# PETScanAnalyzer Class

---

## Overview
The `PETScanAnalyzer` class provides advanced tools for analyzing PET (Positron Emission Tomography) scan volumes. It supports conversion to Standard Uptake Values (SUV), metabolic region segmentation, anomaly detection, lesion characterization, and comprehensive reporting. The class integrates machine learning for anomaly detection and provides visualization capabilities.

---

## Class Definition

```python
class PETScanAnalyzer:
    def __init__(self, image_analyzer):
        """
        Initialize a new PETScanAnalyzer object.
        :param image_analyzer: ImageAnalyzer instance for image processing
        """
        ...
```

---

## Attributes

| Attribute | Type | Description |
|-----------|------|-------------|
| `image_analyzer` | `ImageAnalyzer` | ImageAnalyzer instance for image processing. |
| `suv_ranges` | `Dict[str, Tuple[float, float]]` | Standard Uptake Value (SUV) ranges for different conditions. |
| `tracer_properties` | `Dict[str, Dict[str, float]]` | Properties of common PET tracers. |

---

## Methods

### Initialization
- **`__init__(self, image_analyzer)`**
  Initializes a new `PETScanAnalyzer` instance with the specified `ImageAnalyzer`.

---

### PET Scan Analysis
- **`analyze_pet_scan(self, image_stack, tracer_type="FDG", slice_thickness=1.0, injection_dose=10.0, patient_weight=70.0, scan_time=60.0) -> Dict[str, Any]`**
  Analyzes a PET scan volume using advanced 3D metabolic analysis.

  - **Parameters**:
    - `image_stack`: Input PET scan volume (3D array or list of 2D slices).
    - `tracer_type`: Type of radiotracer used (FDG, F-DOPA, F-Choline).
    - `slice_thickness`: Thickness of each slice in mm.
    - `injection_dose`: Injected dose in MBq.
    - `patient_weight`: Patient weight in kg.
    - `scan_time`: Time from injection to scan in minutes.

  - **Returns**: Dictionary containing comprehensive PET analysis results.

---

### SUV Conversion
- **`convert_to_suv(self, volume, injection_dose, patient_weight, scan_time) -> np.ndarray`**
  Converts raw PET values to Standardized Uptake Values (SUV).

  - **Parameters**:
    - `volume`: Input PET volume (activity concentration).
    - `injection_dose`: Injected dose in MBq.
    - `patient_weight`: Patient weight in kg.
    - `scan_time`: Time from injection in minutes.

  - **Returns**: Volume in SUV units.

---

### Volume Enhancement
- **`enhance_pet_volume(self, suv_volume) -> np.ndarray`**
  Enhances PET volume using specialized filtering for metabolic data.

  - **Parameters**:
    - `suv_volume`: Input volume in SUV units.

  - **Returns**: Enhanced volume.

---

### Metabolic Region Segmentation
- **`segment_metabolic_regions(self, suv_volume, tracer_type) -> Dict[str, np.ndarray]`**
  Segments different metabolic activity regions based on SUV values.

  - **Parameters**:
    - `suv_volume`: Volume in SUV units.
    - `tracer_type`: Type of radiotracer.

  - **Returns**: Dictionary of metabolic region masks.

---

### Metabolic Edge Detection
- **`detect_metabolic_edges(self, enhanced_volume) -> np.ndarray`**
  Detects metabolic boundaries and transitions.

  - **Parameters**:
    - `enhanced_volume`: Enhanced SUV volume.

  - **Returns**: 3D metabolic edge volume.

---

### Metabolic Anomaly Detection
- **`detect_metabolic_anomalies(self, suv_volume, metabolic_segmentation) -> List[Dict[str, Any]]`**
  Detects metabolic anomalies using machine learning on SUV patterns.

  - **Parameters**:
    - `suv_volume`: Volume in SUV units.
    - `metabolic_segmentation`: Metabolic segmentation masks.

  - **Returns**: List of metabolic anomaly locations and characteristics.

- **`_classify_metabolic_anomaly(self, suv_value) -> str`**
  Classifies metabolic anomaly based on SUV value.

  - **Parameters**:
    - `suv_value`: SUV value.

  - **Returns**: Classification of the anomaly.

---

### Metabolic Measurements
- **`calculate_metabolic_measurements(self, metabolic_segmentation, suv_volume, slice_thickness) -> Dict[str, Dict[str, float]]`**
  Calculates metabolic measurements for different regions.

  - **Parameters**:
    - `metabolic_segmentation`: Metabolic segmentation masks.
    - `suv_volume`: Volume in SUV units.
    - `slice_thickness`: Slice thickness in mm.

  - **Returns**: Dictionary of metabolic measurements.

---

### Uptake Pattern Analysis
- **`analyze_uptake_patterns(self, suv_volume) -> Dict[str, Any]`**
  Analyzes uptake patterns throughout the volume.

  - **Parameters**:
    - `suv_volume`: Volume in SUV units.

  - **Returns**: Uptake pattern analysis.

---

### Hypermetabolic Lesion Detection
- **`detect_hypermetabolic_lesions(self, suv_volume, metabolic_segmentation, tracer_type) -> List[Dict[str, Any]]`**
  Detects and characterizes hypermetabolic lesions.

  - **Parameters**:
    - `suv_volume`: Volume in SUV units.
    - `metabolic_segmentation`: Metabolic segmentation masks.
    - `tracer_type`: Type of radiotracer.

  - **Returns**: List of detected lesions.

- **`_calculate_malignancy_score(self, suv_max, suv_mean, mtv) -> float`**
  Calculates a simple malignancy risk score.

  - **Parameters**:
    - `suv_max`: Maximum SUV value.
    - `suv_mean`: Mean SUV value.
    - `mtv`: Metabolic tumor volume.

  - **Returns**: Malignancy risk score.

---

### Quantitative Metrics
- **`calculate_quantitative_metrics(self, suv_volume) -> Dict[str, float]`**
  Calculates standard quantitative PET metrics.

  - **Parameters**:
    - `suv_volume`: Volume in SUV units.

  - **Returns**: Dictionary of quantitative metrics.

---

### Tracer Kinetics Analysis
- **`analyze_tracer_kinetics(self, suv_volume) -> Dict[str, Any]`**
  Analyzes tracer kinetics (simplified for single time point).

  - **Parameters**:
    - `suv_volume`: Volume in SUV units.

  - **Returns**: Kinetic analysis results.

---

### Visualization
- **`visualize_pet_analysis(self, analysis_results, slice_index=None)`**
  Visualizes PET analysis results for a specific slice.

  - **Parameters**:
    - `analysis_results`: Results from `analyze_pet_scan` method.
    - `slice_index`: Index of slice to visualize.

---

### Metabolic Tissue Analysis
- **`create_metabolic_tissue_analysis(self, suv_volume, lesions, tissue_name: str) -> Dict[str, Any]`**
  Creates metabolic tissue analysis from PET scan results.

  - **Parameters**:
    - `suv_volume`: Volume in SUV units.
    - `lesions`: Detected lesions.
    - `tissue_name`: Name for the tissue analysis.

  - **Returns**: Dictionary with metabolic tissue characteristics.

---

### PET/CT Comparison
- **`compare_pet_ct(self, pet_results, ct_results) -> Dict[str, Any]`**
  Compares PET and CT scan results for comprehensive analysis.

  - **Parameters**:
    - `pet_results`: Results from PET scan analysis.
    - `ct_results`: Results from CT scan analysis.

  - **Returns**: Combined analysis results.

- **`_is_in_bone(self, location, bone_mask) -> bool`**
  Checks if a location is within bone tissue.

  - **Parameters**:
    - `location`: Location coordinates.
    - `bone_mask`: Bone tissue mask.

  - **Returns**: Boolean indicating if the location is within bone tissue.

- **`_is_in_lung(self, location, lung_mask) -> bool`**
  Checks if a location is within lung tissue.

  - **Parameters**:
    - `location`: Location coordinates.
    - `lung_mask`: Lung tissue mask.

  - **Returns**: Boolean indicating if the location is within lung tissue.

- **`_calculate_combined_risk(self, correlations, tissue_analysis) -> float`**
  Calculates combined risk assessment from PET/CT correlation.

  - **Parameters**:
    - `correlations`: Structural-metabolic correlations.
    - `tissue_analysis`: Tissue-specific analysis.

  - **Returns**: Combined risk score.

- **`_calculate_diagnostic_confidence(self, correlations) -> float`**
  Calculates diagnostic confidence based on correlations.

  - **Parameters**:
    - `correlations`: Structural-metabolic correlations.

  - **Returns**: Diagnostic confidence score.

- **`_generate_recommendations(self, correlations, tissue_analysis) -> List[str]`**
  Generates clinical recommendations based on analysis.

  - **Parameters**:
    - `correlations`: Structural-metabolic correlations.
    - `tissue_analysis`: Tissue-specific analysis.

  - **Returns**: List of clinical recommendations.

---

### Report Generation
- **`generate_pet_report(self, analysis_results) -> str`**
  Generates a comprehensive PET scan report.

  - **Parameters**:
    - `analysis_results`: Results from `analyze_pet_scan` method.

  - **Returns**: Formatted report string.

---

## Example Usage

```python
# Initialize ImageAnalyzer
image_analyzer = ImageAnalyzer()

# Initialize PETScanAnalyzer
pet_analyzer = PETScanAnalyzer(image_analyzer)

# Load a PET scan image
image = image_analyzer.load_image("path/to/pet_scan.tif")

# Analyze the PET scan
analysis_results = pet_analyzer.analyze_pet_scan(image, tracer_type="FDG")

# Visualize the PET analysis
pet_analyzer.visualize_pet_analysis(analysis_results)

# Create a metabolic tissue analysis
tissue_analysis = pet_analyzer.create_metabolic_tissue_analysis(
    analysis_results["suv_volume"], analysis_results["lesions"], "Liver Tissue"
)
print(tissue_analysis)

# Generate a PET report
report = pet_analyzer.generate_pet_report(analysis_results)
print(report)
```

---

## Dependencies
- **`numpy`**: For numerical operations and array handling.
- **`scipy`**: For scientific computing and image processing.
- **`skimage`**: For image processing and analysis.
- **`sklearn.ensemble.IsolationForest`**: For anomaly detection.
- **`sklearn.preprocessing.StandardScaler`**: For feature normalization.
- **`matplotlib`**: For visualization.
- **`biobridge.blocks.cell.Cell`**: For creating cell objects.

---

## Error Handling
- The class includes checks for valid input data and handles potential errors during image processing and analysis.

---

## Notes
- The `PETScanAnalyzer` class is designed for advanced PET scan analysis.
- It supports multiple tracer types and provides detailed analysis of metabolic activity, lesions, and anomalies.
- The `visualize_pet_analysis` method provides a graphical representation of the analysis results.
- The `generate_pet_report` method creates a comprehensive report suitable for clinical interpretation.
- The class integrates machine learning for anomaly detection and provides methods for comparing PET and CT scan results.
