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

Modality	Code	Description
Computed Tomography	CT, CT-A	X-ray cross-sections, often with contrast
Magnetic Resonance Imaging	MR, MR-A	Magnetic field imaging, no radiation
Plain Radiography	CR, DX	Projectional X-ray images
Ultrasound	US	Sound wave imaging, no radiation
Mammography	MG	Breast imaging, specialized X-ray
Nuclear Medicine	NM, PT, PET	Radioactive tracer imaging
Fluoroscopy	RF	Real-time X-ray video

Hybrid/Advanced Modalities

Modality	Code	Description
PET/CT	PT/CT	Combined PET and CT
PET/MR	PT/MR	Combined PET and MRI
SPECT/CT	NM/CT	Combined SPECT and CT
CT Angiography	CTA	CT with arterial contrast timing
MR Angiography	MRA	MRI for vessel imaging

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:

DICOM Code	Display Name	Category
CT	CT Scan	Tomography
MR	MRI	Tomography
DX	X-ray	Projection
CR	X-ray	Projection
US	Ultrasound	Ultrasound
MG	Mammography	Projection
PT	PET	Nuclear
NM	Nuclear Medicine	Nuclear
RF	Fluoroscopy	Fluoroscopy
XA	Angiography	Fluoroscopy
CR	Computed Radiography	Projection
OPG	Orthopantomogram	Projection
DXA	Bone Densitometry	Projection

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

Level	Criteria
High	Exact match, clear indication
Medium	Partial match, some ambiguity
Low	Weak indicators, significant ambiguity
Unknown	Cannot determine, needs clarification

Handling Ambiguous Cases

When input is ambiguous:

Request clarification: Ask user for more specific information
List possibilities: Provide options if multiple modalities match
Use context: Consider clinical context if available
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

Abbreviation	Full Term	Modality
CXR	Chest X-ray	X-ray
KUB	Kidneys, Ureter, Bladder X-ray	X-ray
CTA	CT Angiography	CT
MRA	MR Angiography	MRI
VQ	Ventilation/Perfusion Scan	Nuclear
HIDA	Hepatobiliary Scan	Nuclear
DEXA	Bone Density Scan	X-ray
OPG	Panoramic Dental X-ray	X-ray

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."

modality-detection