Magnetic Resonance Imaging - Science method

Why not start with serial observations on how the different forms of neuroinflammation develop, Sneha Majumder?

In possibly considering the famous pieces of evidence presented at www.ms-info.net on the best-studied kind of neuroinflammation?

A3 and a4 fractures in neuro intact patients may be classified as B2 injuries and change initial treatment. I think for A3 and A4 when considering non operative management an Mri is highly recommended.

Hi, you would have to first create a mask image of the tumor using 3D Slicer segment editor or Mricron software (easy to use).

once you have generated a mask for the tumor area, use SPM12 segmentation method to generate additional mask (use the tutor template mask to edit that area out of segmentation process).Also combining the 4 independent mask in singe file you would have to use colorcode for each set of mask and use fslmaths command to combine them into single file. you have to make sure to carry out segmentation mask having same dimension as the raw data as lesion is going to be present in subject space not a template.

I assume that FDF files are data acquired on an Agilent/Varian scanner? If so, mrTools (https://github.com/justingardner/mrTools) contains a routine (fdf2nifti.m) for converting FDF files to NIFTI files. mrTools is a Matlab based fMRI analysis package. If you do a bit of Googling I think you can find other similar converters as well.

Hi,

maybe trying to run a despeckling filter to the images, removing features smaller than a certain amount of voxels?

Cortical blindness is in the middle of two extremes, 1. Anton's syndrome where patient is suffering from CB but denies and says that I am OKAY so much so that he/she tries to attempt crossing through wall or closed door. 2. Malingering. In true CB it better to straight away go for MRI brain to save time for management.

I agree with the previous comments; computed tomography is the most common because it is widely available and accessible.

The output table shows the degrees of freedom (df). The table also already tells you which modalities specifically differ. Please see my previous comments.

Thank you Sir.

yes it is possible, see the link below:

The following articles may help with your question:

it depends on your SPION properties including longitudinal and transverse relaxivity. Most SPION, especially with a core size above 5 nm, is a T2 contrast agent and if you have an ideal SPION, then a lower concentration of SPION is needed.

Pleasure Guelib Bouchra

Thanks for the answers ! In the meantime I have changed my job, and now work with another vendor :-)

Sven

Hello,

Our research group has developed and uses Pynovisão for feature extraction and processing with machine learning and deep learning algorithms. For feature extraction we have implemented: Color (HSV, RGB), texture (GLCM (gray-level co-occurrence matrix, Gabor filter, LBP-local binary patterns), Shape, and Gradients (Hu moments, HOG - histogram of oriented gradients). We have used this software for MRI images in a paper in press.

The Pynovisão is available at: http://git.inovisao.ucdb.br/inovisao/pynovisao

Hi, I'm sorry because I'm not sure if you mean exterior flesh as in skin. If that's the case then CT would be fine. If you're referring to ligaments, which attach bone to bone then an MRI is generally needed.

For example, I've attached two images, the first is a CT scan of a knee (named Knee CT). It's not my scan (I don't own it and did not perform the scan), but it gives you an idea of what you can see from a CT.

The second is a knee scan I've personally worked with in 3D slicer, this is an MRI scan ( named Knee MRI) and you can see the difference. In the CT scan you cannot really visualize the ligaments, while the MRI makes them very easy to see. They are there and you may be able to make them out, but it will be work to do it that way.

Again, I assume there is a slight language barrier here, so hopefully i'm answering the question you're asking. If not, just let me know and I'll do my best to explain further. Best of luck!

Hello

1. Change the image format.

2. Modify the modules.

3. If the image still encounters an error, you certainly did not observe a point in the defined code section, it will probably explain the reason for the error in the error section.

In the ([]) imshow field, set the resolution value range, put a number that may be so low that the image is completely dark, for example, write imshow (squeeze (V (:,:, round (end / 2))), [4000]).

The role of imaging in diagnosis of disease is quite extensive.

Esp in fields of oncology where in the not only in diagnosis but in pre operative planning, during the CCRT or in follow up of these cases, imaging is an important paraphernalia in management of the patients of malignancies like breast, colorectal, sarcomas, Musculoskeletal, gynecological as well as CNS malignancies.

The list is not exhaustive.

Regards

X-rays are enough for identification of arthritis. MRI would help to identify early arthritis and more importantly soft tissue problems.

Shahzad Akbar

Hi,

Have you already found the answer to your question?

EEG pearls

Atlas in pediatric EEG

For fetal brain 3D MRIs with manual segmentation, you can have a look to:

- the FeTA challenge 2021 dataset

- the spinal bifida spatio-temporal atlas

You can check the IXI dataset, it contains 600 3D MRIs of normal healthy adults including T1 MRIs.

One minor point: If you plan to use ROIs for tractography that are considerably small already in template space, transformation of these ROIs to subject space could effectively remove any ROI voxels due to (normally) smaller voxel sizes in subject space and the transformation itself. This could be one of the rare cases to transform the DWI data itself to the template space, but as Jerome Maller mentioned, the proper correction of the bvec tables is important.

Unfortunately, there’s no reliable way to do this with any current biochemical method.

Hopefully, this will become possible in the future.

thanks a lot! I´ll try it for matlab.

Otherwise, there is any way to do it directly in ITKsnap or another segmentation program (Osyrix, Horos, 3DSlicer)?

I used the GLCM along with the LBP to classify vegetation density from RGB satellite images please reefer to this study:

Thank you for your information Rafal Z Slapa

Thank you so much

I got image like this

This would be a great question to post in our new free medical imaging question and answer forum ( www.imagingQA.com ), there are a number of MRI / MRS experts in the community. If useful, please feel free to open a new topic at the link below :

Service Unavailable Reza Amini Gougeh

Hi Driss,

You could check out the free datasets listed on www.imagingQA.com at the following link :

If you don't find what your looking for, its free to join so please open a new discussion topic (there are lots of data scientists and imaging specialists in the community) :

Olaf Dietrich, on a non-clinical scanner you could probably try something like that. But you most likely would not be able to tune the ¹H transmit coil to the ¹⁹F frequency, without at least replacing capacitors. Trying to pulse at the ¹⁹F frequency without re-tuning, will result in a large amount of reflected power which will either trip the safety system that protects the RF amplifier or damage the RF amplifier.

On a clinical scanner, I doubt the scanner software will permit shifting the transmit frequency that far without an x-nuclei license. If you are able to, the prescan should detect that the transmit coil is not tuned and cancel the scan (and probably give you a pop-up window saying the RF coil is damaged).

google search

Thank you very much all those who have helped me.

This would be a great question to post in our new free medical imaging question and answer forum ( www.imagingQA.com ), there are a number of MRI experts in the community. If useful, please feel free to open a new topic at the link below :

Try American society of radiology website

Although elevated ALT levels often signify ongoing hepatic inflammation, many patients with chronic liver disease and progressive fibrosis may have average values. Thus, a “normal” aminotransferase value does not exist in clinical medicine.

You can find what you ask for in databases like https://portal.brain-map.org/explore/toolkit/neuroimaging OR you can use the healthy hemisphere for control

BIRADS 3 ( Suspicious lesion)

dear all,

thank you for your answers. i believe I was a little bit naive, thinking this technology would be more accessible by now :)

we managed to change out setup, so a touchpad is not necessary anymore.

thanks

If the existence of a pathology does not matter you can use TCIA .

You can find any kind of data set in there and I assume it is free

Generally when a vendor gives a spec for gradient strength, they are referring to the maximum gradient an individual gradient coil can produce. However, as Samuel mentioned there are 3 gradient coils(X, Y, Z), if you pulse them simultaneously you get a gradient along the diagonal that is stronger than what any of the individual coils can produce. if you pulse the three gradient coils at 45 mT/m, the gradient along the diagonal would be 78 mT/m. So the vendor advertising this vector amplitude, is trying to make it sound like their gradient coils are stronger than they actually are.

Maximum gradient strength really only matters for DTI. The higher your maximum gradient strength, the larger the maximum b-value you can achieve at a give TE. Or conversely you can achieve the same maximum b-value at a shorter TE, if you have stronger gradients. The shorter TE means higher SNR. For DTI experiments, you want to use the same b-value in many different directions, so the maximum gradient strength of the individual gradient coils is the important number. For the system you have mentioned, 78 mT/m can only be achieved for 3 direction.

For other demanding pulse sequences, such as EPI, the gradient coil slew-rate is much more important than the maximum gradient strength. The optimum gradient amplitude for typical read-out gradient pulses or slice selection gradient pulses, is usually well below the maximum gradient that available gradient coils can achieve. However the slew-rates are all software limited in order to avoid peripheral nerve stimulation, so the vendors don't talk much about slew-rate.

Hi,

With surface coils, the image will be of better quality. I recommend SE T1W, FSE T2W, PD, STIR/ T1W FAT SAT and DWI. Anatomical detail is good in SE T1W sequence, while T2W and PD sequences show pathology well, DWI and fat-suppressed sequences provide better characterization of pathology.

Contrast-enhanced imaging is absolutely necessary in patients without contrast allergy.

Technical parameters; TR 5000-6000ms, TE 80-110ms, NEX (AVRAGE) 7, matrix

192x192, section thickness 5.0mm/1.0mm (10-20% GAP), FOV 210-230, phase direction A>P, b value 0-800/1000 should be preferred as.

Hope it helps, have a nice day

Try searching the image dataset library here :

Regards

Dear Krzystof

Are you interested with a test data with high b-values weighted diffusion MRI ?

matrix size of 196×196×16 (field of view=2.7cm×2cm×1.2cm)

native resolution 0.137755102 x 0.102040816 x 0.75 mm

31 b-values :

6 independent directions

As the b-value increase the signal decrease , then the SNR decrease but with 31x6 equivalent estimations of the noise baseline level

Images of animals. One hemisphere with high SNR, the contralateral has less signal, in the same image (pathological model).

You should have the noisy to ultra noisy image you want to deal with.

dear Sreepriya P.

Look at the link, maybe useful

Dear Deevyankar Agarwal

I know that training neural network models in Python is up to 15% faster compared to that of R.

The value of sNfL as a sensitive and clinically meaningful blood biomarker to monitor tissue damage and the effects of therapies in MS, so i think , if there is active lesions in MRI, the serum and CSF NFL will be abnormal. we need to study this important correlation on our patients with MS

Good morning, any news?

Hi,

PET scan with radio tagging of molecules has been used to identify the density and distribution of specific groups of neurons, mostly in pharmacodynamic studies..This has been employed on Dopaminergic neuron distribution and density in Parkinsonism and Schizophrenia.

Probably you will get leads from some of these articles:

Falkenburger BH, Saridaki T, Dinter E. Cellular models for Parkinson's disease. J Neurochem. 2016 Oct;139 Suppl 1:121-130. doi: 10.1111/jnc.13618

Tomasella E, Falasco G, Urrutia L, Bechelli L, Padilla L, Gelman DM. Impaired brain glucose metabolism and presynaptic dopaminergic functioning in a mouse model of schizophrenia. EJNMMI Res. 2020 Apr 17;10(1):39. doi: 10.1186/s13550-020-00629-x

Bossert I, Marinelli L, Castaldi A, De Carli F, Campus C, Abbruzzese G, Nobili F. Brain (18)F-DOPA PET and cognition in de novo Parkinson's disease. Eur J Nucl Med Mol Imaging. 2015 Jun;42(7):1062-70. doi: 10.1007/s00259-015-3039-0

Welcome Bharathi Ak

I've done similar DCE-MRI evaluations with "3D Slicer" <https://www.slicer.org/> using the "MultiVolumeExplorer", the "SegmentEditor", and, for the actual evaluation, the Module "PkModeling" <https://www.slicer.org/wiki/Documentation/4.10/Modules/PkModeling>.

An alternative might be MITK <https://www.mitk.org/wiki/The_Medical_Imaging_Interaction_Toolkit_(MITK)> with the MITK-ModelFit module (may require building MITK from source to include this module).

Dear Nelson,

You need high computational power for the analysis of MRI images. Such as: with Intelr CoreTM i7-8700k CPU 3.20 GHz, 32GB RAM and GeForce RTX 2080 8 GB.

Thank you!

By MRI with contrast study

I would recommend looking at these two tools:

hey segmentation expert there a way to compare stl image of root canal volume of a lower six exporeted from itsnap in a way that i can compare geometric changes

Hi, you may use mango Dicom viewer and can add overlays on Dicom.

here is the link

I second Allen's response that raw MRI signals (including BOLD fMRI) are generally arbitrarily scaled and do not have a direct quantitative meaning. For this reason, percent signal change is more meaningful in BOLD studies (although it too is not a direct measure of a single physiological process).

One thing I would add is that the absolute value does start to become meaningful in the context of signal-to-noise ratio (SNR) or temporal SNR (tSNR). tSNR is one of the most important factors for predicting your ability to detect a significant signal change. If it is too low, noise in the images will dominate, and you will need to perform many more runs or subjects to achieve a desired statistical significance. For instance, if you are acquiring BOLD data on the same scanner with the exact same sequence, and with one set of parameters (e.g., voxel size, TR, etc.) you get intensities that are 100x greater than for another set of parameters, these two data sets will likely have very different SNRs (and probably tSNRs).

For more on these SNR relationships, you can check out papers from Christina Triantafyllou et al., e.g.,

Dr. Chu, Decoupling the coils in the array eliminates the splitting effect. This is easily seen by on a S12 or S22 measurement on a spectrum analyzer where reducing the overlap of two circular resonant RF coils to 11% of the coil area will reach a minimum and the spectral splitting will collapse.

This is described in the original NMR array coil paper by Roemer et. al - Magnetic Resonance in Medicine 16, 192-225 (1990). Since that paper additional methods that use capacitive and low-impedence pre-amps have been used to reduce coupling. The inductive and capacitive methods allow a high current to be run in the conductor to produce a strong RF magnetic field. The low-impedence amplifier technique reduces coil current to a very low value to reduce the coupling field. The first two techniques are strongly configuration dependent. The latter not as sensitive to orientation.

There is an extensive literature in this field. Much larger than can be listed here. I' m attaching the Roemer paper for your use.

Good luck in your research.

I have YOLO-based algorithm dataset annotations for MRI brain dataset.

You can message me for more information. Refat Khan Pathan

Hira Waqar

One can try the hysteresis ( Magnetization (B-H) curve) experiment for knowing how long does the nanoparticle retains the magnetization.

Try this link:

Best regards

There are different experimental procedures to investigate cellular Oxygen Concentration, like Microelectrodes, EPR (electron paramagnetic resonance), and optical methods.

For details go through the link

You can do sample cross-correlation, but for the fMRI data, you have to reshape the data matrix or write the function by yourself. MATLAB cross-correlation only covers one-dimensional data.

I suggest :

Best regards

Check the following links:

Thanks a lot Babak Saravi, I've made headway with identifying methods and tools for my work but your comprehensive answer certainly adds some avenues to explore. Cheers, Ben

It depends on how you are going to analyze the data. If you are going to use CONN Toolbox, you can easily define two sessions for each subject, then you don't need to merge the data.

If possible try to get from original MRI unit by downloading images of scans of MR Pelvis.

If you need if for any research or project purpose.

'Raw data'  :https://www.radiology-tip.com/serv1.php?type=db1&dbs=Raw%20data

Hi. You can try this journal which is an Open Access Journal: Open Journal of Medical Imaging (https://www.scirp.org/journal/ojmi/)

I would suggest to collaborate with nearby hospital and get the MRI because later you have to mention it the research papers from where you have received the MRI and its details like slice thickness, Age, gender etc.