Archive for category Radiology

[WEB PAGE] Taking magnetic resonance imaging into a new dimension

EU-funded ATTRACT consortium explores how to enrich MRI scans with mixed reality headsets and other high-tech wizardry.

Copyright: MRBrainS

The complexity of the brain makes operating on this organ one of the most challenging tasks in medicine. But a group of Italian researchers is trying to make intricate neurosurgery easier with holographic brain-mapping software, which highlights and labels crucial areas and blood vessels right before the surgeon’s eyes.

Their project, called MRbrainS, feeds brain activity data from functional magnetic resonance imaging (fMRI) into dedicated software for brain-mapping (called a neuronavigator), then integrates that into a mixed reality headset, which overlays 3D digital images on to the wearer’s view of the real world.  These images can be linked to objects in the real world and remain anchored to them as the wearer looks around.MRBrainS is one of eight research projects focused on magnetic resonance imaging (MRI) with support from ATTRACT, a €20 million consortium funded by the EU and led by CERN that has awarded €100,000 each to 170 technology projects. First developed in the 1970s, MRI is a medical imaging technology that uses magnetic fields and radio waves to create detailed images of internal organs, tissues and bones.Today’s neuronavigators display 2D images on screens, forcing the surgeon to mentally link what’s on the display with the patient lying on the operating table. That’s difficult and “slows down the whole procedure,” says principal investigator Antonio Ferretti. But using a mixed reality headset to tie 3D information directly to what surgeons see in front of them means they can rely on hand-eye coordination, “which is easier if your hands are in front of you, in the same direction you are looking,” explains Ferretti.Another problem MRbrainS aims to solve is that today’s neuronavigators don’t incorporate fMRI data. Unlike regular MRI or computed topography (CT), fMRI shows activity in different parts of the patient’s brain, something that previously could only be determined during invasive surgery with targeted electrical pulses, called direct cortical stimulation. The different steps necessary to push fMRI data to a neuronavigator currently require various different software packages, most of which are designed for researchers, not surgeons. Turning the neuronavigator’s output into a mixed reality image is yet another challenge.For now, MRbrainS uses the Microsoft HoloLens mixed-reality headset. But Ferreti said a longer-term goal could be to design a new headset that integrates the feed from a surgical microscope with all the other data sources in a single device. “This is why we hope that in future development we could involve larger companies,” says Ferretti, an assistant professor of physics at Annunzio University’s Institute for Advanced Biomedical Technologies (ITAB), which runs MRbrainS in partnership with the University of Pavia startup SerVE.

Operating inside the womb with mixed reality

Similarly, the ATTRACT-funded MIFI project is developing a mixed reality system that integrates MRI, ultrasound, and endoscopic video for surgery on unborn children. In-utero surgery is especially difficult, because doctors “need to operate on a patient inside another patient,” notes Mario Ceresa, MIFI’s principal investigator. That patient is very small and delicate, and depends on an amniotic sac that can quickly collapse, so “the interventions are very difficult, because there is a lot of time pressure,” adds Ceresa, a postdoctoral researcher at Pompeu Fabra University (UPF) in Barcelona. Another challenge is that since the operation is keyhole surgery, the surgeon has only a very narrow field of view inside the womb through an endoscope, a tiny camera on the end of a long, thin fibre optic cable.MIFI aims to improve the surgeon’s field of view by displaying a virtual 3D image of the mother’s womb in mixed reality, on top of what the doctor sees in front of them. The project applies machine learning to pre-operative ultrasound and MRI scans to identify relevant blood vessels—some of which are extremely small—and to help the surgeon find them in the womb, even if the baby has moved in the meantime.Though the system isn’t meant to be specific to one condition, for development purposes MIFI is focusing on surgery to correct a condition called Twin-to-Twin Transfusion Syndrome (TTTS). This is where twins that share a placenta — monochorionic twins — are threatened by a blood flow imbalance; the condition occurs in one third of monochorionic twins, and kills both in more than 90 per cent of cases. TTTS can be treated by separating the twins’ blood circulation, using a laser to coagulate the tiny blood vessels. MIFI is a partnership between UPF, research firm Vicomtech, and Sant Joan de Déu Hospital in Barcelona.

Finding scars fast        

MERIT-VA is another ATTRACT project trying to improve the way major surgery is carried out. The researchers, based at the Teknon Medical Centre in Barcelona, UPF, and software firm Galgo Medical, are using machine learning to analyse data from MRI scans and electrocardiograms (ECGs) to improve planning of a particular type heart surgery.Scar tissue formed after a heart attack can disrupt the heart’s natural electrical pulses by directing the current where it shouldn’t go, causing an irregular heartbeat (arrythmia). The condition is treated by inserting tiny catheters into the heart that destroy the problem tissues with radio waves. These catheters contain sensors that provide their position in 3D and detect electrical signals to identify the tissues that need removing. This information can then be displayed on an electro-anatomical map (EAM) to guide the surgeon.But building this map using the catheters can take hours, increasing the risk that something will go wrong during surgery. The condition also frequently recurs after treatment. The more the surgeon knows about which scars to target and where to find them, the quicker the procedure and the greater the chance of curing the condition without destroying excess tissue unnecessarily.Doctors can predict where in the heart the problem is likely to be found by looking at ECG charts, and MRI has been shown to improve this pre-op planning. MERIT-VA, therefore, aims to improve such predictions further by using machine learning to analyse and integrate ECG and MRI information. The goal is to make the surgery quicker, less risky, and more successful.In another project, called QP-MRI, researchers at the University of Turin and the University of Aberdeen are using a variable-field strength MRI scanner to monitor the structural integrity of a new type of medical implant. The implants, used to repair bodily tissues, such as bone, cartilage or corneas, are made from a biodegradable polymer lattice, bonded to an amino acid called polyhistidine, which shows up brightly in MRI scans. When the lattice begins to break down, the MRI signature of the polyhistidine fades.The lattices are supposed to break down once their job is done, but the point is to ensure they don’t deteriorate too early. Such polymer lattices are already in medical use; QP-MRI’s novelty is the use of polyhistidine as a contrast agent, along with an MRI scanner capable of operating at variable magnetic field strengths, designed by the team at Aberdeen.“Our system uses a completely new mechanism in order to produce contrast in an MRI machine,” says principal investigator Simonetta Geninatti Crich, a professor of molecular biology at Turin. Geninatti explains the existing MRI contrast agents carry health risks, which is why polyhistidine is a desirable alternative. But in order for it to work, new, lower-field strength scanners are needed. “You can detect this signal only if you are able to work at a low magnetic field strengths of about 30 milliteslas, more or less,” whereas conventional MRI scanners work at around 1 tesla, she adds.

Machine learning dissects the detail

The MAGRes project aims to make MRI more effective at monitoring glioblastoma—an extremely aggressive form of brain cancer—by identifying subtle variations in MRI scans. The MAGRes researchers use magnetic resonance spectroscopy imaging (MRSI) to identify metabolic changes in the tumour. They then link these results to barely-perceptible changes in ordinary MRI scans, in order to develop new machine learning models for analysing MRI. The idea is not for glioblastoma patients to undergo MRSI—which takes much longer than MRI—but for MRSI research to make MRI analysis more effective.“This metabolic information can appear before anatomical information seen by MRI,” explains Ana Paula Candiota, MAGRes principal investigator and postdoctoral researcher at the Network Centre for Biomedical Research and the Autonomous University of Catalonia. The hypothesis is that “we can use the metabolic information to try to guide ourselves to find things on the [MRI] image that maybe we did not know,” she adds.The purpose of MAGRes is to detect as early as possible whether a patient’s treatment is having any effect or whether it needs to be changed, since glioblastoma patients are tragically short on time. Average life expectancy with treatment is a little over one year, and only a small percentage of patients survive five years.Candiota says she also took part in experiments that eliminated glioblastoma in half of affected mice, without recurrence, by reducing the frequency of chemotherapy treatment to give the immune system more time to attack the tumour. But trying this method in humans is difficult because doctors and patients are suspicious of the hope of getting better results from less chemotherapy, she warns. The only human trials so far involved patients who “were in the last days” and had failed to respond to any treatment, so in all likelihood, nothing could be done for them. “That’s not fair,” notes Candiota.In a similar vein to MAGRes, the IMAGO project aims to develop new models of MRI analysis using a technique called single particle tracking (SPT) to monitor the behaviour of light in sample tissues. Unlike MRI, SPT can identify tiny, sub-microscopic features, but MRI can “see” inside the body whereas SPT can’t. The IMAGO experiments aim to link the characteristics of different samples to subtle variations in MRI data, so that more information can be gleaned from MRI scans. The project is a partnership between Italy’s National Research Council and the Sapienza University of Rome.Meanwhile, the DentMRI project is using low-strength MRI scanning to improve dental care, by providing the first ever images of teeth and gums together that are good enough for medical diagnosis. The researchers, based at the Polytechnic University of Valencia and MRI equipment manufacturer Tesoro Imaging, have developed a prototype scanner that can accommodate objects of up to a cubic centimetre, and the goal is to build one large enough for a person to put their head inside for a dental scan.

Enabling electronics at extreme temperatures

The Low Temperature Communication Link (LTCL) project could help to make MRI equipment more efficient by redesigning the way the powerful magnets inside an MRI scanner are connected to the rest of the system.MRI magnets are kept cool with liquid helium, which has a boiling point of -269° Celsius, or about four Kelvins. Normal electronics can’t function at such low temperatures, so they are built outside the cryogenic vessel that contains the magnets and connected with wires. But LTCL aims to develop electronics that could work inside the cryogenic container, with a wireless communications link and wireless power supply to the normal temperature environment outside.The LTCL researchers at CERN, startup Oxford Instruments, and the French Alternative Energies and Atomic Energy Commission (CEA) say this would bring a number of advantages—not just for MRI, but for any technology that relies on cryogenic equipment. For example, engineers would have more freedom in how they design the cryogenic containers because there would be no need for physical connections to the outside. Furthermore, bringing the electronics closer to the data source—the magnet—would cut interference and give more accurate readings.

Discover more ATTRACT projects developing MRI technologies and innovative solutions for society here. Also, save the date for the ATTRACT online conference – Igniting the Deep Tech Revolution.

via Taking magnetic resonance imaging into a new dimension | Science|Business

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[Abstract + References] Improving wrist imaging through a multicentre educational intervention: The challenge of orthogonal projections

In relation to wrist imaging, the accepted requirement is two orthogonal projections obtained at 90°, each with the wrist in neutral position. However, the literature and anecdotal experience suggests that this principle is not universally applied.

This multiphase study was undertaken across eight different hospitals sites. Compliance with standard UK technique was confirmed if there was a change in ulna orientation between the dorsi-palmar (DP) and lateral wrist projections. A baseline evaluation for three days was randomly identified from the preceding three months. An educational intervention was implemented using a poster to demonstrate standard positioning. To measure the impact of the intervention, further evaluation took place at two weeks (early) and three months (late).

Across the study phases, only a minority of radiographs demonstrated compliance with the standard technique, with an identical anatomical appearance of the distal ulna across the projections. Initial compliance was 16.8% (n = 40/238), and this improved to 47.8% (n = 77/161) post-intervention, but declined to 32.8% (n = 41/125) within three months. The presence of pathology appeared to influence practice, with a greater proportion of those with an abnormal radiographic examination demonstrating a change in ulna appearances in the baseline cohort (p < 0.001) and the late post-intervention group (p = 0.002) but not in the examinations performed two weeks after staff education (p = 0.239).

Assessment of image quality is critical for diagnosis and treatment monitoring. Yet poor compliance with standard anatomical principles was evident. A simple educational intervention resulted in a transient improvement in wrist positioning, but the impact was not sustained over time.

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via Improving wrist imaging through a multicentre educational intervention: The challenge of orthogonal projections – Beverly Snaith, Scott Raine, Lynsey Fowler, Christopher Osborne, Sophie House, Ryan Holmes, Emma Tattersall, Emma Pierce, Melanie Dobson, James W Harcus,

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[WEB PAGE] What Does Chocolate and Peanut Butter Have to Do with Stroke? Find Out Here

Posted by Debbie Overman | Jan 16, 2020   

What Does Chocolate and Peanut Butter Have to Do with Stroke? Find Out Here

Calling it his “chocolate and peanut butter moment,” a University at Buffalo researcher has developed a brain model designed to offer insights into damage caused by stroke and other injuries that represents a combination of two existing approaches that seems obvious.

The model is designed to create a digital simulation environment that can serve as a testing ground for hypotheses about specific damage caused by neurological issues. The researcher’s background in computer modeling made his advancement of combining existing approaches seem obvious–hence, the “chocolate and peanut butter moment,” a media release from University at Buffalo explains.

“This model is tied accurately to the functional connectivity of the brain and is able to demonstrate realistic patterns of cognitive impairment,” says Christopher McNorgan, an assistant professor of psychology in UB’s College of Arts and Sciences, in the release. “Since the model reflects how the brain is connected, we can manipulate it in ways that provide insights, for example, into the areas of a patient’s brain that might be damaged.

“This recent work doesn’t prove that we have a digital facsimile of the human brain, but the findings indicate that the model is performing in a way that is consistent with how the brain performs, and that at least suggests that the model is taking on properties that are moving in the direction of possibly one day creating a facsimile.”

The findings, published in NeuroImage, provide a powerful means of identifying and understanding brain networks and how they function, which could lead to what once were unrealized possibilities for discovery and understanding.

Explaining McNorgan’s model starts with a look at the two fundamental components of its design: functional connectivity and multivariate pattern analyses (MVPA).

For many years, traditional brain-based models have relied on a general linear approach. This method looks at every spot in the brain and how those areas respond to stimuli. This approach is used in traditional studies of functional connectivity, which rely on functional magnetic resonance imaging (fMRI) to explore how the brain is wired. A linear model assumes a direct relationship between two things, such as the visual region of the brain becoming more or less active when a light flickers on or off.

While linear models excel at identifying which areas are active under certain conditions, they often fail to detect complicated relationships potentially existing among multiple areas. That’s the domain of more recent advances, like MVPA, a “teachable” machine-learning technique that operates on a more holistic level to evaluate how activity is patterned across brain regions.

MVPA is non-linear. Assume for instance that there’s a set of neurons dedicated to recognizing the meaning of a stop sign. These neurons are not active when we see something red or something octagonal because there’s not a one-to-one linear mapping between being red and being a stop sign (an apple isn’t a stop sign), nor between being octagonal and being a stop sign (a board room table isn’t a stop sign).

“A non-linear response ensures that they do light up when we see an object that is both red and octagonal,” McNorgan explains, the release continues.

“For this reason, non-linear methods like MVPA have been at the core of so-called ‘Deep Learning’ approaches behind technologies, such as the computer vision software required for self-driving cars.”

But MVPA uses brute force machine-learning techniques. The process is opportunistic, sometimes confusing coincidence with correlation. Even ideal models require researchers to provide evidence that activity in the theoretical model would also be present under the same conditions in the brain.

On their own, both traditional functional connectivity and MVPA approaches have limitations, and integrating results generated by each of these approaches requires considerable effort and expertise for brain researchers to puzzle out the evidence.

When combined, however, the limitations are mutually constrained —  and McNorgan is the first researcher to successfully integrate functional connectivity and MVPA to develop a machine-learning model that’s explicitly grounded in real-world functional connections among brain regions. In other words, the mutually constrained results are a self-assembling puzzle.

“It was my chocolate and peanut butter moment,” says McNorgan, an expert in neuroimaging and computational modeling.

“I’ve had a particular career trajectory that has allowed me to work extensively with different theoretical models. That background provided a particular set of experiences that made the combination seem obvious in hindsight,” he comments.

To build his models, McNorgan begins by gathering the brain data that will teach them the patterns of brain activity that are associated with each of three categories – in this case, tools, musical instruments and fruits. These data came from 11 participants who imagined the appearance and sound of familiar category examples, like hammers, guitars and apples, while undergoing an MRI scan. These scans indicate which areas are more or less active based on blood oxygen levels.

“There are certain patterns of activity across the brain that are consistent with thinking about one category versus another,” says McNorgan. “We might think of this as a neural fingerprint.”

These MRI patterns were then digitized and used to train a series of computer models to recognize which activity patterns were associated with each category.

“After training, models are given previously unseen activity patterns,” he explains. “Significantly above-chance classification accuracy indicates that the models have learned a generalizable relationship between specific brain activity patterns and thinking about a specific category.”

To test whether the digital brain models produced by this new method were more realistic, McNorgan gave them “virtual lesions” by disrupting activations in regions known to be important for each of the categories.

He found that the mutually constrained models showed classification errors consistent with the lesion location. For example, lesions to areas thought to be important for representing tools disrupted accuracy for tool patterns, but not the other two categories. By comparison, other versions of models not trained using the new method did not show this behavior.

“The model now suggests how brain areas that might not appear to be important for encoding information when considered individually may be important when it’s functioning as part of a larger configuration or network,” he says. “Knowing these areas may help us understand why someone who suffered a stroke or other injury is having trouble making these distinctions.”

[Source(s): University at Buffalo, Newswise]

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[Abstract] How brain imaging provides predictive biomarkers for therapeutic success in the context of virtual reality cognitive training

Highlight

VR environments help improve rehabilitation of impaired complex cognitive functions

Combining neuroimaging and VR boosts ecological validity, generates practical gains

These are the first neurofunctional predictive biomarkers of VR cognitive training

Abstract

As Virtual reality (VR) is increasingly used in neurological disorders such as stroke, traumatic brain injury, or attention deficit disorder, the question of how it impacts the brain’s neuronal activity and function becomes essential. VR can be combined with neuroimaging to offer invaluable insight into how the targeted brain areas respond to stimulation during neurorehabilitation training. That, in turn, could eventually serve as a predictive marker for therapeutic success. Functional magnetic resonance imaging (fMRI) identified neuronal activity related to blood flow to reveal with a high spatial resolution how activation patterns change, and restructuring occurs after VR training. Portable and quiet, electroencephalography (EEG) conveniently allows the clinician to track spontaneous electrical brain activity in high temporal resolution. Then, functional near-infrared spectroscopy (fNIRS) combines the spatial precision level of fMRIs with the portability and high temporal resolution of EEG to constitute an ideal measuring tool in virtual environments (VEs). This narrative review explores the role of VR and concurrent neuroimaging in cognitive rehabilitation.

Source: https://www.sciencedirect.com/science/article/abs/pii/S0149763420304218?dgcid=rss_sd_all&utm_campaign=RESR_MRKT_Researcher_inbound&utm_medium=referral&utm_source=researcher_app

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[BOOK] Neuroimaging In Epilepsy Surgery

Russell A. Reeves; Richard Gorniak.Author Information

Introduction

A seizure is a transient occurrence of abnormal excessive or synchronous neuronal activity in the brain. Seizures manifest in different ways based on the anatomic regions of hyperactive neuronal activity. For example, patients may develop focal symptoms due to abnormal activity in the temporal lobe, whereas global signs represent widespread aberrant neuronal activity. Seizures may initially manifest as focal symptoms with subsequent generalization to the remaining cortex. Furthermore, patients may or may not lose consciousness during a seizure, depending on whether or not the limbic structures and brainstem are involved.[1]

Seizure activity in the brain can be caused by numerous anatomic abnormalities such as tumors, infection, inflammatory/autoimmune processes, vascular malformations, stroke, trauma, cortical malformations/dysplasias, gray matter heterotopias, mesial temporal sclerosis, encephaloceles or other acquired or developmental abnormalities.[2] Patients may have seizures due to medical factors such as metabolic derangement, withdrawal, hyperthermia, or toxins as well. However, patients may also suffer from recurrent seizures without known underlying etiology. Patients with at least two unprovoked seizures separated by at least 24 hours may be diagnosed with epilepsy.

Seizure management relies on the treatment of the underlying etiology and/or anti-seizure drug therapy, and, for most patients, part of the evaluation for the underlying cause requires diagnostic workup with imaging. Various diagnostic imaging modalities may be used for patients with recurrent seizures, many adding complementary information for the care of these patients. Furthermore, diagnostic imaging can provide information that localizes epileptogenic lesions in patients with refractory epilepsy that require surgical intervention, potentially obviating the need for invasive electroencephalography (EEG). As such, understanding the uses and limitations of each modality is of critical importance for the treatment of these patients.Go to:

Anatomy

Seizures can manifest as a result of a wide variety of anatomic abnormalities within the brain as well as toxic or metabolic derangements. Anatomic abnormalities that result in seizures can be located nearly anywhere within the brain, though usually involve the neocortex or mesial temporal region, particularly the hippocampi. Because of this, imaging is typically employed to adequately scrutinize all structures of the brain with careful attention directed towards the hippocampi.

The hippocampi are situated within the medial aspect of the temporal lobes bilaterally and occupy the medial floor of the lateral ventricles. They are a core limbic structure, responsible for learning and memory formation. The hippocampus is composed of two distinct gray matter structures known as the cornu ammonis and the dentate gyrus. Anterior to posterior it is divided into the head, body, and tail segments. On coronal imaging, the hippocampal head is characterized by digitations which give its superior surface an undulating contour. The hippocampal body can be recognized by its “jelly roll” or “swiss roll” appearance of the interlocking dentate gyrus, Ammon horn, and intervening strata. White matter tracts from the hippocampus traverse its superior surface, forming the alveus, which condenses into bundles called fimbria, which continue posteriorly as the fornix. The fornix terminates just off of midline within the mamillary body; this white matter tract plays a vital role in the Papez circuit. Adjacent to the head of the hippocampus lies the amygdala and entorhinal cortex.[3]

Hippocampal sulcus remnant cysts and incomplete hippocampal inversion are developmental variants in the hippocampus, which should not be confused with pathology.

The bilateral and symmetric nature of the hippocampi allows for direct comparison during imaging. Unilateral abnormalities may shed light on the underlying etiology of a patient’s refractory epilepsy. However, gross abnormalities of the hippocampi may be bilateral in up to 10% of cases.[3] Additionally, hippocampal lesions can be associated with extra hippocampal epileptogenic lesions. Proper identification of hippocampal abnormalities is critical for patients with medically refractory epilepsy, as surgical resection of the epileptogenic focus is the standard treatment for these patients. Go to:

Plain Films

Plain radiography represents the earliest form of diagnostic imaging. X-rays are used to generate image contrast based on differences in tissue attenuation. Because the soft tissues of the brain exhibit similar attenuation characteristics, the use of plain radiographs to evaluate for structural lesions within the brain is extremely limited. As such, plain radiographs play no role in the diagnostic workup for patients suffering from seizures. Go to:

Computed Tomography

Computed tomography (CT) utilizes helically acquired x-rays and postprocessing techniques to generate cross-sectional images. Modern CT scanners receive x-ray attenuation data in a nearly isotropic manner, which allows the generation of voxels that can be reconstructed in coronal, sagittal, and three-dimensional formats. As with the limitations of plain radiographs, the brain soft tissues are poorly evaluated on routine CT examinations because the attenuation characteristics of the brain soft tissues and many pathologies are similar. Iodinated intravenous contrast can highlight the vascular structures of the brain or areas of enhancement. Seizure activity may result in increased cortical enhancement due to increased cortical perfusion but is typically an unanticipated observation in patients that are not suspected of seizures rather than a sought out diagnostic finding. As such, CT plays a limited role in the imaging workup of patients considering epilepsy surgery.Go to:

Magnetic Resonance

Magnetic resonance imaging (MRI) is the preferred diagnostic modality for patients with seizures. MRI offers excellent signal-to-noise and contrast within the brain. Seizures that are attributed to known metabolic arrangements may not necessarily require further diagnostic studies; however, nearly every patient that suffers from an unexplained seizure should undergo an MRI to evaluate for underlying structural brain abnormalities. Virtually any MRI can be used to assess for mass lesions within the brain, but high field strength scanners, more than 1.5 Tesla, should be used for evaluating patients with epilepsy when possible.[4] Specialized imaging protocols have been developed which optimize subtle signal intensity alterations and anatomic abnormalities within the hippocampi. This is particularly critical for patients undergoing evaluation for surgical management of intractable epilepsy, as small abnormalities within the hippocampi may be undetectable without specialized techniques. 

Patients with medically intractable partial complex epilepsy are most commonly affected by mesial temporal sclerosis (MTS). MRI is essential in identifying MTS, as it has characteristic findings of volume loss and increased T2/FLAIR signal intensity due to hippocampal neuronal cell death and gliosis. There may also be associated atrophy within the ipsilateral amygdala, entorhinal cortex, fornix, or mammillary body. Identifying these subtle differences requires the acquisition of a 1 mm isotropic series with T1 weighting and FLAIR. Reconstructions must be performed perpendicular to the plane of the hippocampi to allow adequate side-to-side comparison. A coronal T2-weighted series should also be obtained with 2 mm slices and sub-mm in-plane resolution to allow both side-to-side comparisons of the hippocampi as well as to delineate the typical internal architecture.[5][6] These sequences are also useful in detecting focal cortical dysplasias, gray matter heterotopias, and small encephaloceles. 

Intravenous contrast may improve the utility of MRI depending on the clinical circumstances. Gadolinium-based contrast agents act to increase the T1 signal, highlighting vascular structures and blood-brain barrier abnormalities. A common approach to patients with seizures is to perform non-enhanced MRI sequences initially and only to administer contrast if the nonenhanced study requires further investigation.[7] That said, patients with intractable epilepsy undergoing evaluation for possible surgical treatment do not routinely require intravenous contrast. Finally, it should be noted that MRI does not require the use of ionizing radiation, where this is a necessary consequence of CT imaging.

Although MRI can be useful for the detection of underlying structural lesions, MRI can also be used to evaluate brain physiology. In patients being evaluated for surgical resection, functional MRI (fMRI) is useful in identifying the language laterality[8] and can, in many instances, replace the invasive Wada test.Go to:

Ultrasonography

Ultrasound utilizes high-frequency sound waves to generate diagnostic images. The advantage of ultrasound is that no ionizing radiation is required for its use. Unfortunately, calcified structures such as the bones of the calvarium preclude adequate sound transmission for an ultrasound to be useful in diagnostic imaging of the brain. As such, ultrasound plays no role in the evaluation of patients with seizures.Go to:

Nuclear Medicine

Nuclear imaging plays an adjunctive role in seizure imaging. Due to its technical limitations, nuclear studies are not considered to be first-line imaging modalities for patients with seizures. However, there are circumstances where nuclear imaging studies add complementary information to that of traditional cross-sectional imaging such as MRI.[4]

Positron emission tomography with fluorodeoxyglucose (FDG-PET) allows for metabolic imaging within the brain. The fluorodeoxyglucose is actively taken up by neuronal cells, in an activation-dependent distribution. Thus, FDG uptake is increased in parts of the brain during a seizure, and conversely, uptake is decreased within the seizure focus interictally. These temporal factors contribute significant limitations to FDG-PET imaging, making it technically challenging to obtain the images either during a seizure or immediately after.[9] Furthermore, PET imaging has low resolution compared to CT and MRI, with a resolution limit of approximately 1 cm. Because of this limitation, FDG-PET imaging is often co-registered with either CT or MRI data to provide useful colocalization between the foci of metabolic abnormality and anatomic structures.[5] However, in patients with suspected temporal lobe epilepsy, interictal FDG-PET is frequently useful in seizure localization, especially in patients with normal MRI scans.[10]

Single-photon emission CT (SPECT) produces images through the use of radioisotope production of gamma rays. These radioisotopes are linked to parent molecules, known as radiopharmaceuticals. Radiopharmaceuticals such as Tc99m-HMPAO do not cross the blood-brain barrier and act as perfusion agents within the brain. Through rapid intravenous administration of a radiopharmaceutical within 90 seconds of seizure onset, regions of increased perfusion within the brain can be identified, which correspond to the seizure focus. Similarly, postictal administration results in decreased cerebral blood flow in the epileptogenic center.[4] Subtracting ictal and interictal SPECT studies with coregistration to an MRI (SISCOM) improves the utility of SPECT imaging.[11] However, radiopharmaceutical administration within 90 seconds of seizure onset is technically challenging, limiting the utility of SPECT imaging.[12] This modality is most commonly used when conventional MRI imaging, electroencephalograms, and other adjunct tests are equivocal in seizure focus localization.Go to:

Angiography

As described previously, both CT and magnetic resonance angiography are uncommonly performed in patients with seizure disorders. A notable exception includes patients who are suspected of having underlying ischemic or vascular disease within the head or neck. Outside of these narrow indications, cross-sectional angiography is seldomly performed during the routine seizure workup. Additionally, more invasive tests such as catheter-based angiography may be used for further delineation of vascular pathology but are rarely required. Before surgical interventions, vascular imaging may be warranted, but this is dependent on the individual clinical scenario.Go to:

Patient Positioning

Before the advent of modern CT and MRI scanners, patient positioning was of critical importance to obtain accurate and useful diagnostic images. However, modern scanners can acquire data in an isotropic fashion, which permits post imaging processing and reconstruction.[13] Prior to this technical development, adjusting for differences in patient positioning was not easily performed. Nearly all studies are performed supine with the patient lying in a comfortable position. This is particularly relevant for MRI studies since image quality is significantly degraded with even small patient movements; as such, ensuring that a patient can maintain a particular position long enough for image acquisition is of considerable technical importance. Similar principles also apply to FDG-PET and SPECT imaging studies.Go to:

Clinical Significance

Gaining a full understanding of the various imaging modalities for patients who suffer from seizures is of critical importance. Nearly one-third of patients with epilepsy will not achieve remission with antiepileptic medications alone, and many patients will have underlying anatomic abnormalities that may offer a surgical cure.[2] Specialized MRI protocols are necessary to thoroughly scrutinize the hippocampi, as many of these patients will develop or demonstrate mesial temporal sclerosis. More subtle abnormalities, such as focal cortical dysplasias, require advanced postprocessing techniques and expert neuroradiologists for a full evaluation.[14] Patients with structural lesions concordant with seizure localization on EEG should receive a preoperative fMRI to map regions of eloquent neocortex. In cases where no anatomic lesion is identified on MRI, FDG-PET may offer complementary information, revealing areas of hypometabolism or help guide the placement of intracranial EEG electrodes. Finally, if PET fails to aid localization, ictal, and interictal SPECT imaging can be performed to evaluate dynamic changes in cerebral perfusion, helping direct the placement of intracranial EEG electrodes for definitive localization of an epileptogenic focus. Further advanced imaging equipment and techniques are under development and available at a limited number of institutions, including the use of 7T MRI. This increased field strength offers an opportunity to detect even more subtle lesions, potentially increasing overall imaging sensitivity and providing curative surgery to a greater proportion of patients.[8] Advanced neuroimaging will continue to play an evolving role in the surgical management of patients with medication-refractory epilepsy. Go to:

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Biochemical and structural magnetic resonance imaging in chronic stroke and the relationship with upper extremity motor function – Full Text

Abstract

Background

Recovery of upper extremity (UE) motor function after stroke is variable from one to another due to heterogeneity of stroke pathology. Structural and biochemical magnetic resonance imaging of the primary motor cortex (M1) have been used to document reorganization of neural activity after stroke.

Objective

To assess cortical biochemical and structural causes of delayed recovery of UE motor function impairment in chronic subcortical ischemic stroke patients.

Methodology

A cross-sectional study with fifty patients were enrolled: thirty patients with chronic (> 6 months) subcortical ischemic stroke suffering from persistent UE motor function impairment (not improved group) and twenty patients with chronic subcortical ischemic stroke and improved UE motor function (improved group). We recruited a group of (16) age-matched healthy subjects. Single voxel proton magnetic resonance spectroscopy (1H-MRS) was performed to measure N-acetylaspartate (NAA) and glutamate+glutamine (Glx) ratios relative to creatine (Cr) in the precentral gyrus which represent M1of hand area in both ipsilesional and contralesional hemispheres. Brain magnetic resonance imaging (MRI) to measure precentral gyral thickness is representing the M1of hand area. UE motor function assessment is using the Fugl Meyer Assessment (FMA-UE) Scale.

Results

The current study found that ipslesional cortical thickness was significantly lower than contralesional cortical thickness among all stroke patients. Our study found that ipsilesional NAA/Cr ratio was lower than contralesional NAA/Cr among stroke patients. UE and hand motor function by FMA-UE showed highly statistically significant correlation with ipsilesional cortical thickness and ipsilesional NAA/Cr ratio, more powerful with NAA/Cr ratio.

Conclusion

We concluded that persistent motor impairment in individuals with chronic subcortical stroke may be at least in part related to ipsilesional structural and biochemical changes in motor areas remote from infarction in form of decreased cortical thickness and NAA/Cr ratio which had the strongest relationship with that impairment.

Introduction

Motor impairment of one side of the body is a major cause of disability in activities of daily living. Recovery from strokes varies from one patient to another due to the heterogeneity of the stroke pathology and rehabilitation strategies. While most stroke patients recover spontaneously, many are left with permanent neurological impairments [1].

Understanding the brain pathologies associated with upper extremity (UE) impairment after stroke, the underlying mechanisms of injury, and the processes associated with recovery is important for achieving good recovery and successful rehabilitation. Advancements in neuroimaging technology have made this possible. Structural and biochemical brain imaging of primary motor cortices has been used to document the reorganization of neural activity after stroke. Ipsilesional and contralesional primary motor cortices, as well as the dorsal premotor cortex, have been identified as areas that can undergo substantial post-stroke neuroplasticity [2].

Single voxel proton magnetic resonance spectroscopy (1H-MRS) is a non-invasive tool to measure the levels of certain metabolites. In acute stroke, the presence of a measurable lactate peak demonstrates a shift from aerobic to anaerobic metabolism in the brain. Previous studies have shown that this is a temporary effect, and levels return to an almost undetectable state within 3 weeks [3].

Biochemical changes have also been observed chronically within ipsilesional normal-appearing grey matter and have been associated with morphological changes in stroke patients [4].

N-Acetylaspartate (NAA) is a metabolite found exclusively in neurons and their processes. It is considered a putative marker of their integrity with specific roles in the central nervous system, including myelin synthesis, neuronal energetics, neuronal osmoregulation, and axonal–glial signaling [5].

In individuals with chronic stroke, lower NAA and higher myo-inositol (an astrocyte marker) concentrations have been reported within ipsilesional and contralesional primary motor cortices [6].

A positive correlation has been drawn between NAA, glutamate (Glu: the principle excitatory neurotransmitter in the human central nervous system), and UE function in a chronic stroke population [7].

Structural changes in regional cortical thickness have also been observed in individuals in the subacute phase (3 months of recovery) after subcortical ischemic stroke and have been linked to functional activation changes in individuals with chronic stroke [89].

The purpose of this study is to assess cortical, biochemical, and structural causes of delayed recovery of UE motor function impairment in patients with chronic, subcortical ischemic stroke.

Subjects and methods

A cross-sectional study with fifty patients were enrolled: thirty patients with chronic (> 6 months) subcortical ischemic stroke suffering from persistent upper extremity motor function impairment (not improved group) and twenty patients with chronic subcortical ischemic stroke and improved upper extremity motor function (improved group).

All patients were recruited from El Sahel Teaching Hospital outpatient clinics.

Inclusion criteria

  1. 1.Eligible right handed patients aged between 50 and 70 years old.
  2. 2.Single clinically diagnosed chronic (> 6 months) subcortical ischemic stroke presented with UE motor impairment at the acute stage.
  3. 3.Radiologically apparent healthy precentral gyrus grey matter.

Exclusion criteria

  1. 1.Patients with disturbed consciousness level.
  2. 2.Patients with aphasia, cognitive impairment, UE apraxia, sensory deficit, or ataxia.
  3. 3.Patients with history of previous stroke.
  4. 4.Patients who had any contraindication for MRI or 1H-MRS.
  5. 5.Patients who underwent neuro-rehabilitation through transmagnetic brain stimulation or transcranial direct current brain stimulation.
  6. 6.Patients with uncontrolled diabetes mellitus.
  7. 7.Patients with chronic renal or hepatic failure.

We recruited right-handed age-matched (16) healthy subject group to be references for our results regarding Egyptian people.

Methods

All participants subjected to:

  1. 1.Full medical history and neurological examination.
  2. 2.Routine lab and imaging.
  3. 3.(1H-MRS) to measure NAA and Glx concentration as ratios of their peak heights to Cr peak height as a stable internal reference (NAA/Cr and Glx/Cr) in ipsilesional and contralesional precentral gyrus which represent the M1 of hand area (landmark of precentral gyrus), determined anatomically by the area that faces and forms the “middle knee” of the central sulcus, located just at the cross point between the precentral sulcus and the central sulcus, which is topographically located at the level of the distal end of the superior frontal sulcus and is therefore also visible on the cortical surface [10] (Fig. 1).
  4. 4.MRI brain to (a) quantify cortical thickness of the precentral gyrus representing the M1of hand area in both ipsilesional and contralesional hemispheres through Picture Archiving and Communication System (PACS), presented in millimeters (mm) and (b) assess white matter disease by fazekas scale which divides the white matter in periventricular (PVWM) and deep white matter (DWM) through fluid attenuated recovery (FLAIR) film with the higher score means more severity.
  5. 5.UE motor function assessment using the Fugl Meyer Assessment (FMA-UE) Scale [11].
  6. 6.Modified rankin scale (mRS) to assess disability degree in stroke patients ranging from 0 to 5 with higher score indicating more disability [12].
figure1
Precentral gyrus, which represents the M1 of hand area (landmark of precentral gyrus), determined anatomically by the area that faces and forms the “middle knee” of the central sulcus, located just at the cross point between the precentral sulcus and the central sulcus, which is topographically located at the level of the distal end of the superior frontal sulcus and is therefore also visible on the cortical surface

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[ARTICLE] Imaging in neuro-oncology – Full Text

Imaging plays several key roles in managing brain tumors, including diagnosis, prognosis, and treatment response assessment. Ongoing challenges remain as new therapies emerge and there are urgent needs to find accurate and clinically feasible methods to noninvasively evaluate brain tumors before and after treatment. This review aims to provide an overview of several advanced imaging modalities including magnetic resonance imaging and positron emission tomography (PET), including advances in new PET agents, and summarize several key areas of their applications, including improving the accuracy of diagnosis and addressing the challenging clinical problems such as evaluation of pseudoprogression and anti-angiogenic therapy, and rising challenges of imaging with immunotherapy.

The roles of imaging in neuro-oncology primarily consist of diagnosis, prognosis, and treatment response assessment of central nervous system (CNS) tumors. Imaging assessment is currently an important surrogate endpoint for clinical trials. With ongoing evaluation and discovery of novel treatment agents, including immunotherapy agents, the ability to accurately assess progression and discern treatment-related changes is a central goal of neuro-oncologic imaging. In this review, we will summarize several clinically available imaging techniques as well as some novel methods under development, and provide an up-to-date review of some clinical challenges in treatment of glioblastomas where imaging can have important roles.

Diffusion-weighted magnetic resonance imaging (DW-MRI) can characterize tissues based on the differences in the degree of free movement of protons. It has been shown that the cellularity or cell density of tumor is associated with apparent diffusion coefficient (ADC), a calculated metric from DW-MRI.1 This property allows one to distinguish between both tumor subtypes and tumor grades (low versus high). More recently, high b-value DW-MRI, using a b-value >3000 s/mm2, has been demonstrated to be superior to standard DW-MRI in distinguishing tumor tissue from normal brain parenchyma.2 DW-MRI data can also be further quantified to generate imaging markers using techniques such as diffusion kurtosis imaging (DKI),3 histogram curve-fitting,4 and functional diffusion map (fDM).5 Restriction spectrum imaging (RSI) is an DW-MRI technique that can isolate the diffusion properties of tumor cells from extracellular process such as edema, potentially improving specificity of tumor detection and characterization.6 Diffusion tensor imaging (DTI) measures the directionality of proton motion as fractional anisotropy (FA), which is often altered in the presence of brain tumors.7 Applications of these methods will be reviewed in the following sections.

Perfusion-weighted magnetic resonance imaging (PW-MRI) techniques assess blood flow to tissue by calculating parameters derived from the time–intensity curve. Using the normal brain as reference, these techniques can detect pathological alterations of tissue vascularity that commonly occur among brain tumors due to increased vascular permeability as well as intravascular blood volume because of tumor-induced angiogenesis. Dynamic susceptibility contrast magnetic resonance imaging (DSC-MRI) quantifies first-pass bolus of paramagnetic contrast agent,8,9 and is currently the most common perfusion-weighted imaging method in clinical use. Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) can characterize vascular permeability within or surrounding tumors by using pharmacokinetic models to quantify the movement of contrast agents crossing the blood–brain barrier.1012 DCE-MRI has an advantage over DSC-MRI due to its greater signal-to-noise ratio and spatial resolution, although imaging acquisition time is also longer. Perfusion imaging measurements are highly dependent on imaging acquisition parameters and postprocessing techniques, including variations in postprocessing software tools.13 Clinical application of this technique therefore requires efforts in standardization, particularly in multicenter settings.

Magnetic resonance spectroscopy (MRS) measures concentrations of metabolites within tissues noninvasively.14 The single-voxel spectroscopy (SVS) method collects average MRS data within a target region of interest selected on standard MRI images. The multivoxel spectroscopy (MVS) method can obtain two- or three-dimensional maps of the region of interest to detect voxel-wise spatial changes of specific metabolites. Both SVS and MVS approaches have been evaluated in tumor diagnosis, grading, pre-therapy planning and post-therapy assessment. One major limitation of the technique is its operator dependency, requiring experienced staff to manually select regions of interest during acquisition. It is also less sensitive to lesions with volume <1.5 cm3.

18F-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET) is an important imaging tool in oncology.15 Similar to systemic cancers, brain tumors often exhibit increased metabolic activity resulting in elevated 18F-FDG uptake that can be detected by PET.16 The role of FDG-PET in brain tumor imaging, however, has been quite limited due to its relative lack of specificity and high background uptake by the normal brain. This limitation is particularly important for small lesions, as currently the resolution of PET imaging is limited to 5 mm. More recently, amino acid PET tracers including 11C-methionine, 18F-fluorothymidine (FLT), 18F-fluoro-ethyl-tyrosine (FET), and 18F-dihydroxyphenylalanine (DOPA) have been developed and evaluated for brain tumor imaging. This class of radiotracers is avidly taken up by malignant brain tumors that have higher cellular proliferation compared to the normal brain.1720 The advantage of high lesion-to-background uptake ratio makes amino acid PET suitable for imaging of brain tumors, including applications such as predicting tumor grade, detecting recurrent tumor, and assessing treatment response. Novel PET radiotracer (18)F-fluoromisonidazole (18F-FMISO) has been evaluated as a marker of tissue hypoxia before and after treatment.21,22

With increasing computing speed and availability of pre-engineered algorithms, imaging data can be analyzed for voxel-level intensity variations to generate texture-type features that can be correlated with tumor biology or treatment response. This approach can be applied to any imaging modality individually or simultaneously through spatial co-registration. As a result, imaging features can be regarded as tumor phenotypes and this type of biomarker can be summarized by the term ‘radiomics’.23 Screening or combining a large number of radiomic features allows generation of models that can aid oncologic diagnosis, prognostication, and treatment response prediction. This approach has been successful in a number of systemic cancers.2428 The radiomic approach is particularly suitable for evaluating high-grade gliomas, a tumor type that is well known for its genetic heterogeneity and highly complex imaging phenotypes.

Imaging plays a key role in the diagnosis of brain tumors and has become one routine management step during preoperative evaluation to aid determination of tumor grade and prognosis. It can also provide important spatial information on tumor tissue characteristics for some tumor subtypes that can influence surgical and radiation treatment planning. In addition, imaging has shown increasing ability to detect tumor genetic profile that can further provide valuable prognostic and predictive information for optimal treatment planning. Finally, imaging findings are often combined with clinical data such as age, gender, and presenting symptoms and signs to increase the accuracy of diagnosis for various tumor types, as well as identifying non-tumor mimics.

One common clinical dilemma during preoperative diagnosis of brain tumors is to distinguish between high-grade glioma and lymphoma. Standard management of CNS lymphoma is nonsurgical and biopsy is the preferred approach if lymphoma is suspected preoperatively, whereas maximal surgical resection provides the best prognosis for high-grade glioma. On conventional imaging sequences, these tumor types commonly exhibit contrast enhancement and peritumoral edema, which make it challenging to differentiate. Lymphomas typically exhibit low ADC values due to high cellularity.29,30 However, this histological feature can be seen in high-grade gliomas and metastases.

Quantitatively, the FA and ADC values of primary cerebral lymphoma are significantly lower than those of glioblastoma.31,32 There is also evidence that DSC-MRI and DCE-MRI parameters of the enhancing regions of the tumor can discriminate between lymphomas and glioblastomas as well as between lymphomas and metastasis,32,33 although a direct comparison of DCE-MRI and DW-MRI shows that ADC measurement is superior to DCE-MRI in differentiating the two tumor types.34 Detection of intratumoral microhemorrhage using the susceptibility-sensitive MRI technique also allows differentiation of glioblastoma and primary CNS lymphomas.35 Texture features generated from post-contrast images of lymphoma and glioblastoma also allow diagnostic differentiation.36

Analysis of nonenhancing signal abnormalities surrounding brain lesions can provide independent diagnostic information. ADC values measured within fluid-attenuated inversion recovery (FLAIR) abnormalities surrounding the enhancing regions can differentiate high-grade gliomas from solitary metastases.37,38 The difference could be due to the presence of tumor infiltration by glioma, resulting in higher cellularity than tumor-induced edema.39 This is also supported by MRS and DSC-MRI measurements of the peritumoral region showing higher choline to N-acetylaspartic acid (NAA) ratio and greater vascularity among high-grade gliomas compared to brain metastases.32,40,41 Combined evaluation of both the enhancing and nonenhancing regions can potentially enhance diagnostic accuracy.32,42 Beyond the margins of signal abnormalities outlined by conventional MRI, including T1- and T2-weighted imaging, MRS can identify regions of brain containing tumor and improve surgical resection and patient outcome.43,44

Molecular data of gliomas have demonstrated prognostic significance and have been incorporated into the 2016 World Health Organization (WHO) criteria.45 The imaging characteristics of brain tumors can be directly related to a specific set of tumor genomics, providing opportunities to noninvasively predict tumor genotype preoperatively. Radiomic models have been developed based on conventional MRI, DTI, and DSC-MRI for predicting gene expression profiles of newly diagnosed glioblastomas.46 Specific genetic alterations of tumors can also be predicted by analysis of MRI data and predictive models have been generated for O6-methylguanine-DNA methyltransferase (MGMT) methylation status,47,48 epidermal growth factor (EGFR) amplification status,25,49 and EGFR receptor variant III status.50 Isocitrate dehydrogenase 1/2 (IDH) mutations are commonly present in low-grade gliomas as well as secondary glioblastomas. These mutant tumors accumulate 2-hydroxyglutarate (2HG), an onco-metabolite that can be detected by MRS (Figure 1).51 Measurement of 2HG concentration allows diagnosis of IDH mutant tumor preoperatively and also opportunities to monitor tumor activity during treatment.52,53 Static and dynamic FET-PET measurements have also been correlated with IDH and 1p/19q status.54 More recently, multimodal MRI imaging can be evaluated by machine learning algorithms to generate predictive models for IDH status in gliomas.5557

Continue —-> Imaging in neuro-oncology – Hari Nandu, Patrick Y. Wen, Raymond Y. Huang, 2018

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[ARTICLE] Advances in brain imaging in multiple sclerosis – Full Text

Brain imaging is increasingly used to support clinicians in diagnosing multiple sclerosis (MS) and monitoring its progression. However, the role of magnetic resonance imaging (MRI) in MS goes far beyond its clinical application. Indeed, advanced imaging techniques have helped to detect different components of MS pathogenesis in vivo, which is now considered a heterogeneous process characterized by widespread damage of the central nervous system, rather than multifocal demyelination of white matter. Recently, MRI biomarkers more sensitive to disease activity than clinical disability outcome measures, have been used to monitor response to anti-inflammatory agents in patients with relapsing–remitting MS. Similarly, MRI markers of neurodegeneration exhibit the potential as primary and secondary outcomes in clinical trials for progressive phenotypes. This review will summarize recent advances in brain neuroimaging in MS from the research setting to clinical applications.

 

In the last decade, magnetic resonance imaging (MRI) has emerged as a fundamental imaging biomarker for multiple sclerosis (MS). Currently, MRI plays a key role in several aspects of the disease including diagnosis,1 prognosis2 and treatment response assessment.3

Over the last few years, developments in brain imaging acquisition and post-processing have advanced the field and have made tremendous contributions to our understanding of disease-specific pathogenetic mechanisms.4 This has improved the accuracy of MS diagnosis and differentiation from other inflammatory diseases of the central nervous system (CNS).5 Furthermore, promising imaging biomarkers are now used to reflect pathological processes occurring in progressive MS.6 This has culminated in the recent use of advanced imaging technique measures as outcomes in phase II and III MS clinical trials of disease-modifying and neuroprotective therapies.7

There is expanding scientific literature on brain imaging in MS. Therefore, we constrained our review to the clinical advances in human brain MRI achieved over the last 5 years in the MS field. Although positron emission tomography (PET)8 and optical coherence tomography (OCT)9 are currently emerging as key tools in the understanding of MS pathophysiology and in monitoring the disease, these neuroimaging techniques were not included in our search criteria.

The aim of this review was to describe advances in brain MRI imaging used to support the diagnosis of MS and to characterize the pathological mechanisms underlying clinical activity and progression. Finally, we intended also to present the recent impact of these advances on clinical trials in MS. For these purposes, the review was conducted using literature from Embase and PubMed using the following keywords: multiple sclerosis; magnetic resonance imaging; brain; pathogenesis; diagnosis; progression. As regards clinical trials, we focused on completed phase II and III trials in relapsing–remitting MS (RR-MS) or progressive MS using clinical trials databases, such as ClinicalTrials.gov and ClinicalTrialsRegister.eu.

Recent advances in neuroimaging considering different brain locations are listed in Figure 1.

                        figure

Figure 1. Advances in brain imaging in multiple sclerosis in different brain locations.
CVS, central vein sign; DGM, deep grey matter; DMD, disease-modifying drug; ihMT, inhomogeneous magnetization transfer; MRI, magnetic resonance imaging; MRS, magnetic resonance spectroscopy; MWF, myelin water fraction; NODDI, neurite orientation dispersion and density imaging; PET, positron emission tomography; qMT, quantitative magnetization transfer; SEL, slowly expanding lesion; TSC, total sodium concentration.

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Continue —-> Advances in brain imaging in multiple sclerosis – Rosa Cortese, Sara Collorone, Olga Ciccarelli, Ahmed T. Toosy, 2019

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[BOOK] Learning Radiology: Recognizing the Basics – William Herring – Google Books

Front Cover

Elsevier Health SciencesApr 14, 2011 – Medical – 318 pages

Learning Radiology: Recognizing the Basics, 2nd Edition, is an image-filled, practical, and clinical introduction to this integral part of the diagnostic process. William Herring, MD, a skilled radiology teacher, masterfully covers everything you need to know to effectively interpret medical images. Learn the latest on ultrasound, MRI, CT, and more, in a time-friendly format with brief, bulleted text and abundant high-quality images. Then ensure your mastery of the material with additional online content, bonus images, and self-assessment exercises at http://www.studentconsult.com.

Identify a wide range of common and uncommon conditions based upon their imaging findings.Quickly grasp the fundamentals you need to know through easy-access bulleted text and more than 700 images.

Arrive at diagnoses by following a pattern recognition approach, and logically overcome difficult diagnostic challenges with the aid of decision trees.

Learn from the best, as Dr. Herring is both a skilled radiology teacher and the host of his own specialty website, http://www.learningradiology.com.

Easily master the fundamental principles of MRI, ultrasound, and CT with new chapters that cover principles of each modality and the recognition of normal and abnormal findings.

Know the basics and be more confident when interpreting diagnostic imaging studies

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[BLOG POST] Brain Imaging: What Are the Different Types? – BrainLine

Positron Emission Topography
Positron Emission Topography (PET) measures brain metabolism. Different applications of PET allow one to “see” pathology associated with Alzheimer’s disease, for instance, that cannot be visualized any other way. Used in a different way, PET also allows doctors to see how different areas of the brain use oxygen or glucose – both very important to understanding not just what the damage might look like but also how the brain provides energy to itself.
T1-Weighted MRI
The T1-Weighted MRI is the standard imaging test and part of every general MRI exam. It provides doctors with a very clear view of brain anatomy and structure. It can also show damage in brain injury but generally only when the damage is very significant.
T2-Weighted MRI
The T2-Weighted MRI is also a standard part of every MRI exam. But unlike T1-weighted imaging, the T2 allows visualization of severe diffuse axonal injury such as what is expected following severe TBI.
Diffusion Weighted Magnetic Resonance Imaging
Diffusion Weighted MRI (DWI) shows alterations in tissue integrity. In ischemic injury — such as many types of stroke or when blood is not able to get to all parts of the brain — there is a chemical reaction in the cells. As the cells die because of lack of blood flow (with oxygen), there is an increase in sodium and this changes (increases) the amount of water in the tissue. DWI is very sensitive to this change. In fact, using DWI, doctors can identify a stroke or ischemic injury within seconds of occurrance.
Fluid-Attenuated Inversion Recovery MRI
Fluid-Attenuated Inversion Recovery (FLAIR) MRI is also sensitive to water content in brain tissue. This is very useful in patients who have reductions in brain tissue following an injury. Most commonly, however, FLAIR is used to visualize alterations in tissue in diseases such as multiple sclerosis.
Diffusion Tensor Imaging
Diffusion Tensor Imaging (DTI) shows white matter tracts in brain tissue. These tracts allow different parts of the brain to talk to each other. Think of the brain as if it were a computer. With DTI doctors can see and measure the “cables” connecting parts of the brain. DTI can provide information about damage to parts of the nervous system as well as about connections among brain regions.
Gradient Record MRI
Gradient Record MRI (GRE) shows blood or hemorrhaging in the brain tissue. This is very important in acute head injury. CT scans are also very useful in this stage but sometimes miss very small bleeds ― or so called microbleeds ― in the brain. MRI and types of MRI more sensitive to blood can identify these and allow doctors to monitor the patient.
Functional MRI
Functional MRI (fMRI) is a newer type of MRI that takes advantage of the iron in blood and the fact that when neurons fire there is ― eventually ― an increase in local iron in the areas where the neurons fired. For this imaging test, doctors ask patients to do something while in the MRI machine like opening and closing their right hand for 30 seconds and then opening and closing their left hand for 30 seconds. Then, the doctors model the change in signal associated with an increase in blood related to that task. So, areas involved in opening the right hand will show increased signal. This allows images to be created that reveal how the brain does tasks. This is potentially useful in TBI when the brain structures all appear normal but the brain is functioning in a different way. It is important to know that fMRI is not approved for clinical use for diagnosis of TBI.

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