V1.0.4.2 Bug fixes in vmat_scp_optimization.py and beams.py

1. Resolving python rounding issue. Use ceil instead of round for step size change
2. Modified beams.py so that beam ids can take integer or string values
3. Minor updates in examples

examples/python_files/inf_matrix_sparsification.py

@@ -68,7 +68,7 @@ def inf_matrix_sparsification():
     beams_full = pp.Beams(data, load_inf_matrix_full=True)
     # load influence matrix based upon beams and structure set
     inf_matrix_full = pp.InfluenceMatrix(ct=ct, structs=structs, beams=beams_full, is_full=True)
-    plan_full = pp.Plan(ct, structs, beams, inf_matrix_full, clinical_criteria)
+    plan_full = pp.Plan(ct=ct, structs=structs, beams=beams, inf_matrix=inf_matrix_full, clinical_criteria=clinical_criteria)
     # use the full influence matrix to calculate the dose for the plan obtained by sparse matrix
     dose_full_1d = plan_full.inf_matrix.A @ (sol_sparse['optimal_intensity'] * plan_full.get_num_of_fractions())
 

examples/python_files/vmat_global_optimal.py

@@ -176,7 +176,7 @@ def vmat_optimization():
     """
     # plot dvh for the structures in the given list. Default dose_1d is in Gy and volume is in relative scale(%).
     struct_names = ['PTV', 'ESOPHAGUS', 'HEART', 'CORD']
-    pp.Visualization.plot_dvh(my_plan, sol=sol, struct_names=struct_names, title=patient_id)
+    pp.Visualization.plot_dvh(my_plan, sol=sol, struct_names=struct_names, title=data.patient_id)
 
     # plot 2d axial slice for the given solution and display the structures contours on the slice
     pp.Visualization.plot_2d_slice(my_plan=my_plan, sol=sol, slice_num=60, struct_names=['PTV'])

examples/python_files/vmat_tps_import.py

@@ -17,29 +17,20 @@
 # 5. Comparing the PortPy plan against the TPS plan for validation and analysis 
 # 
 
-# In[1]:
-
-
 import sys
 sys.path.append('..')
 
-
-# In[8]:
-
-
 import portpy.photon as pp
 import os
 import matplotlib.pyplot as plt
 import cvxpy as cp
 import numpy as np
+import pandas as pd
 
 
 # ### 1) Creating a simple IMRT plan (Plan class, Optimization class)
 # 
 
-# In[3]:
-
-
 # specify the patient data location.
 data_dir = r'../data'
 # Use PortPy DataExplorer class to explore PortPy data
@@ -50,10 +41,6 @@
 # # display the data of the patient in console or browser.
 # data.display_patient_metadata()
 
-
-# In[9]:
-
-
 # Load ct and structure set for the above patient using CT and Structures class
 ct = pp.CT(data)
 structs = pp.Structures(data)
@@ -88,11 +75,6 @@
 my_plan = pp.Plan(ct=ct, structs=structs, beams=beams, inf_matrix=inf_matrix, clinical_criteria=clinical_criteria, arcs=arcs)
 
 
-# #### Optimize VMAT plan using sequential convex programming
-
-# In[ ]:
-
-
 # Initialize Optimization
 vmat_opt = pp.VmatScpOptimization(my_plan=my_plan,
                                   opt_params=vmat_opt_params)
@@ -117,35 +99,21 @@
 # 3. Finally, in TPS, execute final dose calculation.
 # 
 
-# In[4]:
-
-
 my_plan = pp.load_plan(plan_name='my_plan_vmat.pkl', path=os.path.join(r'C:\temp', data.patient_id))
 sol = pp.load_optimal_sol(sol_name='sol_vmat.pkl', path=os.path.join(r'C:\temp', data.patient_id))
 
-
-# In[ ]:
-
-
 # create dicom RT Plan file to be imported in TPS
 out_rt_plan_file = r'C:\Temp\Lung_Patient_3\rt_plan_portpy_vmat.dcm'  # change this file directory based upon your needs
 in_rt_plan_file = r'C:\Temp\Lung_Patient_3\rt_plan_echo_vmat.dcm'  # change this directory as per your
 pp.write_rt_plan_vmat(my_plan=my_plan, in_rt_plan_file=in_rt_plan_file, out_rt_plan_file=out_rt_plan_file)
 
-
-# In[7]:
-
-
 # get the corresponding tcia collection/subject ID
 data.get_tcia_metadata()
 
 
 # ### 3) Exporting the finally calculated dose from the TPS system into PortPy
 # After the final dose calculation in Eclipse, export the dose in DICOM RT-Dose format using the Eclipse Export module. Then, utilize the following lines of code to convert the exported dose into the PortPy format for visualization or evaluation purposes
 
-# In[10]:
-
-
 # Specify the location and name of the DICOM RT Dose file
 dose_file_name = os.path.join(r'C:\temp', data.patient_id, 'rt_dose_portpy_vmat.dcm')  
 # Convert the DICOM dose into PortPy format
@@ -156,31 +124,19 @@
 # ### 4)  Comparing the PortPy VMAT plan against the TPS plan for validation and analysis 
 # 
 # Finally dose exported from TPS in RT-dose DICOM file can be compared against PortPy dose using full influence matrix
-# 
-
-# In[12]:
-
 
 beams_full = pp.Beams(data, beam_ids=beam_ids, load_inf_matrix_full=True)
 # load influence matrix based upon beams and structure set
 inf_matrix_full = pp.InfluenceMatrix(ct=ct, structs=structs, beams=beams_full, is_full=True)
 dose_full_1d = inf_matrix_full.A @ (sol['optimal_intensity'] * my_plan.get_num_of_fractions())  # calculate dose using full matrix
 
-
-# In[13]:
-
-
 # Visualize the DVH discrepancy between eclipse dose and dose using full matrix in portpy
 struct_names = ['PTV', 'ESOPHAGUS', 'HEART', 'CORD', 'LUNGS_NOT_GTV']
 fig, ax = plt.subplots(figsize=(12, 8))
 ax = pp.Visualization.plot_dvh(my_plan, dose_1d=dose_full_1d, struct_names=struct_names, style='solid', ax=ax, norm_flag=True)
 ax = pp.Visualization.plot_dvh(my_plan, dose_1d=ecl_dose_1d, struct_names=struct_names, style='dotted', ax=ax, norm_flag=True)
 ax.set_title('- PortPy VMAT(Using full matrix) .. Eclipse')
 plt.show()
-
-
-# In[ ]:
-
-
+print('Done')
 
 

examples/vmat_scp_tutorial.ipynb

@@ -7,7 +7,7 @@
    "source": [
     "# <center>VMAT SCP Tutorial </center>\n",
     "\n",
-    "Volumetric Modulated Arc Therapy (VMAT) has increasingly become the preferred treatment method for many disease sites, primarily due to its fast plan delivery and dose distribution comparable to that of IMRT. However, the optimization of numerous apertures and their corresponding monitor units results in challenging large-scale non-convex optimization problems. To address these challenges, researchers often resort to computationally efficient heuristic methods. Having a globally optimal solution as a benchmark can significantly aid in the development and validation of new VMAT algorithms (see [vmat_optimization.ipynb](https://github.com/PortPy-Project/PortPy/blob/master/examples/benchmark_algorithms/vmat_optimization.ipynb) notebook).\n",
+    "Volumetric Modulated Arc Therapy (VMAT) has increasingly become the preferred treatment method for many disease sites, primarily due to its fast plan delivery and dose distribution comparable to that of IMRT. However, the optimization of numerous apertures and their corresponding monitor units results in challenging large-scale non-convex optimization problems. To address these challenges, researchers often resort to computationally efficient heuristic methods. Having a globally optimal solution as a benchmark can significantly aid in the development and validation of new VMAT algorithms (see [vmat_global_optimal.ipynb](https://github.com/PortPy-Project/PortPy/blob/master/examples/vmat_global_optimal.ipynb) notebook).\n",
     "\n",
     "In this notebook, we introduce a VMAT optimization algorithm based on our recent publications ([Dursun et al 2021](https://iopscience.iop.org/article/10.1088/1361-6560/abee58/meta) and [Dursun et al 2023](https://iopscience.iop.org/article/10.1088/1361-6560/ace09e/meta)). This algorithm, called Sequential Convex Programming (SCP), solves a sequence of convex optimization problems that converge to the original non-convex VMAT problem. This technique should not be confused with hierarchical optimization (also known as prioritized optimization), which we utilized in our publication ([Dursun et al 2023](https://iopscience.iop.org/article/10.1088/1361-6560/ace09e/meta)) to automate the planning process. The SCP-VMAT algorithm with hierarchical optimization will be shared in a separate repository ([ECHO-VMAT](https://github.com/PortPy-Project/ECHO-VMAT)). The SCP technique can, in principle, be used in conjunction with any automation technique, including AI-based methods (see [vmat_scp_dose_prediction.ipynb](https://githubcom/PortPy-Project/PortPy/blob/master/examples/vmat_scp_dose_prediction.ipynb) notebook).\n",
     "\n",