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added diffusion values and solved scaling
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‎phantoms/MR_XCAT_qMRI/sim_ivim_sig.py‎

Lines changed: 61 additions & 69 deletions
Original file line numberDiff line numberDiff line change
@@ -82,94 +82,94 @@ def contrast_curve_calc():
8282
tissue_included = np.array([1, 2, 3, 4, 5, 6, 7, 8, 13, 17, 18, 20, 22, 23, 24, 25, 26, 30, 36, 37, 40, 41, 42, 43, 50, 73])
8383

8484
D = np.zeros(74)
85-
D[1] = 2e-3 # 1 Myocardium LV
86-
D[2] = 2e-3 # 2 myocardium RV
85+
D[1] = 2.4e-3 # 1 Myocardium LV : Delattre et al. doi: 10.1097/RLI.0b013e31826ef901
86+
D[2] = 2.4e-3 # 2 myocardium RV: Delattre et al. doi: 10.1097/RLI.0b013e31826ef901
8787
D[3] = 2e-3 # 3 myocardium la
8888
D[4] = 1.5e-3 # 4 myocardium ra
8989
D[5] = 3e-3 # 5 Blood LV
9090
D[6] = 3e-3 # 6 Blood RV
9191
D[7] = 3e-3 # 7 Blood la
9292
D[8] = 3e-3 # 8 Blood ra
93-
D[13] = 1.2e-3 # 13 liver
94-
D[17] = 1e-3 # 17 esophagus
95-
D[18] = 1e-3 # 18 esophagus cont
96-
D[20] = 1e-3 # 20 stomach wall
93+
D[13] = 1.5e-3 # 13 liver: Delattre et al. doi: 10.1097/RLI.0b013e31826ef901
94+
D[17] = 1.67e-3 # 17 esophagus : Huang et al. doi: 10.1259/bjr.20170421
95+
D[18] = 1.67e-3 # 18 esophagus cont : Huang et al. doi: 10.1259/bjr.20170421
96+
D[20] = 1.5e-3 # 20 stomach wall: Li et al. doi: 10.3389/fonc.2022.821586
9797
D[22] = 1.3e-3 # 22 Pancreas (from literature)
98-
D[23] = 2e-3 # 23 right kydney cortex
99-
D[24] = 1e-3 # 23 right kydney medulla
100-
D[25] = 2e-3 # 23 left kydney cortex
101-
D[26] = 1e-3 # 23 left kydney medulla
102-
D[30] = 0.8e-3 # 30 spleen
98+
D[23] = 2.12e-3 # 23 right kydney cortex : van Baalen et al. Doi: jmri.25519
99+
D[24] = 2.09e-3 # 23 right kydney medulla : van Baalen et al. Doi: jmri.25519
100+
D[25] = 2.12e-3 # 23 left kydney cortex : van Baalen et al. Doi: jmri.25519
101+
D[26] = 2.09e-3 # 23 left kydney medulla : van Baalen et al. Doi: jmri.25519
102+
D[30] = 1.3e-3 # 30 spleen : Taimouri et al. Doi: 10.1118/1.4915495
103103
D[36] = 3e-3 # 36 artery
104-
D[37] = 3e-3 # 37 vein
105-
D[40] = 3e-3 # 40 asc lower intestine
106-
D[41] = 3e-3 # 41 trans lower intestine
107-
D[42] = 3e-3 # 42 desc lower intestine
108-
D[43] = 3e-3 # 43 small intestine
104+
D[37] = 3e-3 # 37 vein
105+
D[40] = 1.31e-3 # 40 asc lower intestine : Hai-Jing et al. doi: 10.1097/RCT.0000000000000926
106+
D[41] = 1.31e-3 # 41 trans lower intestine : Hai-Jing et al. doi: 10.1097/RCT.0000000000000926
107+
D[42] = 1.31e-3 # 42 desc lower intestine : Hai-Jing et al. doi: 10.1097/RCT.0000000000000926
108+
D[43] = 1.31e-3 # 43 small intestine : Hai-Jing et al. doi: 10.1097/RCT.0000000000000926
109109
D[50] = 3e-3 # 50 pericardium
110110
D[73] = 1.8e-3 # 73 Pancreatic tumor (advanced state, from literature)
111111

112112
f = np.zeros(74)
113-
f[1] = 0.7 # 1 Myocardium LV
114-
f[2] = 0.7 # 2 myocardium RV
115-
f[3] = 0.7 # 3 myocardium la
116-
f[4] = 0.7 # 4 myocardium ra
113+
f[1] = 0.15 # 1 Myocardium LV : Delattre et al. doi: 10.1097/RLI.0b013e31826ef901
114+
f[2] = 0.15 # 2 myocardium RV : Delattre et al. doi: 10.1097/RLI.0b013e31826ef901
115+
f[3] = 0.07 # 3 myocardium la
116+
f[4] = 0.07 # 4 myocardium ra
117117
f[5] = 1.00 # 5 Blood LV
118118
f[6] = 1.00 # 6 Blood RV
119119
f[7] = 1.00 # 7 Blood la
120120
f[8] = 1.00 # 8 Blood ra
121-
f[13] = 0.25 # 13 liver
122-
f[17] = 0.1 # 17 esophagus
123-
f[18] = 0.1 # 18 esophagus cont
124-
f[20] = 0.3 # 20 stomach wall
121+
f[13] = 0.11 # 13 liver : Delattre et al. doi: 10.1097/RLI.0b013e31826ef901
122+
f[17] = 0.32 # 17 esophagus : Huang et al. doi: 10.1259/bjr.20170421
123+
f[18] = 0.32 # 18 esophagus cont : Huang et al. doi: 10.1259/bjr.20170421
124+
f[20] = 0.3 # 20 stomach wall: Li et al. doi: 10.3389/fonc.2022.821586
125125
f[22] = 0.15 # 22 Pancreas (from literature)
126-
f[23] = 0.3 # 23 right kydney cortex
127-
f[24] = 0.2 # 23 right kydney medulla
128-
f[25] = 0.3 # 23 left kydney cortex
129-
f[26] = 0.2 # 23 left kydney medulla
130-
f[30] = 0.4 # 30 spleen
131-
f[36] = 1.0 # 36 artery
132-
f[37] = 1.0 # 37 vein
133-
f[40] = 0.19 # 40 asc lower intestine
134-
f[41] = 0.19 # 41 trans lower intestine
135-
f[42] = 0.19 # 42 desc lower intestine
136-
f[43] = 0.19 # 43 small intestine
126+
f[23] = 0.097 # 23 right kydney cortex : van Baalen et al. Doi: jmri.25519
127+
f[24] = 0.158 # 23 right kydney medulla : van Baalen et al. Doi: jmri.25519
128+
f[25] = 0.097 # 23 left kydney cortex : van Baalen et al. Doi: jmri.25519
129+
f[26] = 0.158 # 23 left kydney medulla : van Baalen et al. Doi: jmri.25519
130+
f[30] = 0.2 # 30 spleen : Taimouri et al. Doi: 10.1118/1.4915495
131+
f[36] = 1.0 # 36 artery
132+
f[37] = 1.0 # 37 vein
133+
f[40] = 0.69 # 40 asc lower intestine : Hai-Jing et al. doi: 10.1097/RCT.0000000000000926
134+
f[41] = 0.69 # 41 trans lower intestine : Hai-Jing et al. doi: 10.1097/RCT.0000000000000926
135+
f[42] = 0.69 # 42 desc lower intestine : Hai-Jing et al. doi: 10.1097/RCT.0000000000000926
136+
f[43] = 0.69 # 43 small intestine : Hai-Jing et al. doi: 10.1097/RCT.0000000000000926
137137
f[50] = 0.07 # 50 pericardium
138138
f[73] = 0.37 # 73 Pancreatic tumor (advanced state, from literature)
139139

140140
Ds = np.zeros(74)
141-
Ds[1] = 0.07 # 1 Myocardium LV
142-
Ds[2] = 0.07 # 2 myocardium RV
141+
Ds[1] = 0.08 # 1 Myocardium LV: Delattre et al. doi: 10.1097/RLI.0b013e31826ef901
142+
Ds[2] = 0.08 # 2 myocardium RV: Delattre et al. doi: 10.1097/RLI.0b013e31826ef901
143143
Ds[3] = 0.07 # 3 myocardium la
144144
Ds[4] = 0.07 # 4 myocardium ra
145145
Ds[5] = 0.1 # 5 Blood LV
146146
Ds[6] = 0.1 # 6 Blood RV
147147
Ds[7] = 0.1 # 7 Blood la
148148
Ds[8] = 0.1 # 8 Blood ra
149-
Ds[13] = 0.05 # 13 liver
150-
Ds[17] = 0.05 # 17 esophagus
151-
Ds[18] = 0.05 # 18 esophagus cont
152-
Ds[20] = 0.07 # 20 stomach wall
149+
Ds[13] = 0.1 # 13 liver: Delattre et al. doi: 10.1097/RLI.0b013e31826ef901
150+
Ds[17] = 0.03 # 17 esophagus : Huang et al. doi: 10.1259/bjr.20170421
151+
Ds[18] = 0.03 # 18 esophagus cont : Huang et al. doi: 10.1259/bjr.20170421
152+
Ds[20] = 0.012 # 20 stomach wall: Li et al. doi: 10.3389/fonc.2022.821586
153153
Ds[22] = 0.01 # 22 Pancreas (from literature)
154-
Ds[23] = 0.02 # 23 right kydney cortex
155-
Ds[24] = 0.07 # 23 right kydney medulla
156-
Ds[25] = 0.02 # 23 left kydney cortex
157-
Ds[26] = 0.07 # 23 left kydney medulla
158-
Ds[30] = 0.08 # 30 spleen
159-
Ds[36] = 0.1 # 36 artery
160-
Ds[37] = 0.1 # 37 vein
161-
Ds[40] = 0.09 # 40 asc lower intestine
162-
Ds[41] = 0.09 # 41 trans lower intestine
163-
Ds[42] = 0.09 # 42 desc lower intestine
164-
Ds[43] = 0.09 # 43 small intestine
154+
Ds[23] = 0.02 # 23 right kydney cortex : van Baalen et al. Doi: jmri.25519
155+
Ds[24] = 0.019 # 23 right kydney medulla : van Baalen et al. Doi: jmri.25519
156+
Ds[25] = 0.02 # 23 left kydney cortex : van Baalen et al. Doi: jmri.25519
157+
Ds[26] = 0.019 # 23 left kydney medulla : van Baalen et al. Doi: jmri.25519
158+
Ds[30] = 0.03 # 30 spleen : Taimouri et al. Doi: 10.1118/1.4915495
159+
Ds[36] = 0.1 # 36 artery
160+
Ds[37] = 0.1 # 37 vein
161+
Ds[40] = 0.029 # 40 asc lower intestine : Hai-Jing et al. doi: 10.1097/RCT.0000000000000926
162+
Ds[41] = 0.029 # 41 trans lower intestine : Hai-Jing et al. doi: 10.1097/RCT.0000000000000926
163+
Ds[42] = 0.029 # 42 desc lower intestine : Hai-Jing et al. doi: 10.1097/RCT.0000000000000926
164+
Ds[43] = 0.029 # 43 small intestine : Hai-Jing et al. doi: 10.1097/RCT.0000000000000926
165165
Ds[50] = 0.01 # 50 pericardium
166166
Ds[73] = 0.01 # 73 Pancreatic tumor (advanced state, from literature)
167167
# Return values
168168

169169
return tissue_included, D, f, Ds
170170

171171

172-
def XCAT_to_MR_DCE(XCAT, TR, TE, bvalue, tissue_included, D, f, Ds, b0=3, ivim_cont = True, Contrast = 'SE', FA=90):
172+
def XCAT_to_MR_DCE(XCAT, TR, TE, bvalue, tissue_included, D, f, Ds, b0=3, ivim_cont = True):
173173
###########################################################################################
174174
# This script converts XCAT tissue values to MR contrast based on the SSFP signal equation.
175175
# Christopher W. Roy 2018-12-04 # fetal.xcmr@gmail.com
@@ -355,19 +355,8 @@ def XCAT_to_MR_DCE(XCAT, TR, TE, bvalue, tissue_included, D, f, Ds, b0=3, ivim_c
355355
#S0 = np.zeros(len(bvalue))
356356
S0 = ivim(bvalue,Dtemp,ftemp,Dstemp)
357357
if T1 > 0 or T2 > 0:
358-
if Contrast == 'GRE':
359-
if iTissue not in [31, 32, 33]:
360-
MR = MR + (XCAT == iTissue) * np.sin(np.deg2rad(FA)) * (1 - np.exp(-TR / T1)) / (
361-
1 - (np.cos(np.deg2rad(FA)) * np.exp(-TR / T1)))
362-
elif Contrast == 'bSSFP':
363-
MR = MR + (XCAT == iTissue) * np.sin(np.deg2rad(FA)) * (1 - np.exp(-TR / T1)) * np.exp(-TE / T2) / (
364-
1 - (np.exp(-TR / T1) - np.exp(-TR / T2)) * np.cos(np.deg2rad(FA)) - np.exp(
365-
-TR / T1) * np.exp(-TR / T2))
366-
elif Contrast == 'SE':
367-
MR = MR + np.tile(np.expand_dims(XCAT == iTissue,3),len(S0)) * S0 * (1 - 2 * np.exp(-(TR - TE / 2) / T1) + np.exp(-TR / T1)) * np.exp(
368-
-TE / T2)
369-
else:
370-
raise ValueError('Unknown MR contrast. Use only GRE, bSSFP, or SE.')
358+
MR = MR + np.tile(np.expand_dims(XCAT == iTissue,3),len(S0)) * S0 * (1 - 2 * np.exp(-(TR - TE / 2) / T1) + np.exp(-TR / T1)) * np.exp(
359+
-TE / T2)
371360
Dim = Dim + (XCAT == iTissue) * Dtemp
372361
fim = fim + (XCAT == iTissue) * ftemp
373362
Dpim = Dpim + (XCAT == iTissue) * Dstemp
@@ -396,18 +385,21 @@ def XCAT_to_MR_DCE(XCAT, TR, TE, bvalue, tissue_included, D, f, Ds, b0=3, ivim_c
396385
output_file = 'output_xcat.nii.gz' # Replace with your desired output file name
397386
nib.save(nifti_img, output_file)
398387

399-
nifti_img = nib.Nifti1Image(Dim*1000000, affine=res) # Replace affine if necessary
388+
nifti_img = nib.Nifti1Image(Dim, affine=res) # Replace affine if necessary
400389
# Save the NIfTI image to a file
390+
nifti_img.header.set_data_dtype(np.float64)
401391
output_file = 'D.nii.gz' # Replace with your desired output file name
402392
nib.save(nifti_img, output_file)
403393

404-
nifti_img = nib.Nifti1Image(fim*1000, affine=res) # Replace affine if necessary
394+
nifti_img = nib.Nifti1Image(fim, affine=res) # Replace affine if necessary
405395
# Save the NIfTI image to a file
396+
nifti_img.header.set_data_dtype(np.float64)
406397
output_file = 'f.nii.gz' # Replace with your desired output file name
407398
nib.save(nifti_img, output_file)
408399

409-
nifti_img = nib.Nifti1Image(Dpim*1000, affine=res) # Replace affine if necessary
400+
nifti_img = nib.Nifti1Image(Dpim, affine=res) # Replace affine if necessary
410401
# Save the NIfTI image to a file
402+
nifti_img.header.set_data_dtype(np.float64)
411403
output_file = 'Dp.nii.gz' # Replace with your desired output file name
412404
nib.save(nifti_img, output_file)
413405

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