Generate DICOM PDF

dicom_sr.py

@@ -95,7 +95,14 @@ def apply(retina_net, dicom,
         instance_number = 1
     )
     
+    sr_object.StudyDate = dicom.StudyDate
+    sr_object.StudyTime = dicom.StudyTime
     sr_object.SeriesDate = datetime.now().strftime("%Y%m%d")
     sr_object.SeriesTime = datetime.now().strftime("%H%M%S")
+    sr_object.PatientID = dicom.PatientID
+    sr_object.PatientName = dicom.PatientName
+    sr_object.PatientSex = dicom.PatientSex
+    sr_object.PatientBirthDate = dicom.PatientBirthDate
+    sr_object.ReferringPhysicianName = sr_object.ReferringPhysicianName
     
     return sr_object

dicom_sr_to_pdf.py

@@ -0,0 +1,163 @@
+import pydicom
+from pydicom.dataset import Dataset
+from pydicom.dataset import FileMetaDataset
+from pydicom.uid import MediaStorageDirectoryStorage, EncapsulatedPDFStorage, generate_uid
+import matplotlib
+matplotlib.use("Agg")  # Use non-GUI backend to avoid Tkinter issues
+import matplotlib.pyplot as plt  # Now import pyplot
+from reportlab.pdfgen import canvas
+from datetime import datetime, date
+
+
+def extract_measurements(sr):
+    """Extracts measurement annotations from an SR."""
+    measurements = []
+    probabilities = []
+
+    if "ContentSequence" in sr:
+        for itemLevel1 in sr.ContentSequence:
+            if len(itemLevel1.ConceptNameCodeSequence) ==  1:
+                if itemLevel1.ConceptNameCodeSequence[0].CodeMeaning == "Imaging Measurements":
+                    for itemLevel2 in itemLevel1.ContentSequence:
+                        for itemLevel3 in itemLevel2.ContentSequence:
+                            if itemLevel3.ValueType == "SCOORD":
+                                measurements.append(itemLevel3.GraphicData)
+                            elif itemLevel3.ValueType == "NUM":
+                                if len(itemLevel3.MeasuredValueSequence) == 1:
+                                    probabilities.append(itemLevel3.MeasuredValueSequence[0].NumericValue)
+    return measurements, probabilities
+
+
+def overlay_measurements(image, measurements, probabilities):
+    """Overlays extracted measurements onto the mammography image."""
+    fig, ax = plt.subplots()
+    ax.imshow(image, cmap='gray')
+
+    # Draw each polyline
+    for i in range(0, len(measurements), 1):
+        measurement = measurements[i]
+        x = measurement[0::2]  # Extract x-coordinates (every other value)
+        y = measurement[1::2]  # Extract y-coordinates (every other value)
+        ax.plot(x, y, 'lime', linewidth=1)  # Plot the entire polyline at once
+        ax.text(x[-3] + 100, y[-3], f"{probabilities[i]:.2f} %", color='lime', fontsize=8)
+
+    ax.axis("off")
+
+    # Save the overlay as an image
+    plt.savefig("temp.png", bbox_inches='tight', pad_inches=0)
+    plt.close(fig)
+
+
+def create_pdf(temp_image_path, measurements, sr, pdf_path):
+    """Creates a PDF with the mammography image and extracted measurements."""
+    c = canvas.Canvas(pdf_path)
+
+    # Set font for the title
+    c.setFont("Helvetica-Bold", 16)
+
+    # Get page width to center the title
+    page_width = 595  # Default A4 width in points
+    title = "Mammography Report"
+    c.drawCentredString(page_width / 2, 820, title)  # Adjust Y-position as needed
+
+    # Reset font for other text
+    c.setFont("Helvetica", 12)
+
+    # Add patient info to the PDF
+    c.drawString(70, 800, f"Patient ID: {sr.PatientID}")
+    c.drawString(70, 785, f"Patient name: {sr.PatientName}")
+    c.drawString(70, 770, f"Patient birth date: {formateted_datetime(sr.PatientBirthDate)}")
+    c.drawString(70, 755, f"Patient sex: {sr.PatientSex}")
+    c.drawString(70, 730, f"Study date: {formateted_datetime(sr.StudyDate, sr.StudyTime)}")
+    c.drawString(70, 715, f"Report date: {formateted_datetime(sr.SeriesDate, sr.SeriesTime)}")
+    c.drawString(70, 700, f"Referring physician: {sr.ReferringPhysicianName}")
+
+    # Add the image to the PDF
+    c.drawImage(temp_image_path, 70, 300)
+
+    c.save()
+
+# Convert DICOM date
+def formateted_datetime(dicom_date, dicom_time = None):
+
+    if dicom_date is None or dicom_date == '':
+        return ''
+
+    # Convert DICOM date
+    formatted_date = datetime.strptime(dicom_date, "%Y%m%d").strftime("%Y-%m-%d")
+
+    if dicom_time is None or dicom_time == '':
+        return formatted_date
+
+    # Convert DICOM time (handling optional fractions of a second)
+    if "." in dicom_time:
+        formatted_time = datetime.strptime(dicom_time, "%H%M%S.%f").strftime("%H:%M:%S.%f")[:-3]  # Keep milliseconds
+    else:
+        formatted_time = datetime.strptime(dicom_time, "%H%M%S").strftime("%H:%M:%S")
+
+    # Combined datetime
+    return f"{formatted_date} {formatted_time}"
+
+def create_dcm_pdf(sr, pdf_path):
+    ds = Dataset()
+
+    # Add general DICOM metadata
+    ds.PatientName = sr.PatientName
+    ds.PatientID = sr.PatientID
+    ds.PatientBirthDate = sr.PatientBirthDate
+    ds.PatientSex = sr.PatientSex
+
+    ds.StudyInstanceUID = sr.StudyInstanceUID
+    ds.StudyDate = sr.StudyDate
+    ds.StudyTime = sr.StudyTime
+    ds.AccessionNumber = sr.AccessionNumber
+    ds.ReferringPhysicianName = sr.ReferringPhysicianName
+    ds.StudyID = sr.StudyID
+
+    ds.SeriesInstanceUID = generate_uid()
+    ds.SeriesDate = sr.SeriesDate
+    ds.SeriesTime = sr.SeriesTime
+    ds.SeriesNumber = 1
+    ds.Modality = "DOC"
+
+    ds.Manufacturer = "MammographyAI"
+    ds.ConversionType = "DI"
+
+    ds.SOPInstanceUID = generate_uid()
+    ds.SOPClassUID = EncapsulatedPDFStorage
+
+    # Open the PDF file and read it as binary data
+    with open(pdf_path, 'rb') as f:
+        pdf_data = f.read()
+
+    # Add the EncapsulatedDocument (PDF content) to the DICOM dataset
+    ds.ContentDate = ds.SeriesDate
+    ds.ContentTime = ds.SeriesTime
+    ds.AcquisitionDateTime = ""
+    ds.InstanceNumber = 1
+    ds.BurnedInAnnotation = "YES"
+    ds.DocumentTitle = ""
+    ds.EncapsulatedDocument = pdf_data
+    ds.MIMETypeOfEncapsulatedDocument = "application/pdf"
+
+    # Create a FileMetaDataset for DICOM file meta information
+    file_meta = FileMetaDataset()
+    file_meta.MediaStorageSOPClassUID = EncapsulatedPDFStorage
+    file_meta.MediaStorageSOPInstanceUID = ds.SOPInstanceUID
+    file_meta.TransferSyntaxUID = pydicom.uid.ImplicitVRLittleEndian
+    file_meta.FileMetaInformationGroupLength = 0
+
+    # Assign the file meta information to the dataset
+    ds.file_meta = file_meta
+
+    # Ensure preamble and "DICM" prefix is included
+    ds.is_implicit_VR = True  # Set to explicit VR
+    ds.is_little_endian = True  # Set to little endian
+    
+    return ds
+
+def create(image, sr):
+    measurements, probabilities = extract_measurements(sr)
+    overlay_measurements(image, measurements, probabilities)
+    create_pdf("temp.png", measurements, sr, "temp.pdf")
+    return create_dcm_pdf(sr, "temp.pdf",)

mammography.py

@@ -100,6 +100,7 @@ import pydicom
 sys.path.append(os.path.join(SCRIPT_DIR, '..'))
 import model
 import dicom_sr
+import dicom_sr_to_pdf
 
 orthanc.LogWarning('Loading the RetinaNet model for mammography')
 my_retina_net = model.load_retina_net()
@@ -126,6 +127,7 @@ def execute_inference(output, uri, **request):
             output.SendHttpStatusCode(400)
         else:
             result = dicom_sr.apply(my_retina_net, dicom, minimum_score=0.2)
+            pdf = dicom_sr_to_pdf.create(dicom.pixel_array, result)
 
             with io.BytesIO() as f:
                 pydicom.dcmwrite(f, result)
@@ -134,4 +136,11 @@ def execute_inference(output, uri, **request):
 
             output.AnswerBuffer(orthanc.RestApiPost('/instances', content), 'application/json')
             
+            with io.BytesIO() as f:
+                pydicom.dcmwrite(f, pdf)
+                f.seek(0)
+                content = f.read()
+            
+            output.AnswerBuffer(orthanc.RestApiPost('/instances', content), 'application/json')
+
 orthanc.RegisterRestCallback('/mammography-apply', execute_inference)

requirements.txt

@@ -1,7 +1,9 @@
 highdicom==0.22.0
-numpy==2.1.0
+numpy==1.24.0
 opencv-python==4.10.0.84
 pydicom==2.4.4
-torch==2.3.0
-torchaudio==2.3.0
-torchvision==0.18.0
+torch==2.4.0
+torchaudio==2.4.0
+torchvision==0.19.0
+reportlab==4.3.1
+matplotlib==3.10.0