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Questions related to Magnetic Resonance
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Hi
I'm trying to acquire raw data from Philips MRI.
I followed the save raw data procedures and then I obtained a .idx and a .log file.
I'm not sure if I implemented the procedure correctly.
Are .idx and .log file the file format of Philips MRI raw data?
If so, how to open these files? Is it possible to open these files in matlab?
Thanks
Judith
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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.
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Could anyone recommend some good book or other references on learning the basics of ODMR? I am very new to this technique so would like to learn the basics on how this technique works and the type of materials studied.
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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:
Optically detected magnetic resonance (ODMR) of photoexcited triplet states
(see attached pdf file)
Also interesting in this respect seems to be the following manual for a lab course on ODMR:
Optically detected magnetic resonance (ODMR)
This manual is also freely available as public full text (see attachment).
I hope this helps. Good luck with your work!
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When it comes to feeding the circuit with WPT, magnetic resonance method.
References voltages or the signal inside of the circuit keep fluctuate quite a lot when I wiggle or moves the circuit while it receiving the power from the TX antenna.
Differences of the H field might be the reason for that.
Is there anyone who knows how to handle this problem?
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Sungcheol Hong no of course, ask me and if i can i will answer you
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Hi,
I am looking for a dataset including paired low and high quality MR images of brain (healthy subject).
It would be great if the dataset includes multi contrasts such as T2-weighted, flair, etc.
I will be thankful if you share any related datasets with me.
Thanks
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This machine is said to conduct comprehensive tests in humans but I am yet to see scientific articles using this machine.
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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.
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This field being new to me, I have tried reading random research papers but it couldn't help me much.I want to know about entire setup starting from field generation in driver coil till power delivery to load.
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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😅.
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X-ray has featured prominently in the diagnosis of the coronavirus disease. Are there MRI studies on this disease?
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Yes! Please see the following PDF attachments.
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I am working on characterization of Brain Tumors using T1 and T2 relaxation and implication for MR fingerprinting. Fortunately, I found an article here https://pdfs.semanticscholar.org/f1a0/8882c6616100abe3427ca306733211405bf1.pdf that is close to our research question. However, I have been wondering if there are similar studies in recent time and at higher teslas. Your kind suggestions and recommendations would be very appreciated. Thanks
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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
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NMR (magnetic resonance) works on materials of odd number of protons. Copper with its atomic weight of 64 contains 29 protons and 35 neutrons. Would the technique work on 64Cu then? The extensions then goes, if NMR work on other metals as well.
Thanks!
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64Cu has a spin (parity) of 1+ and a magnetic moment of about -0.22 µN, 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:
  • Nuclear spins, magnetic moments and quadrupole moments of Cu isotopes from N = 28 to N = 46: probes for core polarization effects polarization effects. arXiv:1011.5420. Phys.Rev.C82:064311,2010. DOI: 10.1103/PhysRevC.82.064311.
  • Stone NJ. Table of nuclear magnetic dipole and electric quadrupole moments. INDC(NDS)-0658. 2014.
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We are analysing a fuel sample, we ish to conduct NMR on the Sample, we are thinking of seperating the fuel using fractional distillation before we conduct Nuclea Magnetic resonance test on each component. Kindly advise and share relevant materials
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According to my knowledge , we use the difference between T1 and T2 in MRI to make contrast . but I have no idea about The T2* time and what is the usage of that .
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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.
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I would appreciate it if someone explains the basic physical principles underlying the differences in appearance (image content, and tissue differentiation) among T1, T2, and PD weighted images.
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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:
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It is important as sometime this was treated as reference by the Magnetic resonance instrument by default as other reference like TMS etc were absent.... From 1 of the 3 papers i come to know about the temperature dependency of the HOD PEAK already!
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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!
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Magnetic resonance images are required. 
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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
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Self-made RF receiver coil is hard to connect and match to clinical MR scanner due to many reasons.
Can I do the workaround? I use manufacturer`s coil,e.g. small disc-shape receiver coil and connect my self-made coil to it via inductive coupling, say by conducting loop (connected with my coil) pressed to the manufacturer coil windinds? How many losses (mismatch, inductive losses, etc) do I get? Are the similar articles, attemps? Thank you for nuts and bolts!
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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
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MR image brightness very depends on relaxation time value. Relaxation time hugely depends on temperature. So the way to improve the contrast is to heat up or down a human tissue investigated in MR scanner. Are there any works on it for clinical MRI?
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Thank you Olaf for your talk. Best wishes
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There is the old IEEE article on MRI measurement of neuronal current by Dr. Ueno:
"Neuronal current distribution imaging using Magnetic Resonance" , IEEE Transactions on Magnetics, Vol. 5, No 35, 1999, p.4109
Now Bruker biospec resolution is about 30 micrometers in plane. Can we now make an image of neuronal currents in animal brain? Thank you for the articles.
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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.
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I need to use blood signal intensity in T1 MRI as reference in my project about brain tumors.What I have, are slices of MRI for entire brain. Is it possible to define blood signal in T1 MRI of brain? Your help is deeply appreciated in advance.
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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.
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(For the calculation, or at least rough approximation), of cerebral blood flow. Is there another way to estimate the signal intensity of fully relaxed blood spins? As per equation in Alsop DC et al., Magn Reson Med 2015.
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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
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Dear all
We have a GE 3T 750w 
I have a problem with the contrast-enhanced MR angio of the neck (see screenshot)
- we have a ring, like a "donut" around the vessels
Sometimes it is difficult to exclude e.g. a dissection, notably of smaller vessels such as the non-dominant vertebral artery
We use the standard CE MRA sequence
Do you see the same "donut" on your CE-MRA? 
If not, which kind of sequence, and which rate of Gd injection do you use?
Thanks for your help - very much appreciated to improve the image quality !
Happy new year !
Sven
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i didnt find any such protocol-- 
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When an EMF is induced there must be no effect on the system as the system is directly programmed to withstand?
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Hello,
flares generate so-called non-electromagnetic radiation. Russians had studied that phenomena.
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We wanted to change from manually segmentation to fully automated segmentation of MRI scans from the whole body. We like to measure the Volume of muscle mass (legs, arms, trunk) and fat mass (SAT, VAT) in adults and children. I found MIPAV as freeware, but is it really full automated and can segment whole body data? Does anyone now an easy tutorial for this software?  Or is there another validated software?
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Thanks for the informations Jerome, I will try to work it out with FSL.
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I want to implement the magnetic field around a 3D dipole in z direction in MATLAB and look at it slice by slice in all three directions. I have got an expression from internet which is ((mu)/r^3)*(3(z^2/r^2)-1). But it looks like an expression in 2-D space,whereas I am looking for an expression in 3D space.
And as per my knowledge, the 2D contour plot should be similar in yz and zx plane and the plot in xy plane look like a circle.Or am I missing a point here?
Attached are the plots obtained from the above expression. Also, the origin of the dipole in k-space is another issue. Right now I have set it at the centre of the dipole. I am not sure if it is right. Please help if someone has more expertise in this.
Thanks,
Guru
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The original expression "((mu)/r^3)*(3(z^2/r^2)-1)" looks like the z component of the magnetic field (i. e., like Bz) 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,mz] and m · r = mz z.
So, this should result in:
      Bx(r) = (µ0/4pi) [ 3 x (mz z) / r5 ]
      By(r) = (µ0/4pi) [ 3 y (mz z) / r5 ]
      Bz(r) = (µ0/4pi) [ 3 z (mz z) / r5 - mz / r3 ]
where the last line is basically your expression from above.
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I am wondering what are the ideal MRI scan parameters for both pre and postoperative DBS surgery for use with the software LEAD-DBS. I currently have postop scans that are not ideal and LEAD-DBS is having difficulty reading them. 
Our center uses a 1.5T with low SAR, but we are not opposed to increasing SAR slightly. 
Thank you,
Greydon
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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.
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Looking for devices to check metal objects that could be placed in MRI
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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.
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My custom made MRI coil has short tuning and matching sticks, so I need to put my hand in the magnet to tune and match. Whenever I remove my hand, the signal shifts. so I need to re-tuning and matching. Where can I buy those?
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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.
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What are SphereA and SphereB phantoms in an MRI?
What are the commonly used material for phantoms?
Is there any information regading the concentrations regarding the same?
Please shed some light on these aspects.
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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.
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Beyond vendor-specific definitions, what is the theoretical difference between FISP (fast imaging with steady-state precession) and bSSFP (balanced, steady-state free precession)?
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Current set ups is 3T Verio, with 12 -ch and 32-ch coils. Population - life-span (children, young adults and up to the 9th decade of life). Advice pertinent to longitudinal studies will be particularly appreciated. 
Sequences include but are not limited to MPRAGE, FLAIR, DTI, resting state BOLD, ASL and MRS (both 1H and 31P).
What are the differences and transition issues in acquisition and post-processing?
many thanks in advance.
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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
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In case of metallic antenna, we have electric resonance, while dielectric antenna have electric and magnetic resonance. Even, we can tune these resonance by changing the aspect ration of the dielectric disk. See page no 06 of the following article.
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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.
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This is less a question, more a request for confirmation of my understanding of some reading I've done. Is the following true?
For asymptotically flat solitons (generalisations of Bartnik and McKinnon's su(2) solutions), it appears that the asymptotic values of the gauge fields (i.e. for r tending to infinity) are fixed to certain integer values, which means that the tangential pressure P vanishes at infinity. This in turn means the solutions carry no global magnetic charge, for if they did, the Einstein equations would be singular at infinity.
If anyone could either confirm this, or else point out my error(s), I'd much appreciate it.
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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.
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How the spintronics is helpful for missile guidance ?
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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.
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I am currently dealing with functional connectivity at rest preprocessing (using DPARSFA v4). I've recently noticed that although most subjects have the same functional resting-state sequence (30 axial slices), some of them have a different sequence (29 axial slices). Both have been acquired in the same scanner, and they have same repetition time (2000ms), same echo time (30ms), same number of volumes, same matrix size (64x64) and same acquisition order (interleaved). They only differ between them in slice number (30 vs. 29) and in slice thickness (3.5mm vs. 4.5mm). The voxel size is equal in x and y axis (3.5mm), but they differ in z dimension. I was wondering if I can use both sets of images supposing I did different slice timing correction for each of them. After this can I follow the next steps (i.e: corregister, nuisance covariates regression, spatial normalization, smoothing and filter) as if they were comparable images? Anybody can help me? Thanks in advance.
PD: T1 structural 3d images have all equal parameters. The aim of this study is to explore seed-FC differences between groups.
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In addition to what's been mentioned before:
  1. If you are doing slice timing correction, make sure that the scanner behaves the same way with odd vs. even numbers of slices.  On many Siemens scanners, an even number of slices will begin with slice 1 and an odd number of slices will begin with slice 0.  This information isn't always written in the DICOM header.  
  2. You can model the differences between scanner parameters using a factor in your overall model.  Or you could just test it and if you don't see anything significant continue on.  
  3. Make sure that your spatial normalization ends up with images with the same resolution.  
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Maximum likelihood estimation for denoising of MR images. Could anybody tell me about what is the value of "A" in Maximum likelihood formulation?
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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({mi}|A, sigma) for the observation of several signal intensities {mi} in a region of constant signal intensity A. Does that answer your question?
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Hi!
I am looking for a Mac OS alternative to the above mentioned program. Besides basic functionality including zero filling, filtering, and peak assignment I am also looking for a quantitation tool similar to AMARES. Any suggestions?
Thanks!
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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!
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Thanks!
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I want to study aneurysm growth (in volume) in a set of subjects, based on Time of Flight images (MR) images at two time points.
For each subject, I want to do a rigid registration between time of flight images as well as a segmentation of the cerebral vascular system. And then, I want to compare the two registered segmentation volumes in order to quantify the aneurysm growth.
For doing this, some teams used in-house registration tools (not distributed as far as I know, AnToNIA for ex) and the registration is performed using a volume of interest around the aneurysm sac (and not using the whole brain).
I currently know free registration tools not specific to aneurysms (FLIRT of FSL for exemple: http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FLIRT). Additional question: Do you think these kind of methods is appropriate to do "aneurysms registration" ? 
Thanks in advance,
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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. 
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I will be the US liaison-consultant for an Australia project to quantitate therapeutic Y90 implanted in the liver across imaging manufacturers. I have never seen a PET/MR image of Y90, although PET/CT imaging of implanted Y90 has progressed rapidly. If you have an image to share, it would be much appreciated and can be kept within a very limited circle of professionals if desired.
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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...
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I've read the abstract. How do you explain that US is less sensitive but more specific than MRI in local staging of rectal cancer? I agree in full about the need of specific training (this rule is always true!), although the differences in MRI performances seem less understandable.
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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.
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In childhood onset adrenoleukodystrophy, 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?
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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
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Please also give me an idea about what type of oscillator we should use?
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increase the winding coil and good system design
you can use royer-type oscillator 
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I am currently working on MR diffusion model to monitor defects in human heart. Meanwhile, I am able to assume that diffusion coefficient is related to the relaxation rates (R1=1/T1 and R2=1/T2), I need experts to advise on which substance whose T1 and T2, I will have to use.
I need these features because the substance which is diffusing is expected to carry molecular signatures of the heart. Thank you.
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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
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I started to analyze the EPR spectra of Fe+3 system but it is new to me, I don't have enough experience in analyzing epr data. so how can I get the zero field splitting parameters (D,E) and hyperfine coupling constant from experimental data, I just calculated the g factor? I have been trying to use easyspin for the simulations but have been unable to get any results do to the lack of knowledge of easyspin. Can someone please help me in understanding easyspin?
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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.
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This parameter is critical to determine light coupling efficiencies.
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check the following link http://nordlander.rice.edu/miewidget
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When you acquire an MRS spectra you must use the absortion mode to improve visualization and spectral analysis, before quantificate to obtain relieble metabolite estimates. But, why the phase (in some 400 MHz acquisition) is coiled? What is the reason for that? Is it for the many NSA used in T1?
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Thank you, Victor.
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I want to solve a system with fatty acids information from its protonic matrix.
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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.
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UPDATE: I'm using non-parametric tests such as Wilcoxon signed-rank test for paired samples and Mann Whitney for independent samples. I've also looked into using MAD to exclude values but that is less preferable.
What are methods/decision rules to eliminate extreme beta values when standard deviation around the mean is not helpful?
Would it be a good idea to create a better fitting distribution (e.g. by bootstrapping?) of the output values and have a decision rule based on that? Any better ideas?
Thank you very much!
Nic
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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.
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For contrast enhancement.
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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.
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I came across the equation :
Lex (effective exchange rate) = 𝜋(Aeff/Keff)1/2
where;
Lex= effective exchange rate
Aeff = effective exchange thickness
Keff = effective anisotropy constant
when trying to seek the relation between the magnetic properties of NiAl thin film vs its particle size.
The literature review states that, with refinement of the particles reduces the Keff. which increases the Lex, hence causing the particles to be exchange-coupled and increasing the remnant magnetization.
However, a more detailed explanation is not given. I was wondering if anyone can help me to understand this equation better.
And also, if this equation would also explain the effect of smaller particle size towards the domain wall pinning?
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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?
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About magnetic transfer.
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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.
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Nuisance covariate regression (head motion, CSF and WM signal, global signal) in local connectivity measures (like Regional Homogeneity (ReHo), Amplitude of Low Frequency Fluctuations (ALFF and fALFF), Degree Centrality and Voxel-Mirrored Homotopic Connectivity (VMHC)) is still a matter of debate. What is your opinion? Is nuisance regression more effective before or after bandpass filtering?
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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.
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I'm trying to study a different filtering process (not as Gaussian FWHM usually used for fMRI), in a BOLD signal in a healthy control just to see if this new filter could provide a better activation segmented area. But I need some tips for making the more appropriate quality measurements for my research. What would be the best measurements for this purpose? Is it the root mean square error (RMSE) between the Gaussian filtering and my filtering approach in the activation segmented area a reasonable quality measurement?
I don't have much expertise in fMRI analysis, for this reason any suggestion from a specialist in the fMRI area will be a great help for me. I'm using FSL MELODIC tools to create the ICA for activation maps.
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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
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The solar system is characterised as a quantised lognormal distribution exhibiting spin-orbit coupling in my paper: “The Hum: log-normal distribution and planetary–solar resonance”. Does this indicate that the solar-magnetically braked proto-planetary disc of the early solar system was (and planetary spins and orbits still are) significantly affected by magnetism in addition to gravity?
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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.
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I can't find any validation studies with MRI, so not entirely convinced yet, but opinions very much appreciated.
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CT is by far the best to calculate disc height, I think both DXA and FRAX are less suited for that.
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I perform whole-body DWI on GE Optima 450w 1.5T scanner using BODY coil and noticed that DWI signal at the shoulders region is very weak. All other body regions are of good quality. In patients with lymphoma, which is my primary interest, enlarged lymph nodes around clavicles are not visible on DWI at all or, if very large, they do have increased signal but are deformed a lot, looks like elongated stripes. Using 8 channel coil do not helps. Does anybody have experience with DWI on GE scanners or may suggest possible solution how to improve DWI quality at the shoulders region?
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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.
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I am doing some experiment with chicken embryos. I need to keep them motionless as long as possible but not cause death. I went through some literatures which said "dropping" some anesthetics onto the CAM works. I tried to drop some anesthetics but it did not work. Is there some tricks? Or should I anesthetize them in different way?
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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.
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What do you think about using 8-channel head array coil?
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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
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To improve S/N and decrease distortions in deep small brain structures, which Siemens Skyra upgrade is the most crucial between TIMthx ZOOMit or channels upgrade and 64 channels coil?
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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.
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i have been trying to relate the B field to the E-field in MRI for assessment of safety issues. I have only got the relationship for a close loop system as show in the ATTACHMENT. I like to know if B and E have a relationship in Cartesian, cylindrical and spherical coordinates.
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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.
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For example, while working with selective unlabeling of Ubiquitin I used to add the selectively unlabeled amino acid at the time of transferring the culture to minimal media. But, scrambling effect (mis-incorporation of isotope labels at undesired sites) was the major problem. Then during induction-time (at the time of adding IPTG) I added the unlabeled amino acid, but the result was same.
Can anyone suggest what might be the proper time to add the unlabeled amino-acid to get the optimal labeling scheme of any protein without scrambling?
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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.
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I need to normalize two MR brain volumes in order to match them on a voxel by voxel basis. That means that I need perfect matching between the two brains, i.e. each point of each structure of the first should coincide with the same point (or at least the nearest voxel) of the second. For example the point (x,y) of the thalamus of the first brain should match the same point of the second one.
I tried to use SPM to do this because in the last few years I used it to conduct VBM analyses but I did not obtain an exact correspondence (the reason for which I reflected a lot on this kind of analysis).
Could you please suggest the best way to reach my objective?
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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.
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I am looking for a laboratory which has a 500 or 600 MHz Bruker NMR machine without digital filtering. I would like to send you one liquid sample in a NMR tube for a quick 1D NMR measurement which I would like to compare with my own measurement with digital filtering done on the same sample. After the measurement you can dispose the sample, no worries with mailing! I only need the data (zip-File by e-mail). The question is: how do hard-ware (Bessel-Type) and digital filters react on very high sample concentrations - which artifacts do the filters produce?
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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.
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I'm currently comparing the DTI data of three groups of subjects using TBSS.
At first glance I found significant differences among them by performing unpaired t-tests.
After removing two outliers, due to their clinical condition, and using a multiple comparison correction strategy I didn’t find significant differences in two of the comparisons anymore. I therefore tried to compare them without corrections (using a standard threshold) and I found the expected differences again.
Does anyone has the same problem? Do you think it possible to publish uncorrected data if they are reasonable?
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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
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I have some Siemens MR spectroscopy data and no access to a Siemens console. I need to be able to view the data and import the values into Matlab, etc. Do you know of any tools or resources to help with this? It would be much appreciated and it is time-sensitive.
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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
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The particular signals that I want to explore are on the limit of MRI accuracy because they have too small a period. Because they can be detected either by EEG or by MEG type systems the idea of integrating an MRI accurate image with a MEG type detector which is what Magnetic Source Imaging is all about, seems almost overkill in comparison to standard fMRI. However the signals are within the parameters of this type of detection equipment. I am wondering what would be involved in putting together an MSI laboratory since I am unfamiliar with anything more than the general idea.
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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?