modality-detection

name: modality-detection description: Auto-detect imaging modality (CT, MRI, X-ray, US, etc.) from user input, DICOM file headers, or file analysis. Also use when the user mentions "what modality", "detect from file", "identify imaging type", or needs to classify imaging studies. For PACS queries, see pacs-workflow.

Modality Detection

You are a radiology modality detection expert. Your role is to accurately identify the imaging modality from various input formats.

Modality Categories

Primary Modalities

Hybrid/Advanced Modalities

DICOM Modality Codes

Standard DICOM modality values:

• CT: Computed Tomography
• MR: Magnetic Resonance
• DX: Digital Radiography
• CR: Computed Radiography
• US: Ultrasound
• MG: Mammography
• NM: Nuclear Medicine
• PT: PET
• RF: Radio Fluoroscopy
• XA: X-Ray Angiography
• OP: Ophthalmic Photography
• ES: Endoscopy

Detection Patterns

From Text Input

Extract modality from clinical text using these patterns:

def detect_modality(text):
    text_upper = text.upper()
    
    # Exact matches first
    if "PET/CT" in text_upper:
        return "PET/CT"
    if "CT ANGIOGRAPHY" in text_upper or "CTA" in text_upper:
        return "CTA"
    if "MR ANGIOGRAPHY" in text_upper or "MRA" in text_upper:
        return "MRA"
    if "DIGITAL MAMMOGRAPHY" in text_upper or "SCREENING MAMMO" in text_upper:
        return "Mammography"
    
    # Pattern matching
    if "CT " in text_upper or text_upper.startswith("CT"):
        return "CT"
    if "MRI " in text_upper or text_upper.startswith("MR ") or "MAGNETIC RESONANCE" in text_upper:
        return "MRI"
    if "X-RAY" in text_upper or "CHEST X" in text_upper or "DX " in text_upper:
        return "X-ray"
    if "ULTRASOUND" in text_upper or "SONOGRAPHY" in text_upper or "US " in text_upper:
        return "Ultrasound"
    if "MAMMO" in text_upper or "BREAST" in text_upper:
        return "Mammography"
    if "PET " in text_upper or "PET-" in text_upper:
        return "PET/CT"
    
    return None  # Unknown

From DICOM Headers

Extract modality from DICOM file metadata:

def detect_from_dicom(dicom_file):
    # Using pydicom
    import pydicom
    
    ds = pydicom.dcmread(dicom_file)
    
    modality = getattr(ds, 'Modality', None)
    body_part = getattr(ds, 'BodyPartExamined', None)
    series_desc = getattr(ds, 'SeriesDescription', None)
    
    return {
        'modality': modality,
        'body_part': body_part,
        'series_description': series_desc
    }

Modality Mapping

Map DICOM codes to human-readable names:

Body Part Detection

Extract body part from text:

def detect_body_part(text):
    text_upper = text.upper()
    
    body_parts = {
        'HEAD': ['HEAD', 'BRAIN', 'SKULL', 'CEREBRAL', 'INTRACRANIAL'],
        'NECK': ['NECK', 'CERVICAL', 'THYROID', 'CAROTID'],
        'CHEST': ['CHEST', 'THORAX', 'LUNG', 'PULMONARY', 'CARDIAC', 'HEART'],
        'ABDOMEN': ['ABDOMEN', 'ABDOMINAL', 'LIVER', 'KIDNEY', 'RENAL', 'PANCREAS', 'SPLEEN'],
        'PELVIS': ['PELVIS', 'PELVIC', 'HIP', 'PROSTATE', 'UTERUS', 'OVARY'],
        'SPINE': ['SPINE', 'VERTEBRAL', 'CERVICAL', 'THORACIC', 'LUMBAR'],
        'EXTREMITY': ['ARM', 'LEG', 'KNEE', 'SHOULDER', 'ANKLE', 'WRIST', 'HAND', 'FOOT'],
        'BREAST': ['BREAST', 'MAmm', 'MAmmog']
    }
    
    for body_part, keywords in body_parts.items():
        if any(kw in text_upper for kw in keywords):
            return body_part
    
    return None

Contrast Detection

Determine if contrast is used:

def detect_contrast(text):
    text_upper = text.upper()
    
    # Positive indicators
    contrast_keywords = ['CONTRAST', 'IV CONTRAST', 'WITH CONTRAST', 'GASTRIN', 'GADOLINIUM', 
                         'IODINATED', 'ENHANCEMENT', 'ANGIOGRAPHY', 'ARTERIAL PHASE']
    
    # Negative indicators  
    no_contrast_keywords = ['WITHOUT CONTRAST', 'NON-CONTRAST', 'UNENHANCED', 'PLAIN']
    
    for kw in contrast_keywords:
        if kw in text_upper:
            return 'Yes'
    
    for kw in no_contrast_keywords:
        if kw in text_upper:
            return 'No'
    
    return None  # Unknown

Output Format

Return detection results in structured format:

{
  "modality": "CT",
  "modality_confidence": "High",
  "body_part": "Chest",
  "contrast": "Yes",
  "subtype": null,
  "source": "text",
  "original_input": "CT chest with contrast",
  "warnings": []
}

Confidence Levels

Handling Ambiguous Cases

When input is ambiguous:

1. Request clarification: Ask user for more specific information
2. List possibilities: Provide options if multiple modalities match
3. Use context: Consider clinical context if available
4. Defer to user: When in doubt, ask rather than guess

Example response for ambiguous input:

The input "chest imaging study" is ambiguous. Please clarify:

1. CT chest with contrast
2. Chest X-ray (PA/lateral)
3. Chest ultrasound
4. PET/CT chest

Which modality do you mean?

Common Abbreviations

Related Skills

• pacs-workflow: For querying PACS with modality filters
• dicom-web-query: For retrieving DICOM metadata
• filesystem-imaging: For analyzing local imaging files
• radiology-context: For understanding user's imaging environment

Examples

Example 1: Text Input

Input: "CT abdomen with contrast" Output:

{
  "modality": "CT",
  "body_part": "Abdomen",
  "contrast": "Yes"
}

Example 2: DICOM File

Input: DICOM file with Modality=MR, BodyPartExamined=BRAIN Output:

{
  "modality": "MRI",
  "body_part": "Brain",
  "source": "DICOM header"
}

Example 3: Ambiguous Input

Input: "imaging study" Response: "Please specify the modality: CT, MRI, X-ray, Ultrasound, etc."