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Merge pull request #34 from OSIPI/dev
Functionality for one pipeline
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""" | ||
============== | ||
Linear model for relationship between R1 and magnitude signal | ||
============== | ||
Demonstrating the linear model for relationship between R1 and magnitude signal, s = k.R1 | ||
""" | ||
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# %% | ||
# Import necessary packages | ||
import matplotlib.pyplot as plt | ||
import numpy as np | ||
import osipi | ||
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# %% | ||
# Convert a series of R1 values to the corresponding signal intensities using the SPGR model. | ||
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R1 = np.linspace(0.1, 10, 50) # R1 in units of /s. | ||
S0 = np.float64(100) # fully T1-relaxed signal in a.u. | ||
TR = np.float64(5e-3) # repetition time in units of s. | ||
a = np.float64(15) # prescribed flip angle in units of deg. | ||
S = osipi.signal_SPGR(R1, S0, TR, a) # signal in a.u. | ||
print(f"Signal: {S}") | ||
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# Plot S vs. R1 | ||
plt.plot(R1, S, "r-") | ||
plt.xlabel("R1 (/s)") | ||
plt.ylabel("S (a.u.)") | ||
plt.show() |
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""" | ||
============== | ||
Linear model for relationship between R_1 and magnitude signal S | ||
============== | ||
Demonstrating the linear model for relationship between R_1 and magnitude signal, S = k.R_1 | ||
""" | ||
|
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# %% | ||
# Import necessary packages | ||
import matplotlib.pyplot as plt | ||
import numpy as np | ||
import osipi | ||
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# %% | ||
# Convert a series of R1 values to the corresponding signal intensities. | ||
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R1 = np.array([0.0, 1.5, 3.0, 4.0, 10.0]) # R_1 in units of /s | ||
k = np.float64(150.0) # constant of proportionality in units of a.u. s | ||
S = osipi.signal_linear(R1, k) # signal in a.u. | ||
print(f"Signal: {S}") | ||
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# Plot S vs. R1 | ||
plt.plot(R1, S, "ro-") | ||
plt.xlabel("R1 (/s)") | ||
plt.ylabel("S (a.u.)") | ||
plt.show() |
29 changes: 29 additions & 0 deletions
29
...xamples/signal_to_concentration/plot_signal_to_concentration_R1_to_C_linear_relaxivity.py
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""" | ||
============== | ||
Electromagnetic property inverse model: longitudinal relaxation rate, linear with relaxivity | ||
============== | ||
Demonstrating the inverse linear relaxivity model | ||
for converting R_1 to tissue indicator concentration C. | ||
""" | ||
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# %% | ||
# Import necessary packages | ||
import matplotlib.pyplot as plt | ||
import numpy as np | ||
import osipi | ||
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# %% | ||
# Convert a series of R1 values to the corresponding indicator concentrations. | ||
R1 = np.array([1, 2, 3, 4, 5, 6], dtype=np.float64) | ||
R10 = np.float64(1) | ||
r1 = np.float64(5) | ||
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C = osipi.R1_to_C_linear_relaxivity(R1, R10, r1) | ||
print(f"Concentration (mM): {C}") | ||
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# Plot C vs. R1 | ||
plt.plot(R1, C, "r-") | ||
plt.xlabel("R1 (/s)") | ||
plt.ylabel("C (mM)") | ||
plt.show() |
69 changes: 69 additions & 0 deletions
69
docs/examples/signal_to_concentration/plot_signal_to_concentration_S_to_C_via_R1_SPGR.py
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""" | ||
============== | ||
Signal to concentration via electromagnetic property (SPGR, FXL, analytical linear relaxivity) | ||
============== | ||
Demonstrating the SPGR model for relationship between | ||
signal S and total tissue indicator concentration, assuming the FXL. | ||
""" | ||
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# %% | ||
# Import necessary packages | ||
import matplotlib.pyplot as plt | ||
import numpy as np | ||
import osipi | ||
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# %% | ||
# Convert a series of S values to the corresponding indicator concentrations. | ||
# Example data adapted from OSIPI repository ("vox_1") | ||
S = np.array( | ||
[ | ||
7, | ||
9, | ||
6, | ||
10, | ||
9, | ||
6, | ||
9, | ||
10, | ||
9, | ||
9, | ||
9, | ||
12, | ||
8, | ||
10, | ||
12, | ||
15, | ||
53, | ||
70, | ||
71, | ||
70, | ||
63, | ||
58, | ||
54, | ||
50, | ||
53, | ||
48, | ||
52, | ||
49, | ||
42, | ||
], | ||
dtype=np.float64, | ||
) | ||
S_baseline = S[0] # use first point for baseline signal | ||
R10 = np.float64(1 / 1.4) | ||
TR = np.float64(0.002) | ||
a = np.float64(13) | ||
r1 = np.float64(4.5) | ||
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C = osipi.S_to_C_via_R1_SPGR(S, S_baseline, R10, TR, a, r1) | ||
print(f"Concentration (mM): {C}") | ||
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# Plot S and C | ||
fig, ax = plt.subplots(2, 1) | ||
ax[0].plot(S, "b-") | ||
ax[0].set_ylabel("S (a.u.)") | ||
ax[1].plot(C, "b-") | ||
ax[1].set_ylabel("C (mM)") | ||
ax[1].set_xlabel("time point") | ||
plt.show() |
67 changes: 67 additions & 0 deletions
67
docs/examples/signal_to_concentration/plot_signal_to_concentration_S_to_R1_SPGR.py
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""" | ||
============== | ||
Signal to electromagnetic properties (R1) (SPGR, FXL, analytical linear relaxivity) | ||
============== | ||
Demonstrating the SPGR model for relationship between signal S and R1, assuming the FXL. | ||
""" | ||
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# %% | ||
# Import necessary packages | ||
import matplotlib.pyplot as plt | ||
import numpy as np | ||
import osipi | ||
|
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# %% | ||
# Convert a series of S values to the corresponding R1 values. | ||
# Example data adapted from OSIPI repository ("vox_1") | ||
S = np.array( | ||
[ | ||
7, | ||
9, | ||
6, | ||
10, | ||
9, | ||
6, | ||
9, | ||
10, | ||
9, | ||
9, | ||
9, | ||
12, | ||
8, | ||
10, | ||
12, | ||
15, | ||
53, | ||
70, | ||
71, | ||
70, | ||
63, | ||
58, | ||
54, | ||
50, | ||
53, | ||
48, | ||
52, | ||
49, | ||
42, | ||
], | ||
dtype=np.float64, | ||
) | ||
S_baseline = np.float64(S[0]) # use first point for baseline signal | ||
R10 = np.float64(1 / 1.4) | ||
TR = np.float64(0.002) | ||
a = np.float64(13) | ||
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R1 = osipi.S_to_R1_SPGR(S, S_baseline, R10, TR, a) | ||
print(f"R_1 (/s): {R1}") | ||
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# Plot S and R1 | ||
fig, ax = plt.subplots(2, 1) | ||
ax[0].plot(S, "b-") | ||
ax[0].set_ylabel("S (a.u.)") | ||
ax[1].plot(R1, "b-") | ||
ax[1].set_ylabel("R1 (/s)") | ||
ax[1].set_xlabel("time point") | ||
plt.show() |
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