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README.md

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+# SynthRAD2025 – Task 1 & Task 2 Solutions
+
+[![MICCAI](https://img.shields.io/badge/MICCAI-Grand%20Challenge-blue)](https://grand-challenge.org/)
+[![Docker](https://img.shields.io/badge/Docker-ready-brightgreen)](https://www.docker.com/)
+[![License](https://img.shields.io/badge/license-MIT-lightgrey)](LICENSE)
+
+This repository contains our solutions for the **MICCAI Grand Challenge – SynthRAD2025**, focusing on **Task 1** and **Task 2**.  
+Our team achieved **1st place** in the **post-challenge leaderboard** for both tasks (with **Task 1 also ranking 1st during the official test phase**).
+
+---
+
+## 🏆 Challenge Overview
+
+- **Task 1**: MRI-to-CT synthesis (**MR → sCT**)  
+- **Task 2**: CBCT-to-CT synthesis (**CBCT → sCT**)  
+
+Our methods emphasize **robust image synthesis**, **reproducible pipelines**, and **multi-region generalization**.
+
+---
+
+
+### File Descriptions
+
+- **`docker_task_1/`**  
+  Contains all necessary files to build and run the Docker image for **Task 1** (MR → sCT).  
+  - `process.py`: Script that performs inference, converting MR images into synthetic CT (sCT).  
+
+- **`docker_task_2/`**  
+  Contains all necessary files to build and run the Docker image for **Task 2** (CBCT → sCT).  
+  - `process.py`: Script that performs inference, converting CBCT images into synthetic CT (sCT).  
+
+- **Normalization Config Files**  
+  - `260_gt_nnUNetResEncUNetLPlans.json`: Normalization configuration for the **Abdomen** region.  
+  - `262_gt_nnUNetResEncUNetLPlans.json`: Normalization configuration for the **Head & Neck** region.  
+  - `266_gt_nnUNetResEncUNetLPlans.json`: Normalization configuration for the **Thorax** region.  
+  These files are essential for inverse normalization, ensuring that the synthesized CT intensities are mapped back to their correct clinical ranges.  
+
+- **`nnunet_results`**  
+  You also need to place your **trained models** under the `nnunet_results/` directory so that inference can correctly locate and load them.  
+  
+- **`Dockerfile`**  
+  Defines all steps and dependencies needed to build the Docker image. It ensures reproducibility and consistency across environments.  
+
+- **`base_algorithm/`**  
+  Contains the baseline algorithm files provided by the official **SynthRAD2025 template**, serving as the foundation for our solution.  
+
+- **`build.sh`**  
+  Shell script for automating the Docker build process.  
+
+- **`export.sh`**  
+  Shell script for exporting the built Docker image into a compressed archive for submission or deployment.  
+
+- **`requirements.txt`**  
+  Lists all Python dependencies required to run the code.  
+
+- **`revert_normalisation.py`**  
+  Script to apply **inverse normalization** to synthesized CT outputs, restoring them to the correct intensity distributions for downstream evaluation.  
+
+
+## 🚀 Getting Started
+
+### 1. Build the Docker Image
+```bash
+cd docker_task_1
+bash build.sh
+
+To test the algorithm locally, you can run the Docker container with GPU support, memory limit, and a larger shared memory (`/dev/shm`) size (e.g., 8 GB).  
+