Mag p

[SWeinmueller]

Return the correct mag _P

[J-Endres]

Do we have consensus that we rather want the magnetization including encoding etc. than without? Or should we provide an additional flag to select this?

[SWeinmueller]

I see no application where we need the signal without relaxation, but we could use another flag to be more general and we don't have to decide now.

[mzaiss]

sounds interesting to have both options... so you could get mri signals and the mag at the same time, still i wonder how spoiling affects this... is running an unencoded experiment then the same? probably not as spoiling is missing in unencoded. do you get mag at any timepoint or only at adc? SWeinmueller ***@***.***> schrieb am Di., 10. Dez. 2024, 09:40:

…

I see no application where we need the signal without relaxation, but we could use another flag to be more general and we don't have to decide now. — Reply to this email directly, view it on GitHub <#97 (comment)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AB7RKGR5YZE4ENSSQKCNEY32E2SHVAVCNFSM6AAAAABTAEQWKKVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZDKMZQHAYTQNZQGY> . You are receiving this because you are subscribed to this thread.Message ID: ***@***.***>

[J-Endres]

We could have the option to return either + states or ADC mag, as made here: #101

If that's okay, merge into mag_p before merging mag_p into main

[J-Endres]

Suggested change: remove return_mag_z and return_mag_p in favor of a clear_mag_state flag that defaults to false - this allows to extract all the data from the graph object, including magnetization.
return_mag_adc stays.

Example usage:

graph = mr0.compute_graph(seq, data)
signal, mag_adc = mr0.execute_graph(graph, seq, data, return_mag_adc=True, clear_state_mag=False)

# Extract all states of the desired type and if it was simulated (mag is not None)
mag_p = [[state.mag for state in rep if state.dist_type == "+" and state.mag is not None] for rep in graph]
mag_z = [[state.mag for state in rep if state.dist_type in ["z", "z0"] and state.mag is not None] for rep in graph]

[SWeinmueller]

It works as desired. Nevertheless, you have to think carefully about which states you take into account, as some of these are also dephased.

[SWeinmueller]

mag_adc:

img = torch.zeros([*sz], dtype=torch.complex64)
mag = torch.stack([m[0] for m in mag_adc])
rows = 8
cols = 8
factor = int(sz[0] / rows)

# Generate plots for each subplot
plt.figure(dpi=300)
plt.suptitle("Transversal adc Mag", fontsize=16)
for i in range(rows):
    for j in range(cols):
        plt.subplot(rows, cols, i * cols + j + 1)
        img[obj_p.recover().PD>0] = mag[i*factor,j*factor,:]
        util.plot3D(img.abs())
        plt.clim(0,0.15)
        plt.axis('off')
# Show the plot
plt.show()

# Generate plots for each subplot
plt.figure(dpi=300)
plt.suptitle("Transversal adc Mag", fontsize=16)
for i in range(rows):
    for j in range(cols):
        plt.subplot(rows, cols, i * cols + j + 1)
        img[obj_p.recover().PD>0] = mag[i*factor,j*factor,:]
        util.plot3D(img.angle())
        plt.clim(0,0.15)
        plt.axis('off')
# Show the plot
plt.show()