FUNCTIONAL MRI

Functional magnetic resonance imaging or functional MRI (fMRI) is a type of specialized MRI scan used to measure the hemodynamic response (change in blood flow) related to neural activity in the brain or spinal cord of humans or other animals. It is one of the most recently developed forms of neuroimaging. Since the early 1990s, fMRI has come to dominate the brain mapping field due to its relatively low invasiveness, absence of radiation exposure, and relatively wide availability.

Since the 1890s it has been known that changes in blood flow and blood oxygenation in the brain (collectively known as hemodynamics) are closely linked to neural activity. When neural cells are active they increase their consumption of energy from glucose and switch to less energetically effective, but more rapid anaerobic glycolysis. The local response to this energy utilization is to increase blood flow to regions of increased neural activity, which occurs after a delay of approximately 1–2 seconds. This hemodynamic response rises to a peak over 4–6 seconds, before falling back to baseline (and typically undershooting slightly). This leads to changes in local cerebral blood volume and local changes in the local concentration of oxyhemoglobin that are detectable through their paramagnetic effects.

Blood-oxygen-level dependence (BOLD) is the MRI contrast of blood deoxyhemoglobin, first discovered in 1990 by Seiji Ogawa at AT&T Bell labs. Ogawa and colleagues had recognized the potential importance of BOLD for functional brain imaging with MRI, but the first successful fMRI study was reported by John W. Belliveau and colleagues in 1991 using an intravenously administered paramagnetic contrast agent (Gadolinium). Using a visual stimulus paradigm, localized increases in blood volume (32 +/- 10 percent, n = 7 subjects) were detected in the primary visual cortex. In 1992, three papers were published using endogenous BOLD contrast MRI. One was submitted by Peter Bandettini at the Medical College of Wisconsin on February 5, revised March 31, accepted March 31 and published in the June 1992 issue of Magnetic Resonance in Medicine (MRM). The second by Kenneth Kwong and colleagues also applied BOLD to image human brain activities with MRI and was submitted on March 26 and published in the June issue of PNAS in 1992. In the same year, Dr. Ogawa submitted their result on March 31 and published in July issue of PNAS. In the following year, Dr. Ogawa published the biophysics model of BOLD contrast in Biophysical Journal. Dr. Bandettini also published a further paper in 1993 demonstrating quantitative determination of functional activation maps.

As neurons do not have internal reserves for glucose and oxygen, more neuronal activity requires more glucose and oxygen to be delivered rapidly through the blood stream. Through a process called the hemodynamic response, blood releases glucose to neurons and astrocytes at a greater rate than in the area of inactive neurons. It results in a surplus of oxyhemoglobin in the veins of the area and distinguishable change of the local ratio of oxyhemoglobin to deoxyhemoglobin, the "marker" of BOLD for MRI.
Hemoglobin is diamagnetic when oxygenated (oxyhemoglobin) but paramagnetic when deoxygenated (deoxyhemoglobin). The magnetic resonance (MR) signal of blood is therefore slightly different depending on the level of oxygenation. Higher BOLD signal intensities arise from increases in the concentration of oxygenated hemoglobin since the blood magnetic susceptibility now more closely matches the tissue magnetic susceptibility. By collecting data in an MRI scanner with sequence parameters sensitive to changes in magnetic susceptibility one can assess changes in BOLD contrast. These changes can be either positive or negative depending upon the relative changes in both cerebral blood flow (CBF) and oxygen consumption. Increases in CBF that outstrip changes in oxygen consumption will lead to increased BOLD signal, conversely decreases in CBF that outstrip changes in oxygen consumption will cause decreased BOLD signal intensity. The signal difference is very small, but given many repetitions of a thought, action or experience, statistical methods can be used to determine the areas of the brain which reliably show more of this difference as a result, and therefore which areas of the brain are active during that thought, action or experience.
Almost all current fMRI research uses BOLD as the method for determining where activity occurs in the brain as the result of various experiences, but because the signals are relative and not individually quantitative, some question its rigor. Other methods which propose to measure neural activity more directly have been attempted (for example measurement of the Oxygen Extraction Fraction (OEF) in regions of the brain, which measures how much of the oxyhemoglobin in the blood has been converted to deoxyhemoglobin or direct detection of magnetic fields generated by neuronal currents), but because the electromagnetic fields created by an active or firing neuron are so weak, the signal-to-noise ratio is extremely low and statistical methods used to extract quantitative data have been largely unsuccessful as of yet.

Neural correlates of BOLD

The precise relationship between neural signals and BOLD is under active research. In general, changes in BOLD signal are well correlated with changes in blood flow. Numerous studies during the past several decades have identified a coupling between blood flow and metabolic rate; that is, the blood supply is tightly regulated in space and time to provide the nutrients for brain metabolism. However, neuroscientists have been seeking a more direct relationship between the blood supply and the neural inputs/outputs that can be related to observable electrical activity and circuit models of brain function.
While current data indicate that local field potentials, an index of integrated electrical activity, form a marginally better correlation with blood flow than the spiking action potentials that are most directly associated with neural communication , no simple measure of electrical activity to date has provided an adequate correlation with metabolism and the blood supply across a wide dynamic range. Presumably, this reflects the complex nature of metabolic processes, which form a superset with regards to electrical activity. Some recent results have suggested that the increase in cerebral blood flow (CBF) following neural activity is not causally related to the metabolic demands of the brain region, but rather is driven by the presence of neurotransmitters, like glutamate, serotonin, nitric oxide, acetylcholine, dopamine and noradrenaline.
Some other recent results suggest that an initial small, negative dip before the main positive BOLD signal is more highly localized and also correlates with measured local decreases in tissue oxygen concentration (perhaps reflecting increased local metabolism during neuron activation). Use of this more localized negative BOLD signal has enabled imaging of human ocular dominance columns in primary visual cortex, with resolution of about 0.5 mm. One problem with this technique is that the early negative BOLD signal is small and can only be seen using larger scanners with magnetic fields of at least 3 Tesla. Further, the signal is much smaller than the normal BOLD signal, making extraction of the signal from noise more difficult. Also, this initial dip occurs within 1–2 seconds of stimulus initiation, which may not be captured when signals are recorded at long repetition (TR). If the TR is sufficiently low, increased speed of the cerebral blood flow response due to consumption of vasoactive drugs (such as caffeine) or natural differences in vascular responsiveness may further obscure observation of the initial dip.
The BOLD signal is composed of CBF contributions from larger arteries and veins, smaller arterioles and venules, and capillaries. Experimental results indicate that the BOLD signal can be weighted to the smaller vessels, and hence closer to the active neurons, by using larger magnetic fields. For example, whereas about 70% of the BOLD signal arises from larger vessels in a 1.5 tesla scanner, about 70% arises from smaller vessels in a 7 tesla scanner. Furthermore, the size of the BOLD signal increases roughly as the square of the magnetic field strength. Hence there has been a push for larger field scanners to both improve localization and increase the signal. A few 7 tesla commercial scanners have become operational, and experimental 8 and 9 tesla scanners are under development.

Technique

BOLD effects are measured using rapid volumetric acquisition of images with contrast weighed by T1 or T2*. Such images can be acquired with moderately good spatial and temporal resolution; images are usually taken every 1–4 seconds, and the voxels in the resulting image typically represent cubes of tissue about 2–4 millimeters on each side in humans. Recent technical advancements, such as the use of high magnetic fields and multichannel RF reception, have advanced spatial resolution to the millimeter scale. Although responses to stimuli presented as close together as one or two seconds can be distinguished from one another, using a method known as event-related fMRI, the full time course of a BOLD response to a briefly presented stimulus lasts about 15 seconds for the robust positive response.

fMRI studies draw from many disciplines

fMRI is a highly interdisciplinary research area and many studies draw on knowledge in several fields:
Physics: Physical principles underlie fMRI signals and many studies require an understanding of these underlying principles.
Psychology: Almost all fMRI studies are essentially cognitive psychological, cognitive psychophysiological, and/or psychophysical experiments in which the MRI scanner is used to obtain an extra set of measurements in addition to behavioral or electroencephalographic measurements.
Neuroanatomy: The fMRI signals can be put into the context of previous knowledge only with an understanding of the neuroanatomy.
Statistics: Correct application of statistics is essential to "tease out" observations and avoid false-positive results.
Electrophysiology: Familiarity with neuronal behavior at the electrophysiological level can help investigators design a useful fMRI study.

Advantages and Disadvantages of fMRI

Like any technique, fMRI has advantages and disadvantages, and in order to be useful, the experiments that employ it must be carefully designed and conducted to maximize its strengths and minimize its weaknesses.

It can noninvasively record brain signals without risks of ionizing radiation inherent in other scanning methods, such as CT or PET scans.
It has high spatial resolution. 2–3 mm is typical but resolution can be as good as 1mm.
It can record signal from all regions of the brain, unlike EEG/MEG which are biased towards the cortical surface.
fMRI is widely used and standard data-analysis approaches have been developed which allow researchers to compare results across labs.
fMRI produces compelling images of brain "activation".

For these reasons, Functional imaging provides insights into neural processing that are complementary to insights of other studies in neurophysiology.

Subjects participating in a fMRI experiment are asked to lie still and are usually restrained with soft pads to prevent movement from disturbing measurements. Some labs also employ bite bars to reduce motion, although these are unpopular as they can be uncomfortable. Small head movements can be corrected for in post-processing of the data, but large transient motion cannot be corrected. Motion in excess of around 3 millimeters results in unusable data. Motion is an issue for all populations, but most especially problematic for subjects with certain medical conditions (e.g. Alzheimer's Disease or schizophrenia) or with young children. Participants can be habituated to the scanning environment and trained to remain still in an MRI simulator.
An fMRI experiment usually lasts between 15 minutes and an hour. Depending on the purpose of study, subjects may view movies, hear sounds, smell odors, perform cognitive tasks such as n-back, memorization or imagination, press a few buttons, or perform other tasks. Researchers are required to give detailed instructions and descriptions of the experiment plan to each subject, who must sign a consent form before the experiment.
Safety is an important issue in all experiments involving MRI. Potential subjects must ensure that they are able to enter the MRI environment. The MRI scanner is built around an extremely strong magnet (1.5 teslas or more), so potential subjects must be thoroughly examined for any ferromagnetic objects (e.g. watches, glasses, hair pins, pacemakers, bone plates and screws, etc.) before entering the scanning environment.

Aside from BOLD fMRI, there are other related ways to probe brain activity using magnetic resonance properties:

Neuronal activity produces some immediate physical changes in cell shape that can be detected because they affect the compartment shape and size for water diffusion. A much improved spatial and temporal resolution for fMRI data collection has now been achieved by using diffusion MRI methodology that can detect these changes in neurons. The abrupt onset of increased neuron cell size occurs before the metabolic response commences, is shorter in duration and does not extend significantly beyond the area of the actual cell population involved. This technique is a diffusion weighted technique (DWI). There is some evidence that similar changes in axonal volume in white matter may accompany activity and this has been observed using a DTI (diffusion tensor imaging) technique. The future importance of diffusion-based functional techniques relative to BOLD techniques is not yet clear.

An injected contrast agent such as an iron oxide that has been coated by a sugar or starch (to hide from the body's defense system), causes a local disturbance in the magnetic field that is measurable by the MRI scanner. The signals associated with these kinds of contrast agents are proportional to the cerebral blood volume. While this semi-invasive method presents a considerable disadvantage in terms of studying brain function in normal subjects, it enables far greater detection sensitivity than BOLD signal, which may increase the viability of fMRI in clinical populations. Other methods of investigating blood volume that do not require an injection are a subject of current research, although no alternative technique in theory can match the high sensitivity provided by injection of contrast agent.

Arterial Spin Labeling (ASL), also known as arterial spin tagging, is an MRI technique capable of measuring cerebral blood flow (CBF) in vivo. ASL is capable of providing cerebral perfusion maps, without requiring the administration of a contrast agent or the use of ionizing radiation, as it uses magnetically-labeled endogenous blood water as a freely-diffusible tracer. It was first proposed in 1992 and has since benefited from a number of modifications aimed at improving its robustness. ASL can monitor changes in CBF with activation and fMRI studies can therefore be conducted using ASL instead of relying on the BOLD effect. ASL fMRI is less popular than BOLD, as it suffers from a lower signal to noise ratio, can be less sensitive to weak stimuli and its temporal resolution is poorer than in BOLD studies. On the plus side, it can provide quantitative measures of a single well-defined parameter, CBF, whose baseline value can also be determined in the same experiment. It has also been found to outperform BOLD in terms of stability to slow signal drifts and localization of the activation area. The ASL activation signal is believed to be dominated by changes in the capillary bed of the activated area of the cortex, where as the BOLD signal is likely to be dominated by changes in the oxygenation of nearby veins.

Magnetic resonance spectroscopic imaging (MRS) is another, NMR-based process for assessing function within the living brain. MRS takes advantage of the fact that protons (hydrogen atoms) residing in differing chemical environments depending upon the molecule they inhabit (H₂O vs. protein, for example) possess slightly different resonant properties (chemical shift). For a given volume of brain (typically > 1 cubic cm), the distribution of these H resonances can be displayed as a spectrum.
The area under the peak for each resonance provides a quantitative measure of the relative abundance of that compound. The largest peak is composed of H2O. However, there are also discernible peaks for choline, creatine, N-acetylaspartate (NAA) and lactate. Fortuitously, NAA is mostly inactive within the neuron, serving as a precursor to glutamate and as storage for acetyl groups (to be used in fatty acid synthesis) — but its relative levels are a reasonable approximation of neuronal integrity and functional status. Brain diseases (schizophrenia, stroke, certain tumors, multiple sclerosis) can be characterized by the regional alteration in NAA levels when compared to healthy subjects. Creatine is used as a relative control value since its levels remain fairly constant, while choline and lactate levels have been used to evaluate brain tumors.

Diffusion tensor imaging (DTI) is a related use of MR to measure anatomical connectivity between areas. Although it is not strictly a functional imaging technique because it does not measure dynamic changes in brain function, the measures of inter-area connectivity it provides are complementary to images of cortical function provided by BOLD fMRI. White matter bundles carry functional information between brain regions. The diffusion of water molecules is hindered across the axes of these bundles, such that measurements of water diffusion can reveal information about the location of large white matter pathways. Illnesses that disrupt the normal organization or integrity of cerebral white matter (such as multiple sclerosis) have a quantitative impact on DTI measures.

Functional MRI has high spatial resolution but relatively poor temporal resolution (of the order of several seconds). Electroencephalography (EEG) directly measures the brain's electrical activity, giving high temporal resolution (~milliseconds) but low spatial resolution. The two techniques are therefore complementary and may be used simultaneously to record brain activity.
Recording an EEG signal inside an MRI system is technically challenging. The MRI system introduces artifacts into the EEG recording by inducing currents in the EEG leads via Faraday induction. This can happen through several different mechanisms. An imaging sequence applies a series of short radiofrequency pulses which induce a signal in the EEG system. The pulses are short and relatively infrequent, so interference may be avoided by blanking (switching off) the EEG system during their transmission. Magnetic field gradients used during imaging also induce a signal, which is harder to remove as it is in a similar frequency range to the EEG signal. Current is also induced when EEG leads move inside the magnet bore (i.e. when the patient moves during the exam). Finally, pulsed blood flow in the patient in the static magnetic field also induces a signal (called a ballistocardiographic artifact), which is also within the frequency range of interest. The EEG system also affects the MRI scan. Metal in the EEG leads and electrodes can introduce susceptibility artifacts into MR images. Care must also be taken to limit currents induced in the EEG leads via the MRI RF system, which could heat the leads sufficiently to burn the subject.
Having simultaneously recorded EEG and fMRI data, the final hurdle is to co-register the two datasets, as each is reconstructed using a different algorithm, subject to different distortions.

Before the advent of fMRI functional neuroimaging was typically performed with positron emission tomography (PET) scanners or more rarely with SPECT scanners. Niels A. Lassen and his coworkers lead the earliest efforts of functional neuroimaging, using radioactive gases to construct images of the working brain.
These nuclear imaging techniques do not use the nuclear magnetic resonance property and employ entirely different scanners.

The ultimate goal of fMRI data analysis is to detect correlations between brain activation and the task the subject performs during the scan. The BOLD signature of activation is relatively weak, however, so other sources of noise in the acquired data must be carefully controlled. This means that a series of processing steps must be performed on the acquired images before the actual statistical search for task-related activation can begin.
For a typical fMRI scan, the 3D volume of the subject's head is imaged every one or two seconds, producing a few hundred to a few thousand complete images per scanning session. The nature of MRI is such that these images are acquired in Fourier transform space, so they must be transformed back to image space to be useful. Because of practical limitations of the scanner the Fourier samples are not acquired on a grid, and scanner imperfections like thermal drift and spike noise introduce additional distortions. Small motions on the part of the subject and the subject's pulse and respiration will also affect the images.
The most common situation is that the researcher uses a pulse sequence supplied by the scanner vendor, such as an echo-planar imaging (EPI) sequence that allows for relatively rapid acquisition of many images. Software in the scanner platform itself then performs the reconstruction of images from Fourier transform space. During this stage some information is lost (specifically the complex phase of the reconstructed signal). Some types of artifacts, for example spike noise, become more difficult to remove after reconstruction, but if the scanner is working well these artifacts are thought to be relatively unimportant. For pulse sequences not provided by the vendor, for example spiral EPI, reconstruction may have to be done by software running on a separate platform.
After reconstruction the output of the scanning session consists of a series of 3D images of the brain. The most common corrections performed on these images are motion correction and correction for physiological effects. Outlier correction and spatial and/or temporal filtering may also be performed. If the task performed by the subject is thought to produce bursts of activation which are short compared to the BOLD response time (on the order of 6 seconds), temporal filtering may be performed at this stage to attempt to deconvolve out the BOLD response and recover the temporal pattern of activation.
At this point the data provides a time series of samples for each voxel in the scanned volume. A variety of methods are used to correlate these voxel time series with the task in order to produce maps of task-dependent activation.
There are many software packages available for analyzing fMRI data.
Reconstruction of MRI data needs to be tested, calibrated and confirmed. MRI can suffer from numerous artifacts that include, geometric distortions, Nyquist ghosting, and signal dropout. Medical Imaging Phantoms are used to provide a consistent geometrical source for calibration and testing purposes. Minute tumor changes can require recalibration by use of a phantom to quantify the change.

Commercial use

Most fMRI scans are for research or clinical use. Commercial use is limited. However, a few companies have been set up that attempt to sell fMRI specific hardware or services for research or clinical use.
At least two companies have been set up to use fMRI in lie detection (No Lie MRI, Inc and Cephos Corporation.
In using fMRI techniques for use in lie detection, activated areas of the brain are observed while the subject is making a statement. Depending on what regions are the most active, the technician might determine whether a subject is telling the truth or not. Since a specific combination of brain functions are needed in order to tell a lie, the simultaneous activation of these regions often indicates deception. This technology is in its early stages of development, and many of its proponents hope to replace older lie detection techniques.
In clinical trials, the usage of fMRI as a method of lie detection has appeared reliable, with studies from 2005 by Kozel et al. indicating a 90% to 93% success rate.
However, there is still a fair amount of controversy over whether these techniques are reliable enough to be used in a legal setting. Some studies indicate that while there is an overall positive correlation, there is a great deal of variation between findings and in some cases considerable difficulty in replicating the findings.

The Neuro fMRI/DTI Combi Package is a bundle of:
- Inline BOLD Imaging :Performing a Motor Cortex Functional Exam
- 3D PACE syngo : Prospective Acquisition CorrEction
- BOLD 3D Evaluation syngo
- fMRI Trigger Converter
- Diffusion Tensor Imaging
- DTI Evaluation
- DTI Tractography syngo

The bundle comprehends all acquisition and postprocessing tools for comprehensive BOLD fMRI and DTI exams. BOLD fMRI experiments can be displayed fused with DTI data and anatomy. The package is particularly valuable for presurgical planning. The 3D display of anatomical images, functional brain mapping results and DTI allows a better understanding of the spatial relationship between eloquent cortices, cortical landmarks, brain lesions and tract shifts of white matter.

Inline BOLD Imaging
The BOLD imaging package allows the user to define protocols which, apart from the measurement, configure automatic evaluation of the measured data during the scan. With Inline Technology it is thus possible to generate statistical images (t-value) based on 3D motion corrected and spatially filtered data automatically in real time without any further user interaction. The Inline display of activation cards allows the user to decide during the scan whether enough statistical power has built up for his brain mapping task or if the examination is corrupted by motion. As a result examinations will be shorter with a higher success rate. Functional brain mapping can be easily integrated into the clinical routine e.g. prior to neurosurgical interventions.

Additional Features:
- Inline retrospective 3D motion detection and correction in 3 rotational and 3 translational directions
- Inline t-statistics calculation for variable paradigms and display of t-value images
- Statistical evaluation by means of “General Linear Model (GLM)”:
- Paradigms can be configured
- Transitions between passive and active states can be modeled by the hemodynamic response function
- Correction of low-frequency trends
- Allows for time delays due to the BOLD-EPI slice order during a measurement
- Display of GLM design matrix
- Display of a continuously updated t-value card during measurement
- Display of colored activation cards continuously updated during measurement, overlaid over the respective BOLD images using Inline technology
- MOSAIC image mode for accelerating display, processing and storage of images

3D PACE syngo
By tracking the patients head 3D PACE reduces motion resulting in increased data quality beyond what can be achieved with a retrospective motion correction. As a result the sensitivity and specificity of BOLD experiments are increased.
Features:
- Real time prospective motion correction: Highest accuracy real time motion detection algorithm feeding a real time feed back loop to the acquisition system with updated positioning information
- 3D motion correction for 6 degrees of freedom (3 translation and 3 rotation)
- Motion related artifacts are avoided in first place instead of correcting for them retrospectively
- Significant reduction of motion-related artifacts in statistical evaluations
- Increased sensitivity and specificity of BOLD experiments

BOLD 3D Evaluation syngo

All tasks from statistical evaluation of the fMRI datasets to reading and exporting results are supported by BOLD 3D Evaluation syngo:

Generation of statistical maps:
- In cases an inline calculated statistical map is not available a statistical map can be generated easily using processing protocols. An intuitive editor UI allows the paradigm definition and offers the selection of head motion correction, image filters and statistical evaluation.
- Predefined processing protocols and paradigms are available, which can be edited if required.

Statistical evaluation using General Linear Model (GLM)
- Transitions between passive and active states modeled by the hemodynamic response function.
- Correction of low-frequency trends.
- Corrects for time delays due to the BOLD-EPI slice order during a measurement.
- Output of a t-value map and the GLM design matrix

Inline monitoring of the fMRI exam
- During an ongoing BOLD imaging exam results are calculated (by Inline BOLD imaging) and displayed in real time.
- The results are displayed and continuously updated as an overlay on online adjustable, free angulated cut planes through the anatomical 3D data set.
- The evolving signal time courses in task-related areas of activation can be displayed and monitored.

Visualization of fMRI Results
- Visualization with 3D volume rendering.
- Superimposing on cut planes through the volume.
- Interactive Navigation: Zoom, pan and rotate in 3D without noticeable delay. Free double oblique angulation of up to 6 cut planes.
- Cine display of the BOLD time series and of EPI volumes in 3 orthogonal cuts for evaluation of non-corrected head motion.

Data Quality Monitoring
- Based on the B0 field map, loaded automatically with the fMRI data, areas with less reliable results are indicated.

Overview:

syngo BOLD 3D Evaluation

syngo BOLD 3D Evaluation is a comprehensive processing and visualization package for BOLD fMRI.

Features

•This package provides statistical map calculations from BOLD datasets and enables the visualization of task-related areas of activation with 2D or 3D anatomical data. This allows the visualization of the spatial relation of eloquent cortices with cortical landmarks or brain lesions
•On the syngo Acquisition Workplace the unique Inline function of syngo BOLD 3D Evaluation merges, in real time, the results of ongoing BOLD imaging measurements with 3D anatomical data
•Additionally, evolving signal time courses in task-related areas of activation can be displayed and monitored
•Functional and anatomical image data can be exported for surgical planning as DICOM datasets, additionally all color fused images and results can be stored or printed
•Statistical map generation: paradigm definition, calculation of t-value map with General Linear Model or t-test
•3D Visualization: fused display of fMRI results, color t-value maps on anatomical datasets
•Inline 3D real time monitoring of the fMRI acquisition
•On-the-Fly adjustment for t-value thresholding, 3D clustering, and opacity control
•Data export to neurosurgical planning software

Clinical Applications

•Neurosurgical planning
•Assess the effects of neurodegenerative diseases, trauma or stroke on brain function
•Brain mapping


BOLD evaluation task cards

Step by step instructions:

1. Load the ep2d_bold_moco series into the BOLD Evaluation Task card. From the patient browser, select this series and go to Applications and choose BOLD Evaluation.
2. Choose the moco filter 3D evaluation program. Automatically the evaluation controller dialog box will appear, when post processing BOLD data it is freely selectable to choose filters or motion
3. Adjust the simple clustering to remove noise from BOLD data. Increasing this value will remove any colored clustered pixels lower than this number. For example when setting this value to 10 any value of activated (colored) adjacent pixels less than 10 will be hidden from view.
4. Load the t1_se_tra sequence into segment 1. From the patient browser select this sequence and drag and drop into the upper left segment. This will fuse the BOLD data with anatomic data
5. Scroll thru the images using the "dog ear tab" of segment one. This will also move the fused anatomic and functional slices.
6. Set the transparency of the functional data. Reducing the Alpha Value will make the functional data more transparent.
7. Save the fused results. Go to patient, and select Save All Alpha As... This will save all slice positions and allow naming of the sequence, for easy access in the patient browser.
This series can now be viewed in the viewing card or sent via PACs for reading.

fMRI Trigger Converter
An optical trigger signal is available to trigger external stimulation devices in fMRI experiments.
With the "fMRI Trigger Converter" this signal can be converted to an electrical signal (TTL/BNC and RS 232 interface for PC; modes: toggle or impulse).

Diffusion Tensor Imaging
Diffusion Tensor Imaging allows for a complete description of the diffusion properties of the brain within the scope of the tensor diffusion model, both for anisotropic and isotropic diffusion. Efficient diffusion direction schemes are pre-defined to allow for optimal diffusion directional resolution. Schemes with up to 256 directions can be selected.
Inline technology enables automatic and immediate calculation of the diffusion tensor, including grey-scale and colored “fractional anisotropy" (FA) map derived from it.

Details:
- Measurements with up to 256 different directions and with up to 16 different b-values
- Inline calculation of tensor, grey-scale and colored FA map, ADC map and trace-weighted image
- Support of parallel imaging (iPAT)
- Clinical protocols with full head coverage, incl. inline calculation of tensor, FA, ADC and trace-weighted images in 4 minutes.

DTI Tractography syngo
syngo DTI Tractography is optimized for the clinical use by providing advanced 3D visualization of white matter tracts in the context of 2D or 3D anatomical datasets and DTI datasets. DTI data sets can be explored fast and intuitively using the interactive QuickTracking. QuickTracking instantaneously displays the tract originating from the mouse pointer position while moving over the DTI data set. This also allows identifying qualified regions to place seeding ROIs. Seed points can be set to assess connectivity by tracking with single ROI and with multiple ROIs. Furthermore they can be placed in fused views displaying the anatomical reference and e.g. the colored FA map simultaneously.
Texture Diffusion, a highly versatile in-plane visualization of white matter tracts, allows to display and read DTI Tractography results on PACS reading stations and in the OR.
At the same time the package provides the scientific user with the flexibility to configure the tracking algorithm and to change display settings for the tracts. Tract and seeding ROI statistics are included to support publications (e.g. mean/max FA value, min/mean/max ADC value).
All views can be exported as DICOM images or bitmaps. Tract and seeding ROI statistics can be exported as html files.

DTI Evaluation
Clinical applications are supported by a dedicated DTI evaluation mode to support diagnostics of white matter diseases (e.g. multiple sclerosis and brain maturation disorders). Based on the tensor, in addition to the already inline-calculated parameter maps, further maps characterizing the anisotropy of diffusion properties can be calculated and stored. Multiple diffusion parameter maps (e.g. Fractional Anisotropy, ADC, b=0) and an anatomical image are displayed next to each other in the same slice position for comparison. The images can be evaluated together based on ROIs and the results can be documented in a table. The display options include 2D and 3D tensor graphics, color-coded images and overlay images on the anatomical images.

In addition, the package offers the scientific user full flexibility of 2- and 3-dimensional visualization of the diffusion tensor with measures of isotropic and anisotropic (fractional and relative) diffusion, Eigen vectors (E1, E2, E3) of the diffusion tensor and shape-descriptive measures of the diffusion tensor (linear, planar, spherical).

Keyhole
acquire full k-space as reference
acquire reduced low-frequency k-space fMRI study
fill in missing k-space from reference
Half-Fourier
acquire 50-60% of k-space starting at highest ky
theoretical symmetry used to fill in missing ky
Sensitivity encoding (SENSE)
Multiple RF coils with independent signal for each (parallel imaging)
Calibration maps from full k-space
each coil part of k-space
2X improvement EPI, 4X for GE
UNFOLD
Acquire k-space in sequential time segments
time 1 acquire lines 1, 5, 9, 13 ...
time 2 acquire lines 2, 6, 10, 14 ...
time 3 acquire lines 3, 7, 11, 15 ...
time 4 acquire lines 4, 8, 12, 16 ...
reorder into k-space
4x faster per segment reduces inter echo distortions