Mri Research Study Template: Fill & Download for Free

This is one of those questions where I am not quite sure if the OP has a very technical research methods question that I may have misunderstood, or if the OP is a layperson that has come across these terms while reading through research papers. 'Region labeling' IS, in fact a big concern in the field of neuroimaging ... but 'tracer injection sites' aren't directly related.There are actually several types of 'connectivity' that can be imaged/measured, which are described in detail here by Karl Friston.1) anatomical connectivity -- physical connections between neurons.One way to look at anatomical connectivity in brain tissue is to use Anterograde or retrograde tracing. This involves injection of a molecule (or viral particle) that is taken up by the axons of neurons all the way back to the cell body (or vice versa), thus "tracing" out the part that neuron takes through the brain tissue. An example of the best type of this work was recently done by the Allen Brain Institute, where researchers created a "mesoscale connectome of the mouse brain" using such methods. If you read through the paper, you will find that they dealt with the variability of this technique by scanning many, many, many brains AND using multiple, precisely mapped injection sites. To label regions, a template atlas was used -- basically an average high-res image of a mouse brain. Brain images can be aligned and sort of morphed to fit a template atlas-- and that's where there can be errors introduced in labeling of regions. The paper has an immense amount of detail about how they dealt with these issues. (here's a summary figure of their process):In living, human brains, most anatomical connectivity studies are done using Diffusion MRI, which does not require injection of a tracer. Instead by mapping the diffusion properties of water in specific places in the brain, anatomical information is extracted about where neurons are connected. Basically, diffusion MRI looks at how water flows or does not flow around axons and nerve fibers, and then deduces from that precise anatomy.Region labelling of diffusion MRI is done by placing the diffusion information on top of an anatomical scan of the brain. And once, again, based on how you choose to mathematically line up (or "coregister") the image of white matter tracts with the image of overall anatomy, you can introduce errors into where your connections actually lead.2) functional connectivity: temporal connections -- that is, if two areas of the brain consistently activate together under the same stimulus (in the context of a neuroimaging study), they are said to be functionally connected.Studies of functional connectivity can use MRI, EEG, PET, and other brain scanning methods. Apart from PET, none of these methods involve injection of tracer. And in the case of PET, tracer is always injected via an artery (usually the radial artery on the wrist). The tracer then circulates and after a delay, is taken up into the brain. The injection site, speed and timing, can affect how long and how much of the tracer makes it up into the brain.Regional labeling in these sorts of studies is a fairly involved process, one which many people have spent a great deal of time grappling with (including yours truly), and again, is usually done with the help of a reference atlas, and the main sources of error is the resolution of your atlas, and also how you process and line up the atlas with your test image. I went into in more detail here: How do you create ROIs of different regions of your brain?*There's also "effective connectivity" but that is likely going out of the scope of this question.

Disclaimer: My answer is completely based on my academic experiences and interaction with radiologists as an undergrad and grad student who studies Biomedical engineering, Image processing and Computer Vision.To look at Computer Vision being applied in medical imaging from a bird’s eye view, I would say that most research work can be grouped into two main categories:1) Augmenting the functionality/efficiency of Radiologists:This category has been more successful than its counterpart, for good reason. As we know, the time of a radiologist is really valuable (and very expensive), so anything to aid their functionality would be a good idea. Few examples of this category would be:(i) Finding the dimensions of various organs(ii) Tools to change contrast/brightness etc.. of the images(iii) Building 3D models of organs from series of 2D images(iv) Segmenting an organ from images, to help in better viewing and detailed study(v) Registering a template to an organ in the image, for comparative study2) Building stand-alone decision systems:With tremendous improvements in Machine Learning (ML), some vision scientists are urging the community to rely more on the ML algorithm to do it’s job, rather than trying to further improve the image features/descriptors. This has its manifestations in Medical Image analysis as well, it has led to the development of stand-alone decision systems. For example, building a tool that is capable of classifying a tumor in Mammogram or even a Brain MRI as malignant vs benign on a well labelled training and test data.In medical imaging research, they are papers that show even >95% accuracy. However, in reality, we have other concerns than just the accuracy of a classifier. For example, consider the cost of labelling a cancerous tumor as benign. From the classifier’s point of view, it’s just one instance of an error. On the other hand, if we rely only on this system, the doctor may not have prescribed the necessary treatments which may ultimately lead to the death of the patient. False negatives and false positives have serious consequences in medical imaging. This is one of the reasons why stand-alone decision systems are not very well adopted despite their good performances. And in labs/hospitals where such systems are used, they are still supervised by the radiologist.Most medical imaging problems are usually translated into the following CV/IP tasks [1]:1) SegmentationExample: Segmenting the gray matter vs white matter in the brain, Segmenting the cartilage from human knee.2) RegistrationExample: Registering an organ of a subject across images from different imaging modalities (such as CT, MRI etc..), Registering an image onto a model (deformable models).3) VisualizationSometimes we need to view surfaces, like the human retina, where a 2D image would not do justice to the actual anatomy. Computer’s 3D models are better than human’s imagination of the same 3D structure from series of 2D images.Machine learning:-People also work on building systems that learn patterns of disease presentation in images and predict (unlabelled images). However I’m not sure about their usage in hospital environment at present times.-Off late, tasks such as content based image retrieval are also growing. It helps radiologists to view examples of patients with similar diseases/characteristics which help them make better decisions.Why medical imaging is a hard task:1) The consequences of false negatives, false positives etc..2) Anatomical structures when captured from most imaging modalities, do not have well defined edges. Hence, problems such as segmentation etc, are harder in medical imaging.3) Lack of generalization. For most organs, each individual might have a different structure and hence learning a model or building a system that automatically detects/segments anatomical structures are not so easy. In a traditional CV problem, for example: Detecting cars in a surveillance camera recording, the object (car) usually has defined edges and we also know that the cars more or less take certain range of shapes [square, rectangle]. Such assumptions cannot be made in medical imaging domain, making it harder.4) It is hard to build a system that can capture fully what a radiologist does. There is a lot of “rule of thumb” scenario in medical imaging and medicine in general.5) FDA and similar such organizations in each country are skeptical (and rightly so) about using the automated processing systems in hospitals and labs.6) People are worried that such systems would replace the radiologists leading to unemployment. [This is too far into the future, at present, people are rather trying to augment the function of a radiologist and improving the set of tools available for them].Why medical imaging is beginning to grow:1) Research is well funded. (Read: Many PhDs can graduate)2) Radiologists themselves feel the need for more tools, to help them do their task better. And this manifests in a lot of collaborative work between academicians (engineers/scientists) with the personnel at hospital.3) My professor once said “In the future would you wait in line for long hours to meet the radiologist, or would you rather throw an image to few lines of code and get the result”. However far fetched this might look, it would be possible some day and that is a good motivation.[1]Lecture 1 of Methods in Medical Image AnalysisP.S: If there are points that you disagree with, please mention it in the comments. I'll edit the post as necessary.

There are many - if not only - benefits to being slighty crazy, none of them are surprising - atleast not to the crazy people.Before we continue, writing “slightly crazy” is not very effective, so I'll call it SC from now on, or SC potencial/quota e.g SQ.I could do SCQ to get the initials for all the words (Slighty Crazy Quota), but that will never trend or be adopted - and since quota means anything from nothing to maximum, hence “slighty” is some where in that range, we can drop the “slighty” and just go for crazy quota.Behold, CQ.With that out of the way, what are the benefits of a high CQ. To figure that out we need to think about what “crazy” is, so let's look at it's opposite: “normal”.Normal (and crazy) are relative and subjective terms depending on societal preference, e.g. flock-mentality.What the majority decides to be “normal” is considered the template of behavior patterns and conformity within that society. Anything opposite or unfamiliar can be considered abnormal - or crazy.So let's look at whats considered normal by majority:Most of us eat flesh from industrially tortured animals, myself included - meaning I'm a hypocrite, I feel guilty, it's evilput into system yet I love meat so I continue. Most of us also believe pseudo-science arguments that humans are omnivores.If you do research you will quickly find out that humans have - as with other planteaters - 6 times longer intestants to be able to break down fibers and get as much nutrients out of vegetation as possible, we also have jaws that can move sideways to “grind” plants, leafs, and flat teeth to help with that task. We also have no enzymes that breaks down acid in our blood.Carnivores however have only up-down motion locked jaws with razor sharp teeth to rip and cut meat and tough skin, they have short intestants to prevent the meat from rotting inside, they also have enzymes to break down acid in the blood, they don't need calsium.This is easy knowledge, yet most just trust a majority opinion that we are “omnivores”, yay - feel better about eating meat. Which brings me to another normal:Most people define true/false/right/wrong from - you guessed it: societal preference, yet again flock-mentality triumphs, oh how great it is to be normal - god forbid to research, study, seek answers/evidence/documentation or even talk with the source subject directly, that would be too energy-consuming, better let majority make decisions for you.Another normal is to complian about world suffering, slavery, hunger, to show that we care, yet the very devices (phones, pc, pads etc) we use to complain about these issues contains metals dug out from mines in ex. Indonesia, by child slaves whom often die of exhaustion after a while. It is hence the norm to use a device that kills children on the other side of the planet, to complain about suffering on the other side of the planet. Throw in all the plants and production-facilities used to produce our cheap clothing, also by the hard work of children getting payed less in year than most of us earn in a day.Not to mention meaningless violence 24/7 as entertainment on all media and channels for all ages, but if Janet Jackson shows her nipple (the most natural harmless thing in the world) by accident on national televison, well then it's a scandal. How could she? I mean, she must be crazy, right?What else is normal, ah - yes: Psychology. We have great trust in psychiatry, they have the power do decide who's crazy and who's not, but how? Do they have a ADHD blood-test? A bipolar MRI-scan? No, they have something much better: opinion.Yes, that's right, they have a licence to have an opinion about what they believe to be normal and healthy.Let's ignore for a second that ADHD-medicine with Aderall ™ on the throne has 14% of the world total pharmaceutical net worth of 1008.6 billion USD, and lets ignore for a second that almost every doctor and psychiatrist gets a 100–800USD fund for each patient they can diagnose with ADHD to put them on a medical regime, and lets forget that ADHD-diagnoses have increased 3600% in 19 years, all without a single scan medical or biological test, all based on opinion, how great is that? That's the norm.Tests show that if you ask 10 psychiatrists to diagnose a (healthy) patient x, you get atleast 5 different diagnoses every single time.So all of the above is normal, and your'e asking the benefits of being the opposite of normal, e.g slighty crazy?Lets go back to CQ, having a high CQ allows you to see the insanity or normalcy, to be curious and fearlessly act on it to break the established truths and dictate the new normal, to progress and innovate, to create an impact ny shocking all the normals out there with your genuis half crazy ideas and “what if”s.High CQ allows you to break free of the narrative of the conformity box without worrying about what others might think, because you have your own compass of right and wrong based on something else than “majority opinion”.I could stretch this further and proclaim that CQ is really indeed IQ, but alas - even IQ has been put under norms of conformity by a elite high IQ-society that seriously believes that mapping only 4 (max 43 with WAIS) out of 2000+ funtions linked to intelligence in IQ-tests frozen in a single snapshot in time assesed and administered by humans, biased by it's creators actually are accurate. I refer again to how “accurate” psychiatry is.Don't get me wrong, both IQ-tests and psychology has an important role, but they are only accurate sciences for relative median group analysis. Both are completaly useless for individual use and accurate assesment of individuals, there are too many incertainties involved on individ levels to trust the accuracy on individual scale.Oh - I forgot one last thing that is normal: Making clubs for adults to believe in adult Santa Claus (also known as “God”) and enforcing actual rules on how to believe it, how to practice it in daily life. Infact 95% of the world population does it, and it has killed millions and been the major cause of all wars the last 10.000 years. The joy of normality never seems to stop.So no SQ is not IQ, and I proclaim SQ as the new measure norm, not IQ. I can do that because I make the rules, after all - I made SQ - because im slighty crazy, your'e welcome.I will finish of by quoting the infamous high SQ George Benard Shaw, the man behind “My fair lady”, born 1856:“The reasonable man adapts himself to the world: the unreasonable one persists in trying to adapt the world to himself. Therefore all progress depends on the unreasonable man.”..And this picture: