brain is not always going to be exactly along one of these directions, so that’s why more directions are often used.

Showing DTI Data

Diffusion Tensor Imaging (DTI) collects information from all the diffusion weighted images (in however many directions was chosen) and tries to sum up all that information about where water can “diffuse to” in each voxel. DTI uses an ellipsoid (a stretched-out sphere, if you like) to represent where water can go. A long thin ellipsoid means very good diffusion for water along the long axis of that ellipsoid. A sphere (not an ellipsoid any more) means the same diffusivity in all directions. The mathematical way of describing the ellipsoid for each voxel is the tensor.

Sorry, what? A tensor?

The tensor is the maths part of DTI. If you like, just think of the diffusion ellipsoid when someone refers to the diffusion tensor. However, it is useful to know that the parametric maps which we produce in DTI (which are images where the pixel values represent some parameter other than signal intensity) are derived from the maths that are used to describe the tensor/ellipsoid at each voxel.

DTI Parametric Maps

It would be nice to draw little 3D ellipsoids at each pixel location, right? Unfortunately that wouldn’t make clinically readable images! So a number of parameters are used which relate to diffusion. There is a choice because the best one for every clinical situation isn’t yet determined. So you can choose. Some examples of parametric maps are now discussed.
Isotropic Image:
If we simply average all the diffusion weighted images which were acquired in all the directions, an image is produced which gives some sense of the total diffusion taking into account all directions. But this Isotropic Image is not generally used clinically, because the ADC is a better map of average diffusion, because the Isotropic Image is affected by T2 shine-through.
ADC: Apparent Diffusion Coefficient (or Constant)
The ADC map shows the average diffusion-freedom water molecules have in each voxel. This parameter can be calculated from all the diffusion weighted images which are acquired in DTI. Note that it is an average of all directions acquired, when performing DTI. The “A” for Apparent is there because the ADC is affected by partial volume averaging, perfusion, and some measurement errors. The average ADC map is sometimes called the trace map, which is to do with the mathematics of how it is calculated. It is not the same as the Isotropic Image; the ADC uses the mathematics of the tensor (the sum of the scalar values of the eigenvalues of the tensor, divided by three (which is the trace/3), sometimes called simply the “trace” image, but let’s not get into the maths now). The ADC is a useful DTI map.
On an ADC map, good diffusion is bright. This is opposite to the diffusion weighted images, where good diffusion is dark. The ADC removes the effect of T2 shine-through.
eADC: enhanced (or exponential) Apparent Diffusion Coefficient (or Constant)
The eADC shows the attenuation of the signal due to diffusion. On an eADC map, good diffusion is dark, just like the diffusion weighted source images. This is opposite to the ADC. Like the ADC, the eADC also removes the effect of T2 shine-through. Clinicians can choose to use ADC or eADC maps depending on whether they want contrast to match (or be opposite to) the diffusion weighted source images.
FA: Fractional Anisotropy
“Anisotropy” refers to how restricted diffusion is. An = not; iso = the same; tropic = direction (from Greek tropos “turn”). So anisotropy means “not the same in all directions”, which is what we are trying to find out about the diffusion of water molecules in each voxel. The ADC and eADC just communicate information about the diffusion in a voxel, whereas anisotropy maps go one step further and communicate information about the orientation of the underlying structure of the fiber tracts in the brain.
There are a number of ways of calculating (and thus, describing) anisotropy. FA is the main one. FA (and RA and VR, below) are rotationally invariant, which is important, because it means that the FA values produced wouldn’t be different if all your diffusion weighting gradients were rotated a bit, or if the patient was in a different position.
RA: Relative Anisotropy
RA is similar to FA, but it is a slightly different calculation (like FA it uses the scalar values from the tensor eigenvectors, but never mind about that now).
FA gives better detail. Use FA.
VR: Volume Ratio
VR is another calculated measure of anisotropy. The SNR and detail of VR is lower than FA and RA. The one thing VR has going for it is that the contrast between regions of low and high anisotropy is stronger than FA or RA.

First prize is a 20q game, which is an incredible little device which can guess what you’re thinking, by asking twenty apparently inane questions like “Is it larger than a duck?”.

Submit your answers to crossword@revisemri.com. Go for it!  Competition ended.

Download a PDF of the crossword here.

Across
3. Twist and bites back (4)
5. Aligned not by confused ballplayer (8)
6. Initial glug with proprietor’s spirits (6)
7. Starting Field Engineer around a band is marginal (6)
10. Initiation of reasoning (9)
14. Throw back ultrasound injection (5)
15. Rot the community in a day (5)
16. Waiting audibly for influence (9)
17. Spurned by Narcissus within the choir (4)
18. One’s rant is made sonorous (8)
19. Since he last lost function (4)

Down
1. Rise from the first reading (8)
2. Encrypting a conclusion swallows the company (8)
4. Shape shifts in cycles (5)
8. The thrill of an old commendation (10)
9. Accommodation that’s barred for flappers (4-4)
11. A pea, initially placed under Lisa’s son’s essay (5)
12. Element of water information (8)
13. Imagine taking in a picture (5)

Download a PDF of the crossword here.

A solution will be posted here January 31^st 2008. You have until then! Congratulations to Sarah Boyson—you’re a winner!

Here’s the solution.

Nobel Prize in Physics lecture, 11th December 1952:

“To follow the analogue of mechanical resonance we must now come back to relaxation, which can be seen to act like a friction, and which counteracts the tilt produced by the alternating field.”

Since the net magnetisation of a sample is the vector sum of many protons, and since there is a constant interaction growth rate of the protons with the lattice, we can write

where M₀ is the equilibrium magnetisation, T1 is the longitudinal, or spin-lattice relaxation time, which is the time constant of the exponential recovery of M_z.^†

The transmission of energy to the local environment of a magnetic moment in a magnetic field which mediates this relaxation is primarily due to its thermal “contact” with that environment (fluctuating magnetic fields).

It is possible to perceive why Bloch likened relaxation to friction.

^†Take the general Bloch equation (dM/dt=γMxB, derivation), and rewrite it in terms of parallel and perpendicular components of M to the external, static main magnetic field: dM_z/dt=0 and dM_xy=γ(M_xyxB). From the former we may write the equation in the post above, and from the latter, dM_xy/dt=γ(M_xyxB)-(1/T2)M_xy (or simply dM_xy/dt=-(1/T2)M_xy in the rotating frame).

After the using the Fourier transform to transform our measured k-space data into image space, the image data is of complex type. This image data is then manipulated for different clinical utility. For example, a magnitude image is used to maximise the signal-to-noise ratio (SNR). Phase images are used to measure flow. Let’s look at how our MR signal is recorded and how these image types are calculated.

The changing magnetic field which is the source of the signal measured in MRI is a vector which we represent using complex notation. This is quadrature detection, which refers to the detection of a circularly polarised magnetic field, and results in two data streams with a 90° phase difference. The digitised values from these signals become the real part and the imaginary part of each complex data point in k-space. The ultimate purpose of quadrature detection is to increase SNR by a factor of √2.

Now, the two signals are the real and imaginary channels which are sometimes denoted I (for in phase, the “real” data) and Q (for quadrature phase, the “imaginary” data). The imaginary data is not imaginary in the colloquial sense; it is a measured quantity. These signals are corrupted by “white” noise, which has a Gaussian probability distribution. After the inverse Fourier transform of the complex data, the noise in the complex image data is still white (Gaussian). However, we don’t generally work with the real or imaginary components of the image data (i.e. calculate images using only the real data, or images using only the imaginary data). To use both parts of the complex data values, we calculate magnitude images and phase images, which have physical meaning (proton density and flow, respectively, ignoring contrast weighting and background phase variation for the moment).

Magnitude images are the real and the imaginary parts combined, calculated after the Fourier transform as √(Real²+Imag²), for the complex data point at each image pixel.

Phase images are calculated after the Fourier transform as tan^-1(Imag/Real) for the complex data point at each image pixel. Phase is also known as the complex argument of a complex number.

After making the calculation of a magnitude image, the noise probability distribution is no longer white, and becomes Rician (tending to a Rayleigh distribution as the SNR goes to zero). This sounds concerning, since most MR quality assurance (QA) programs rely on SNR as the primary parameter of choice as a daily-QA metric. However, it seems that if the SNR is above a very low value (<2), the noise probability distribution is approximately Gaussian again anyway. And note that an MR image with an SNR of 2 would not be practically useful.

*Apologies to Philip Mazzei.

The World Report on Knowledge for Better Health (WHO 2004) has identified a large gap between what is known and what is actually being done in health systems. The so-called know-do gap. Increased communication and interaction between health systems and health research systems was called for. How do researchers achieve this? At first we may think of typical dissemination activities at the end of a study such as:

• publishing research articles in high-impact peer-reviewed research journals
• producing specialist press articles on the subject of (e.g. “The value of MRI in the diagnosis of X”)
• producing press articles on the subject of (e.g. “How MRI can help X sufferers”)

These are valuable and valid dissemination routes, but does this get your research recognised in clinical practice? According to the WHO, not well enough. KT is about translating, synthesising, and communicating research to inform policy, practice and opinion. The three suggestions above are likely to influence these goals to some extent. KT, however, is a much more involved and active method of achieving those goals.

A KT strategy can may be considered under these headings (Lavis et al, 2003):

1. What knowledge do we want to transfer?
This involves identification of take-home messages for each audience. These messages must be immediately actionable. KT specifics must be fine-tuned for each audience for the types of decisions they face and the types of decision-making environments in which they live or work.
2. To whom is the knowledge to be transferred?
Key stakeholders must be identified (persons, groups or institutions interested in the research). These audiences are not only traditional audiences (medical and scientific to whom peer-reviewed reports are appropriate), but any audience which will influence policy, practise and opinion (or other KT goals which are identified for the study).
3. By whom is the knowledge to be transferred?
The messenger must be a credible witness in the eyes of each target audience. This has big implications in a well designed KT strategy; developing credibility throughout a research study—long before dissemination—is a highly effective component of KT.
4. How the knowledge to be transferred?
This facet may be where planned KT can differ most from traditional dissemination strategies. Effective KT requires interactive engagement with opinion leaders within each target audience. By building on existing relationships and relational networks throughout a research study, it is possible to assess how a stakeholder takes up knowledge. It is possible to assess readiness for change and interact appropriately, working around scepticism, distrust or resistance. Technological communication should not be a substitute for face-to-face engagement, by which tacit knowledge is communicated.

KT is a knowledge brokering exercise, as opposed to a “hand-off” of results.

Developing Credibility
Developing credibility must begin before the dissemination stage of a project begins; two-way dialogue / relationship / networking will engender exchange of information between those who generate and those who use knowledge. Development of credibility in this way will facilitate the use of research in practice. It is not sufficient to only transfer evidence or practices to the field in the absence of understanding what is needed to prepare organisations and practitioners to receive and implement this new knowledge. This understanding may be gathered in the relational networking throughout the project.

How can you generate interest in such networking? The recipients of your research results would be contributing their time and effort, and so developing a perceived value is critical. This can be generated by creating opportunities for stakeholders to have access to privileged information, innovation and collaborative experience through workshops, special events, newsletters / summaries of relevant information and so on. The “knowledge network’s” activities would need to be purposeful, collaborative and engaging. It would be necessary to encourage members to contribute to foster a feeling of belonging as well as privilege. For example, it may be an idea to begin to drip-feed quality resources or services to help certain stakeholders set up a disease-X imaging service such as the one used in the study.

Funding for these activities should not be overlooked; travel / access to information / ongoing communication would be costed. An added benefit would be an increase in the credibility and recognition of those involved in the KT activities. Other benefits include reduction of duplication and destructive competition, enhancement of mutual learning, increased access to new audiences and the fostering of ongoing collaboration.

A knowledge network is hard to create from scratch; it needs to be nurtured over time which is why it is necessary to start early. Existing relational foundations may be built upon, in terms of trust, respect and collaboration.

Evaluation of Success
Evaluation of the success of KT initiatives are a part of KT. Success will depend on the objective of the KT, which depends on the target audience. What did we set out to achieve? A change in behaviour? An increase of awareness? A change in policy? A change in practice? A change in culture? Introduction of an issue into a debate?

Summary
KT is a knowledge brokering exercise, and occurs throughout a research project. It includes more audiences and is wider-ranging that traditional dissemination routes. Funding organisations have begun to realise that the usual research-study-success metric (journal articles) does not necessarily address the priorities of their stakeholders, and a research organisation’s success with a particular funding source will be judged on how it meets the funders’ stakeholders’ goals. For the success of future funding applications, effective KT of a current project may prove invaluable. Thus, an effective KT network would attract participators as much for what they can put into it, as what they might take from it.

KT includes the provision of tailored, actionable messages to stakeholders. Credibility with stakeholders is developed over the course of the project. And KT requires relational, interactive engagement with target audiences, not just the golden high-impact journal article!

An introduction to Knowledge Transfer: Lavis et al, How can research organizations more effectively transfer research knowledge to decision makers? The Milbank Quarterly, Vol. 81, No. 2, 2003.

“an indisputable connection between the size of the brain and the mental energy displayed by the individual man.”
(Hamilton JA 1935. The association between brain size and maze ability in the white rat. Doctoral dissertation, University of California, Berkeley.)

Brain volumes can be measured accurately with MRI, and using MRI, research has shown that intelligence and brain volume are meaningfully related. The correlation is higher in adult women. Another study in elderly men showed that fluid intelligence, premorbid intelligence, and visuospatial memory are affected, but not verbal memory and verbal fluency.

It’s OK to be big-headed after all…

Most of my computing knowledge comes from a history of Microsoft Windows use. Like most people, I would have preferred an environment familiar to me. But though command-line use can be daunting at first, the power of the Linux command window (or shell) is formidable, especially for researchers and scientists. You can get Linux to run scripts in any of the many languages it already understands (think of post-processing your acquired images, or your raw k-space data for research purposes). Want a secure connection for remote access to the scanner host PC? No problem, use ssh. Want a screenshot? ImageMagick comes pre-installed (and GIMP, incidentally). This and all the usual powerful commands included in Linux such as diff and grep.

For example, the other day I wanted to look through the whole MR host system for PDF files. I also wanted to see if they would fit onto a USB stick. So my command needed to be:

Look through the whole system for filenames ending ‘.pdf’, but don’t look in other mounted drives or network locations, then estimate the disk usage of those files, then sort the results into a list, by files sizes, biggest first, and send that to a file on my USB stick. Please.

I cobbled together this single line command (it is wrapped onto two lines here), which worked a treat:

find / -mount -name '*.pdf' -exec du {} \; | sort -nr -o /path/to/usb/pdf_files.txt

No doubt such commands are possible with Windows too, but my point is this: the flexibility and utility of Linux for research use is very apparent, and best of all, easy to learn.

The same goes for web hosting; most websites in the world are running on Apache servers, which run on Linux machines.