modality-detection

# 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: ```python 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: ```python 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: ```python 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: ```python 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: ```json { "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: 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 | 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**: ```json { "modality": "CT", "body_part": "Abdomen", "contrast": "Yes" } ``` ### Example 2: DICOM File **Input**: DICOM file with Modality=MR, BodyPartExamined=BRAIN **Output**: ```json { "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."