Update snr_star.md

Author: Motohiro-TABUCHI

_pages/plugins/snr_star.md

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 ---
-title: SNR_star – Covariance-based SNR measurement
+
+title: SNR_star – Covariance-based SNR measurement (ImageJ macro)
 categories: [Analysis]
+tags: [SNR, image quality, CT, covariance, ImageJ macro]
+author: Motohiro TABUCHI
 doi: 10.6009/jjrt.2022-1154
+---------------------------
+
+# SNR_star – Covariance-based SNR measurement
+
+**Author:** Motohiro TABUCHI
+
+SNR_star is an ImageJ macro that estimates signal variance and noise variance using the covariance between two repeated images acquired under identical imaging conditions.
+It provides an unbiased and statistically optimal estimation of signal-to-noise ratio (SNR).
+
+The method is particularly useful for image quality evaluation in X-ray CT where direct measurement of signal variance is difficult.
+
+---
+
+## Principle
+
+SNR* estimates the signal variance from the covariance between two observed images acquired under identical imaging conditions.
+
+Signal variance:
+
+σ_s² = Cov(I₁, I₂)
+
+Noise variance:
+
+σ_n² = Var(I₁ − I₂) / 2
+
+The SNR* value in decibels is defined as
+
+SNR* [dB] = 10 log10 (σ_s² / σ_n²)
+
+![Principle](./snr_star_principle.jpg)
+
+---
+
+## Features
+
+* Covariance-based signal variance estimation
+* Noise variance estimation from the difference of images
+* Outputs SNR* [dB], ROI size, signal variance, and noise variance
+* Simple workflow using standard ImageJ ROI tools
+* Suitable for quantitative image quality evaluation
+
+---
+
+## Requirements
+
+* ImageJ 1.53 or later
+* Two observed images of identical dimensions
+* Images must be acquired under identical imaging conditions
+
+---
+
+## Usage
+
+![Usage](./snr_star_usage.jpg)
+
+1. Prepare a folder containing two observed images
+2. Open the first image in ImageJ
+3. Draw a region of interest (ROI)
+4. Run the SNR_star macro
+5. The macro outputs the following values in the Log window:
+
+* SNR* [dB]
+* ROI size
+* signal variance
+* noise variance
+
 ---
 
-## SNR_star – Covariance-based SNR measurement
+## Video
 
-Author: Motohiro TABUCHI
+Demonstration of the SNR* tool:
 
-SNR_star is an ImageJ macro that estimates signal variance and noise variance using covariance between two repeated images acquired under identical imaging conditions.  
-It provides an unbiased and statistically optimal estimation of SNR.
+https://youtube.com/shorts/qBz2MlNHiCE
 
-### Features
+---
 
-- Covariance-based signal variance estimation
-- Noise variance estimation from difference of images
-- Outputs SNR* [dB], ROI size, signal variance, and noise variance
-- Simple workflow using ImageJ ROI tools
+## Download
 
-### Requirements
+GitHub repository
 
-- ImageJ 1.53 or later
-- Exactly two observed images of identical dimensions acquired under identical imaging conditions
+https://github.com/Motohiro-TABUCHI/SNR_star_Tool
 
-### Download
+Archived release (Zenodo)
 
-[GitHub Repository](https://github.com/Motohiro-TABUCHI/SNR_star_Tool)
+https://zenodo.org/record/18666471
 
-### Notes
+---
+
+## Notes
 
-- Assumes additive, zero-mean, independent noise between the two images
-- Larger ROIs improve estimation stability
-- Negative covariance may indicate unsuitable imaging conditions
+* The method assumes additive, zero-mean, independent noise between the two images
+* Larger ROIs improve estimation stability
+* Negative covariance may indicate unsuitable imaging conditions
+* The method is applicable to CT and other imaging modalities with repeated acquisitions
+
+---
 
-### Reference
+## Reference
 
 {% include citation %}