Download presentation
Presentation is loading. Please wait.
Published byMeghan Bennett Modified over 9 years ago
1
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 and M Hartley for collaboration on Propeller productization.
2
pitch/frequency 392Hz G 660Hz E 523.2Hz C pitch/frequency time temporal frequency
3
log of k-space magnitude data. apodized reconstructed image. checkerboard pattern strong k-space signal along axes
4
Heisenberg, Riemann & Lebesgue Heisenberg Functions cannot be space- and band-limited. implies Riemann-Lebesgue k-space data decays with frequency
5
Cartesian sampling reconstruct directly with Fast Fourier Transform (FFT) Ringing near the edge of a disc. Solid line for k-space data sampled on 512x512; dashed for 128x128; dashed-dot on 64x64 grid.
6
spirals – fast acquisition From Handbook of MRI Pulse Sequences. non-Cartesian sampling requires gridding additional errors Propeller – redundant data permits motion correction.
7
CT errors high-frequency & localized MR errors low-frequency & global CT vs. MRI
8
high-order interp overshootslow-order interp smoothsnaive k-space griddingcorrected for gridding errors linear interpolation = convolve w/“tent” function “gridding” = convolve w/kernel (typically smooth, w/small support)
9
convolution – “shift & sum”
10
convolution – properties 2x Field-of-View Avoid Aliasing Artifacts sinc interp in k-space
11
Avoid Aliasing Artifacts Propeller k-space data interpolated onto 4x fine grid
12
sinc interp convolution – properties Image Space Upsampling
13
image from a phase corrected Propeller blade with ETL=36 and readout length=320. sinc-interpolated up to 64x512. Image Space Upsampling
14
Ringing near the edge of a disc. Solid line for k-space data sampled on 512x512; dashed for 128x128; dashed-dot on 64x64 grid. Reprinted with permission from Handbook of MRI Pulse Sequences. Elsevier, 2004. Tukey window function in k-space PSF in image space. k-space apodization
15
Low-frequency Gridding Errors linear interpolation “tent” function against which k-space data is convolved no interpolation-no shading; interpolation onto k/4 lattice 4xFOV cubic interp linear interp k-space data sampled at ‘X’s and linearly interpolated onto ‘ ’s. cubic interp linear interp no interpolation no shading high-order interp overshootsw/o gridding deconvolutionafter gridding deconv
16
sinc interp Cartesian sampling suited to sinc-interpolation
17
Radial sampling (PR, spiral, Propeller) suited to jinc-interpolation
18
64 256 “fast” conv kernel perfect jinc kernel multiply image
19
Propeller – Phase Correct Redundant data must agree, remove phase from each blade image
20
RAW Propeller – Phase Correct one blade CORRECTED
21
Propeller - Motion Correct 2 scans – sans motion sans motion correction w/motion correction artifacts due to blade #1 errors
22
1 blade # 23 shifts in pixels rotations in degrees blade weights Propeller – Blade Correlation throw out bad – or difficult to interpret - data blade #1 Propeller – Blade Correlation throw out bad – or difficult to interpolate - data
23
Fourier Transform Properties shift image phase roll across data b is blade image, r is reference image
24
max at x No correction, with correction shifts in pixels
25
rotate image rotate data Fourier Transform Properties “holes” in k-space
26
no correction correlation correction only motion correction only full corrections
27
Backup Slides Simulations show Cartesian acquisitions are robust to field inhomogeneity. (top left) Field inhomogeneity translates and distorts k-space sampling more coherently than in spiral scans. (top right) magnitude image suffers fewer artifacts than spiral, despite (bottom left) severe phase roll. (bottom right) Image distortion displayed in difference image between magnitude images with and without field inhomogeneity. k-space stretching decreases the field- of-view (FOV), essentially stretching the imaging object.
28
Backup Slides Propeller blades sample at points denoted with ‘o’ and are upsampled via sinc interpolation to the points denoted with ‘ ’
Similar presentations
© 2025 SlidePlayer.com. Inc.
All rights reserved.