HST.583 Functional Magnetic Resonance Imaging: Data ... fileHST.583: Functional Magnetic Resonance Imaging: Data Acquisition and Analysis, Fall 2006 Harvard-MIT Division of Health

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HST.583 Functional Magnetic Resonance Imaging: Data Acquisition and Analysis Fall 2006

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HST.583: Functional Magnetic Resonance Imaging: Data Acquisition and Analysis, Fall 2006 Harvard-MIT Division of Health Sciences and Technology Course Director: Dr. Randy Gollub.

INTRODUCTION

This Lab consists of three parts: Part 1 : SNR Measurements – Temporal and Spatial Characteristics in Signal and Noise Part 2 : Determination of MR parameters (T1, T2, T2*) across tissue types and regions of interest. Part 3 : EPI distortions due to B0 Inhomogeneity

All experiments will be performed on a human subject. The SNR measurements will also be run on a phantom for comparison. Some of the data analysis will be performed on the scanner console, however you will be asked to note the measurements obtained as you will need them to solve the exercises given in the lab report. The main goals of this lab are to:

1) Become familiar with basic principles of MRI Physics and measurements (i.e. SNR, relaxation times, etc).

2) Understand the T1, T2 and T2* properties of various tissue compartments.

3) Acquire and evaluate phantom data. 4) Perform a human scanning experiment and investigate the

various sources of noise in the fMRI time series. 5) Evaluate EPI distortions through field maps and by varying the

readout properties.

Cite as: Cristina Triantafyllou and Larry Wald , HST.583 Functional Magnetic Resonance Imaging: Data Acquisition and Analysis, Fall 2006. (Massachusetts Institute of Technology: MIT OpenCourseWare), http://ocw.mit.edu(Accessed MM DD, YYYY). License: Creative Commons BY-NC-SA.

1. SNR Measurements

Background At the most basic level, the SNR depends on the number of protons present in the voxel (proportional to Voxel Volume if we assume a constant density) and the

MeasurementTime . The latter is a standard aspect of signal averaging assuming the noise is uncorrelated and distributed in a Gaussian distribution. Each acquisition in the k-space matrix is essentially averaged when the Fourier Transform produces the image. Therefore the total measurement time is the total amount of time the digitizers are actually recording k-space samples. For a readout line of 256 samples acquired with a dwell time (time per sample) of 25μs, this yields an acquisition time of 6.4ms. Sometimes acquisition time for the readout is expressed in terms of the bandwidth of frequencies

1present across the image ( BWread ). BWread = equal to 40kHz for this example. dwell time

On the Siemens system the BW is expressed in Hz per pixel across the image, so for the 256 matrix above, this is a BWread = 156 Hz/px . The total image acquisition time is the time per line multiplied by the number of lines (# phase encode steps NPE) and the number of times each line was averaged (NAVG)

Equation 1 shows the dependence of SNR on some of the above parameters: Voxel Volume ⋅

SNR ∝NAVG (1)

BWread

For fMRI, it is the time-course SNR that is important. Functional MRI is restricted by multiple sources of variance, such as instrumental sources of error including thermal noise and shot-to-shot electronic instability, and subject dependent modulations of the MR signal associated with physiological processes. In addition to respiratory and cardiac cycle contributions, the physiological noise also consists of a noise element with BOLD-like TE dependence (Triantafyllou et al. 2005), (Krueger and Glover 2001), and spatial correlation within gray matter (Krueger and Glover 2001). The origin of this “BOLD noise” is still not fully understood, but is generally associated with hemodynamic and metabolic fluctuations in the gray matter. Since the physiological fluctuations represent a multiplicative modulation of the image signal (Krueger and Glover 2001) their amplitude scales with the MR image intensity. This is in contrast to the thermal noise sources which can be represented by the addition of a fixed amount of Gaussian noise power whose amplitude is determined primarily by the coil loading.

If the noise sources are assumed to be uncorrelated, the total noise in the image time-course (σ) is related to its thermal (σ0) and physiological (σp) components via:

22 0 pσσσ += , (2)

In our measurements, shot-to-shot scanner instabilities will contribute to both terms, σ0

and σp, depending on their signal dependence. Phantom measurements, however, show that they comprise only a small fraction of the in vivo time course noise. The time-course SNR (tSNR) is then defined as:


StSNR = , (3)σ 0

2 +σ 2 p

where S is the mean image signal intensity. Defining the SNR in an individual image as SSNR0 = and combining with Eq. (3), we determine the relationship between tSNR σ 0

and SNR0: SNR0 (4)

1 + λ2 ⋅ SNR02 tSNR =

where λ is a constant.

References 1. Triantafyllou C., et al., Comparison of physiological noise at 1.5 T, 3 T and 7 T and optimization

of fMRI acquisition parameters. NeuroImage 26, 243–250; 2005. 2. Krueger G, Glover GH. Physiological noise in oxygenation-sensitive magnetic resonance imaging.

Magn Reson Med 46: 631-7; 2001.


Experiments

In this exercise we will acquire human MRI data in order to characterize image signal and noise characteristics and the time-course signal and noise characteristics. Data will be collected on a 3T Siemens Imager. We will examine the spatial SNR, time-course SNR and their relationship for three different image resolutions, 5x5x5mm3, 3x3x3mm3 and 1.5x1.5x1.5mm3. In all cases, images with zero RF will also be obtained to capture the thermal image noise. For comparison, time series data using the same parameters will also be acquired on a loading phantom.

Acquisition: 1) Localizer. 2) EPI time series at low resolution 5mm x 5mm x 5mm, 9 slices, 50 time points,

TR=2000ms, TE=32ms, (see protocol epi_5x5x5_signal). 3) Same as #2 but with no RF excitation (just thermal noise) 4) EPI time series at medium resolution 3mm x 3mm x 3mm, 9 slices, 50 time

points, TR=2000ms, TE=32ms (see protocol epi_3x3x3_signal). 5) Same as #4 but with no RF excitation (just thermal noise). 6) EPI time series at higher resolution 1.5mm x 1.5mm x 1.5mm, 9 slices, 50 time

points, TR=2000ms, TE=32ms (see protocol epi_1.5x1.5x1.5_signal). 7) Same as #6 but with no RF excitation (just thermal noise).

Note 1: Typically, thermal noise would be calculated by drawing an ROI outside the signal area in an image. However in EPI acquisition there are a lot of artifacts present. To avoid misreading the noise we thus acquire a separate image without an RF that provides a better representation of thermal noise.

Note 2: Since the thermal noise is random we need to characterize it in terms of its mean, and standard deviation (or variance). Before we can calculate these quantities, we also need to know what kind of statistical distribution this noise belongs to. For example, the most common type of statistical distribution is the Gaussian or normal distribution but the spatial MRI noise outside of the brain has been empirically determined to follow a Rayleigh distribution. It is thus simple to compute the mean and variance of the thermal noise by first computing its variance and mean as though it were Gaussian and applying a Rayleigh correction factor to account for this difference.

Spatial SNR (SNR0)

The SNR in an individual image (SNR0) is a measure or the image quality. In our experiments we will evaluate the impact of the spatial resolution on the SNR0. In human data, ROIs will be defined in cortical gray matter. The SNR0 for a given pixel will be calculated as the mean pixel value for all the images in the time-series divided by the standard deviation of the thermal noise of the time-series acquired with no RF excitation (zero flip angle images).


1. Load the EPI images and the Noise time-courses on the mean curve task card. 2. Select the EPI time-course, draw an ROI within the signal area, and record the

mean signal value. 3. Select the Noise time-course, draw an ROI and record the standard deviation. 4. Record your measures in Table 1 and calculate the SNR0. 5. Repeat steps 1-4 for all three spatial resolutions. 6. Repeat steps 1-4 for the phantom data at all three resolutions and record results on

Table 2.

� Lab Question 1 : Draw the calculated SNR0 as a function of voxel size and comment on your findings. Describe the differences if any, between the human and phantom data.

Temporal SNR (tSNR)

Temporal SNR is defined as the image-to-image variance in the time-course and will be measured on a ROIs based analysis in the cortical gray matter. Temporal SNR in a given pixel will be determined from the mean pixel value across the 50 time points divided by its temporal standard deviation.

1. Load the EPI time series images on the viewer. 2. Calculate the Standard Deviation map from the EPI time series through the

scanner UI. Open the Patient Browser, go to Evaluation -> Dynamic Analysis -> Standard Deviation. Press within series, test and assign a name to the new image (STD_mymap). A new series is created on your patient browser, named STD_mymap.

3. Load the images on the mean curve task card. Select both the EPI time course and the standard deviation map and draw an ROI within the signal area.

4. Record the mean value within the ROI on the EPI images; that is your temporal mean of the signal.

5. Record the mean value within the ROI on the standard deviation map; that is your temporal noise.

6. Calculate the temporal SNR from the above quantities and note on Table 1. 7. Repeat steps 1-6 for all three spatial resolutions. 8. Repeat steps 1-6 for the phantom data at all three resolutions and record results on

Table 2.

� Lab Question 2: Draw the calculated tSNR as a function of voxel size and comment on your findings. Describe the differences if any, between the human and phantom data.

Relationship between SNR0 and tSNR

The tSNR will be analyzed as a function of SNR0 for the given set of resolutions. Use the recorded values from Tables 1 and 2 incorporating the model for tSNR from Eq. 4 (where λ=0.0107).


� Lab Question 3 : Show the relationship of tSNR as a function of SNR0 when SNR0 is modulated by the voxel size. Describe the differences, if any, between the human and phantom data. What is the asymptotic limit for tSNR?

� Lab Question 4 : You are asked to perform an fMRI study of medial temporal lobe activation at a high field strength. Which acquisition parameters would you consider most important to optimize in order to achieve the best activation results? For a 3T scanner provide a suggested set of acquisition parameters.

� Lab Question 5 : Draw ROIS on various tissue types, generate the tSNR as a function of SNR0 in gray matter, white matter and CSF. Record mean signal and standard deviation of the noise. Comment on your findings.

Table 1 – Human Data Average values for SNR measurements as a function of image resolution. SNR0 corrected for Rayleigh distribution.

Resolution

(mm3)

Signal Thermal

Noise

Time-Series

Noise

Spatial

SNR

Temporal

SNR

1.5x1.5x1.5

3x3x3

5x5x5

Table 2 – Phantom Data Average values for SNR measurements as a function of image resolution. SNR0 corrected for Rayleigh distribution.

Resolution

(mm3)

Signal Thermal

Noise

Time-Series

Noise

Spatial

SNR

Temporal

SNR

1.5x1.5x1.5

3x3x3

5x5x5


Table 3 – Human Data - Gray Matter Average values for SNR measurements as a function of image resolution. SNR0 corrected for Rayleigh distribution.

Resolution

(mm3)

Signal Thermal

Noise

Time-Series

Noise

Spatial

SNR

Temporal

SNR

1.5x1.5x1.5

3x3x3

5x5x5

Table 4 – Human Data - White Matter Average values for SNR measurements as a function of image resolution. SNR0 corrected for Rayleigh distribution.

Resolution

(mm3)

Signal Thermal

Noise

Time-Series

Noise

Spatial

SNR

Temporal

SNR

1.5x1.5x1.5

3x3x3

5x5x5

Table 5 – Human Data - CSF Average values for SNR measurements as a function of image resolution. SNR0 corrected for Rayleigh distribution.

Resolution

(mm3)

Signal Thermal

Noise

Time-Series

Noise

Spatial

SNR

Temporal

SNR

1.5x1.5x1.5

3x3x3

5x5x5


2. T1, T2 and T2* Measurements

Background

To calculate T1 values of the tissue we will be using an inversion recovery turbo spin echo sequence. We will be sampling the magnetization as a function of the inversion time (TI). The observed signal intensity is given by the following equation:

S(t) ∝ [1− 2exp(−TI /T1)] (1)

To obtain the transverse relaxation time, T2, values in the brain a series of spin echo images will be acquired by stepping through a range of TEs. The image intensity is given by the following equation:

S(t) ∝ exp(−TE /T 2) (2)

T2star (T2*) is the time constant that describes the decay of the transverse magnetization including local magnetic field inhomogeneities. T2* is an important relaxation time for functional studies because it is related to the amount of deoxygenated hemoglobin present in the brain. To determine the T2* values in gray, white matter and CSF, we will be using a gradient echo sequence and the signal is described by:

S(t) ∝ exp(−TE / T 2*) (3)


Experiments

In this exercise we will acquire and evaluate human data in order to characterize T1, T2 and T2* values in brain tissue compartments. Various sequences will be used with the imaging parameters manipulated so that they provide the appropriate contrast weightings.

T1 measurements Acqusition: Total scan time: 5m 38sec

A. Inversion Recovery Turbo Spin Echo (tse_IR) sequence with TR=3000ms, FOV=240x240, matrix=96x128, repeated for multiple inversion times TI=200, 300, 400, 500, 600, 700, 800, 1000, 1300, 1500, 1700, 2000, 2200 ms.

1. Load all the images on the viewer. 2. Draw ROIs in gray matter areas. 3. Record the mean signal values in Table 1 for each inversion time and ROI. 4. Repeat steps 1-3 for regions of white matter and CSF.

� Lab Question 6 : Draw the measured signal as a function of inversion time for each of the tissue compartments. Calculate the T1 for gray, white matter and CSF by fitting Eq. (1) to your data. Hint: you will need to use a nonlinear fitting function such as nlinfit in matlab to accomplish this.

T2 measurements Acqusition: Total scan time: 9m 42sec

B. SE sequence with TR=4000ms, FOV=220x220, matrix=192x192, 8 echo times in a range between 14.3ms and 114.4ms and inter-echo spacing 14.3 ms.

5. Load all the images on the viewer. 6. Draw ROIs in gray matter areas. 7. Record the mean signal values in Table 2 for each echo time and ROI. 8. Repeat steps 1-3 for regions of white matter and CSF.

� Lab Question 7 : Draw the measured signal as a function of echo time for each of the tissue compartments. Calculate the T2 for gray, white matter and CSF by fitting Eq. (2) to your data.

T2* measurements Acqusition: Total scan time: 1m 20sec

C. 2D FLASH sequence with TR=100msec, FOV=256x256, matrix=128x128, sequence was repeated for multiple echo times TE=6, 10, 15, 20, 30, 50, 70, 90 msec.


9. Load all the images on the viewer. 10. Draw ROIs in gray matter areas. 11. Record the mean signal values in Table 3 for each echo time and ROI. 12. Repeat steps 1-3 for regions of white matter and CSF.

� Lab Question 8 : Draw the measured signal as a function of echo time for each of the tissue compartments. Calculate the T2* of gray and white matter and CSF by fitting Eq. (3) to your data.

� Lab Question 9 : According to your measurements how do T2 and T2* compare for the same tissue compartment? Did you expect these findings? Explain why.

Table 1 – T1 Measurements

TI (ms) Gray

Matter

White

Matter

CSF

200

300

400

500

600

700

800

1000

1300

1500

1700

2000

2200


Table 2 – T2 Measurements

TE (ms) Gray

Matter

White

Matter

CSF

14.3

28.6

42.9

57.2

71.5

85.8

100.1

114.4

Table 3 – T2* Measurements

TE (ms) Gray

Matter

White

Matter

CSF

6

10

15

20

30

50

70

90


3. Distortion in EPI due to B0 Inhomogeneity

Background The echo-planar image (EPI) is distorted due to local field gradients present in the head during imaging. Since the acquisition of k-space data is very asymmetric in EPI, with the readout direction points (kx) collected quickly and the phase encode steps (ky) relatively slowly, the distortion is essentially entirely in the y direction (phase encode direction).

The relative distortion between the two directions can be estimated from the relative speed of the acquisition. In kx, at a typical readout BW (say 2.2kHz/pixel or 140kHz in frequency across the 64 pixel image), the dwell time (time between kx samples) is 7.1μs. The ky sampling rate is slower because of the zig-zag trajectory, and is about 64 times slower. This gives a sampling spacing of 64 * 7.1μs = 0.45ms. We call this .the "echo-spacing", (esp), of the sequence, the time between gradient echos (acquisition of the kx=0 point).

1The effective "bandwidth" in the phase encode direction is therefore across the esp

image or 1/64*esp = 34.7Hz/pixel. Therefore a B0 shift which induces a 34.7Hz frequency shift will induce a shift in this region of 1pixel. The frequency shifts in the "bad susceptibility" regions of the brain are easily in the 100Hz range. Therefore, the principle metric of how distorted the EPI sequence will be is its effective echo-spacing. For conventional echo-planar imaging, this is basically limited by the gradient slew rate and amplitude. Using parallel acceleration such as SENSE or GRAPPA effectively decreases the esp by a factor of the acceleration factor, (i.e. 2 or 3 fold).


Experiments

In this lab we will acquire images with different effective esp and compare the distortion in the brain.

Acquisition : 1) epi_sp460: voxel size = 3.1 x 3.1 x 3.1, Matrix size = 64x64, BW=2520Hz/px,

ESP=0.46ms, effective esp =0.46ms 2) epi_sp470_grappa: voxel size = 3.1 x 3.1 x 3, Matrix size = 64x64,

BW=2520Hz/px, ESP=0.46ms, effective esp =0.46ms 3) epi_sp730: voxel size = 3.1 x 3.1 x 3, Matrix size = 64x64, BW=1502Hz/px,

ESP=0.73ms, effective esp =0.73ms

� Lab Question 10: Estimate the distortion in frontal lobes by measuring distance on the scanner to some reference feature. Plot the distortion in mm as a function of effective esp.


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SIEMENS MAGNETOM TrioTim syngo MR 2006T

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TA: 1:44 PAT: Off Voxel size: 5.0×5.0×5.0 [mm] Rel. SNR: 1.00 USER: benner\ep2d_bold_MGH_pro_tb

Routine Slice group 1

Slices 9 Dist. factor 0 [%] Position R3.0 A0.3 H27.1 [mm] Orientation T > C-12.5 Phase enc. dir. A >> P Rotation 0.340792 [deg]

Phase oversampling 0 [%] FoV read 240 [mm] FoV phase 100.0 [%] Slice thickness 5 [mm] TR 2000 [ms] TE 32 [ms] Averages 1 Concatenations 1 Filter None Coil elements HEA;HEP

Contrast MTC Off Flip angle 90 [deg] Reconstruction Magnitude Fat suppr. Fat sat. Measurements 50 Delay in TR 0 [ms] Multiple series Off

Resolution Base resolution 48 Phase resolution 100 [%] Phase partial Fourier Off Filter 1

Raw filter Off Interpolation Off

PAT mode None Matrix Coil Mode Auto (CP)

Geometry Multi-slice mode Interleaved Series Interleaved

Special sat. None

System Body Off HEP On HEA On

Scan at current TP Off Scan region position H Scan region position 0 [mm] MSMA S - C - T Sagittal R >> L Coronal A >> P Transversal F >> H

Shim mode Standard Adjust with body coil Off Confirm freq. adjustment Off Assume Silicone Off Ref. amplitude [1H] 240.360 [V] Adjust volume

Position R3.0 A0.3 H27.1 [mm] Orientation T > C-12.5 Rotation 0.340792 [deg]

R >> L 240 [mm] A >> P 240 [mm] F >> H 45 [mm]

Physio 1st Signal/Mode None

BOLD t-Test 0 Threshold 4.00 Window Growing Starting ignore meas 0 Paradigm size 1 Meas Ignore Motion correction 0 Spatial filter 0

Sequence Introduction Off Averaging mode Long term Bandwidth 3472 [Hz/Px] Free echo spacing Off Echo spacing 0.36 [ms]

EPI factor 48 RF pulse type Normal Gradient mode Fast

Dummy Scans 2

1/+ Cite as: Cristina Triantafyllou and Larry Wald , HST.583 Functional Magnetic Resonance Imaging: Data Acquisition and Analysis, Fall 2006. (Massachusetts Institute of Technology: MIT OpenCourseWare), http://ocw.mit.edu(Accessed MM DD, YYYY). License: Creative Commons BY-NC-SA.

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\\USER\INVESTIGATORS\HST_583\PhysicsClass\epi_3x3x3_signal


R >> L 192 [mm]Routine

A >> P 192 [mm]Slice group 1



Contrast

F >> H 44 [mm]



Sequence Introduction Off Averaging mode Long term Bandwidth 2520 [Hz/Px] Free echo spacing Off

MTC Off Flip angle 90 [deg] Reconstruction Magnitude Fat suppr. Fat sat. Measurements 50 Delay in TR 0 [ms] Multiple series Off

Resolution

Echo spacing 0.47 [ms]


Dummy Scans 2

Base resolution 64 Phase resolution 100 [%] Phase partial Fourier Off Filter 1




Special sat. None





2/+


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Phase oversampling 0 [%] FoV read 192 [mm] FoV phase 100.0 [%] Slice thickness 1.5 [mm] TR 2000 [ms] TE 30 [ms] Averages 1 Concatenations 1 Filter None Coil elements HEA;HEP

Contrast MTC Off Flip angle 90 [deg] Reconstruction Magnitude Fat suppr. Fat sat. Measurements 50 Delay in TR 0 [ms] Multiple series Off

Resolution Base resolution 128 Phase resolution 100 [%] Phase partial Fourier 5/8 Filter 1




Special sat. None





R >> L 192 [mm] A >> P 192 [mm] F >> H 42 [mm]





Dummy Scans 2

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TA: 9:42 Voxel size: 1.1×1.1×5.0 [mm] Rel. SNR: 1.00 SIEMENS: se_mc

Scan region position HRoutine

Scan region position 0 [mm]Slice group 1

Slices 1 Dist. factor 100 [%] Position R0.7 A10.8 H12.2 [mm] Orientation T > C-10.3 Phase enc. dir. A >> P Rotation 0 [deg]

Phase oversampling 0 [%] FoV read 220 [mm] FoV phase 100.0 [%] Slice thickness 5 [mm] TR 4000 [ms] TE[1] 14.3 [ms] TE[2] 28.6 [ms] TE[3] 42.9 [ms] TE[4] 57.2 [ms] TE[5] 71.5 [ms] TE[6] 85.8 [ms] TE[7] 100.1 [ms] TE[8] 114.4 [ms] Averages 1 Concatenations 1 Filter Raw filter Coil elements HEA;HEP

Contrast

MSMA S - C - T Sagittal R >> L Coronal A >> P Transversal F >> H Matrix Coil Mode Auto (CP)

Shim mode Tune up Adjust with body coil Off Confirm freq. adjustment Off Assume Silicone Off Ref. amplitude [1H] 330.070 [V] Adjust volume

Position Isocenter Orientation Transversal Rotation 0 [deg] R >> L 350 [mm] A >> P 263 [mm] F >> H 350 [mm]


Dark blood Off

Inline Subtract 0 Std-Dev-Sag 0

MTC Off Magn. preparation None Flip angle 180 [deg] Reconstruction Magnitude Fat suppr. None Fat sat. mode Strong Water suppr. None Measurements 1

Resolution

Std-Dev-Cor 0 Std-Dev-Tra 0 Std-Dev-Time 0 MIP-Sag 0 MIP-Cor 0 MIP-Tra 0 MIP-Time 0 Save original images 1

Sequence Base resolution 192 Phase resolution 100 [%] Phase partial Fourier 6/8 Filter 1

Raw filter On Intensity Weak Slope 25

Filter 2 Large FoV Off

Filter 3 Prescan Normalize Off

Filter 4 Normalize Off

Filter 5 Elliptical filter Off

Interpolation Off


Special sat. None

System

Introduction On Averaging mode Short term Contrasts 8 Bandwidth 202 [Hz/Px] Allowed delay 0 [s]

RF pulse type Normal Gradient mode Fast

Body Off HEP On HEA On

Save uncombined Off Scan at current TP Off

1/-


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\\USER\INVESTIGATORS\HST_583\PhysicsClass\FLASH_T2star_TE6

+ TA: 9.6 [s] Voxel size: 2.0×2.0×5.0 [mm] Rel. SNR: 1.00 USER: FLASH


Slices Dist. factor Position Orientation Phase enc. dir. Rotation

Phase oversampling FoV read FoV phase Slice thickness TR TE Averages Concatenations Filter Coil elements

Contrast

1 20 [%] R0.7 A10.8 H12.2 [mm] T > C-10.3 A >> P 0 [deg] 0 [%] 256 [mm] 100.0 [%] 5 [mm] 100 [ms] 6 [ms] 1 1 Raw filter HEA;HEP

Adjust with body coil Off Confirm freq. adjustment Off Assume Silicone Off Ref. amplitude [1H] 319.952 [V] Adjust volume



Inline Subtract 0 Std-Dev-Sag 0 Std-Dev-Cor 0 Std-Dev-Tra 0 Std-Dev-Time 0 MIP-Sag 0 MIP-Cor 0 MIP-Tra 0 MIP-Time 0 Save original images 1

Sequence

MTC Off Flip angle 15 [deg] Reconstruction Magnitude Fat suppr. None Water suppr. None Measurements 1

Resolution Base resolution 128 Phase resolution 100 [%] Phase partial Fourier 6/8 Filter 1

Raw filter On Intensity Weak Slope 25

Filter 2 Large FoV Off

Filter 3 Prescan Normalize Off

Filter 4 Normalize Off

Filter 5 Elliptical filter Off

Interpolation Off

Geometry Multi-slice mode Series

Sequential Interleaved

Introduction Off Dimension 2D Averaging mode Long term Contrasts 1 Bandwidth 300 [Hz/Px]

Gradient mode Fast RF spoiling On

Online ICE Off Selection box Second Choice Spoil me! On Test Time 10 ms dARRAY [1] 1 [UnitArr] dARRAY [2] 12 [UnitArr] dARRAY [3] 22 [UnitArr]

Special sat. None


Save uncombined Off Scan at current TP Off Scan region position H Scan region position 0 [mm] MSMA S - C - T Sagittal R >> L Coronal A >> P Transversal F >> H Matrix Coil Mode Auto (CP)

Shim mode Tune up

1/-Cite as: Cristina Triantafyllou and Larry Wald , HST.583 Functional Magnetic Resonance Imaging: Data Acquisition and Analysis, Fall 2006. (Massachusetts Institute of Technology: MIT OpenCourseWare), http://ocw.mit.edu(Accessed MM DD, YYYY). License: Creative Commons BY-NC-SA.

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\\USER\INVESTIGATORS\HST_583\PhysicsClass\tse_IR1

TA: 0:26 PAT: Off Voxel size: 1.9×1.9×5.0 [mm] Rel. SNR: 1.00 SIEMENS: tse


Slices 1 Dist. factor 50 [%] Position R0.7 A10.8 H12.2 [mm] Orientation T > C-10.3 Phase enc. dir. R >> L Rotation 90 [deg]


Contrast MTC Off Magn. preparation Slice-sel. IR TI 1000 [ms] Flip angle 180 [deg] Reconstruction Real Fat suppr. None Fat sat. mode Strong Water suppr. None Measurements 1


Raw filter Off Filter 2

Large FoV Off Filter 3

Prescan Normalize Off Filter 4

Normalize Off Filter 5

Elliptical filter Off Interpolation Off



Special sat. None


Save uncombined Off Scan at current TP Off Scan region position H Scan region position 0 [mm] MSMA S - C - T Sagittal R >> L

Coronal A >> P Transversal F >> H

Shim mode Tune up Adjust with body coil Off Confirm freq. adjustment Off Assume Silicone Off Ref. amplitude [1H] 330.070 [V] Adjust volume



Dark blood Off

Resp. control Off

Inline Subtract 0 Std-Dev-Sag 0 Std-Dev-Cor 0 Std-Dev-Tra 0 Std-Dev-Time 0 MIP-Sag 0 MIP-Cor 0 MIP-Tra 0 MIP-Time 0 Save original images 1

Sequence Introduction On Dimension 2D Compensate T2 decay Off Averaging mode Short term Contrasts 1 Bandwidth 130 [Hz/Px] Flow comp. No Allowed delay 0 [s] Echo spacing 11.7 [ms]

Turbo factor 15 RF pulse type Normal Gradient mode Fast Hyperecho Off


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\\USER\INVESTIGATORS\HST_583\PhysicsClass\epi_esp730

TA: 2.0 [s] PAT: Off Voxel size: 3.1×3.1×3.0 [mm] Rel. SNR: 1.00 USER: benner\ep2d_bold_MGH_pro_tb


Slices 9 Dist. factor 67 [%] Position R0.3 A11.0 F2.1 [mm] Orientation T > C-10.4 Phase enc. dir. A >> P Rotation 0.340792 [deg]


Contrast MTC Off Flip angle 90 [deg] Reconstruction Magnitude Fat suppr. Fat sat. Measurements 1 Delay in TR 0 [ms]





Special sat. None




Position R0.3 A11.0 F2.1 [mm] Orientation T > C-10.4 Rotation 0.340792 [deg] R >> L 200 [mm]

A >> P 200 [mm] F >> H 44 [mm]





Dummy Scans 0


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\\USER\INVESTIGATORS\HST_583\PhysicsClass\epi_esp460

TA: 2.0 [s] PAT: Off Voxel size: 3.1×3.1×3.1 [mm] Rel. SNR: 1.00 USER: benner\ep2d_bold_MGH_pro_tb


Slices 9 Dist. factor 67 [%] Position R0.3 A11.0 F2.1 [mm] Orientation T > C-10.4 Phase enc. dir. A >> P Rotation 0.340792 [deg]

Phase oversampling 0 [%] FoV read 200 [mm] FoV phase 100.0 [%] Slice thickness 3.1 [mm] TR 2000 [ms] TE 30 [ms] Averages 1 Concatenations 1 Filter None Coil elements HEA;HEP






Special sat. None




Position R0.3 A11.0 F2.1 [mm] Orientation T > C-10.4 Rotation 0.340792 [deg] R >> L 200 [mm]

A >> P 200 [mm] F >> H 45 [mm]





Dummy Scans 0


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\\USER\INVESTIGATORS\HST_583\PhysicsClass\epi_esp460_grappa2

TA: 8.0 [s] PAT: 2 Voxel size: 3.0×3.0×3.0 [mm] Rel. SNR: 1.00 USER: benner\ep2d_bold_MGH_pro_tb


Slices 9 Dist. factor 67 [%] Position R0.3 A23.9 H8.1 [mm] Orientation Transversal Phase enc. dir. A >> P Rotation 0.340792 [deg]





PAT mode GRAPPA Accel. factor PE 2 Ref. lines PE 32 Matrix Coil Mode Auto (Triple)


Special sat. None




Position R0.3 A23.9 H8.1 [mm] Orientation Transversal

Rotation R >> L A >> P F >> H

0.340792 [deg] 192 [mm] 192 [mm] 44 [mm]


BOLD t-Test Threshold Window Starting ignore meas Paradigm size Meas Motion correction Spatial filter

0 4.00 Growing 0 1 Ignore 0 0



Dummy Scans 0



Documents

HST.583 Functional Magnetic Resonance Imaging: Data ... fileHST.583: Functional Magnetic Resonance Imaging: Data Acquisition and Analysis, Fall 2006 Harvard-MIT Division of Health