% setup MRI-education-resources path and requirements
cd ../
startup

loading image
loading signal

Artifacts

This notebook includes a simulation of various MRI artifacts, as well as a high-level Artifact Comparison below. The wikipedia entry https://en.wikipedia.org/wiki/MRI_artifact is also very comprehensive.

Learning Goals

1. Identify artifacts and how to mitigate them

Introduction

Many of the artifacts that occur in MRI can be understood and analyzed using the k-space perspective. In particular, they can be understood as the k-space data being modified by some function. This is described mathematically in MRI Signal Equation and K-space in the K-space Data Weighting section.

Artifact Comparison

Artifact Name	Appearance	Frequency or Phase encoding
Aliasing	Signal folds across image	Phase encoding
Spike Noise	Specific spatial frequency on top of entire image	N/A
Gibbs Ringing/Truncation	Ripples/ringing at sharp edges	N/A
RF interference/Zipper	Line of noisy signal on top of image	At a specific frequency encoding location
Motion/Flow	Copies or “Ghosts” of image regions that are changing due to motion or flow	Phase encoding
Displacement/Distortion	Shifts in location due to chemical shift (fat) and off-resonance (e.g. magnetic susceptibility differences)	Frequency encoding (2DFT), Phase encoding (EPI)
T2*	Signal loss around magnetic suscpetibility differences (e.g. implants, air)	N/A
Slice Misregistration	Slice shifting due to chemical shift or off-resonance	N/A
Dielectric Shading	Shading across entire image, particularly large FOV, higher B0	N/A
Boundary Artifacts	Artificial dark lines at tissue boundaries due to inversion or chemical shift	N/A
Gradient non-linearity	Distortion of subject, particularly near edges of large FOV	N/A

Simulations of Artifacts

% load k-space data
dataname = 'Data/se_t1_sag_data';
load(dataname)
kdata = data;
S = size(kdata);

im_original = ifft2c(kdata);

subplot(121)
imagesc(log(abs(kdata)), [0 max(log(abs(kdata(:))))])
colormap(gray), axis equal tight off
subplot(122)
imagesc(abs(im_original), [0 max(abs(im_original(:)))])
colormap(gray), axis equal tight off

warning: load: 'C:\Users\PLarson\Documents\GitHub\MRI-education-resources\Data\se_t1_sag_data.mat' found by searching load path

%% aliasing
N_undersamp = 2;  % >= 1
data_undersamp = zeros(size(kdata));
Iundersamp = 1:N_undersamp:S(1);
data_undersamp(round(Iundersamp),:) = kdata(round(Iundersamp),:);
im_undersamp = ifft2c(data_undersamp);


subplot(121)
imagesc(log(abs(data_undersamp)), [0 max(log(abs(kdata(:))))])
colormap(gray), axis equal tight off
subplot(122)
imagesc(abs(im_undersamp), [0 max(abs(im_original(:)))])
colormap(gray), axis equal tight off

%% spike noise
spike_location = [.6 .53];

data_spike = zeros(size(kdata));
data_spike(round(S(1)*spike_location(1)), round(S(2)*spike_location(2))) = max(kdata(:))/1.5;
im_spike = ifft2c(kdata + data_spike);


subplot(121)
imagesc(log(abs(kdata + data_spike)), [0 max(log(abs(kdata(:))))])
colormap(gray), axis equal tight off
subplot(122)
imagesc(abs(im_spike), [0 max(abs(im_original(:)))])
colormap(gray), axis equal tight off

%% Ringing (with rect object)
N = 32;
kx = [-N/2:N/2-1]/N;
N_rect = N/2+2;
kdata = sinc(kx *N_rect).' * sinc(kx *N_rect);

rect_ringing = ifft2c(kdata);

subplot(221)
imagesc((abs(kdata)), [0 (1)])
colormap(gray), axis equal tight off
subplot(222)
imagesc(abs(rect_ringing), [0 max(abs(rect_ringing(:)))])
colormap(gray), axis equal tight off
ylabel('Ringing')

kdata_windowed = kdata .* (hamming(N) * hamming(N).');
rect_windowed = ifft2c(kdata_windowed);


subplot(223)
imagesc((abs(kdata_windowed)), [0 (1)])
colormap(gray), axis equal tight off
subplot(224)
imagesc(abs(rect_windowed), [0 max(abs(rect_windowed(:)))])
colormap(gray), axis equal tight off
ylabel('Windowed')

%% RF interference
relative_RF_frequency = 0.6;

%single frequency, phase modulated
ph_int = pi*rand(S(1),1);
rfint = exp(i*2*pi* [1:S(1)] *relative_RF_frequency/2 ) *  max(abs(kdata(:)))/S(1)/1.5;
data_rfint = repmat(rfint, [S(1), 1]) .* repmat(exp(i*ph_int), [1, S(2)]);
im_rfint = ifft2c(kdata + data_rfint);

figure
subplot(121)
imagesc(log(abs(kdata + data_rfint)), [0 max(log(abs(kdata(:))))])
colormap(gray), axis equal tight off
subplot(122)
imagesc(abs(im_rfint), [0 max(abs(im_original(:)))])
colormap(gray), axis equal tight off

% RF interference -simulate amplitude modulated interference
relative_RF_frequency = 0.2;
rfint = exp(i*2*pi* [1:S(1)]/S(1) * S(1)*relative_RF_frequency/2) *  max(abs(kdata(:)))/S(1)/3;
data_rfint = randn(S(1),1) * rfint;
im_rfint = ifft2c(kdata + data_rfint);

figure
subplot(121)
imagesc(log(abs(kdata + data_rfint)), [0 max(log(abs(kdata(:))))])
colormap(gray), axis equal tight off
subplot(122)
imagesc(abs(im_rfint), [0 max(abs(im_original(:)))])
colormap(gray), axis equal tight off

% RF interference - simulate frequency modulated interference
relative_RF_frequency = 1;
f = randn(S(1),1)*relative_RF_frequency/2;
data_rfint = exp(i*2*pi* f*[1:S(1)]/S(1)  ) *  max(abs(kdata(:)))/S(1)/1.5;
im_rfint = ifft2c(kdata + data_rfint);

figure
subplot(121)
imagesc(log(abs(kdata + data_rfint)), [0 max(log(abs(kdata(:))))])
colormap(gray), axis equal tight off
subplot(122)
imagesc(abs(im_rfint), [0 max(abs(im_original(:)))])
colormap(gray), axis equal tight off