Published March 9, 2022 | Version v1
Dataset Open

A volumetric model of rabbit heart and torso including ECG data and ventricular activation sequence

  • 1. Institute for Experimental Cardiovascular Medicine, University Heart Center Freiburg · Bad Krozingen, Medical Center—University of Freiburg, Freiburg, Germany; Faculty of Medicine, University of Freiburg, Freiburg, Germany
  • 2. Institute for Experimental Cardiovascular Medicine, University Heart Center Freiburg · Bad Krozingen, Medical Center—University of Freiburg, Freiburg, Germany; Department of Cardiology and Angiology I, Heart Center University of Freiburg, Medical Faculty, Freiburg, Germany
  • 3. Institute for Experimental Cardiovascular Medicine, University Heart Center Freiburg · Bad Krozingen, Medical Center—University of Freiburg, Freiburg, Germany; Translational Cardiology, Department of Cardiology and Department of Physiology, University Hospital Bern, Bern, Switzerland
  • 4. Institute for Experimental Cardiovascular Medicine, University Heart Center Freiburg · Bad Krozingen, Medical Center—University of Freiburg, Freiburg, Germany; Faculty of Medicine, University of Freiburg, Freiburg, Germany; Department of Cardiology and Angiology I, Heart Center University of Freiburg, Medical Faculty, Freiburg, Germany
  • 5. Faculty of Medicine, University of Freiburg, Freiburg, Germany; Department of Radiology, Medical Physics, University Medical Center Freiburg, Freiburg, Germany
  • 6. Department of Cardiology and Angiology I, Heart Center University of Freiburg, Medical Faculty, Freiburg, Germany; Translational Cardiology, Department of Cardiology and Department of Physiology, University Hospital Bern, Bern, Switzerland

Description

Data generated and analyzed of our work titled "A computational model of rabbit geometry and ECG: Optimizing ventricular activation sequence and APD distribution". Please see the respective publication for more context.

 

  • BSPM_filtered.dat
    • Contains the filtered ECG Data
  • BSPM_original.bdf
  • CT_DataDCM.zip
    • Contains the recorded CT images of heart and torso in DCM file format
  • ECG_NodeIndices.txt
    • Contains the the node IDs of the torso mesh corresponding to the electrode positions of the ECG Vest
  • Endocardial_Surface_Papillary.stl
    • Segmented endocardial surface including papillary muscles
  • Mat_LeadField.dat
    • Contains the calculated lead field matrix to be used in combination with the provided Mesh_Ven.vtu. Make sure to keep the node order
    • #  Python example of usage
      #  Define a read_vm_vec function which reads your calculated data beforehand
      import numpy as np
      
      t_begin = 0
      t_end = 400
      LF_mat = np.loadtxt('Mat_LeadField.dat')
      times = np.linspace(t_begin, t_end, t_end-t_begin)
      result = np.zeros((len(times), 31))
      for i,t in enumerate(times):
          vm_vec = read_vm_vec(t)
          result[i, :] = LF_mat.dot(vm_vec)[0:31]
      result = np.insert(result, 0, times, axis=1)
      np.savetxt('BSPM.dat', result)

       

  • Mesh_PurkinjeTree.vtp
    • The resulting optimized Purkinje Node tree. We recommend using ParaView for visualization
  • Mesh_StimPoints.vtp
    • The resulting points of stimulation.
  • Stim_IndexTime.dat
    • Contains the stimulation pattern in terms the node index of Mesh_Ven.vtu and the respective stimulation time
  • Mesh_Ven.vtu
    • Contains the ventricular mesh as well as the repective lead field matrix values for each surface node.
    • Material:
      Right Ventricle 30
      Left Ventricle 31
  • Mesh_Torso.vtu
    • Contains the whole torso mesh.
    • Material:
      Fat 2
      Bones 3
      Blood 9
      Cartilage 14
      Liver 20
      Lungs 17
      Right Ventricle 30
      Left Ventricle 31
      Right Atrium 32
      Left Atrium 33
      Aorta 60
      Pulmonary artery 61
      Left Vena Jugularis 62
      Right Vena Jugularis 62
      Post Vena Cava 62

 

Notes

RM, GS and KEO gratefully acknowledge financial support by the Deutsche Forschungsgemeinschaft (DFG #394630089). EMW and GS gratefully acknowledge financial support by the Deutsche Forschungsgemeinschaft (DFG #183027722). RM, EMW, and GS are members of the Collaborative Research Centre SFB 1425 (DFG #422681845).

Files

CT_DataDCM.zip

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