Magnetic Resonance - Science topic

Hi, medical image are in DICOM format. But you can manipulate the raw data by using viewer such as Osirix and Horos (Apple). But it still depend on what you want to look at. Certain Philips workstation like ISP can process raw data that you want to extract.

Dear Derek, that's a good technical question. Personally I'm not familiar with this method. However, I can suggest to you the following potential useful review article in which the basic principles of Optically Detected Magnetic Resonance (ODMR) and some examples of applications in photosynthesis are outlined:

(see attached pdf file)

Also interesting in this respect seems to be the following manual for a lab course on ODMR:

This manual is also freely available as public full text (see attachment).

I hope this helps. Good luck with your work!

Sungcheol Hong no of course, ask me and if i can i will answer you

Please check the following datasets, might help.

Hello, i am finding it very difficult to make use of QRMA in my research now. do we have studies that have replicated its use and we can validate it on another study. How reliable is the analyser with other conventional test methods.

Wireless power Transmission is possible at all non ionizing electromagnetic spectrum.basic u need is inspiration from NIKOLA TESLA then follows engineering math(advanced matrix theory, vector calculus, PDE, probability, algebra) Antenna designs, physics of electromagnetics, communication theory,electronics, signals and systems,DSP, wireless communication, digital electronics and communication. I guess this would build the fundamentals enough to explore ideas on wireless power. For further deep reserch in this field it shall involve ferromagnetic, electronics and magnetics at quantum level😅.

Yes! Please see the following PDF attachments.

Lescher et al. Quantitative T1 and T2 Mapping in Recurrent Glioblastomas Under Bevacizumab: Earlier Detection of Tumor Progression Compared to Conventional MRI Neuroradiology. 2015

Kjaer et al. Tissue Characterization of Intracranial Tumors by MR Imaging. In Vivo Evaluation of T1- And T2-relaxation Behavior at 1.5 T Acta Radiol. 1991

Just et al. Tissue Characterization With T1, T2, and Proton Density Values: Results in 160 Patients With Brain Tumors Radiology. 1988

⁶⁴Cu has a spin (parity) of 1⁺ and a magnetic moment of about -0.22 µ_N, resulting in a Larmor frequency of about (-)1.7 MHz/T. In general, NMR spectroscopy of metal nuclei is feasible; check e.g. Benn R, Rufińska A. High‐resolution metal NMR spectroscopy of organometallic compounds. <https://doi.org/10.1002/anie.198608611>.

Data about ⁶⁴Cu is from:

Let me add some more technical details to Samson Nivins' answer:

Both T₂ and T₂* describe the transverse relaxation (i.e., the decay of the MRI signal induced by the precessing transverse nuclear magnetization). T₂ describes the decay observed in spin-echo (or turbo-spin-echo, fast-spin-echo, RARE, HASTE, SE-EPI, ...) measurements. T₂* describes the decay in gradient-echo (or FLASH, SPGR, FID-EPI, ...) measurements.

Technically, T₂* includes additional (static) effects due to macroscopic and microscopic magnetic field inhomogeneities (caused e.g. by blood) and is always shorter than (or at most equal to) T₂.

Mathematically, this is expressed by 1/T₂* = 1/T₂ + 1/T₂' (or, equivalently, T₂* = (T₂' + T₂)/(T₂ × T₂')), where T₂' describes the transverse relaxation only due to static magnetic field inhomogeneities.

This is a broad question that would require a detailed introduction to MRI for a full answer.

In short, different tissues are characterized by different proton densities (PD) and different relaxation time constants (T₁ for the longitudinal relaxation, T₂ for the transverse relaxation).

Depending on the MRI acquisition technique (pulse sequence) and the sequence parameters (such as repetition time TR, echo time TE, or inversion time TI), the resulting image can reflect one of the tissue parameters much stronger than the others. E.g., a T₂-weighted image (with very long TR and TE in the range of T₂) emphasizes differences in T₂ (showing tissues with long T₂ such as fluids much brighter than those with shorter T₂). In a T₁-weighted image (with very short TE and TR in the range of T₁), differences in T₂ are hardly visible, but tissues with long T₁ appear darker than those with shorter T₁.

For a conventional spin-echo sequence, this is described by the signal equation:

S(TE, TR; T₁, T₂) = S₀ (1 – exp(–TR/T₁)) exp(–TE/T₂)

See also my answer to the following related question:

(and e.g. http://mriquestions.com/image-contrast-trte.html )

I'm not shure why CDCl3 ?

1) You can chose from at least 10 common deuterated solvents with resonable price.

If you can not change the solvent:

2) you can add a reference compound to your sample.

If you don't want to add anything to your sample:

3) you can use an insert (coaxial tube or capilary) with anything you want to use as an reference.

If you don't like anything form 1-3:

4) You can use external reference technique- this one is a litle bit tricky, but you can pick up any NMR tube found in the room with anything in it and reference your spectrum on it.

There are many solutions for NMR spectrum calibration and you should choose the one that suits best your sample.

Cheers!

Dear Professor Gillebert,

Thank you very much for your detailed reply to my question and for your editorial. I am lucky enough to work at the same hospital as Professor Chowienczyk and his group.

The main aim of my PhD project at the moment is to assess CBP from MRI images coupled with a brachial cuff BP measurement. I have been mostly following McEniery et al.'s 2014 review on CBP (doi:10.1093/eurheartj/eht565) to justify the clinical importance of CBP over peripheral BP. I would be very grateful if you could share your views on this review with me.

Kind regards,

Jorge

Yes this is quite doable.  You make a secondary coil that is inductively coupled.  The mutual inductive splitting can be handled in a couple of ways.  The best is to use one lobe as your resonant detector.  Most of this was worked out a number of years ago but has not been developed in the last 10 years.  See the attached paper from Zhou et al in 1994.

Good luck with your project.

Doug Morris

Thank you Olaf for your talk. Best wishes

I am not a specialist in neurology but  I think that neuronal current is alternative current of some frequency (it is seen in EEG). The frequency is small and it gives a rise to J(0) (spectral density of magnetic field fluctuations at zero frequency). We know that only T2 (spin-spin relaxation time) is sensitive to this spectral density. So I have a question. Why do we not see neuronal currents at high field MRI. What "dwarfs" means? Could we use the method of T1 in rotation coordinate system ? Low field MRI is very unsensitive.

It depends if your T1 sequence is flow sensitive. If you have a flow sensitive T1 sequence, then the sagittal sinus intensity will not be equivalent to 100% blood volume. I'm guessing you want to normalize to estimate a blood volume. There are issues with tissue T1 intensities also. There are many reasons why this is a bad idea, though people do claim to do it all the time. If your goal is T1 relaxivity, that is a whole other technique.

Hi Maria, many thanks for this! It looks suitable - I just need to get some of the time from our protocol. Wasn't aware of it!

Best wishes,

Colm

i didnt find any such protocol-- 

Hello,

flares generate so-called non-electromagnetic radiation. Russians had studied that phenomena.

Thanks for the informations Jerome, I will try to work it out with FSL.

The original expression "((mu)/r^3)*(3(z^2/r^2)-1)" looks like the z component of the magnetic field (i. e., like B_z) of a dipole in z direction (perhaps up to some unit-system-depending factor).

In SI units, the B field (vector) of a dipole m is (cf. link below):

     B(r) = (µ₀/4pi) [ 3 r (m · r) / r⁵  -  m / r³ ]

with r = [x,y,z] and r = (x²+y²+z²)^1/2, and for a dipole in z direction we have m = [0,0,m_z] and m · r = m_z z.

So, this should result in:

      B_x(r) = (µ₀/4pi) [ 3 x (m_z z) / r⁵ ]

      B_y(r) = (µ₀/4pi) [ 3 y (m_z z) / r⁵ ]

      B_z(r) = (µ₀/4pi) [ 3 z (m_z z) / r⁵ - m_z / r³ ]

where the last line is basically your expression from above.

You might want to take a look at published articles and see what scan parameters they use as a starting point. I am not familiar with the software specifically, but modifying locally accepted parameters is not a process to be taken lightly. Certain parameters (such as increasing TE) are likely to improve your lead discrimination without causing significant SAR changes, but this should be done with the assistance of competent local MR physicist help.

I assume you are aware of this website (http://www.mrisafety.com/). They do discuss lightly the testing methods, and provide references for a deeper exploration. For simple objects, the motion/force/torque induced by the field can be calculated, but I doubt it extends well to mildly complex medical devices. MRI safety is also two components - the induction of current and the force generated by the magnetic field, further complicating the analysis.

Your hand is adding capacitance to the circuit by it's proximity to either the cable attaching the coil to the magnet or by a distortion of the capacitor that you are tuning by the force on the tuning stick.  

Leaving that aside just tape a plastic rod to the tuning rods and see if doing that solve your probe.  If it does then your problem is the effect of cable capacitance.  You can have your shop fabricate longer tuning sticks.  If it does not solve the problem then its mechanical distortion and your MRI coil may need repair.

Good luck with your experiment.

At 3 Tesla, the best materials for MRI are agarose and oil (better if they're doped with nickel)

At less then 3 T (1-1.5 T), usually water doped with copper solution. 

For concentrations, it depends on what you're looking for.

Dear Naftali,

I have experienced a transition from Siemens 3T Trio to Prisma. I thought my experience might help with the transition from Verio especially in relation to 31P MRS.

Shimming: On Trio we just had to go to "Options>Adjustments>3D Shim" and perform shimming. On the Prisma, you will first have to go to "Sequence>Nuclei" and change "Tx Nucleus" to 1H before the system will allow you to shim and probe the linewidth with "Inter. Shim". You shouldn't bother with this if you are using FASTMAP

Data processing: This was a major headache in the beginning as the software for analysis (jMRUI and in-house Matlab scripts) could not load the spectra data. I contacted the team behind jMRUI some months ago and sent them sample datasets from Prisma. They are still working on a fix which will be released as a plugin to allow the import of 31P data from Prisma. The problem is in how Prisma exports the data; it kind of adds image data to the spectra data. We therefore resulted to using in-house Matlab scripts and LCModel for the preprocessing and quantification of 31P data (Deelchand et al., NMR Biomed 2015)

I hope this helps.

Isaac Adanyeguh

Microstrip Antenna (MA) and Dielectric Resonator Antenna (DRA) are investigated by treating as a “cavity”. For thin substrate thickness (d<< lamda), it can be shown that the 3D eigenfunction (chi_mnp) besomes a function of x-y coordinates only (chi_mn where p=0).Different shaped MAs (rectangular, triangular) are simulated using 3D EM simulator HFSS and the same are investigated analytically. It is found that MA shows TM modes (Electric resonance). They do not show TE modes because TE modes get short circuited for p=0. On the other hand, DRA shows TE/TM/HEM modes. For example cylindrical DRA shows TE (magnetic resonance), TM (electric resonance) and HEM (mixed) modes where as rectangular DRA shows TE modes only. Excitation of TM mode in practices is in doubt for Rectangular DRA (R K Mongia, IEEE AP, 1997). Therefore rectangular DRA shows magnetic resonance. Triangular DRA shows TM (electric) resonance.

Basically, DRA is 3D object whose mode are defined by m,n and p whereas the same are defined by m and n for microstrip antenna.

To identify electric or magnetic resonance, investigated a DRA using TE and TM mode in a general sense. Obtain different resonant frequency. Plot internal fields at that resonant frequency. Simulate the same using 3D EM simulator (HFSS or CST). From input impedance plot, identify different resonant frequency. Plot the internal simulated fields at that frequency and compare with theoretical plot. In this way, we can easily identify TE/TM/HEM modes.

In the static situation, using Bogomol'nyi type analysis, it can be derived a positive-definite energy functional which has a lower bound. Specializing to the gauge group SU(2) and the t'Hooft-Polyakov ansatz for the gauge and Higgs fields, we seek static, spherically symmetric solutions to the coupled system of equations in both the isotropic and standard coordinate systems. In both cases, in the spontaneously broken symmetry situation, it is found great simplications reducing the solutions of the coupled system to the solution of a single non-linear differential equation, different one in each case, but well-known in other contexts of physics. They found abelian and non-abelian monopole solutions with gravitational fields playing the role of Higgs fields in providing attraction that balances the repulsion due to the gauge fields. Numerical solutions indicate the possibility of blackhole horizons inside the monopoles enclosing the singularity at the origin. Such non-abelian monopoles are then the analogs of Reissner-Nordstr\"om blackholes with magnetic charge.

Hi

Spintronics is a blend of electronics with spin and also based on the spin of electrons rather than its charge.The application based, it's used in the magnetic version of RAM used in computer nonvolatile and another one of MRAM is faster and less power consumption.

In addition to what's been mentioned before:

It would be helpful if you could either provide the ML equation itself or a literature reference, in which this A occurs. E.g., in the paper by Rajan et al. (listed below), A occurs in the signal probability distribution function (pdf) p(m|A, sigma) and is defined as the "underlying signal", i.e. the MR image signal without noise (which is to be estimated by the ML denoising approach); sigma is the standard deviation of the (original, normally distributed) noise.

The same A also appears then in the joint pdf p({m_i}|A, sigma) for the observation of several signal intensities {m_i} in a region of constant signal intensity A. Does that answer your question?

I may be biased, but I also like the functionality that SIVIC http://sourceforge.net/projects/sivic/ offers. It is also cross-platform, which is a major plus. And free!

Thanks!

If you are willing to get into a bit of programming, you could try the vmtk libraries (http://www.vmtk.org/). The authors would probably mix in 3d Slicer for free registration and visualization. They seem now to have a commercial offering which presumably makes life simpler, but not free. 

Comment to Edward Russak: We do regularly pet/ct imaging with the positron emission of Y90 after SIRT of liver tumors. It takes time because of the low emission probability but it works much better than bremsstrahlung imaging. Physics can be studied here: http://www.mdpi.com/2218-2004/1/1/2/pdf

but sorry, we got no pet mr yet...

The transrectal ultrasonography (TRUS) has high resolution and can accurately identify the commitment layer of the rectal wall . Thus, it is expected that TRUS has higher specificity (83% ) compared to MRI (74 %), which is shown in the article for the skilled reader.

Honestly, greater sensitivity of 96 % (MRI ) versus 93 % (USTR ) might be only numeric, not significant from a statistical point of view. Besides, the authors consider in the article the differences between the readers, not the difference between the methods, by specialist reader .

Personally , I like TRUS for initial staging lesions only, because it allows better decision making, mainly when less agressive therapy can be performed.

can white matter abnormalities be observed exclusively in the brainstem corticospinal tracts and internal capsule on MRI in absence of occipital or frontal white matter involvement?

Initially, involves predominantly the parietal-occipital lobes and posterior visual pathways, but it extends forward into the frontal and temporal lobes as the disease progresses.

Unlike the focal plaque-like character of multiple sclerosis, adrenoleukodystrophy tends to be contiguous within fiber tracts and often is confluent within the larger white matter bundles of the centrum semiovale.

According to image finding,  Both periventricular and subcortical white matter are affected, and in advanced disease the internal capsule, corpus callosum, corticospinal tracts and other white matter fiber tracts in the brain stem can be involved.

I hope it is helpful  Thanks

increase the winding coil and good system design

you can use royer-type oscillator 

If I understand correctly, you want to build a gel phantom that reflects the T1/T2/ADC characteristics of the myocardium ? If so, look into the following articles:

You will have to adjust the formulas to match T1/T2/ADC values of the myocardium. You can find T1/T2 in this paper:

And depending on the purpose of your study, you should look into papers reporting ADC values for in vivo or ex vivo. They can differ quite a lot! Finally you should validate your phantom with a gold standard technique for each characteristic value (T1/T2/ADC).

Good luck on all that

First you should check whether you have high spin or low spin Fe3+.

For low spin (S = 1/2): Assuming you have no hyperfine coupling then you expect up to 3 transitions in the solid state/frozen solution corresponding to the Ms = -1/2 to +1/2 transition (the three components relating to x,y and z direction ( If x~ y then you'll observe 'an axial spectrum' whereas if x <>y <> z then the spectrum can be described as rhombic.

For high spin (S = 5/2): you'll see transitions from -5/2 to -3/2, -3/2 to -1/2, -1/2 to +1/2, +1/2 to +3/2 and +3/2 to +5/2 for x,y and z directions giving up to 15 lines. However the number you observe depends on the microwave frequency and the magnitude of the zero-field splitting parameters D and E.

My first approach would be to compare your spectra with other spectra in the literature for Fe3+ ions for structurally similar compounds to confirm qualitatively whether you have high spin or low spin. If you have similar spectra note the g values (plus D and E parameters fro high spin) from the literature as these will be a good starting point for simulation. A qualitative comparison will at least help you confirm (hopefully) the value of S and the geometry.

In the next couple of days you will get a lot of suggestions for different pieces of software. Easyspin is often used but I have no direct experience with that and from experience know that not all EPR oftware is simple to use. I use a program called PIP written by Mark Nilges at the Illinois EPR centre which is really good for simulating anisotropic S = 1/2 spectra. It also works for S > 1/2 and I've successfully used it for organic radical systems with S = 1 (small D and E). I haven't applied it to systems with large D and E but I think it should work on those too based on the documentation. I have written a GUI which runs under Windows XP and Windows 7 Professional  in XP mode (I've only just migrated to Windows 8 last week so not checked compatibility yet). A simulation of an S = 1/2 system can be seen here:

If you want to try the software drop me an email. I can send you the GUI and documentation on how to install. At some point i'll add a downloadable version of the software to the website but haven't had chance to do that yet either!

Best of luck.

check the following link http://nordlander.rice.edu/miewidget

Thank you, Victor.

Do you need to have the inverse, or do you want to find the solution of a system of linear equations where the matrix is singular. If it's only the latter, then you don't need the inverse of the matrix (which doesn't exist anyway) to find the solution.

Dear Nic,

The simple descriptive method of outlier removel is Inter Quartile Range (Q3-Q1) or Inter Decile Range (D9-D1) whereby extreme values are removed from the data set. 

Outliers are, however the part of data set. It is better not to exclude them but follow some suitable transformation like log, sin, square, root, reciprocal etc. Transformations are allowed in econometrics/ research.

Hope it helps.

Stelar http://stelar.it/applications_biomedical.htm designs and manufactures equipment for testing contrast agents at different magnetic fields. They have many papers on this topic. Just let me know what range of fields you are interested in (e.g. 1-2Tesla, ABove 2 Tesla, below 1 Tesla) then I will have a look for you what information is available.

Hi Dr. Gutowski,

Thank you for the link. Unfortunately, I am unable to obtain the article due to limited access. I was wondering if you can provide me the softcopy, instead?

What I am trying to understand , is what effects the magnetic properties (Hc, Ms) of thin films? Could it be the size of the particles, the domain wall pinning, the thickness of the thin film, the stress induced during the thin film formation?

The life time of a given spin state influences the spectral line width via the Heisenberg’s Uncertainty Principle given as:

∆E. ∆T ≈ ħ

Now ∆E=h ṿ

And

∆T= T2, the life time of the excited state. So the range of frequencies can be given by:

∆ ṿ ≈ 1/T2

From the above relation:

[A] If T2 is large(Long), ∆v is small, I,e. resonance occurs over a very small range of frequencies[ Sharp lines]. [B] If T2 is small(Short), ∆v is large, i,e. resonance occurs over a large (Broader) range of frequencies[ Broad lines].

Note: v = nu; ħ is h bar.

Just my 2 cents:

In general whole brain BOLD signal all have common source of error introduced by physiological noise (CSF & blood flow, pulse, etc.), thus regressing out signals from CSF & WM should increase signal-to-noise ratio. Regressing head motion should in theory reduce error variance at the edges, but if head movements is too vigorous it may not help too much (see http://www.sciencedirect.com/science/article/pii/S1053811905003095).

Whether regressing global signal is still in debate, as it has been shown to cause artificial deactivation for network analysis using seed regions or ICA. However for indices concern regional connectivity it may not matter that much because the calculation is based on coherence/concordance of timeseries.

I would probably perform regression before bandpass filtering because it you do it the other way round you'll temporally smooth your error estimation (CSF & WM signal), which will in turn affect the signal after regression.

Hi!

Did you create the filter yourself? Do you already have some experience with simulated data and different types of noise and how your filter reacts? Starting with real data is nice only if you have true-positive and true-negative control conditions to compare. If your filter is dynamic you will need that. Another thing you may want to do is to filter the data twice, first with your filter and then with the conventional one. If your filter is better, you should get better results after filtering data twice or after filtering it with your filter only. Better than only looking at the rmse would be to run some bootstrapping and to have a look at the posterior distribution of activated voxels for your true-positives and true-negatives.

Cheers,

G

Good progress this week. The publication of McCracken et al 2014

And my co-researcher R.J. Salvador has modeled 2000yrs of TSI proxy to R^2 0.81 using planetary periods and harmonics.

CT is by far the best to calculate disc height, I think both DXA and FRAX are less suited for that.

Beyound the problem of shimming, which is addressed by previous answers, I would like to mention the influence of echo train length. Since shim is always a problem in the neck-shoulder region, shortening of echo train length will improve quality of DWI images. This can be done by parallel imaging (don't know how it's called by GE) which requires multiple receive coil elements and receiver input channels. If you want to work with the body coil, try segmented EPI (sometimes called multishot EPI) which shortens the echo train length by acquiring only part of k-space after an excitation and applying several excitation pulses to get all the data needed.

A completly different approach which will definitly help in this critical anatomic region is to work with propeller diffusion which should be available on GE scanners, maybe as optional software package. This will give you very good image quality at the cost of long acquisition time.

how old are them?

If less than, let's say, 9-10 days you might want to try to just cool them a bit. If you remove them from the incubator to a room at 22-24ºC and let them cool down for 20-30 min, they will enter a "rest" mode with almost no motion and a very low heart rate. When you finish you put them back in the incubator and they recover rapidly.

If they are less than 9 days old this will definetly work; if older, Just check with a few of them for survival

Good luck.

Multichannel receiver coils of greater density have the advantages listed above related to higher acceleration capabilities and greater surface SNR. Several complexities need to be considered when evaluating a coil for a given indication:

1. The old quadrature (single channel) head coils were far more uniform than phased array coils but have lower SNR, particularly at the cortical surface that PA coils. They also accentuated the central brain brightness commonly seen at 3T attributed to dielectric resonance effects (though this term may not be precise.)

2. The advent of phased array coil technology opened the door to parallel imaging and afforded greater SNR. This had the fortuitous virtue of greater signal at the superficial aspects of the brain, which balanced the 3T brightness of the central aspects of the brain.

3. Greater superficial signal with PA coils is due to the signal dropping by the square of the distance from a coil element. There is often a trade off of less "coil penetration" with greater coil density (higher number of coil elements) so that the fall off of signal at deep brain structures becomes more problematic.

4. Newer technology seems to be better at addressing #3 and I am looking forward to working with the Siemens 64 channel Head and Neck coil that recently received FDA approval.

5. Depending on your vendor and the sequences at your disposal, image intensity corrections may have an impact on the discussion. There are uniformity issues inherent in some of these algorithms. GE has SCIC and PURE and Siemens has "prescan normalize" and I would be grateful for insight from others as to the appropriateness of using these in fMRI data collection and analysis.

6. Dual transmit capabilities may also impact the discussion of uniformity. Looking forward to a Prisma installation this Spring, but again I would be interested in others experience.

Mark

Well, the ZOOMit has the advantage that you can make the FoV smaller without foldover artifacts. Therefore you can either reduce the measurement time or get a higher resolution with the same measurement time compared to "standard" sequences. Currently you are limited to EPI based sequences and a 3D SPACE sequence. Standard GRE/TSE/etc. sequences won't work as ZOOMit.

If I understand you correctly, Maxwell equations are what you are looking for. I suggest you to try to google:

"Maxwell equations cylindrical coordinates"

"Maxwell equations spherical coordinates"

Usually, there is an analytic solution only in simple cases. To find electromagnetic field in more complex cases, you may need to do some FEM modeling.

Hi all - interesting and practical discussion here, I'd like to join in with a couple of points.

1). Somnath: I completely agree with prior posters on two key points: 1). unlabeling 13C for I/L should work well; 15N can be problematic. 2). Asp will be challenging. With a bit more information about your application (e.g. do you need to completely unlabel all of the aa, or just sidechain? can you confirm 13C is what you need?), I think there are some good resources that you can be pointed to.

2). Loren: Interesting idea to handle the poor solubility of certain aa, but I've got a slight concern about the potential for setting up two distinct pools of bacteria when you're inducing: one which has seen the aa for only 1 hour, and another which has seen the aa for 1hr + preceding incubation time. As such, one could potentially produce protein in a sample with two different labeling efficiencies but overlapping signals in many experiments. For many experiments, this wouldn't have an effect; for others, particularly those where you're looking to quantitate (isotope-filtered NOESY?), one might interpret intensity differences incorrectly as a result.

This concern might simply be academic --- aa uptake/incorporation rates might be fast compared to various incubation times, quantitation might not be needed, etc. --- but I think I'd err on the safe side by growing that initial culture as simply 700mL, reserving the other 300mL to incubate the aa without bacteria present. Then add as you suggest.

This last comment makes a very important point. Even with the best and most careful registration, you still have no way to confirm that voxels are perfectly matched in a normalized brain space. The other option is to work in native space and create your regions using anatomical boundaries within each individual, which can account for individual differences in anatomy. You can then make strong claims that you are looking at the same brain structure, within a certain degree of confidence based on your inter-rater reliability.

Digital filtering would produce spurious peak(s). Analog acquisition results in severe base-line distortion. These effects disappear if you use appropriate receiver gain and reduced pulse length.

The general rule of thumb is that if you predicted changes in particular areas before you collected the data, you are justified in reporting uncorrected results. But you must explain your a priori hypothesis. If you didn't have any idea where you would see changes appear in the brain, you must use corrected data. For TBSS presentation, a threshold of p<0.05 corrected is considered acceptable.

Tim

Hi all. I found a working solution. The data are in Siemens IMA format and Tarquin does most of what I need along with this Matlab script: http://www.mathworks.com/matlabcentral/fileexchange/9768-dicom-utilities

For instance would it be possible to project the magnetic source dipoles onto the MRI image to place them upon the shape of the brain?