2D FT Imaging MP/BME 574. Frequency Encoding Time (t) Temporal Frequency (f) FT Proportionality Position (x, or y) FT Proportionality Spatial Frequency.

Slides:



Advertisements
Similar presentations
Signal Processing for MRI
Advertisements

Fund BioImag : Echo formation and spatial encoding 1.What makes the magnetic resonance signal spatially dependent ? 2.How is the position of.
Fund BioImag : Echo formation and spatial encoding 1.What makes the magnetic resonance signal spatially dependent ? 2.How is the position of.
Imaging Sequences part I
In Chan Song, Ph.D. Seoul National University Hospital
M R I Pulse Sequences Jerry Allison Ph.D..
Statistical Parametric Mapping
Figure 2. Signal level (left) degrades with slice offset and slice thickness when Z2 SEM is used in GradLoc imaging (ROI = FOV/2). To recover the full.
Parameters and Trade-offs
Topics spatial encoding - part 2. Slice Selection  z y x 0 imaging plane    z gradient.
Principles of MRI: Image Formation
Basics of fMRI Preprocessing Douglas N. Greve
Chapter 9 Basic MRI I Mark D. Herbst, MD, PhD. Notice This lecture contained many drawings on the whiteboard, so get these from one of the other students.
Basic Principles MRI related to Neuroimaging Xiaoping Hu Department of Biomedical Engineering Emory University/Georgia Tech
Wald, fMRI MR Physics Massachusetts General Hospital Athinoula A. Martinos Center MR physics and safety for fMRI Lawrence L. Wald, Ph.D.
K-space Data Pre-processing for Artifact Reduction in MRI SK Patch UW-Milwaukee thanks KF King, L Estkowski, S Rand for comments on presentation A Gaddipatti.
Terry M. Button, Ph.D. Principals of Magnetic Resonance Image Formation.
Real-Time MRI – Outline Biomed NMR JF11/59 Technical Considerations - Data Acquisition - Image Reconstruction Preliminary Applications - Joint Movements,
Encoding and Image Formation
MRI, FBP and phase encoding. Spins Precession RF pulse.
A b c d e f Original imageReconstruction/ No smoothing 4mm FWHM12mm FWHM PSWF Spatial Smoothing in fMRI using Prolate Spheroidal Wave Functions Martin.
EPI – Echo Planar Imaging Joakim Rydell
Image reproduction. Slice selection FBP Filtered Back Projection.
Basics of Magnetic Resonance Imaging
Psy 8960, Fall ‘06 Fourier transforms1 –1D: square wave –2D: k x and k y Spatial encoding with gradients Common artifacts Phase map of pineapple slice.
FMRI: Biological Basis and Experiment Design Lecture 7: Gradients and k-space FFT examples –Sampling and aliasing Gradient Gradient echo K-space
A novel technique has been proposed for dynamic MRI: Dynamic KWIC Permits image reconstruction at both high spatial and high temporal resolutions Technique.
Image reproduction +fMRI. Filtered Back Projection.
Compressed Sensing for Chemical Shift-Based Water-Fat Separation Doneva M., Bornert P., Eggers H., Mertins A., Pauly J., and Lustig M., Magnetic Resonance.
Lecture 4 MR: 2D Projection Reconstruction, 2D FT Longitudinal Magnetization returns to equilibrium as MR Review Transverse Magnetization Gradients’ effect.
Tissue Contrast intrinsic factors –relative quantity of protons tissue proton density –relaxation properties of tissues T1 & T2 relaxation secondary factors.
MRI Image Formation Karla Miller FMRIB Physics Group.
Medical Imaging Systems: MRI Image Formation
Principles of MRI Physics and Engineering
Lecture 24: Cross-correlation and spectral analysis MP574.
Imaging Sequences part II
ELEG 479 Lecture #12 Magnetic Resonance (MR) Imaging
Gradients (Continued), Signal Acquisition and K-Space Sampling
The comparison of MRI imaging In 3.0 T & 1.5T By a.r.shoaie.
Statistical Parametric Mapping Lecture 9 - Chapter 11 Overview of fMRI analysis Textbook: Functional MRI an introduction to methods, Peter Jezzard, Paul.
Parallel Imaging Reconstruction
Medical Imaging Systems: MRI Image Formation
Partial Parallel imaging (PPI) in MR for faster imaging IMA Compressed Sensing June, 2007 Acknowledgement: NIH Grants 5RO1CA and 5P41RR008079, Pierre-Francois.
Basic of Magnetic Resonance Imaging Seong-Gi Kim Paul C. Lauterbur Chair in Imaging Research Professor of Radiology, Neurobiology and Bioengineering University.
In vivo MR Spectroscopy
Correcting for Center Frequency Variations in MRSI Data Using the Partially Suppressed Water Signal Lawrence P Panych, Ph.D., Joseph R Roebuck, Ph.D.,
RSL/MRSL Journal Club VIPR/Radial MRI Kitty Moran.
HighlY Constrained Back PRojection (HYPR) Thank you to Oliver Wieben!!
G Practical MRI 1 Gradients.
NEW SEQUENCES LAVA Liver Acquisition with Volume Acceleration.
Anna Beaumont FRCR Part I Physics
MRI Physics: Spatial Encoding Anna Beaumont FRCR Part I Physics.
Declaration of Relevant Financial Interests or Relationships David Atkinson: I have no relevant financial interest or relationship to disclose with regard.
Magnetic Resonance Learning Objectives
Principles of MRI Physics and Engineering Allen W. Song Brain Imaging and Analysis Center Duke University.
DTI Acquisition Guide Donald Brien February 2016.
Charged particle. Moving charge = current Associated magnetic field - B.
Principles of MRI Physics and Engineering Allen W. Song Brain Imaging and Analysis Center Duke University.
Lecture 1: Magnetic Resonance
Chapter 5 Mark D. Herbst, M.D., Ph.D.. The MR Imaging Process Two major functions –Acquisition of RF signals –Reconstruction of images.
BOLD functional MRI Magnetic properties of oxyhemoglobin and deoxyhemoglobin L. Pauling and C. Coryell, PNAS USA 22: (1936) BOLD effects in vivo.
He-Kwon Song, PhD Associate Professor & Co-investigator in the CMROI, Department of Radiology, University of Pennsylvania School of Medicine, Philadelphia,
FMRI data acquisition.
MRI Physics in a Nutshell Christian Schwarzbauer
بسم الله الرحمن الرحيم.
2D FT Review MP/BME 574.
Magnetic Resonance Imaging: Physical Principles
Assume object does not vary in y
An Optimal Design Method for MRI Teardrop Gradient Waveforms
Basic MRI I Mark D. Herbst, MD, PhD
Presentation transcript:

2D FT Imaging MP/BME 574

Frequency Encoding Time (t) Temporal Frequency (f) FT Proportionality Position (x, or y) FT Proportionality Spatial Frequency (k)

2D Fast GRE Imaging GyGy RF GxGx TE Dephasing/ Rewinder Dephasing/ Rewinder Shinnar- LaRoux RF Phase Encode Asymmetric Readout GzGz TR = 6.6 msec

Summary Frequency encoding –Bandwidth of precessing frequencies Phase –Incremental phase in image space Implies shift in k-space Entirely separable –1D column-wise FFT –1D row-wise FFT

2D FT y x k k Start Finish

3D FT y z k k k x T scan =N y N z TR NEX i.e. Time consuming!

Zero-padding/Sinc Interpolation Recall that the sampling theorem –Restoration of a compactly supported (band- limited) function –Equivalent to convolution of the sampled points with a sinc function

Case II FT k-space: Image Space: kzkz kyky

Case III FT k-space: Image Space: Methods: Sampling kzkz kyky

Case II Nyquist Case III Corner

Case II: Zero-filled FT k-space: Image Space: kzkz kyky

kzkz kyky Case III: Zero-Filled FT k-space: Image Space: Methods: Sampling

Case II: Nyquist Zero-filled Case III: Corner Zero-filled

Apodization Rect windowing implies covolution with a truncated sinc function leading to Gibbs’ Ringing Desire to smooth the windowing function so as to diminish ringing. –Gaussian is one option discussed by Prof. Holden –MRI often uses “Fermi” Filter:

Point Spread Functions Un-windowed: Radial Window:

RefCornersRadial

Angular Dependence w/o Zero-filling

Angular Dependence with Zero-filling

Experimental Results  = 45º 0 Degrees 45 Degrees Methods: Point response function

Summary Samples in 2D k-space represent 2D sinusoids at specific harmonics and at specific rotation angles Interpolation by zero-filling leads to: –Reduced partial volume artifact –Increased spatial resolution at specific angles Role of Apodization window –Increases SNR –Decreases ringing artifact –Choice effects the angular symmetry of the PSF

Point response function due to time-dependent contrast Example showing mapping on contrast- enhanced signal to model the point response function –Predict attainable resolution –Application to carotid artery MR angiography

Fain SB, Bernstein MA, Huston J III, Riederer SJ Point Spread Function (PSF) Analysis Step 1: Measure enhancement curves in patients Step 2: Map enhancement curves to k-space Step 3: Transform result to image space to obtain the point spread function

Fain SB, et al., MRM 42 (1999) Step 1: Enhancement Model

Fain SB, et al., MRM 42 (1999) Start y z k Finish Overall Image Contrast High Detail Information Sampled Points k Step 2: Mapping to k-Space

Fain SB, et al., MRM 42 (1999) Step 2: Mapping to k-Space

Fain SB, et al., MRM 42 (1999) The Hankel Transform

Fain SB, et al., MRM 42 (1999) Step 3: Transform to Image Space

Fain SB, et al., MRM 42 (1999) Analysis: Spatial Resolution FWHM  2 FOV y  z  TR  11 Full Width at Half Maximum (FWHM) of the Point Spread Function is given by: where, FOV y and FOV z are the phase encoding Fields of View TR is the repetition time  1 is the time to peak enhancement of the bolus curve

Fain SB, et al., MRM 42 (1999) PSF Dependence on Acquisition Time

Fain SB, et al., MRM 42 (1999) 213 sec Z Y 10 sec 50 sec 90 sec Line Pairs/mm Acquisition Time (sec) PSF Dependence on Acquisition Time

Fain SB, et al., MRM 42 (1999) Experiment: FOV z Reduction 13 cm X 6.4 cm 13 cm X 4.0 cm Z Y

Fain SB, et al., MRM 42 (1999) Carotid and Vertebral Arteries: Acquisition Parameters –FOV: 22 cm (S/I) X 15 cm (R/L) X 6 cm (A/P) –Matrix: 256 X 168 X –Acquired Voxel: 0.9 mm X 0.9 mm X 1.4 mm –2X Zerofilling in all three directions –TR/TE 6.6 msec/1.4 msec –Acquisition Time: seconds –20 cc Gd

Fain SB, et al., MRM 42 (1999) Left Carotid Artery Stenosis: Reconstruction at Multiple Time Points 33 sec22 sec11 sec44 sec Acquisition Time: X Z X Z Coronal MIP, Full Data Set: MIP Reprojec- tions

Fain SB, et al., MRM 42 (1999) Right Carotid Artery Stenosis: Reconstruction at Multiple Time Points 11 sec22 sec33 sec44 sec Acquisition Time: X Z X Z

Fain SB, et al., MRM 42 (1999) Decreased FOV

Fain SB, et al., MRM 42 (1999) Increased Scan Time

Partial k-Space Acquisition Means of accelerating image acquisition at the expense of minor artifacts –¾ k-space –½ k-space -> Hermetian symmetry Phase in the image space complicates matters –In practice, MR images have non-zero phase due to magnetic field variations Susceptibility General field inhomogeneity –“Homodyne” reconstruction required Low spatial frequency estimation of the phase

FI = fftshift(fft(fftshift(I))); for i = 1:192, FI_34(i,:) =FI(i,:); end I_34 = fftshift(ifft(fftshift(FI_34))); figure;subplot(2,2,1),imagesc(abs(I_34));axis('image'); colorbar;colormap('gray');title('Magnitude') subplot(2,2,2),imagesc(angle(I_34));axis('image'); colorbar;colormap('gray');title('Phase') subplot(2,2,3),imagesc(abs(I-I_34));axis('image'); colorbar;colormap('gray');title('Error') gtext('Three-quarter k-space')

for i = 1:129, FI_Herm(i,:) =FI(i,:); end I_Herm = fftshift(ifft(fftshift(FI_Herm))); figure;subplot(2,2,1),imagesc(abs(I_Herm2));axis('image'); colorbar;colormap('gray');title('Magnitude') figure;subplot(2,2,1),imagesc(abs(I_Herm));axis('image'); colorbar;colormap('gray');title('Magnitude') subplot(2,2,2),imagesc(angle(I_Herm));axis('image'); colorbar;colormap('gray');title('Phase') subplot(2,2,3),imagesc(abs(I-I_Herm));axis('image');colorbar; colormap('gray');title('Error') gtext('One-half k-space')

count2 = 128; for i = 130:256, FI_Herm(i,:) =conj(FI(count2,:)); count2 = count2-1; end I_Herm2 = fftshift(ifft(fftshift(FI_Herm))); figure;subplot(2,2,1),imagesc(abs(I_Herm2));axis('image'); colorbar;colormap('gray');title('Magnitude') save phase_phantom subplot(2,2,2),imagesc(angle(I_Herm2));axis('image'); colorbar;colormap('gray');title('Phase') subplot(2,2,3),imagesc(abs(I-I_Herm2));axis('image'); colorbar;colormap('gray');title('Error') gtext('Hermetian k-space')

FIp = fftshift(fft(fftshift(IIII))); FIp_Herm = zeros(256); for i = 1:129, FIp_Herm(i,:) =FIp(i,:); end count2 = 128; for i = 130:256, FIp_Herm(i,:) =conj(FIp(count2,:)); count2 = count2-1; end Ip_Herm = fftshift(ifft(fftshift(FIp_Herm))); figure;subplot(2,2,1),imagesc(abs(Ip_Herm));axis('image'); colorbar;colormap('gray');title('Magnitude') subplot(2,2,2),imagesc(angle(Ip_Herm));axis('image'); colorbar;colormap('gray');title('Phase') subplot(2,2,3),imagesc(abs(I-Ip_Herm));axis('image'); colorbar;colormap('gray');title('Error') gtext('Attempt at Hermetian k-space for Image with Phase')