F kiel2025

README.md

@@ -12,6 +12,12 @@ The most up-to-date information can be found at the official BART website: http:
 **Quick Installation**
 See the [Quick-Install guide](doc/quick-install.md) for quick installation instructions for BART.
 
+## DACH_NORDIC ISMRM 2025
+**Title**: Non-Cartesian T1 | Subspace-Constrained Reconstruction
+**Meeting**: [2025 DACH-Nordic ISMRM 2025, Kiel, Germany](https://ismrm-dach-2025.moincc.de/)
+**Material**: [`./dach_ismrm2025/subspace/`](./dach_ismrm2025/subspace)
+**Date**: September 14, 2025
+
 ## DS-ISMRM PhD Training 2025
 **Title**: Translational cardiovascular MR Imaging
 **Meeting**: [2025 DS-ISMRM PhD Training, Ulm, Germany](https://www.uni-ulm.de/einrichtungen/moman/forschungsbereiche/academy/phd-training-2025)

dach_ismrm2025/subspace/.gitignore

@@ -0,0 +1,4 @@
+.venv
+__pycache__
+*.hdr
+*.cfl

dach_ismrm2025/subspace/README.md

@@ -0,0 +1,22 @@
+# Subspace Tutorial for ISMRM DACH-NORDIC 2025
+Demonstrate subspace reconstruction for inversion recovery FLASH acquisition both on numerical phantom and data acquired with pulseq sequence generated with `bart seq`.
+
+- [Jupyter Notebook](./tutorial.ipynb)
+- [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/mrirecon/bart-workshop/blob/master/dach_ismrm2025/subspace/tutorial_colab.ipynb)
+
+
+
+
+
+## Requirements
+```
+bart version v0.9.00-1131-g7dd53c3 
+```
+
+Install python packages in virtual environment. 
+```
+python3 -m venv .venv
+source ./venv/bin/activate
+pip install -r requirements
+```
+

dach_ismrm2025/subspace/requirements.txt

@@ -0,0 +1,38 @@
+asttokens==3.0.0
+comm==0.2.3
+contourpy==1.3.3
+cycler==0.12.1
+debugpy==1.8.16
+decorator==5.2.1
+executing==2.2.0
+fonttools==4.59.1
+ipykernel==6.30.1
+ipython==9.4.0
+ipython_pygments_lexers==1.1.1
+jedi==0.19.2
+jupyter_client==8.6.3
+jupyter_core==5.8.1
+kiwisolver==1.4.9
+matplotlib==3.10.5
+matplotlib-inline==0.1.7
+nest-asyncio==1.6.0
+numpy==2.3.2
+packaging==25.0
+parso==0.8.4
+pexpect==4.9.0
+pillow==11.3.0
+platformdirs==4.3.8
+prompt_toolkit==3.0.51
+psutil==7.0.0
+ptyprocess==0.7.0
+pure_eval==0.2.3
+Pygments==2.19.2
+pyparsing==3.2.3
+python-dateutil==2.9.0.post0
+pyzmq==27.0.2
+six==1.17.0
+stack-data==0.6.3
+tornado==6.5.2
+traitlets==5.14.3
+typing_extensions==4.14.1
+wcwidth==0.2.13

dach_ismrm2025/subspace/src/customStyle.mplstyle

@@ -0,0 +1,19 @@
+savefig.dpi : 300
+savefig.bbox : tight
+axes.titlepad : 20
+axes.facecolor : w
+axes.edgecolor : grey
+axes.grid : False
+grid.color : grey
+grid.linewidth : 1
+grid.alpha : 0.5
+xtick.color : k
+ytick.color : k
+xtick.bottom : True
+xtick.major.size : 5
+ytick.major.size : 5
+font.size: 12.0
+
+legend.frameon : True
+legend.edgecolor : grey
+legend.facecolor : w

dach_ismrm2025/subspace/src/plotSubspace.py

@@ -0,0 +1,107 @@
+import matplotlib as mpl
+from matplotlib import pyplot as plt
+import cfl
+import numpy as np
+
+
+
+
+def plotCoefficientMaps():
+    fig, axs = plt.subplots(1,4, figsize=(12,4))
+    axs =axs.ravel()
+    img = absImageFromCFL("subspace_reco")
+    for i, ax in enumerate(axs):
+        ax.set_xticks([])
+        ax.set_yticks([])
+        ax.set_title("Coefficient # {}".format(i+1), fontdict={'fontsize':12})
+        ax.imshow(img[:,:,i], cmap=createNewMap("gray"), clim=(None, 1.2))
+    plt.show()
+
+def plotTemporalEvolution():
+    fig, axs = plt.subplots(1,4, figsize=(12,4))
+    axs =axs.ravel()
+    timesteps=[0,100, 200,600]
+    img = absImageFromCFL("imgs")
+    for i, ax in zip(timesteps,axs):
+        ax.set_xticks([])
+        ax.set_yticks([])
+        ax.set_title("t={}".format(i), fontdict={'fontsize':12})
+        ax.imshow(img[:,:,i], cmap=createNewMap("gray"), clim=(None, 0.125), 
+                  origin="upper")
+
+    plt.tight_layout()
+    plt.show()
+
+def plotT1Map():
+    fig, axs = plt.subplots(1, figsize=(4,3))
+    axs.set_xticks([])
+    axs.set_yticks([])
+    img =absImageFromCFL("t1map")
+    axs.imshow(img, cmap=createNewMap("gray"),clim=(None,1.5))
+    plt.show()
+
+def createNewMap(mapName):
+    newmap = mpl.colormaps[mapName].copy()
+    newmap.set_bad(color="black")
+    if mapName == "RdBu_r":
+        newmap.set_bad(color="white")
+    return newmap
+
+def realImageFromCFL(filename):
+    return np.real(cfl.readcfl(filename).squeeze())
+def absImageFromCFL(filename):
+    return np.abs(cfl.readcfl(filename).squeeze())
+
+
+def plotDict(ax, n=5):
+    indx = np.random.randint(0,100000, size=n)
+    filename="./subspace_dict"
+    signals= realImageFromCFL(filename)[:,indx].T
+    cmap=mpl.colormaps["Set1"]
+    ax.set_xlabel("time [s]")
+    ax.set_ylabel("signal")
+    ax.set_title("Selection from Simulated Dictionary",fontdict={'fontsize':12})
+    time =np.arange(0, len(signals[0])) * 6e-3
+    for i,signal in enumerate(signals):
+        ax.plot(time, signal, color=cmap(i))
+
+    return ax
+
+def plotPCACoeff(ax,nCoef=4,n=30):
+    ax.set_xlabel("principal component")
+    ax.set_ylabel("rel. contribution")
+    ax.set_title("Accumulated PCA Coefficients",fontdict={'fontsize':12})
+    S = realImageFromCFL("./S")
+
+    cumSum = np.cumsum(S)
+    cumSum /= cumSum[-1]
+    coeffIndx = np.arange(0,n)
+    ax.plot(coeffIndx, cumSum[:n], 'ko-')
+    annotation = "{} coeff. -> {:.3}% contribution".format(nCoef, cumSum[nCoef]*100)
+
+    ax.annotate(annotation,xy=(0.1,.5), xycoords="axes fraction")
+
+    return ax
+
+
+def plotTemporalBasis(ax, n=5):
+    ax.set_title("Temporal Basis",fontdict={'fontsize':12})
+    ax.set_xlabel("time [s]")
+    ax.set_ylabel("signal")
+    U = realImageFromCFL("./U").T[:n]
+    time =np.arange(0, len(U[0])) * 6e-3
+    cmap=mpl.colormaps["Set1"]
+    for i,signal in enumerate(U):
+        ax.plot(time, signal, color=cmap(i), label="# {}".format(i+1))
+
+    ax.legend()
+
+
+def plotSubspace():
+    fig, axs = plt.subplots(1,3, figsize = (12,4))
+    plotDict(axs[0],n=5)
+    plotPCACoeff(axs[1], nCoef=4, n=17)
+    plotTemporalBasis(axs[2],n=4)
+    plt.tight_layout()
+    plt.show()
+