mirror of
https://gitlab.tugraz.at/ibi/projects/julia-pfitzer/esmrmb-educational.git
synced 2024-11-24 02:22:27 +00:00
Added ky kz undersampling pattern.
This commit is contained in:
jupfi
parent
7ee78882d7
commit
a27b222671
1 changed files with 207 additions and 75 deletions
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@ -130,7 +130,7 @@
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"cell_type": "code",
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"execution_count": 3,
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"execution_count": 4,
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"id": "5abe217f-02ba-4ca4-9de2-7862af2d0ce6",
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"metadata": {},
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@ -215,7 +215,7 @@
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"cell_type": "code",
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"execution_count": 4,
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"execution_count": 7,
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"id": "c60a8e73-8a0b-4cb8-9724-891d860307e4",
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"metadata": {},
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@ -245,7 +245,7 @@
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"cell_type": "code",
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"execution_count": 11,
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"execution_count": 8,
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"id": "f7b7800e-8a4c-4852-b94c-2b47cd4a52de",
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"metadata": {},
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"outputs": [],
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@ -378,7 +378,7 @@
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"cell_type": "code",
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"execution_count": 12,
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"execution_count": 9,
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"id": "f1883fa9-f260-47a3-9598-474905d10f89",
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"metadata": {},
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"outputs": [
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@ -428,7 +428,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"execution_count": 10,
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"id": "96b28fec-ff5a-44a3-8a0c-9b01b60cff0c",
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"metadata": {},
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"outputs": [],
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@ -447,7 +447,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"execution_count": 11,
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"id": "5f93fcca-48ab-4d42-9061-91361aadfb35",
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"metadata": {},
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"outputs": [
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@ -475,7 +475,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 17,
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"execution_count": 12,
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"id": "77d36186-04bb-422d-9ec3-a2032fcbceda",
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"metadata": {},
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"outputs": [],
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@ -686,11 +686,64 @@
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 14,
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"id": "a0a45c91-da70-4fdf-b1ab-a35b5d92b63c",
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"metadata": {},
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"outputs": [],
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"source": []
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"source": [
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"%%bash\n",
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"bart ecalib -m1 data/ksp data/coilsens_fully"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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"id": "3db83274-63d1-4ae1-abcf-3c5d7baac3cc",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Size: 2048000 Samples: 2047999 Acc: 1.00\n",
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"l1 regularization: 0.030000\n",
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"Regularization terms: 1, Supporting variables: 0\n",
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"FISTA\n",
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"Total Time: 9.526724\n"
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]
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}
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],
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"source": [
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"%%bash\n",
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"bart pics -RI:$(bart bitmask 5):3e-2 -i150 -S data/ksp data/coilsens_fully data/pics_fully\n",
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"#bart pics -l1 -r 0.03 -i 150 -S data/kSpace_upat_combined data/nlinv_coilsens data/pics3_upat"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 16,
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"id": "0a2bf699-4d04-4a76-a717-3085154e096c",
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"metadata": {},
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"outputs": [],
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"source": [
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"%%bash\n",
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"# Fit the model:\n",
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"bart mobafit -T data/echo_times_final data/pics_fully data/fit_pics_fully\n",
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"\n",
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"# Now some values will be very large so we can apply a threshold to obtain a mask\n",
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"MAX_T2=1000\n",
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"\n",
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"bart threshold -M $MAX_T2 data/fit_pics_fully data/mask_pics_fully\n",
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"\n",
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"# Multiply the fit with the mask\n",
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"bart fmac data/fit_pics_fully data/mask_pics_fully data/fit_mask_pics_fully\n",
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"\n",
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"# Select slice\n",
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"bart slice 6 1 data/fit_mask_pics_fully data/R2_map_pics_fully\n",
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"\n",
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"# Invert the data to get T2\n",
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"bart invert data/R2_map_pics_fully data/T2_map_pics_fully"
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]
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},
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{
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"cell_type": "markdown",
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@ -959,14 +1012,14 @@
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},
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{
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"cell_type": "code",
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"execution_count": 229,
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"execution_count": 17,
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"id": "a9aa3845-2dc3-4e83-b68f-49b965af11ab",
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"metadata": {},
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"outputs": [],
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"source": [
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"%%bash\n",
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"ACC_Y=2\n",
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"ACC_Z=2\n",
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"ACC_Y=4\n",
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"ACC_Z=4\n",
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"CENTER_SIZE=8\n",
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"\n",
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"bart upat -Y 80 -Z 16 -y $ACC_Y -z $ACC_Z -c $CENTER_SIZE data/upat"
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@ -974,18 +1027,10 @@
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},
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{
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"cell_type": "code",
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"execution_count": 230,
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"execution_count": 18,
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"id": "ad44e09a-a74e-493e-bb3f-3cd3f7607eb6",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Scaling: 0.349421\n"
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]
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}
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],
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"outputs": [],
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"source": [
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"%%bash\n",
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"bart fmac data/upat data/kSpace_transposed data/kSpace_upat\n",
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@ -995,9 +1040,13 @@
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"\n",
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"bart nlinv -i 30 -x 80:80:16 -S data/kSpace_upat_transposed data/nlinv_upat\n",
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"# bart fft -i $(bart bitmask 0 1 2) data/kSpace_upat data/fft_upat\n",
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"#bart pics -RI:$(bart bitmask 5):3e-2 -i150 -S data/kSpace_upat_transposed data/nlinv_coilsens data/nlinv_upat \n",
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"\n",
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"# bart transpose 3 5 data/fft_upat data/fft_upat_transposed\n",
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"\n",
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"# Calculate the pattern\n",
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"bart pattern data/kSpace_upat_transposed data/calc_pattern_upat\n",
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"\n",
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"# Fit the model:\n",
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"bart mobafit -T data/echo_times_final data/nlinv_upat data/fit_upat\n",
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"\n",
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@ -1018,7 +1067,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 231,
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"execution_count": 6,
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"id": "5132b2af-0e1d-44b9-90ba-d6031ca4d10a",
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"metadata": {},
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"outputs": [
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@ -1030,19 +1079,15 @@
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]
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},
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{
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"data": {
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"image/png": "iVBORw0KGgoAAAANSUhEUgAAAFAAAAAQCAIAAAAOK2+WAAAARklEQVRIie3RIRIAMAgDwdD//5mKeqYiqemtwMJw6m5J/8xS3tl0qSp70nrz13sUNqNwAIUnFDajcACFJxQ2o3AAhScUNtsCZEIXIDnDpQAAAABJRU5ErkJggg==",
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"text/plain": [
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"<IPython.core.display.Image object>"
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]
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},
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"execution_count": 231,
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"metadata": {
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"image/png": {
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"width": 1000
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}
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},
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"output_type": "execute_result"
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"ename": "NameError",
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"evalue": "name 'Image' is not defined",
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"output_type": "error",
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"traceback": [
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
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"Cell \u001b[0;32mIn[6], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m get_ipython()\u001b[38;5;241m.\u001b[39msystem(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbart transpose 1 2 data/upat data/img\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 2\u001b[0m get_ipython()\u001b[38;5;241m.\u001b[39msystem(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbart toimg -w 1.0 data/img fig/upat_mask.png\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m----> 3\u001b[0m \u001b[43mImage\u001b[49m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfig/upat_mask.png\u001b[39m\u001b[38;5;124m\"\u001b[39m, width\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1000\u001b[39m)\n",
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"\u001b[0;31mNameError\u001b[0m: name 'Image' is not defined"
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]
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}
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],
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"source": [
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},
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{
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"cell_type": "code",
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"execution_count": 32,
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"execution_count": 18,
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"id": "0882a788-ecd2-4968-9715-a57816d66e9c",
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"metadata": {},
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"outputs": [
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@ -1117,18 +1162,10 @@
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},
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{
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"cell_type": "code",
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"execution_count": 33,
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"execution_count": 19,
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"id": "d14780bc-dd5a-4710-a52b-ef491caae213",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Scaling: 0.347030\n"
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]
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}
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],
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"outputs": [],
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"source": [
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"%%bash\n",
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"# For nlinv the echos can't be in the coil dimension (3) also to make it work with the upat_combined\n",
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},
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{
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"cell_type": "code",
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"execution_count": 34,
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"execution_count": 20,
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"id": "821f937e-64ef-4ad3-9848-df50ee3faac3",
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"metadata": {},
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"outputs": [
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"<IPython.core.display.Image object>"
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]
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},
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"execution_count": 34,
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"execution_count": 20,
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"metadata": {
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"image/png": {
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"width": 1000
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},
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{
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"cell_type": "code",
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"execution_count": 35,
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"execution_count": 21,
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"id": "3ea47c87-480b-434e-a451-f5dc246968c2",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Done.\n"
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]
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}
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],
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"outputs": [],
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"source": [
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"%%bash\n",
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"bart ecalib -m1 data/kSpace_upat_combined data/upat_coilsens"
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},
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{
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"cell_type": "code",
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"execution_count": 36,
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"execution_count": 1,
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"id": "5b6a3b3d-9bd1-4e2f-b4b1-fd33513f984c",
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"metadata": {},
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"outputs": [
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"l1 regularization: 0.030000\n",
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"Regularization terms: 1, Supporting variables: 0\n",
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"FISTA\n",
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"Total Time: 6.242878\n"
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"Total Time: 9.607770\n"
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]
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}
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],
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},
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{
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"cell_type": "code",
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"execution_count": 37,
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"execution_count": 2,
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"id": "c28b7fa1-4acb-4cfc-98b3-d8af9cf21025",
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"metadata": {},
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"outputs": [],
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},
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{
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"cell_type": "code",
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"execution_count": 21,
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"execution_count": 30,
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"id": "c74d227b-ff58-4ffb-a511-be3865c5618e",
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"metadata": {},
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"outputs": [
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"Basis: [ 80 80 16 1 1 20 3 1 1 1 1 1 1 1 1 1 ]\n",
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"Max: [ 80 80 16 1 1 1 3 1 1 1 1 1 1 1 1 1 ]\n",
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"Size: 2048000 Samples: 2047999 Acc: 1.00\n",
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"Regularization terms: 0, Supporting variables: 0\n",
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"conjugate gradients\n",
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"Total Time: 2.095993\n"
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"l1 regularization: 0.030000\n",
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"Regularization terms: 1, Supporting variables: 0\n",
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"FISTA\n",
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"Total Time: 9.596245\n"
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]
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}
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],
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"\n",
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"# Now we get a coefficient map\n",
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"# bart nlinv -B data/basis data/kSpace data/coeff data/sens\n",
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"bart pics -B data/basis data/ksp data/nlinv_coilsens data/coeff\n",
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"bart pics -B data/basis -RI:$(bart bitmask 5):3e-2 -i150 -S data/ksp data/nlinv_coilsens data/coeff\n",
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"\n",
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"# From the coefficients and the basis we can get the image\n",
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"bart fmac -s $(bart bitmask 6) data/basis data/coeff data/imgs\n",
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},
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{
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"cell_type": "code",
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"execution_count": 22,
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"execution_count": 31,
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"id": "bd942687-35f8-455f-a794-922bf19eb822",
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"metadata": {},
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"outputs": [],
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},
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{
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"cell_type": "code",
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"execution_count": 23,
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"execution_count": 36,
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"id": "450ddbe5-dad6-4f34-89c1-58db4ca1968c",
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"metadata": {},
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"outputs": [
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"text": [
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"Basis: [ 80 80 16 1 1 20 3 1 1 1 1 1 1 1 1 1 ]\n",
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"Max: [ 80 80 16 1 1 1 3 1 1 1 1 1 1 1 1 1 ]\n",
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"Size: 2048000 Samples: 1228800 Acc: 1.67\n",
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"Regularization terms: 0, Supporting variables: 0\n",
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"conjugate gradients\n",
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"Total Time: 2.317959\n"
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"Size: 2048000 Samples: 819199 Acc: 2.50\n",
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"l1 regularization: 0.030000\n",
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"Regularization terms: 1, Supporting variables: 0\n",
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"FISTA\n",
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"Total Time: 9.529222\n"
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]
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}
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],
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"%%bash\n",
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"# Now we get a coefficient map\n",
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"# bart nlinv -B data/basis data/kSpace_upat_combined data/coeff_upat data/sens_upat\n",
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"bart pics -B data/basis data/kSpace_upat_combined data/nlinv_coilsens data/coeff_upat\n",
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"bart pics -B data/basis -RI:$(bart bitmask 5):3e-2 -i150 -S data/kSpace_upat_combined data/nlinv_coilsens data/coeff_upat\n",
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"\n",
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"## Coeffs are on dim 7\n",
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"\n",
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@ -1547,10 +1578,111 @@
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"bart invert data/R2_fit_subreco_upat data/T2_fit_subreco_upat"
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]
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},
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{
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"cell_type": "markdown",
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"id": "bb1744d3-9ee0-4af0-a3a3-09ddbea9db47",
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"metadata": {},
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"source": [
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"## Two dim USP"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 23,
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"id": "d491fd0f-5131-4496-944d-e07cb6bf7a79",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Pattern creation completed. Output file: upat_combined_new\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"%%bash\n",
|
||||
"# Parameters\n",
|
||||
"R_Y=2 # Undersampling factor in the Y dimension\n",
|
||||
"R_Z=2 # Undersampling factor in the Z dimension\n",
|
||||
"Y=80 # Dimension Y\n",
|
||||
"Z=16 # Dimension Z\n",
|
||||
"N_ECHO=20 # Number of ECHO shifts\n",
|
||||
"CENTER_SIZE=16\n",
|
||||
"\n",
|
||||
"# Create the center\n",
|
||||
"bart ones 2 $CENTER_SIZE $CENTER_SIZE data/mask_center_new\n",
|
||||
"bart transpose 0 2 data/mask_center_new data/mask_center_new_transposed\n",
|
||||
"\n",
|
||||
"# Zero-pad the second (index 1) dimension to the full k-space dimension \n",
|
||||
"bart resize -c 1 $Y 2 $Z data/mask_center_new_transposed data/mask_full_new\n",
|
||||
"\n",
|
||||
"# Create pattern with undersampling in phase and slice directions by R_Y and R_Z\n",
|
||||
"bart upat -Y $Y -Z $Z -y $R_Y -z $R_Z -c 1 data/upat_new\n",
|
||||
"\n",
|
||||
"# Initialize the final combined pattern file\n",
|
||||
"bart saxpy 1 data/mask_full_new data/upat_new data/upat_combined_new\n",
|
||||
"\n",
|
||||
"# Loop over N_ECHO times to apply shifts and join them\n",
|
||||
"for ((i=1; i<N_ECHO; i++))\n",
|
||||
"do\n",
|
||||
" # Apply a circular shift of one along the phase direction\n",
|
||||
" bart circshift 1 $i data/upat_new data/upat_shifted_new\n",
|
||||
"\n",
|
||||
" # Combine the center mask and the \n",
|
||||
" bart saxpy 1 data/mask_full_new data/upat_shifted_new data/tmp\n",
|
||||
"\n",
|
||||
" # Join the shifted pattern along the 5th dimension \n",
|
||||
" bart join 5 data/upat_combined_new data/tmp data/upat_combined_new\n",
|
||||
"done\n",
|
||||
"\n",
|
||||
"bart threshold -B 0.1 data/upat_combined_new data/upat_combined_new\n",
|
||||
"\n",
|
||||
"echo \"Pattern creation completed. Output file: upat_combined_new\"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 24,
|
||||
"id": "1c045e2a-3180-4d06-8cb5-d8d7fe2c4531",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%%bash\n",
|
||||
"\n",
|
||||
"bart fmac data/upat_combined_new data/ksp data/ksp_upat_new\n",
|
||||
"\n",
|
||||
"# Compute pattern (just to check)\n",
|
||||
"bart pattern data/ksp_upat_new data/calc_pattern_upat_new\n",
|
||||
"\n",
|
||||
"# PICS reco\n",
|
||||
"# bart pics -RI:$(bart bitmask 5):3e-2 -i150 -S data/ksp_upat_new data/nlinv_coilsens data/pics_upat_new\n",
|
||||
"\n",
|
||||
"# nlinv\n",
|
||||
"bart nlinv -i 30 -x 80:80:16 -S data/ksp_upat_new data/pics_upat_new\n",
|
||||
"\n",
|
||||
"# Fit the model:\n",
|
||||
"bart mobafit -T data/echo_times_final data/pics_upat_new data/fit_upat_new\n",
|
||||
"\n",
|
||||
"# Now some values will be very large so we can apply a threshold to obtain a mask\n",
|
||||
"MAX_T2=1000\n",
|
||||
"\n",
|
||||
"bart threshold -M $MAX_T2 data/fit_upat_new data/mask_upat_combined_new\n",
|
||||
"\n",
|
||||
"# Multiply the fit with the mask\n",
|
||||
"bart fmac data/fit_upat_new data/mask_upat_combined_new data/fit_mask_upat_combined_new\n",
|
||||
"\n",
|
||||
"# Select slice\n",
|
||||
"bart slice 6 1 data/fit_mask_upat_combined_new data/R2_map_upat_combined_new\n",
|
||||
"\n",
|
||||
"# Invert the data to get T2\n",
|
||||
"bart invert data/R2_map_upat_combined_new data/T2_map_upat_combined_new"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "cde1499e-4d97-4d21-b413-a2cccc21bd04",
|
||||
"id": "3754d681-ba36-4d7f-9c30-573af2cf624a",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
|
|
|
|||
Loading…
Reference in a new issue