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An electromagnetic wave able to pass through many materials opaque to light.

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X-ray of Deep Brain stimulation for OCD.

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Magnetic resonance imaging is a medical imaging technique used in radiology to form pictures of the anatomy and the physiological processes of the body in both health and disease. MRI scanners use strong magnetic fields, magnetic field gradients, and radio waves to generate images of the organs in the body.

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A CT scan, also known as a computed tomography scan, uses X-ray measurements to produce cross-sectional images of the brain

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An electroencephalogram (EEG) is a test that detects electrical activity in your brain using small, metal discs (electrodes) attached to your scalp.

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Positron emission tomography (PET)  is a nuclear medical imaging test that looks for a variety of diseases in your body using radioactive substances.

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Functional magnetic resonance imaging or functional MRI (fMRI) measures brain activity by detecting changes associated with blood flow. This technique relies on the fact that cerebral blood flow and neuronal activation are coupled.

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Cancer Type | PET Scan | CT or mammogram
Lung Cancer | 81% | 52%
Colon Cancer | 95% | 68%
Breast Cancer | 85% | 67%

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Normal brain MRI

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scan lesions mri findings human brain covid incidental findings brain tumor normal brain brain abnormalities brain scans

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A SPECT scan is a type of nuclear imaging test, which means it uses a radioactive substance and a special camera to create 3-D pictures

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Early Alzheimer's

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fMRI scans are used to take pictures of the brain to understand what changes occur when blood flow changes. fMRI is often used to diagnose weak, diseased, or dead tissue in the brain.

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fMRI scans are used to take pictures of the brain to understand what changes occur when blood flow changes. fMRI is often used to diagnose weak, diseased, or dead tissue in the brain.

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Fluoroscopy is a type of medical imaging that shows a continuous X-ray image on a monitor, much like an X-ray movie. During a fluoroscopy procedure, an X-ray beam is passed through the body.

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Lesions in the central nervous system (CNS) are aggressive and frequently fatal. Individuals diagnosed with CNS tumors often suffer from disabilities caused by dysfunctional neurological state and deterioration in systemic activity, leading to relative short expected life-span post diagnosis. Lesions of the CNS are highly variable in both position and biochemical activity, with no common origin pinpointed to date. Causes for CNS lesions range between injuries, strokes, exposure to chemical or radiation, metastases, infection, genetic abnormalities, whereas other lesions' origin remains elusive. The World Health Organization (WHO, 2007) has classified CNS tumors into a total of more than 50 subtypes, with new entries presented annually. An estimated 80,000 new cases of primary malignant cases will be diagnosed in 2016 in the US alone, with incident rates increasing annually.

Brain lesion detection is most commonly performed by Magnetic Resonance Imaging (MRI). MRI enables a relative clear view of CNS tissues and distinguishes soft white and gray matter in the brain after injection of contrasting agents (gadolinium). Monitoring the characteristics of the lesion, such as its size, shape, position, together with its histological composition and rate of spread, allows oncologists to grade the tumor according to scale of severity and to decide a course of action.

Physiological features of the tumor are an important aspect in the central nervous system since the visible size in MRI and the actual size and geometry might significantly differ. The soft matter in the brain provides means for tumors (glioblastoma, see figure above) to spread beyond the bulky sphere into a more elongated thin shape which invades surrounding tissues. Expert knowledge is required in such situations to distinguish edema from the tumor mass and estimate the invasion to neighboring tissues. According to the shape of the tumor, oncologists assess the extent of its spread to vital parts of the brain and plan a resection (when applicable) in a rational manner.

The use of imaging techniques in brain lesion detection can significantly reduce bias in diagnosis caused by oncologist subjectivity. MRI and other modalities used in practice (PET, CT, etc.) open up new opportunities for automated processes to detect, monitor, integrate information from other modalities and grade CNS lesions. Automatic computer vision and machine learning algorithms are constructed to segment MRI volumetric data into all voxels classified as lesions and all the rest, generating a binary classification problem.

Due to the unpredictable shape of the tumor, statistical shape representations are employed in brain lesion detection algorithms. Such statistical models form as shape priors to restrict segmentation in later stages. Binary classification assigns each voxel to the most probable class assuming the shape prior (Bayesian theory). Prior models can be formed by training a classifier on a group of extracted features from the neighborhood of each voxel in a training set. The actual set of feature can include intensity values as well as Haar-like features. Optimization of the segmentation based on the prior is performed by max-flow/min-cut algorithm, which is optimal for this Bayesian classification problem.

An automated approach to segment irregular 3D shapes from MRI volumetric data can greatly assist oncologists in their prognosis of CNS lesions. Automated methods based on computer vision, image processing and machine learning methodologies are highly flexible to inclusion of expert knowledge and have been demonstrated in numerous applications to perform accurately and robustly.

Cutting edge algorithms to match cutting edge applications requires the hands of experts, like RSIP Vision's engineers and scientists. This is even truer for medical applications software.

Do you want to be always up to date regarding computer vision news and technology? Subscribe to Computer Vision News, it's free!

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Brain Lesion Detection in MRI Images with Graph-cut Algorithms

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Lesions in the central nervous system (CNS) are aggressive and frequently fatal. Individuals diagnosed with CNS tumors often suffer from disabilities caused by dysfunctional neurological state and deterioration in systemic activity, leading to relative short expected life-span post diagnosis. Lesions of the CNS are highly variable in both position and biochemical activity, with no common origin pinpointed to date.

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3D modeling technique used to visually represent nerve tracts using data collected by diffusion MRI.

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Psychedelic mushrooms have been used for medical, ceremonial and spiritual purposes for thousands of years, due their mind-alterating properties which induce hallucinations, perception disorders or altered states of awareness. It's been found that the active ingredient responsible for the psychedelic state, which many associated with a religious experience, is a substance called psilocybin. Though a lot is known about the substance, chemically-wise, how exactly it affects the brain remained unclear until now, but a recent studying involving fMRI brain scans changed all that.

Scientists at the Neuropsychopharmacology Unit at Imperial College London, used blood-oxygen level dependent (BOLD) functional MRI (fMRI), in conjunction with a technique that images the transition from normal, conscious state to psychedelic state, to scan the brains of volunteers. These were separated into two groups, the ones which were administrated with active psilocybin, and those who were given a placebo.

What the researchers found was a decreased blood flow and BOLD in the thalamus, anterior and posterior cingulate cortex, and medial prefrontal cortex. These areas of the brain are key connector hubs, which when faced with a decreased activity, enable a state of unconstrained cognition, which would explain the psychedelic effect.

The study, though highly successful, wasn't without hardship. Lead researcher Dr. Robin L. Carhart-Harris recalls how a number of impediments made their research one big tough nut to crack. For instance, an exact dosage and delivery protocol was necessary for accurate fMRI imaging, insulating placebo effects like pre-administration anxiety, and a number of other issues, which were fortunately resolved by the scientists.

Now that the exact regions of the brain where the psychedelic substance is active have been pinpointed, research which might lead to beneficial psychedelic drug therapy will not only commence soon, but will be taken more seriously by the rest of the community, as hard evidence of key receptors and their interaction with substances are presented. Psilocybin therapy might help a great deal people suffering from depression.

"Psilocybin decreases brain activity in regions such as the medial prefrontal cortex," Dr. Carhart explains, "that are overactive in depression."

In addition to depression, Carhart-Harris observes, there are other research and applications that might benefit from the team's findings.

"Those suffering from cluster headaches," he notes, "report excruciating pain that is difficult to treat, sometimes describing it as worse than the pain childbirth. During such headaches, they show an increase in hypothalamic activity to date has only been ameliorated by deep brain stimulation. However," he concludes, "when administered psilocybin, they display a decrease in hypothalamic activity and a corresponding suspension of cluster headaches."

The findings were reported in the journal PNAS.

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fMRI scans reveals how 'magic mushrooms' inflict psychedelic effect on the brain

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Psychedelic mushrooms have been used for medical, ceremonial and spiritual purposes for thousands of years, due their mind-alterating properties which induce hallucinations, perception disorders or altered states of awareness. It's been found that the active ingredient responsible for the psychedelic state, which many associated with a religious experience, is a substance called psilocybin.

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A Magnetic resonance imaging scan helps doctors take a look at what's happening inside your body. It uses a combination of strong magnetic fields and radio waves to produce the detailed look at things like your brain, spinal cord, bones, joints, heart, blood vessels and internal organs.

The one thing that you need to know about an MRI machine is that no metal is allowed inside the room in which it's housed, as it will get sucked into the machine by the powerful magnets.

Unfortunately for Rajesh Maru, he tragically died because he did just that.

Credit: Facebook

It's reported that the 32-year-old, from India, walked into a room in Nair Hospital carrying a metal, cylindrical oxygen tank. It's not yet clear why he was moving the tank into the room, but what is evident is that it played a serious role in his death.

Rajesh was rapidly pulled into the MRI machine and it's understood that at some point the tank ruptured - spilling liquid oxygen everywhere. The victim reportedly inhaled this toxic substance and died within 10 minutes, according to the the Independent.

An autopsy revealed Rajesh died as a result of a pneumothorax, otherwise known as a collapsed lung. ANI news agency says Mr Maru was in the hospital to see a sick relative.

Credit: Facebook

Right now you may be thinking, 'but we need oxygen to survive; it couldn't possibly kill you.' Liquid oxygen, however, is very different to the air we breathe.

7 days ago

It has a freezing point of −218.79 °C and a boiling point of −182.96 °C, meaning if it got into your body you would freeze, burn or simply explode.

This incident in India has led to police starting an investigation to see whether hospital negligence played a role in Rajesh's death. Police spokesman Deepak Deoraj released a statement saying: "We have arrested a doctor and another junior staff member under section 304 of the Indian penal code for causing death due to negligence."

CCTV footage of the horrific event has been handed over to authorities as a part of their investigation.

According to the Daily Mail, Mr Maru's uncle, Jitendra Maru says his nephew was assured the MRI machine was turned off: "The ward boy who was supposed to prevent such incidents told my family members to go inside when the machine was turned on. We are shocked and devastated."

Rajesh's family has been awarded 500,000 rupees (£5,590) in compensation.

Featured Image Credit: PA

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Man Killed After Being Sucked Into MRI Machine And Inhaling Liquid Oxygen

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A Magnetic resonance imaging scan helps doctors take a look at what's happening inside your body. It uses a combination of strong magnetic fields and radio waves to produce the detailed look at things like your brain, spinal cord, bones, joints, heart, blood vessels and internal organs.

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picture on left is  CT scan in upper mediastinum with lymph nodes and vessels in a patient with  lung cancer. The PET scan (on the right) lights up the structures that contain cancer, thus helping prove that the cancer has already spread to these lymph nodes and changing the patient's stage from stage I to stage III and eliminating surgery as a good treatment option.

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multiple sclerosis × lesions radiology ucl differential diagnosis primary progressive contrast mri spinal cord brain lesions cns symptoms

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same patient lower in the mediastinum, again the PET helps identify the malignant lymph nodes and avoids confusing these structures with normal blood vessels

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Over the years, scientists have developed many techniques to observe what's going on in the human brain as we think or move. Unfortunately, few of the insights we have made so far have resulted in any improvements in standard clinical mental care. Recent advances in neuroimaging may be changing this. Studies from the past few years have shown that various measures of brain activity and structure can predict who will develop certain mental disorders as well as how individuals will respond to particular treatments. In fact, measurements of brain activity can be better at diagnosing depression than standard clinical assessments. This is just one of many examples of modern neuroimaging techniques beginning to contribute to how we diagnose and treat diseases.

Developing tools to look at what's inside our heads

Despite centuries-or perhaps millennia-of questions about how the brain works, there weren't tools for looking at the living brain until the second half of the twentieth century. Before that, our knowledge of the brain's organization and function largely came from dissecting patients' brains after death and matching up any physical abnormalities with their reported symptoms during life.

Starting in the 1960s with the development of computerized axial tomography (CT or CAT scans) and magnetic resonance imaging (MRI), it became possible to take images of the structure of a living person's brain. Next, in the 1980s, scientists figured out how to produce functional brain images using positron emission tomography, better known as PET. Instead of one image of the brain's structure, they could now take snapshots of the brain over time to observe how brain activity changed.

However, a downside to PET is that it requires injecting the participant with a radioactive isotope, which is then tracked by the scanner while the participant performs a given task. The radioactivity is equivalent to what you'd receive normally just by living on Earth for about 5 years. Inspired by functional PET imaging, researchers figured out how to use an MRI scanner to create functional images without any radioactive isotopes.

Instead, the MRI scanner uses a large magnet to measure differences in various tissues in the body (Figure 2). During functional MRI, or fMRI, the scanner is specifically sensitive to oxygenated blood. Since active parts of the brain use up a lot of oxygen, new oxygen must be delivered through the blood stream. As a result, fMRI infers brain activity by tracking where oxygenated blood is increasing in the brain (see the image in Figure 1D). With this information we can look at brain activity differences between people with different mental states and disorders.

From voxels to discoveries: Analyzing brain imaging data

An fMRI scan results in a three-dimensional image made up of "voxels," or 3-D pixels. Each voxel represents a volume in the brain about 2-3 cubic millimeters in size-pretty small, but big enough to contain hundreds of thousands of neurons. With fMRI, the scanner takes one image every two seconds or so, resulting in a time series of brain images, each with hundreds of thousands of voxels. Each voxel for each time point has a particular level of activity. This activity is "Blood Oxygenation-Level Dependent, " meaning more oxygen leads to greater signal, so it goes by the name BOLD.

However, this time series isn't ready to be published as a pretty picture yet. There are differences from one image in the time series to the next that aren't caused just by changes in brain activity. This can be due to the participant moving in the scanner or even just due to normal breathing. If this isn't fixed before the images are combined, then a voxel that's in brain area A in one image could be in brain area B in another image! A computer program aligns all the images so they are lined up and can be compared across time.

After the fMRI data have been processed, they next go through rigorous statistical tests to figure out which parts of the brain were active during the scan. A common misconception is that only about 10% of your brain is doing anything at any particular time. In fact, your whole brain is always active! What this fMRI statistical analysis does is look for which brain areas are more or less active than the rest of the brain during a particular task. These differences are very small-BOLD activity might increase only 1% during a task-but historically, the differences have been noticeable if we combine results from many people, each of whom does the task a number of times.

Making new discoveries with brain imaging

Functional human brain imaging makes it possible for scientists and doctors to "see inside" the working brain. As a result, brain imaging can be used to investigate how the brain functions differently in people with various psychiatric disorders. Studies showed that during tasks involving emotions, people with depression had different activity in a region in the middle of the front of the brain than people without depression. Other studies showed that other frontal areas involved in attentional control are less active in people with attention deficit hyperactivity disorder.

Not all brain imaging discoveries had to do with how participants performed tasks. In fact, one of the biggest insights came from observing brain activity when people were asked to do nothing at all! When researchers looked at how similar activity across different brain areas was, they found fascinating networks of "resting state" activity.

What can brain imaging tell us about a single person?

New discoveries about brain functions and disorders are made using brain imaging every day. These findings tell us how most people use different brain areas, or how people with a particular disorder differ from people without that disorder as a group. However, it has been extremely difficult to use brain imaging to tell us information about a single person. So as we mentioned in the previous paragraph, people with depression process emotions differently than non-depressed people, on average. But we haven't been able to pinpoint whether one specific person processes emotions differently enough that they're probably depressed.

As mentioned earlier, some of the difficulty is due to BOLD activations being relatively small, so researchers need to collect data from 20 or 30 participants and average their results together in order to see any effects. Other issues arise from the computational challenges of brain imaging-each image can be made up of 100,000 voxels, so doing statistical analyses on these data have historically taken powerful computers a long time.

However, recent advances in computational power and machine learning algorithms have opened the door to single-subject data analysis. Faster, more powerful computers means that we can use higher resolution brain scans with more sophisticated processing steps. Improved data processing steps result in "cleaner" data and make BOLD activity easier to see. This means participants don't have to do a task for quite as long, and we don't need to combine results from as many participants in order to see brain activity differences.

Machine learning algorithms-along the same lines as what Netflix uses to recommend you movies based on what you've enjoyed previously-make predictions about new data based on earlier data. (See the special edition article on computational neuroscience for more.) These same principles can be applied to neuroimaging data. An algorithm can look at the fMRI activity of some participants, along with important information about the participants (such as their age, gender, or intelligence). Then, with only the fMRI activity of a new group of participants, the algorithm can "predict" information about this new group (like which ones are male, old, or average IQ).

In the past few years, neuroscientists have used these computational advances to investigate various mental health disorders. For instance, a study of patients with social anxiety disorder took brain scans before the patients underwent cognitive behavioral therapy. The patients looked at pictures of faces with various emotions. The study found that the patients who went on to respond best to the therapy had the greatest brain responses to angry faces before the therapy.

A recent study from MIT and Harvard Medical School scanned children whose parents have major depressive disorder. Children with a parent who is depressed are three times more likely to develop depression themselves. The study also scanned a group of children whose parents were not depressed. By looking at brain activity when there was no task, the researchers found that the resting state network was more synchronized with areas that process emotions in the brain in the at-risk children. That is, when the fMRI participants were lying in the scanner without an explicit task, the emotion-processing regions were active in similar patterns as the resting state network (which is shown in Figure 4),. This synchronicity could represent overactive emotion processing and could explain why children whose parents have depression are more susceptible to developing depression themselves.

Beyond the findings that compared the two groups directly, the researchers also trained a computer classification model to guess whether each child's brain activity makes them more likely to be in the at-risk group or the control group. The computer model based on brain activity ended up classifying the children correctly 80% of the time with very few misclassifications of control children as at-risk (20%). In contrast, a computer model that was built on behavioral tests that clinicians use to diagnose mental disorders was correct only 64% of the time, with many more misclassifications of controls as at-risk (73%). These results suggest that brain activity is actually a better predictor of whether someone is at risk for a psychiatric disorder than the behavioral data used by clinicians!

So are my next doctors an MRI and a computer?

Despite promising results using computer algorithms to predict whether someone has a disease or not, we won't be replacing MDs with MRIs anytime soon. For one, most studies have been successful at picking out who has a disorder from the pool of study participants that were included because they had the disorder (the test participants) or because they didn't (they were control participants). It is much more difficult to pick someone out of the general population and accurately predict which disorder they may have-the resting state networks of two very different disorders could actually look quite similar.

However, it is extremely encouraging that researchers may soon be able to make accurate medical predictions with a "data-driven" approach. Diagnosis of mental health disorders is notoriously subjective, with clinicians diagnosing patients differently. Recent advances in human brain imaging show that diagnosing medical disorders will be more consistent and more accurate in the near future.
Kevin Sitek is a graduate student in the Harvard Program in Speech and Hearing Bioscience and Technology and conducts research at the McGovern Institute for Brain Research at MIT.
This article is part of the April 2016 Special Edition on Neurotechnology.

For more information:
"Diagnosing depression before it starts" "Twenty years of fMRI" "Idle minds: Neuroscientists are trying to work out why the brain does so much when it seems to be doing nothing at all"
Featured image (top) created by Kevin Sitek.

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Can Computers Use Brain Scans to Diagnose Psychiatric Disorders? - Science in the News

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by Kevin Sitek figures by Daniel Utter Over the years, scientists have developed many techniques to observe what's going on in the human brain as we think or move. Unfortunately, few of the insights we have made so far have resulted in any improvements in standard clinical mental care.

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With and without contrast brain MRI after a stroke