光声图像处理 / Photoacoustic Image Processing

Overview

This skill enables automated analysis of photoacoustic (PA) imaging data: read multi-format PA data, compute quantitative image quality metrics, generate publication-quality visualizations, and produce structured experiment evaluation reports (Markdown / Word / PDF / PPT).

When to Use

Trigger this skill when the user:

• Uploads PA raw signal data (.mat, .npy, .csv) or reconstructed images (.tiff, .png, .dcm, .nii) and asks for analysis
• Asks to evaluate PA image quality with quantitative metrics (SNR, CNR, spatial resolution, contrast-to-noise ratio)
• Requests a structured experiment report with statistics and figures
• Wants side-by-side comparison of multiple PA reconstruction methods
• Uses keywords: 光声/photoacoustic/PAI/optoacoustic + 分析/报告/评价/指标

Workflow

Step 1: Data Ingestion

Use scripts/read_pa_data.py to load input data.

python scripts/read_pa_data.py INPUT_PATH [--format auto|mat|npy|dcm|nii|img]

The script auto-detects format and returns a standardized data dict: {"data": ndarray, "metadata": {...}, "shape": tuple, "dtype": str}.

Supported formats:

| Format | Extensions | Typical PA Use Case | |--------|-----------|---------------------| | MATLAB | .mat | k-Wave simulation output, raw channel data | | NumPy | .npy, .npz | Pre-processed PA arrays | | DICOM | .dcm | Clinical PA/CT systems | | NIfTI | .nii, .nii.gz | 3D volumetric PA | | Image | .tiff, .png, .jpg | Reconstructed 2D PA images |

Step 2: Quantitative Analysis

Use scripts/analyze_pa.py to compute quality metrics.

python scripts/analyze_pa.py DATA_PATH [--roi ROI_PATH] [--metrics snr,cnr,resolution,contrast,all] [--output OUTPUT_DIR]

Core metrics (defined in references/metrics.md):

| Metric | Formula | What It Tells | |--------|---------|---------------| | SNR | 20·log₁₀(μ_signal / σ_background) | Signal-to-noise ratio in dB | | CNR | |μ_ROI - μ_bg| / σ_bg | Contrast-to-noise ratio | | Resolution | FWHM of edge spread function | Spatial resolution in μm/pixels | | Contrast | (I_max - I_min) / (I_max + I_min) | Michelson contrast | | SSIM | Structural similarity index | Perceptual similarity to reference |

Step 3: Report Generation

Use scripts/generate_report.py to produce the final deliverable.

python scripts/generate_report.py ANALYSIS_DIR [--format md|docx|pptx|pdf|all] [--template assets/report_template.md] [--output REPORT_PATH]

The report template (see assets/report_template.md) organizes content into:

1. 实验概述 — data source, acquisition parameters, sample info
2. 方法 — reconstruction algorithm, processing pipeline
3. 结果 — figures with metric tables
4. 讨论 — interpretation of results, comparison across methods
5. 结论与评价 — quantitative summary and quality assessment

Prerequisites

Before using this skill, ensure the Python environment has required packages:

pip install numpy scipy matplotlib pandas pydicom nibabel Pillow scikit-image python-docx python-pptx reportlab

Verify with:

python -c "import numpy, scipy, matplotlib, pandas, pydicom, nibabel, PIL, skimage; print('All dependencies OK')"

Resources

scripts/

• read_pa_data.py — Multi-format PA data loader (MAT/NPY/DICOM/NIfTI/images)
• analyze_pa.py — Quantitative metrics: SNR, CNR, resolution, contrast, SSIM, statistics
• generate_report.py — Report generator: Markdown, DOCX, PPTX, PDF

references/

• metrics.md — Detailed definitions and formulas for all PA image quality metrics Load this when the user asks for specific metric definitions or custom metrics.

assets/

• report_template.md — Structured experiment report template (Markdown) Copy and customize per experiment.