From c79a1f806e6b83c7f2543f17e893cfcd085c531a Mon Sep 17 00:00:00 2001
From: fahimirfanalam <fahim@cu.ac.bd>
Date: Wed, 27 Mar 2024 14:47:20 +1100
Subject: [PATCH 1/4] pseudocode added for multi-dose grid functionality

---
 pydicer/convert/data.py | 44 ++++++++++++++++++++++++++++++++++++++++-
 1 file changed, 43 insertions(+), 1 deletion(-)

diff --git a/pydicer/convert/data.py b/pydicer/convert/data.py
index 85d130c..3b48b80 100644
--- a/pydicer/convert/data.py
+++ b/pydicer/convert/data.py
@@ -10,8 +10,10 @@
 import SimpleITK as sitk
 import pydicom
 import matplotlib
+import pydicom
+
 
-from platipy.dicom.io.rtdose_to_nifti import convert_rtdose
+#from platipy.dicom.io.rtdose_to_nifti import convert_rtdose
 from pydicer.config import PyDicerConfig
 
 from pydicer.convert.pt import convert_dicom_to_nifti_pt
@@ -53,6 +55,34 @@
 ]
 
 
+
+## TODO
+# Call this function in the 'convert' function rather than platipy
+# Add Ali's summation logic
+def convert_rtdose(dcm_dose, force=False, dose_output_path=None):
+    """Convert DICOM RTDose to SimpleITK image, saving as NiFTI if needed.
+
+    Args:
+        dcm_dose (str|path): Path to DICOM dose file
+        force (bool): force flag to pass to pydicom
+        dose_output_path (str|path, optional): If set, NiFTI file will be written. Defaults to
+            None.
+
+    Returns:
+        SimpleITK.Image: The dose grid as a SimpleITK image
+    """
+
+    ds = pydicom.read_file(dcm_dose, force=force)
+    dose = sitk.ReadImage(str(dcm_dose))
+    dose = sitk.Cast(dose, sitk.sitkFloat32)
+    dose = dose * ds.DoseGridScaling
+
+    if dose_output_path is not None:
+        sitk.WriteImage(dose, str(dose_output_path))
+
+    return dose
+
+
 def get_object_type(sop_class_uid: str) -> str:
     """Get the type of the object (used for the output path)
 
@@ -600,9 +630,21 @@ def convert(self, patient: Union[str, list]=None, force: bool=True):
 
                 elif sop_class_uid == RT_DOSE_STORAGE_UID:
                     # If we have multiple doses with the same sop_instance_uid we'll just drop them
+                    # TODO 
+                    # check if dosegrids with the same sop_instance_uid are always duplicates
+
+
                     df_files = df_files.drop_duplicates(subset=["sop_instance_uid"])
 
+
                     # If there are multiple RTDOSEs in the same series then just save them all
+                    # TODO
+                    # check if multiple dose grids are linked to the same RT structure set (and base CT)
+                    # if so, then check time frames for cases such as exporting the plans within a day aprat (verifying
+                    # dates between dosegrids exports are within a certain timeframe) and double checking
+                    # to make sure that we are not adding largest dosegrid (upper and lowerbound in Ali's PDCP)
+
+
                     for _, rt_dose_file in df_files.iterrows():
                         sop_instance_hash = hash_uid(rt_dose_file.sop_instance_uid)
 

From 30b0f07991cb93c49fe1920c8b735c44d166242e Mon Sep 17 00:00:00 2001
From: fahimirfanalam <fahim@cu.ac.bd>
Date: Thu, 16 May 2024 10:47:05 +1000
Subject: [PATCH 2/4] Multi-dose-grid-first-version

---
 .../select-combine-multi-dose-grids.ipynb     | 503 ++++++++++++++++++
 1 file changed, 503 insertions(+)
 create mode 100644 Multi dose-grids/select-combine-multi-dose-grids.ipynb

diff --git a/Multi dose-grids/select-combine-multi-dose-grids.ipynb b/Multi dose-grids/select-combine-multi-dose-grids.ipynb
new file mode 100644
index 0000000..131b7f7
--- /dev/null
+++ b/Multi dose-grids/select-combine-multi-dose-grids.ipynb	
@@ -0,0 +1,503 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## This code implements the necessary functionalities for selecting & combining multiple dose grids."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pydicom\n",
+    "import numpy as np\n",
+    "import os\n",
+    "import SimpleITK as sitk"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Function to Load DICOM Files:\n",
+    "\n",
+    "•\tPurpose: This function scans the specified directory and reads each DICOM file, organizing them by their modality (CT, RTDOSE, RTPLAN, RTSTRUCT). \n",
+    "\n",
+    "•\tDictionary Initialization: The dictionary dicoms is used to categorize the files based on the Modality attribute, which is standard across DICOM files. It initializes a dictionary with keys corresponding to the different DICOM modalities we expect to handle: CT, RTDOSE, RTPLAN, and RTSTRUCT. Each key maps to an empty list intended to store DICOM files of that modality.\n",
+    "\n",
+    "•\tSpecific Handling for RTDOSE: While all modalities are processed uniformly by storing the files in the appropriate lists, additional processing is done for RTDOSE files. If the file has a pixel_array attribute (which contains the dose data), the function prints the maximum value found in this array. This step is particularly useful for quickly assessing the dose values directly from the DICOM files without needing further processing steps.\n",
+    "\n",
+    "•\tError Handling: Includes a try-except block to catch and report errors that occur during the reading of DICOM files. This could be due to corrupt files, incorrect file formats, or other issues preventing a file from being read as a DICOM file."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import pydicom\n",
+    "import numpy as np\n",
+    "import SimpleITK as sitk\n",
+    "\n",
+    "def load_dicom_files(folder_path):\n",
+    "    # Dictionary to hold lists of DICOM files by their modality\n",
+    "    dicoms = {'CT': [], 'RTDOSE': [], 'RTPLAN': [], 'RTSTRUCT': []}\n",
+    "\n",
+    "    for root, dirs, files in os.walk(folder_path):\n",
+    "        for file in files:\n",
+    "            path = os.path.join(root, file)\n",
+    "            try:\n",
+    "                dicom = pydicom.dcmread(path)\n",
+    "                # Check the modality of each DICOM file and process accordingly\n",
+    "                if dicom.Modality in dicoms:\n",
+    "                    # Add the DICOM file to the appropriate list based on its Modality\n",
+    "                    dicoms[dicom.Modality].append(dicom)\n",
+    "                    # Print specific information for RTDOSE files\n",
+    "                    if dicom.Modality == 'RTDOSE':\n",
+    "                        # Use SimpleITK to read the dose grid\n",
+    "                        dose = sitk.ReadImage(path)\n",
+    "                        # Convert the dose grid to float \n",
+    "                        dose = sitk.Cast(dose, sitk.sitkFloat32)\n",
+    "                        # Apply the DoseGridScaling from the DICOM file\n",
+    "                        if hasattr(dicom, 'DoseGridScaling'):\n",
+    "                            dose_array = sitk.GetArrayFromImage(dose) * float(dicom.DoseGridScaling)\n",
+    "                            print(f\"Loaded Dose Grid for {file}: Max Value - {np.max(dose_array)}\")\n",
+    "                        else:\n",
+    "                            print(f\"DoseGridScaling attribute missing in {file}\")\n",
+    "            except Exception as e:\n",
+    "                print(f\"Failed to read {path}: {str(e)}\")\n",
+    "\n",
+    "    # After all files have been processed, return the dictionary\n",
+    "    return dicoms\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Function to Verify Linkages\n",
+    "\n",
+    "Purpose: This function checks and verifies the relationships between RTPLAN and RTSTRUCT, and then RTDOSE and RTPLAN based on UIDs to ensure that each RTDOSE is linked to the correct plan and structure set. It also checks the uniqueness of RTPLAN and RTDOSE individually before allowing the individual dose grids to be added. We have divided this function in separate components.\n",
+    "\n",
+    "Implementation: RTPLAN Processing:\n",
+    "\n",
+    "•\tUniqueness check of RTPLAN using composite key: It uses a combination of PlanName, NumberOfFractionsPlanned, and TreatmentMachineName to create a unique identifier for each RTPLAN. It uses a set to track which composite keys have been seen, ensuring each plan is processed only if it’s unique. \n",
+    "\n",
+    "•\tLinking with RTSTRUCT: If the RTPLAN is either unique or a duplicate but still needs processing for linking, the function checks if it has a ReferencedStructureSetSequence linking it to an RTSTRUCT. This link (corresponding plain_uid) is then stored in the stored_plans dictionary, mapping RTPLAN UIDs to RTSTRUCT UIDs, allowing for future reference when processing RTDOSE files.\n",
+    "\n",
+    "RTDOSE Processing:\n",
+    "\n",
+    "•\tUniqueness Check: Each RTDOSE file is checked for a pixel_array, ensuring it's a valid dose file and then is checked for uniqueness based on its dose grid. The function compares the current dose grid with previously stored dose grids in existing_dose_grids. If an identical grid is found, the RTDOSE is flagged as not unique; otherwise, it's added to the list of stored grids.\n",
+    "\n",
+    "•\tVisualization of Differences: For each comparison, whether the grid is unique or not, the differences between dose grids are visualized. This helps in visually confirming the results of the comparison.\n",
+    "\n",
+    "•\tLinkage Check: If the RTDOSE grid is unique, the function then checks if this RTDOSE has a ReferencedRTPlanSequence. If it does, the function verifies that the referenced RTPLAN UID is stored in stored_plans. This confirms that the RTDOSE is correctly linked to an RTPLAN (and indirectly to an RTSTRUCT, assuming the RTPLAN-RTSTRUCT link was correctly established).\n",
+    "\n",
+    "Final Validation:\n",
+    "\n",
+    "•\tIf all checks are passed (unique dose grid and correct linkage to RTPLAN and RTSTRUCT), the RTDOSE is considered valid and added to valid_doses. This dictionary collects all RTDOSE files that are correctly linked and unique, ready for further processing or analysis."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def verify_links(dicoms):\n",
+    "    plans_to_structs = {}\n",
+    "    existing_composite_keys = set() # Tracks composite keys to identify duplicate RTPLANs.\n",
+    "    stored_plans = {}  # Dictionary to store all plans regardless of composite key uniqueness\n",
+    "    valid_doses = {}  # Initialize the dictionary to store validated RTDOSE files\n",
+    "    existing_dose_grids = []  # List to store dose grids for comparison\n",
+    "\n",
+    "    # Handle RTPLAN for composite key uniqueness and link to RTSTRUCT\n",
+    "    for plan in dicoms['RTPLAN']:\n",
+    "        composite_key = (\n",
+    "            plan.PlanName if 'PlanName' in plan else \"Unnamed\",\n",
+    "            plan.FractionGroupSequence[0].NumberOfFractionsPlanned if 'FractionGroupSequence' in plan else \"Undefined\",\n",
+    "            plan.TreatmentMachineName if 'TreatmentMachineName' in plan else \"No Machine\"\n",
+    "        )\n",
+    "        plan_uid = str(plan.SOPInstanceUID)\n",
+    "        if composite_key not in existing_composite_keys:\n",
+    "            existing_composite_keys.add(composite_key)\n",
+    "            print(f\"Processing unique RTPLAN UID: {plan_uid}, Composite Key: {composite_key}\")\n",
+    "        else:\n",
+    "            print(f\"Duplicate RTPLAN based on composite key: {plan_uid}, Composite Key: {composite_key}\")\n",
+    "\n",
+    "        # Store all RTPLAN UIDs for potential RTDOSE linkage\n",
+    "        if 'ReferencedStructureSetSequence' in plan:\n",
+    "             # Convert UIDs to strings and check RTPLAN to RTSTRUCT linkage\n",
+    "            struct_uid = str(plan.ReferencedStructureSetSequence[0].ReferencedSOPInstanceUID)\n",
+    "            stored_plans[plan_uid] = struct_uid\n",
+    "            print(f\"Linking RTPLAN {plan_uid} to RTSTRUCT {struct_uid}\")\n",
+    "\n",
+    "   # Process RTDOSE files for uniqueness and check linkage to RTPLAN\n",
+    "    for dose in dicoms['RTDOSE']:\n",
+    "        if hasattr(dose, 'pixel_array'):\n",
+    "            dose_uid = str(dose.SOPInstanceUID)\n",
+    "            unique = True  # Flag to track uniqueness\n",
+    "\n",
+    "            # Compare with existing dose grids\n",
+    "            for existing_grid in existing_dose_grids:\n",
+    "                if np.array_equal(dose.pixel_array, existing_grid):\n",
+    "                    print(f\"Duplicate dose grid found for RTDOSE {dose_uid}.\")\n",
+    "                    unique = False\n",
+    "                    break\n",
+    "\n",
+    "            if unique:\n",
+    "                existing_dose_grids.append(dose.pixel_array)\n",
+    "\n",
+    "                if 'ReferencedRTPlanSequence' in dose:\n",
+    "                    # Convert UIDs to strings and check RTDOSE to RTPLAN linkage\n",
+    "                    ref_plan_uid = str(dose.ReferencedRTPlanSequence[0].ReferencedSOPInstanceUID)\n",
+    "                    if ref_plan_uid in stored_plans and any(str(struct.SOPInstanceUID) == stored_plans[ref_plan_uid] for struct in dicoms['RTSTRUCT']):\n",
+    "                        valid_doses[dose_uid] = dose\n",
+    "                        print(f\"RTDOSE {dose_uid} is unique and correctly linked to RTPLAN and RTSTRUCT.\")\n",
+    "                    else:\n",
+    "                        print(f\"RTDOSE {dose_uid} does not link correctly to any RTSTRUCT or RTPLAN.\")\n",
+    "                else:\n",
+    "                    print(f\"RTDOSE {dose_uid} does not have ReferencedRTPlanSequence.\")\n",
+    "            else:\n",
+    "                print(f\"RTDOSE {dose_uid} is not unique based on dose grid.\")\n",
+    "\n",
+    "    return valid_doses"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Function to Sum Doses\n",
+    "\n",
+    "Purpose: Sums up all the dose grids from the validated RTDOSE files.\n",
+    "\n",
+    "Implementation: It initializes a dose grid with zeros and then iteratively adds up all valid dose grids."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import SimpleITK as sitk\n",
+    "import numpy as np\n",
+    "\n",
+    "def sum_doses(valid_doses):\n",
+    "    sum_dose = None  # Initialize the sum of doses as None\n",
+    "\n",
+    "    for dose_uid, dose_dicom in valid_doses.items():\n",
+    "        try:\n",
+    "            # Convert pixel_array to SimpleITK Image for processing\n",
+    "            dose_image = sitk.GetImageFromArray(dose_dicom.pixel_array.astype(np.float32))\n",
+    "            # Check and apply the DoseGridScaling factor\n",
+    "            if hasattr(dose_dicom, 'DoseGridScaling'):\n",
+    "                dose_image = sitk.Cast(dose_image, sitk.sitkFloat32)\n",
+    "                scaled_dose = dose_image * float(dose_dicom.DoseGridScaling)\n",
+    "            else:\n",
+    "                print(f\"DoseGridScaling attribute missing in {dose_uid}, using unmodified dose\")\n",
+    "                scaled_dose = dose_image\n",
+    "\n",
+    "            # Sum the doses\n",
+    "            if sum_dose is None:\n",
+    "                sum_dose = scaled_dose  # If sum_dose is None, initialize it with the first dose\n",
+    "            else:\n",
+    "                sum_dose += scaled_dose  # Add the current dose to the sum of previous doses\n",
+    "\n",
+    "        except Exception as e:\n",
+    "            print(f\"Failed to process dose {dose_uid}: {str(e)}\")\n",
+    "\n",
+    "    if sum_dose is not None:\n",
+    "        # Convert the summed dose back to a numpy array for compatibility with existing code or further processing\n",
+    "        summed_dose_array = sitk.GetArrayFromImage(sum_dose)\n",
+    "        print(\"Summed Dose Grid:\\n\", summed_dose_array)\n",
+    "        return summed_dose_array\n",
+    "\n",
+    "    return None"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Function to Compare with a Max Dose\n",
+    "\n",
+    "This function checks the maximum value in the summed dose grid against a specified threshold (max_threshold). If the maximum dose exceeds this limit, it prints a warning and returns False, indicating the validation failed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def validate_dose(summed_dose, max_threshold):\n",
+    "    max_dose = np.max(summed_dose)\n",
+    "    print(f\"Maximum dose in the summed dose grid: {max_dose}\")\n",
+    "    if max_dose > max_threshold:\n",
+    "        print(\"Warning: Dose exceeds the maximum threshold.\")\n",
+    "        return False\n",
+    "    else:\n",
+    "        print(\"Dose is within the safe limits.\")\n",
+    "        return True"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Create A New RTDOSE DICOM\n",
+    "\n",
+    "1. Create a New DICOM Dataset\n",
+    "\n",
+    "2. Populate Standard DICOM Tags by copying the necessary tags from a reference dicom file. \n",
+    "\n",
+    "3. Assigning new identifiers like SOPInstanceUID and SeriesInstanceUID to ensure that the new file is uniquely identifiable within a DICOM system, along with important metadata and image-specific attributes like BitsAllocated, BitsStored, HighBit, and PixelRepresentation to define how pixel data (in this case, dose data) is encoded in the DICOM file.\n",
+    "\n",
+    "4. Inserting the summed dose grid with pixel data attribute, specifically using the summed_dose.tobytes() method to convert into a byte string appropriate for DICOM file storage.\n",
+    "\n",
+    "5. Creating and saving the new DICOM file."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pydicom\n",
+    "from pydicom.dataset import Dataset, FileMetaDataset\n",
+    "from pydicom.uid import ExplicitVRLittleEndian, PYDICOM_IMPLEMENTATION_UID, RTDoseStorage\n",
+    "\n",
+    "def create_rt_dose_dicom(summed_dose, reference_dicom):\n",
+    "    # Create a new DICOM dataset\n",
+    "    new_rt_dose = Dataset()\n",
+    "\n",
+    "    # Copy relevant tags from the reference RTDOSE DICOM file\n",
+    "    tags_to_copy = ['PatientID', 'StudyInstanceUID', 'SeriesInstanceUID', 'FrameOfReferenceUID', 'StudyID', 'PatientName']\n",
+    "    for tag in tags_to_copy:\n",
+    "        if hasattr(reference_dicom, tag):\n",
+    "            setattr(new_rt_dose, tag, getattr(reference_dicom, tag))\n",
+    "\n",
+    "    # Mandatory DICOM attributes for RTDOSE\n",
+    "    new_rt_dose.Modality = 'RTDOSE'\n",
+    "    new_rt_dose.Manufacturer = 'Your Manufacturer'  # Adjust as appropriate\n",
+    "    new_rt_dose.DoseSummationType = 'PLAN'  # Adjust based on how doses are summed\n",
+    "    new_rt_dose.SOPClassUID = RTDoseStorage\n",
+    "    new_rt_dose.SOPInstanceUID = pydicom.uid.generate_uid()\n",
+    "    new_rt_dose.StudyDate = reference_dicom.StudyDate if 'StudyDate' in reference_dicom else ''\n",
+    "    new_rt_dose.SeriesInstanceUID = pydicom.uid.generate_uid()  # Optionally, generate a new series UID\n",
+    "\n",
+    "    # Image specific attributes\n",
+    "    new_rt_dose.BitsAllocated = 16  # Commonly set to 16 for RTDOSE\n",
+    "    new_rt_dose.BitsStored = 16\n",
+    "    new_rt_dose.HighBit = 15\n",
+    "    new_rt_dose.PixelRepresentation = 1  # 0 for unsigned, 1 for signed\n",
+    "\n",
+    "    # Setting the dose grid data\n",
+    "    new_rt_dose.Rows, new_rt_dose.Columns = summed_dose.shape[1], summed_dose.shape[2]\n",
+    "    new_rt_dose.NumberOfFrames = summed_dose.shape[0]\n",
+    "    new_rt_dose.PixelData = summed_dose.tobytes()\n",
+    "\n",
+    "    # File meta information, required for DICOM files\n",
+    "    file_meta = FileMetaDataset()\n",
+    "    file_meta.MediaStorageSOPClassUID = new_rt_dose.SOPClassUID\n",
+    "    file_meta.MediaStorageSOPInstanceUID = new_rt_dose.SOPInstanceUID\n",
+    "    file_meta.ImplementationClassUID = PYDICOM_IMPLEMENTATION_UID\n",
+    "    file_meta.TransferSyntaxUID = ExplicitVRLittleEndian  # Assuming the use of Explicit VR Little Endian\n",
+    "\n",
+    "    # Combine the dataset and the file meta into a new DICOM file\n",
+    "    new_rt_dose.file_meta = file_meta\n",
+    "    new_rt_dose.is_little_endian = True\n",
+    "    new_rt_dose.is_implicit_VR = False\n",
+    "\n",
+    "    # Save the new DICOM file\n",
+    "    new_rt_dose.save_as('new_summed_rt_dose.dcm')\n",
+    "\n",
+    "# This function now properly sets all necessary DICOM attributes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import SimpleITK as sitk\n",
+    "def create_nifti(summed_dose):\n",
+    "\n",
+    "    ds = pydicom.read_file('/Users/fahimalam/Multi-Dose-Westmead/Physics_JS/RD.Physics_JS.zD4 ImBH.dcm')\n",
+    "    dose = sitk.ReadImage('/Users/fahimalam/Multi-Dose-Westmead/Physics_JS/RD.Physics_JS.zD4 ImBH.dcm')\n",
+    "    dose.SetPixel(summed_dose)\n",
+    "    dose = sitk.Cast(dose, sitk.sitkFloat32)\n",
+    "    dose = dose * ds.DoseGridScaling\n",
+    "\n",
+    "\n",
+    "    sitk.WriteImage(dose, str(summed_dose.nii.gz))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Main Function\n",
+    "\n",
+    "Execution Flow: This is where the functions are called in sequence to load DICOM files, validate their linkages, sum the doses, and then output the results.\n",
+    "\n",
+    "1.\tLoading all DICOM files.\n",
+    "\n",
+    "2.\tVerify linkage between RTDOSE, RTPLAN and RTSTRUCT and further confirms the uniqueness of the RTPLAN and RTDOSE. If all passed, then valid doses are obtained and summed accordingly by again comparing the summed dose against a maximum threshold.\n",
+    "\n",
+    "3.\tPassing the reference DICOM (typically the first available RTDOSE file) and summed dose grid for creating a new RTDOSE DICOM. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loaded Dose Grid for RD.Physics_JS.zD4 OpBH.dcm: Max Value - 28.842092514038086\n",
+      "Loaded Dose Grid for RD.Physics_JS.zD4 ImBH.dcm: Max Value - 23.033327102661133\n",
+      "Loaded Dose Grid for RD.Physics_JS.zD4 SCF.dcm: Max Value - 43.64101028442383\n",
+      "Processing unique RTPLAN UID: 1.2.246.352.71.5.76690441797.3107721.20240219144537, Composite Key: ('Unnamed', '15', 'No Machine')\n",
+      "Linking RTPLAN 1.2.246.352.71.5.76690441797.3107721.20240219144537 to RTSTRUCT 1.2.246.352.71.4.76690441797.620525.20240219151440\n",
+      "Duplicate RTPLAN based on composite key: 1.2.246.352.71.5.76690441797.3107708.20240219150747, Composite Key: ('Unnamed', '15', 'No Machine')\n",
+      "Linking RTPLAN 1.2.246.352.71.5.76690441797.3107708.20240219150747 to RTSTRUCT 1.2.246.352.71.4.76690441797.620525.20240219151440\n",
+      "Duplicate RTPLAN based on composite key: 1.2.246.352.71.5.76690441797.3107707.20240219150503, Composite Key: ('Unnamed', '15', 'No Machine')\n",
+      "Linking RTPLAN 1.2.246.352.71.5.76690441797.3107707.20240219150503 to RTSTRUCT 1.2.246.352.71.4.76690441797.620525.20240219151440\n",
+      "RTDOSE 1.2.246.352.71.7.76690441797.5185432.20240219151030 is unique and correctly linked to RTPLAN and RTSTRUCT.\n",
+      "RTDOSE 1.2.246.352.71.7.76690441797.5185427.20240219151059 is unique and correctly linked to RTPLAN and RTSTRUCT.\n",
+      "RTDOSE 1.2.246.352.71.7.76690441797.5185435.20240219152400 is unique and correctly linked to RTPLAN and RTSTRUCT.\n",
+      "Summed Dose Grid:\n",
+      " [[[0. 0. 0. ... 0. 0. 0.]\n",
+      "  [0. 0. 0. ... 0. 0. 0.]\n",
+      "  [0. 0. 0. ... 0. 0. 0.]\n",
+      "  ...\n",
+      "  [0. 0. 0. ... 0. 0. 0.]\n",
+      "  [0. 0. 0. ... 0. 0. 0.]\n",
+      "  [0. 0. 0. ... 0. 0. 0.]]\n",
+      "\n",
+      " [[0. 0. 0. ... 0. 0. 0.]\n",
+      "  [0. 0. 0. ... 0. 0. 0.]\n",
+      "  [0. 0. 0. ... 0. 0. 0.]\n",
+      "  ...\n",
+      "  [0. 0. 0. ... 0. 0. 0.]\n",
+      "  [0. 0. 0. ... 0. 0. 0.]\n",
+      "  [0. 0. 0. ... 0. 0. 0.]]\n",
+      "\n",
+      " [[0. 0. 0. ... 0. 0. 0.]\n",
+      "  [0. 0. 0. ... 0. 0. 0.]\n",
+      "  [0. 0. 0. ... 0. 0. 0.]\n",
+      "  ...\n",
+      "  [0. 0. 0. ... 0. 0. 0.]\n",
+      "  [0. 0. 0. ... 0. 0. 0.]\n",
+      "  [0. 0. 0. ... 0. 0. 0.]]\n",
+      "\n",
+      " ...\n",
+      "\n",
+      " [[0. 0. 0. ... 0. 0. 0.]\n",
+      "  [0. 0. 0. ... 0. 0. 0.]\n",
+      "  [0. 0. 0. ... 0. 0. 0.]\n",
+      "  ...\n",
+      "  [0. 0. 0. ... 0. 0. 0.]\n",
+      "  [0. 0. 0. ... 0. 0. 0.]\n",
+      "  [0. 0. 0. ... 0. 0. 0.]]\n",
+      "\n",
+      " [[0. 0. 0. ... 0. 0. 0.]\n",
+      "  [0. 0. 0. ... 0. 0. 0.]\n",
+      "  [0. 0. 0. ... 0. 0. 0.]\n",
+      "  ...\n",
+      "  [0. 0. 0. ... 0. 0. 0.]\n",
+      "  [0. 0. 0. ... 0. 0. 0.]\n",
+      "  [0. 0. 0. ... 0. 0. 0.]]\n",
+      "\n",
+      " [[0. 0. 0. ... 0. 0. 0.]\n",
+      "  [0. 0. 0. ... 0. 0. 0.]\n",
+      "  [0. 0. 0. ... 0. 0. 0.]\n",
+      "  ...\n",
+      "  [0. 0. 0. ... 0. 0. 0.]\n",
+      "  [0. 0. 0. ... 0. 0. 0.]\n",
+      "  [0. 0. 0. ... 0. 0. 0.]]]\n",
+      "Maximum dose in the summed dose grid: 51.77133560180664\n",
+      "Dose is within the safe limits.\n",
+      "Dose validated and within limits.\n",
+      "New RTDOSE DICOM file has been created.\n"
+     ]
+    }
+   ],
+   "source": [
+    "def main():\n",
+    "    folder_path = 'Physics_JS'\n",
+    "    max_dose_threshold = 150  # Define an appropriate threshold based on clinical needs\n",
+    "\n",
+    "    # Load DICOM files\n",
+    "    dicoms = load_dicom_files(folder_path)\n",
+    "\n",
+    "    # Verify links and get valid doses\n",
+    "    valid_doses = verify_links(dicoms)\n",
+    "\n",
+    "    # Sum the valid doses\n",
+    "    if valid_doses:\n",
+    "        summed_dose = sum_doses(valid_doses)\n",
+    "\n",
+    "        # Validate the summed dose against the threshold\n",
+    "        if summed_dose is not None and validate_dose(summed_dose, max_dose_threshold):\n",
+    "            print(\"Dose validated and within limits.\")\n",
+    "\n",
+    "            # Reference DICOM file (for copying some metadata)\n",
+    "            reference_dicom = next(iter(valid_doses.values()))  # Use the first available RTDOSE file\n",
+    "\n",
+    "            # Create and save new RTDOSE DICOM\n",
+    "            create_rt_dose_dicom(summed_dose, reference_dicom)\n",
+    "            #create_nifti(summed_dose)\n",
+    "            print(\"New RTDOSE DICOM file has been created.\")\n",
+    "        else:\n",
+    "            print(\"Dose validation failed or dose exceeds the maximum threshold.\")\n",
+    "    else:\n",
+    "        print(\"No valid doses found or all were duplicates.\")\n",
+    "\n",
+    "if __name__ == \"__main__\":\n",
+    "    main()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

From eb46d4b236beec93bb8e47c1b99de4e247d74959 Mon Sep 17 00:00:00 2001
From: Daniel Al Mouiee <d.almouiee@gmail.com>
Date: Thu, 16 May 2024 13:24:41 +1000
Subject: [PATCH 3/4] Made changes to multi dose grid conversion handling

---
 .../select-combine-multi-dose-grids.ipynb     | 224 +++++++++++++++---
 1 file changed, 196 insertions(+), 28 deletions(-)

diff --git a/Multi dose-grids/select-combine-multi-dose-grids.ipynb b/Multi dose-grids/select-combine-multi-dose-grids.ipynb
index 131b7f7..30e1dfd 100644
--- a/Multi dose-grids/select-combine-multi-dose-grids.ipynb	
+++ b/Multi dose-grids/select-combine-multi-dose-grids.ipynb	
@@ -36,7 +36,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -106,7 +106,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -183,7 +183,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -326,21 +326,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
-    "import SimpleITK as sitk\n",
-    "def create_nifti(summed_dose):\n",
-    "\n",
-    "    ds = pydicom.read_file('/Users/fahimalam/Multi-Dose-Westmead/Physics_JS/RD.Physics_JS.zD4 ImBH.dcm')\n",
-    "    dose = sitk.ReadImage('/Users/fahimalam/Multi-Dose-Westmead/Physics_JS/RD.Physics_JS.zD4 ImBH.dcm')\n",
-    "    dose.SetPixel(summed_dose)\n",
-    "    dose = sitk.Cast(dose, sitk.sitkFloat32)\n",
-    "    dose = dose * ds.DoseGridScaling\n",
-    "\n",
-    "\n",
-    "    sitk.WriteImage(dose, str(summed_dose.nii.gz))\n"
+    "import matplotlib.pyplot as plt\n",
+    " \n",
+    "def visualize_dose_grid(dose_grid):\n",
+    "    # Assuming dose_grid is a 3D numpy array\n",
+    "    # Visualize the middle slice of the grid along the z-axis\n",
+    "    mid_slice = dose_grid.shape[2] // 2\n",
+    "    plt.imshow(dose_grid[:, :, mid_slice], cmap='hot')\n",
+    "    plt.colorbar()\n",
+    "    plt.title(\"Dose Distribution on Middle Slice\")\n",
+    "    plt.show()"
    ]
   },
   {
@@ -360,7 +359,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -369,7 +368,21 @@
      "text": [
       "Loaded Dose Grid for RD.Physics_JS.zD4 OpBH.dcm: Max Value - 28.842092514038086\n",
       "Loaded Dose Grid for RD.Physics_JS.zD4 ImBH.dcm: Max Value - 23.033327102661133\n",
-      "Loaded Dose Grid for RD.Physics_JS.zD4 SCF.dcm: Max Value - 43.64101028442383\n",
+      "Loaded Dose Grid for RD.Physics_JS.zD4 SCF.dcm: Max Value - 43.64101028442383\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/60228808/dev/pydicer/.venv/lib/python3.8/site-packages/pydicom/dataset.py:594: UserWarning: Invalid value 'PlanName' used with the 'in' operator: must be an element tag as a 2-tuple or int, or an element keyword\n",
+      "  warnings.warn(msg)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "Processing unique RTPLAN UID: 1.2.246.352.71.5.76690441797.3107721.20240219144537, Composite Key: ('Unnamed', '15', 'No Machine')\n",
       "Linking RTPLAN 1.2.246.352.71.5.76690441797.3107721.20240219144537 to RTSTRUCT 1.2.246.352.71.4.76690441797.620525.20240219151440\n",
       "Duplicate RTPLAN based on composite key: 1.2.246.352.71.5.76690441797.3107708.20240219150747, Composite Key: ('Unnamed', '15', 'No Machine')\n",
@@ -428,7 +441,108 @@
       "  ...\n",
       "  [0. 0. 0. ... 0. 0. 0.]\n",
       "  [0. 0. 0. ... 0. 0. 0.]\n",
-      "  [0. 0. 0. ... 0. 0. 0.]]]\n",
+      "  [0. 0. 0. ... 0. 0. 0.]]]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[[0. 0. 0. ... 0. 0. 0.]\n",
+      "  [0. 0. 0. ... 0. 0. 0.]\n",
+      "  [0. 0. 0. ... 0. 0. 0.]\n",
+      "  ...\n",
+      "  [0. 0. 0. ... 0. 0. 0.]\n",
+      "  [0. 0. 0. ... 0. 0. 0.]\n",
+      "  [0. 0. 0. ... 0. 0. 0.]]\n",
+      "\n",
+      " [[0. 0. 0. ... 0. 0. 0.]\n",
+      "  [0. 0. 0. ... 0. 0. 0.]\n",
+      "  [0. 0. 0. ... 0. 0. 0.]\n",
+      "  ...\n",
+      "  [0. 0. 0. ... 0. 0. 0.]\n",
+      "  [0. 0. 0. ... 0. 0. 0.]\n",
+      "  [0. 0. 0. ... 0. 0. 0.]]\n",
+      "\n",
+      " [[0. 0. 0. ... 0. 0. 0.]\n",
+      "  [0. 0. 0. ... 0. 0. 0.]\n",
+      "  [0. 0. 0. ... 0. 0. 0.]\n",
+      "  ...\n",
+      "  [0. 0. 0. ... 0. 0. 0.]\n",
+      "  [0. 0. 0. ... 0. 0. 0.]\n",
+      "  [0. 0. 0. ... 0. 0. 0.]]\n",
+      "\n",
+      " ...\n",
+      "\n",
+      " [[0. 0. 0. ... 0. 0. 0.]\n",
+      "  [0. 0. 0. ... 0. 0. 0.]\n",
+      "  [0. 0. 0. ... 0. 0. 0.]\n",
+      "  ...\n",
+      "  [0. 0. 0. ... 0. 0. 0.]\n",
+      "  [0. 0. 0. ... 0. 0. 0.]\n",
+      "  [0. 0. 0. ... 0. 0. 0.]]\n",
+      "\n",
+      " [[0. 0. 0. ... 0. 0. 0.]\n",
+      "  [0. 0. 0. ... 0. 0. 0.]\n",
+      "  [0. 0. 0. ... 0. 0. 0.]\n",
+      "  ...\n",
+      "  [0. 0. 0. ... 0. 0. 0.]\n",
+      "  [0. 0. 0. ... 0. 0. 0.]\n",
+      "  [0. 0. 0. ... 0. 0. 0.]]\n",
+      "\n",
+      " [[0. 0. 0. ... 0. 0. 0.]\n",
+      "  [0. 0. 0. ... 0. 0. 0.]\n",
+      "  [0. 0. 0. ... 0. 0. 0.]\n",
+      "  ...\n",
+      "  [0. 0. 0. ... 0. 0. 0.]\n",
+      "  [0. 0. 0. ... 0. 0. 0.]\n",
+      "  [0. 0. 0. ... 0. 0. 0.]]]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "Maximum dose in the summed dose grid: 51.77133560180664\n",
       "Dose is within the safe limits.\n",
       "Dose validated and within limits.\n",
@@ -440,24 +554,35 @@
     "def main():\n",
     "    folder_path = 'Physics_JS'\n",
     "    max_dose_threshold = 150  # Define an appropriate threshold based on clinical needs\n",
-    "\n",
+    " \n",
     "    # Load DICOM files\n",
     "    dicoms = load_dicom_files(folder_path)\n",
-    "\n",
+    " \n",
     "    # Verify links and get valid doses\n",
     "    valid_doses = verify_links(dicoms)\n",
-    "\n",
+    " \n",
     "    # Sum the valid doses\n",
     "    if valid_doses:\n",
     "        summed_dose = sum_doses(valid_doses)\n",
-    "\n",
+    " \n",
+    "        for dose in valid_doses.values():\n",
+    "            visualize_dose_grid(dose.pixel_array)\n",
+    " \n",
+    "            # Visualize the summed dose grid\n",
+    "        print(summed_dose)\n",
+    "        if summed_dose is not None:\n",
+    "            visualize_dose_grid(summed_dose)\n",
+    "        else:\n",
+    "            print(\"Summed dose calculation failed or returned None.\")\n",
+    " \n",
     "        # Validate the summed dose against the threshold\n",
     "        if summed_dose is not None and validate_dose(summed_dose, max_dose_threshold):\n",
     "            print(\"Dose validated and within limits.\")\n",
-    "\n",
+    "          \n",
+    " \n",
     "            # Reference DICOM file (for copying some metadata)\n",
     "            reference_dicom = next(iter(valid_doses.values()))  # Use the first available RTDOSE file\n",
-    "\n",
+    " \n",
     "            # Create and save new RTDOSE DICOM\n",
     "            create_rt_dose_dicom(summed_dose, reference_dicom)\n",
     "            #create_nifti(summed_dose)\n",
@@ -466,11 +591,54 @@
     "            print(\"Dose validation failed or dose exceeds the maximum threshold.\")\n",
     "    else:\n",
     "        print(\"No valid doses found or all were duplicates.\")\n",
-    "\n",
+    " \n",
     "if __name__ == \"__main__\":\n",
     "    main()"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dose_path_1 = \"./Physics_JS/RD.Physics_JS.zD4 ImBH.dcm\"\n",
+    "dose_1 = sitk.ReadImage(str(dose_path_1))\n",
+    "dose_1 = sitk.Cast(dose_1, sitk.sitkFloat64)\n",
+    "dose_1 = dose_1 * (pydicom.read_file(dose_path_1)).DoseGridScaling\n",
+    "\n",
+    "dose_path_2 = \"./Physics_JS/RD.Physics_JS.zD4 OpBH.dcm\"\n",
+    "dose_2 = sitk.ReadImage(str(dose_path_2))\n",
+    "dose_2 = sitk.Cast(dose_2, sitk.sitkFloat64)\n",
+    "dose_2 = dose_2 * (pydicom.read_file(dose_path_2)).DoseGridScaling\n",
+    "\n",
+    "dose_path_3 = \"./Physics_JS/RD.Physics_JS.zD4 SCF.dcm\"\n",
+    "dose_3 = sitk.ReadImage(str(dose_path_3))\n",
+    "dose_3 = sitk.Cast(dose_3, sitk.sitkFloat64)\n",
+    "dose_3 = dose_3 * (pydicom.read_file(dose_path_3)).DoseGridScaling\n",
+    "\n",
+    "summed_dose = dose_1 + dose_2 + dose_3\n",
+    "sitk.WriteImage(summed_dose, str(\"output.nii.gz\"))\n",
+    "\n",
+    "# visualize_dose_grid(sitk.GetArrayFromImage(summed_dose))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# from platipy.imaging.visualisation.dose import visualise_dose\n",
+    "\n",
+    "# vis = ImageVisualiser(\"\", cut=get_com(structures[\"PTV60\"]))\n",
+    "\n",
+    "# vis.add_scalar_overlay(dose, discrete_levels=20, colormap=matplotlib.colormaps.get_cmap(\"inferno\"), name=\"Dose (Gy)\")\n",
+    "# vis.add_contour(structures)\n",
+    "\n",
+    "# fig = vis.show()"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -481,7 +649,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -495,9 +663,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.13"
+   "version": "3.8.16"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }

From 310ca11be2ffa58fe8cb9f4dc5be51645f849584 Mon Sep 17 00:00:00 2001
From: fahimirfanalam <fahim@cu.ac.bd>
Date: Thu, 16 May 2024 16:21:07 +1000
Subject: [PATCH 4/4] synch changes

---
 Multi dose-grids/.pydicer/preprocessed.csv    |      89 +
 Multi dose-grids/Physics_JS.zip               |     Bin 0 -> 7371664 bytes
 .../data/Physics_JS/converted.csv             |       9 +
 .../Physics_JS/doses/28bb61/dvh_5fd59b.csv    |       8 +
 .../Physics_JS/doses/28bb61/dvh_5fd59b.png    |     Bin 0 -> 109054 bytes
 .../Physics_JS/doses/28bb61/metadata.json     |     500 +
 .../Physics_JS/doses/28bb61/vis_5fd59b.png    |     Bin 0 -> 88345 bytes
 .../Physics_JS/doses/37320e/dvh_5fd59b.csv    |       8 +
 .../Physics_JS/doses/37320e/dvh_5fd59b.png    |     Bin 0 -> 107338 bytes
 .../Physics_JS/doses/37320e/metadata.json     |     500 +
 .../Physics_JS/doses/37320e/vis_5fd59b.png    |     Bin 0 -> 87195 bytes
 .../Physics_JS/doses/4de6b1/dvh_5fd59b.csv    |       8 +
 .../Physics_JS/doses/4de6b1/dvh_5fd59b.png    |     Bin 0 -> 80886 bytes
 .../Physics_JS/doses/4de6b1/metadata.json     |     500 +
 .../Physics_JS/doses/4de6b1/vis_5fd59b.png    |     Bin 0 -> 77737 bytes
 .../data/Physics_JS/images/09ea70/CT.png      |     Bin 0 -> 29099 bytes
 .../Physics_JS/images/09ea70/metadata.json    |     395 +
 .../Physics_JS/plans/13715a/metadata.json     |   12601 +
 .../Physics_JS/plans/ce6168/metadata.json     |    1935 +
 .../Physics_JS/plans/f1571e/metadata.json     |    1303 +
 .../structures/5fd59b/STRUCTURE_SET.nrrd      |     Bin 0 -> 212893 bytes
 .../structures/5fd59b/metadata.json           | 1134440 ++++++++++++++
 .../structures/5fd59b/radiomics_09ea70.csv    |       6 +
 .../structures/5fd59b/vis_09ea70.png          |     Bin 0 -> 50841 bytes
 Multi dose-grids/data/Physics_JS/summary.csv  |      14 +
 examples/Testing-multi-dose-grid.ipynb        |     236 +
 26 files changed, 1152552 insertions(+)
 create mode 100644 Multi dose-grids/.pydicer/preprocessed.csv
 create mode 100644 Multi dose-grids/Physics_JS.zip
 create mode 100644 Multi dose-grids/data/Physics_JS/converted.csv
 create mode 100644 Multi dose-grids/data/Physics_JS/doses/28bb61/dvh_5fd59b.csv
 create mode 100644 Multi dose-grids/data/Physics_JS/doses/28bb61/dvh_5fd59b.png
 create mode 100644 Multi dose-grids/data/Physics_JS/doses/28bb61/metadata.json
 create mode 100644 Multi dose-grids/data/Physics_JS/doses/28bb61/vis_5fd59b.png
 create mode 100644 Multi dose-grids/data/Physics_JS/doses/37320e/dvh_5fd59b.csv
 create mode 100644 Multi dose-grids/data/Physics_JS/doses/37320e/dvh_5fd59b.png
 create mode 100644 Multi dose-grids/data/Physics_JS/doses/37320e/metadata.json
 create mode 100644 Multi dose-grids/data/Physics_JS/doses/37320e/vis_5fd59b.png
 create mode 100644 Multi dose-grids/data/Physics_JS/doses/4de6b1/dvh_5fd59b.csv
 create mode 100644 Multi dose-grids/data/Physics_JS/doses/4de6b1/dvh_5fd59b.png
 create mode 100644 Multi dose-grids/data/Physics_JS/doses/4de6b1/metadata.json
 create mode 100644 Multi dose-grids/data/Physics_JS/doses/4de6b1/vis_5fd59b.png
 create mode 100644 Multi dose-grids/data/Physics_JS/images/09ea70/CT.png
 create mode 100644 Multi dose-grids/data/Physics_JS/images/09ea70/metadata.json
 create mode 100644 Multi dose-grids/data/Physics_JS/plans/13715a/metadata.json
 create mode 100644 Multi dose-grids/data/Physics_JS/plans/ce6168/metadata.json
 create mode 100644 Multi dose-grids/data/Physics_JS/plans/f1571e/metadata.json
 create mode 100644 Multi dose-grids/data/Physics_JS/structures/5fd59b/STRUCTURE_SET.nrrd
 create mode 100644 Multi dose-grids/data/Physics_JS/structures/5fd59b/metadata.json
 create mode 100644 Multi dose-grids/data/Physics_JS/structures/5fd59b/radiomics_09ea70.csv
 create mode 100644 Multi dose-grids/data/Physics_JS/structures/5fd59b/vis_09ea70.png
 create mode 100644 Multi dose-grids/data/Physics_JS/summary.csv
 create mode 100644 examples/Testing-multi-dose-grid.ipynb

diff --git a/Multi dose-grids/.pydicer/preprocessed.csv b/Multi dose-grids/.pydicer/preprocessed.csv
new file mode 100644
index 0000000..87abadc
--- /dev/null
+++ b/Multi dose-grids/.pydicer/preprocessed.csv	
@@ -0,0 +1,89 @@
+,patient_id,study_uid,series_uid,modality,sop_class_uid,sop_instance_uid,for_uid,file_path,slice_location,referenced_uid,referenced_for_uid
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diff --git a/Multi dose-grids/data/Physics_JS/converted.csv b/Multi dose-grids/data/Physics_JS/converted.csv
new file mode 100644
index 0000000..cb566e6
--- /dev/null
+++ b/Multi dose-grids/data/Physics_JS/converted.csv	
@@ -0,0 +1,9 @@
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diff --git a/Multi dose-grids/data/Physics_JS/doses/28bb61/dvh_5fd59b.csv b/Multi dose-grids/data/Physics_JS/doses/28bb61/dvh_5fd59b.csv
new file mode 100644
index 0000000..b7eff4b
--- /dev/null
+++ b/Multi dose-grids/data/Physics_JS/doses/28bb61/dvh_5fd59b.csv	
@@ -0,0 +1,8 @@
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diff --git a/Multi dose-grids/data/Physics_JS/doses/28bb61/metadata.json b/Multi dose-grids/data/Physics_JS/doses/28bb61/metadata.json
new file mode 100644
index 0000000..452e906
--- /dev/null
+++ b/Multi dose-grids/data/Physics_JS/doses/28bb61/metadata.json	
@@ -0,0 +1,500 @@
+{
+  "00080005": {
+    "vr": "CS",
+    "Value": [
+      "ISO_IR 192"
+    ]
+  },
+  "00080012": {
+    "vr": "DA",
+    "Value": [
+      "20240219"
+    ]
+  },
+  "00080013": {
+    "vr": "TM",
+    "Value": [
+      "152910"
+    ]
+  },
+  "00080016": {
+    "vr": "UI",
+    "Value": [
+      "1.2.840.10008.5.1.4.1.1.481.2"
+    ]
+  },
+  "00080018": {
+    "vr": "UI",
+    "Value": [
+      "1.2.246.352.71.7.76690441797.5185427.20240219151059"
+    ]
+  },
+  "00080020": {
+    "vr": "DA",
+    "Value": [
+      "20240219"
+    ]
+  },
+  "00080021": {
+    "vr": "DA",
+    "Value": [
+      "20240219"
+    ]
+  },
+  "00080023": {
+    "vr": "DA",
+    "Value": [
+      "20240219"
+    ]
+  },
+  "00080030": {
+    "vr": "TM",
+    "Value": [
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diff --git a/Multi dose-grids/data/Physics_JS/doses/37320e/dvh_5fd59b.csv b/Multi dose-grids/data/Physics_JS/doses/37320e/dvh_5fd59b.csv
new file mode 100644
index 0000000..b268430
--- /dev/null
+++ b/Multi dose-grids/data/Physics_JS/doses/37320e/dvh_5fd59b.csv	
@@ -0,0 +1,8 @@
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new file mode 100644
index 0000000..dc2ffac
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diff --git a/Multi dose-grids/data/Physics_JS/doses/4de6b1/dvh_5fd59b.csv b/Multi dose-grids/data/Physics_JS/doses/4de6b1/dvh_5fd59b.csv
new file mode 100644
index 0000000..2ce6912
--- /dev/null
+++ b/Multi dose-grids/data/Physics_JS/doses/4de6b1/dvh_5fd59b.csv	
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literal 0
HcmV?d00001

diff --git a/Multi dose-grids/data/Physics_JS/doses/4de6b1/metadata.json b/Multi dose-grids/data/Physics_JS/doses/4de6b1/metadata.json
new file mode 100644
index 0000000..a7e1198
--- /dev/null
+++ b/Multi dose-grids/data/Physics_JS/doses/4de6b1/metadata.json	
@@ -0,0 +1,500 @@
+{
+  "00080005": {
+    "vr": "CS",
+    "Value": [
+      "ISO_IR 192"
+    ]
+  },
+  "00080012": {
+    "vr": "DA",
+    "Value": [
+      "20240219"
+    ]
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+                        -51.0,
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+}
\ No newline at end of file
diff --git a/Multi dose-grids/data/Physics_JS/plans/ce6168/metadata.json b/Multi dose-grids/data/Physics_JS/plans/ce6168/metadata.json
new file mode 100644
index 0000000..24afa8c
--- /dev/null
+++ b/Multi dose-grids/data/Physics_JS/plans/ce6168/metadata.json	
@@ -0,0 +1,1935 @@
+{
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+    "vr": "ST",
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+      "Blacktown Road\r\n2148\r\nBlacktown\r\nNSW\r\nAustralia"
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diff --git a/Multi dose-grids/data/Physics_JS/plans/f1571e/metadata.json b/Multi dose-grids/data/Physics_JS/plans/f1571e/metadata.json
new file mode 100644
index 0000000..e4cafde
--- /dev/null
+++ b/Multi dose-grids/data/Physics_JS/plans/f1571e/metadata.json	
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diff --git a/Multi dose-grids/data/Physics_JS/structures/5fd59b/metadata.json b/Multi dose-grids/data/Physics_JS/structures/5fd59b/metadata.json
new file mode 100644
index 0000000..acd9f0a
--- /dev/null
+++ b/Multi dose-grids/data/Physics_JS/structures/5fd59b/metadata.json	
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diff --git a/Multi dose-grids/data/Physics_JS/summary.csv b/Multi dose-grids/data/Physics_JS/summary.csv
new file mode 100644
index 0000000..be146d0
--- /dev/null
+++ b/Multi dose-grids/data/Physics_JS/summary.csv	
@@ -0,0 +1,14 @@
+module,hashed_uid,success,log,start_time,end_time
+convert,4de6b1,0,,2024-03-15 11:47:24.286410,2024-03-15 11:47:24.457603
+convert,28bb61,0,,2024-03-15 11:47:24.466409,2024-03-15 11:47:24.624751
+convert,37320e,0,,2024-03-15 11:47:24.632997,2024-03-15 11:47:24.792460
+convert,f1571e,0,,2024-03-15 11:47:24.801989,2024-03-15 11:47:24.845477
+convert,ce6168,0,,2024-03-15 11:47:24.801989,2024-03-15 11:47:24.902875
+convert,13715a,0,,2024-03-15 11:47:24.801989,2024-03-15 11:47:25.012570
+convert,5fd59b,0,,2024-03-15 11:47:25.019869,2024-03-15 11:48:28.285642
+visualise,09ea70,0,,2024-03-15 11:48:28.333447,2024-03-15 11:48:29.415174
+visualise,4de6b1,0,,2024-03-15 11:48:29.422412,2024-03-15 11:48:31.838880
+visualise,28bb61,0,,2024-03-15 11:48:31.844911,2024-03-15 11:48:33.981739
+visualise,37320e,0,,2024-03-15 11:48:33.988154,2024-03-15 11:48:36.765643
+visualise,5fd59b,0,,2024-03-15 11:48:36.774235,2024-03-15 11:48:38.575903
+analyse_compute_radiomics,5fd59b,0,,2024-03-15 11:48:38.666721,2024-03-15 11:48:52.978556
diff --git a/examples/Testing-multi-dose-grid.ipynb b/examples/Testing-multi-dose-grid.ipynb
new file mode 100644
index 0000000..8261f10
--- /dev/null
+++ b/examples/Testing-multi-dose-grid.ipynb
@@ -0,0 +1,236 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Getting Started\n",
+    "\n",
+    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/AustralianCancerDataNetwork/pydicer/blob/main/examples/GettingStarted.ipynb)\n",
+    "\n",
+    "This notebook provides a basic example to run the PyDicer pipeline using some test data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "try:\n",
+    "    from pydicer import PyDicer\n",
+    "except ImportError:\n",
+    "    !pip install pydicer\n",
+    "    from pydicer import PyDicer\n",
+    "\n",
+    "from pathlib import Path\n",
+    "\n",
+    "from pydicer.input.test import TestInput"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Setup working directory\n",
+    "\n",
+    "First we'll create a directory for our project. Change the `directory` location to a folder on your\n",
+    "system where you'd like PyDicer to work with this data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "directory = Path(\"../Phantom_Breast_Test\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create a PyDicer object\n",
+    "\n",
+    "The [PyDicer class](https://australiancancerdatanetwork.github.io/pydicer/tool.html) provides all functionlity to run the pipeline and work with the data stored and\n",
+    "converted in your project directory"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pydicer = PyDicer(directory)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Fetch some data\n",
+    "\n",
+    "A [TestInput class](https://australiancancerdatanetwork.github.io/pydicer/input.html#pydicer.input.test.TestInput) is provided in pydicer to download some sample data to work with. Several other\n",
+    "input classes exist if you'd like to retrieve DICOM data for conversion from somewhere else, [see \n",
+    "the docs for information on how these work](https://australiancancerdatanetwork.github.io/pydicer/html/input.html)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dicom_directory = directory.joinpath(\"Physics_JS\")\n",
+    "\n",
+    "# Add the input DICOM location to the pydicer object\n",
+    "pydicer.add_input(dicom_directory)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Run the pipeline\n",
+    "\n",
+    "The function runs the entire PyDicer pipeline on the test DICOM data. This includes:\n",
+    "- [Preprocessing](https://australiancancerdatanetwork.github.io/pydicer/preprocess.html) the DICOM data (data which can't be handled or is corrupt will be placed in Quarantine)\n",
+    "- [Convert](https://australiancancerdatanetwork.github.io/pydicer/convert.html) the data to Nifti format (see the output in the `data` directory)\n",
+    "- [Visualise](https://australiancancerdatanetwork.github.io/pydicer/visualise.html) the data (png files will be placed alongside the converted Nifti files)\n",
+    "- [Compute Radiomics features](https://australiancancerdatanetwork.github.io/pydicer/analyse.html#pydicer.analyse.data.AnalyseData.compute_radiomics) (Results are stored in a csv alongside the converted structures)\n",
+    "- [Compute Dose Volume Histograms](https://australiancancerdatanetwork.github.io/pydicer/analyse.html#pydicer.analyse.data.AnalyseData.compute_dvh) (results are stored alongside converted dose data)\n",
+    "\n",
+    "> Note that the entire Pipeline can be quite time consuming to run. Depending on your project's\n",
+    "> dataset you will likely want to run only portions of the pipeline with finer control over each\n",
+    "> step. For this reason we only run the pipeline for one patient here as a demonstration."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 88/88 [00:00<00:00, 307.33files/s, preprocess]\n",
+      "100%|██████████| 6/6 [01:04<00:00, 10.75s/objects, convert]\n",
+      "100%|██████████| 5/5 [00:10<00:00,  2.05s/objects, visualise]\n",
+      "100%|██████████| 1/1 [00:14<00:00, 14.32s/objects, Compute Radiomics]\n",
+      "  0%|          | 0/3 [00:00<?, ?objects/s, Compute DVH]/Users/fahimalam/pydicer/.venv/lib/python3.9/site-packages/platipy/imaging/dose/dvh.py:102: RuntimeWarning: Mean of empty slice.\n",
+      "  mean_dose = dose_array[mask_array > 0].mean()\n",
+      "/Users/fahimalam/pydicer/.venv/lib/python3.9/site-packages/numpy/core/_methods.py:192: RuntimeWarning: invalid value encountered in divide\n",
+      "  ret = ret.dtype.type(ret / rcount)\n",
+      " 33%|███▎      | 1/3 [00:07<00:15,  7.76s/objects, Compute DVH]/Users/fahimalam/pydicer/.venv/lib/python3.9/site-packages/platipy/imaging/dose/dvh.py:102: RuntimeWarning: Mean of empty slice.\n",
+      "  mean_dose = dose_array[mask_array > 0].mean()\n",
+      "/Users/fahimalam/pydicer/.venv/lib/python3.9/site-packages/numpy/core/_methods.py:192: RuntimeWarning: invalid value encountered in divide\n",
+      "  ret = ret.dtype.type(ret / rcount)\n",
+      " 67%|██████▋   | 2/3 [00:14<00:07,  7.36s/objects, Compute DVH]/Users/fahimalam/pydicer/.venv/lib/python3.9/site-packages/platipy/imaging/dose/dvh.py:102: RuntimeWarning: Mean of empty slice.\n",
+      "  mean_dose = dose_array[mask_array > 0].mean()\n",
+      "/Users/fahimalam/pydicer/.venv/lib/python3.9/site-packages/numpy/core/_methods.py:192: RuntimeWarning: invalid value encountered in divide\n",
+      "  ret = ret.dtype.type(ret / rcount)\n",
+      "100%|██████████| 3/3 [00:22<00:00,  7.41s/objects, Compute DVH]\n"
+     ]
+    }
+   ],
+   "source": [
+    "pydicer.run_pipeline()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Prepare a dataset\n",
+    "\n",
+    "Datasets which are extracted in DICOM format can often be a bit messy and require some cleaning up\n",
+    "after conversion. Exactly what data objects to extract for the clean dataset will differ by project\n",
+    "but here we use a somewhat common approach of extracting the latest structure set for each patient\n",
+    "and the image linked to that.\n",
+    "\n",
+    "The resulting dataset is stored in a folder with your dataset name (`clean` for this example).\n",
+    "\n",
+    "See the [dataset preparation example](https://australiancancerdatanetwork.github.io/pydicer/_examples/DatasetPreparation.html) for a more detailed description on how this works.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pydicer.dataset.prepare(dataset_name=\"clean\", preparation_function=\"rt_latest_dose\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Analyse the dataset\n",
+    "\n",
+    "The pipeline computes first-order radiomics features by default, as well as dose volume histograms.\n",
+    "Here we can extract out the results easily into a Pandas DataFrame for analysis.\n",
+    "\n",
+    "Check out the [Compute Radiomics](https://australiancancerdatanetwork.github.io/pydicer/_examples/Radiomics.html) and the [Dose Metrics](https://australiancancerdatanetwork.github.io/pydicer/_examples/DoseMetrics.html) examples for further details on how to use these functions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Display the DataFrame of radiomics computed\n",
+    "df_radiomics = pydicer.analyse.get_all_computed_radiomics_for_dataset(dataset_name=\"clean\")\n",
+    "df_radiomics"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Extract the D95, D50 and V3 dose metrics\n",
+    "df_dose_metrics = pydicer.analyse.compute_dose_metrics(dataset_name=\"clean\", d_point=[95, 50], v_point=[3])\n",
+    "df_dose_metrics"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "interpreter": {
+   "hash": "814af119db7f8f2860617be3dcd1d37c560587d11c65bd58c45b1679d3ee6ea4"
+  },
+  "kernelspec": {
+   "display_name": "Python 3.8.0 64-bit ('pydicer': pyenv)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.13"
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