{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "1bfdbc37",
   "metadata": {},
   "source": [
    "# MR-to-synthetic CT generation\n",
    "\n",
    "## Goal\n",
    "Run an end-to-end OrcaImage `synthesis-v1` smoke baseline inspired by SynthRAD2023. The bundled cohort is synthetic; it contains no patient data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c294cae4",
   "metadata": {},
   "source": [
    "## Submission contract\n",
    "`images/<case_id>.nii.gz synthetic CT with unchanged geometry`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d1e51b25",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "import importlib.util,subprocess,sys\n",
    "missing=[p for p in ['numpy','nibabel','scipy'] if importlib.util.find_spec(p) is None]\n",
    "if missing: subprocess.check_call([sys.executable,'-m','pip','install','numpy','nibabel','scipy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e628b1d",
   "metadata": {},
   "source": [
    "## Bundled smoke cohort\n",
    "This cell restores the small dataset carried inside the notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2cfa2412",
   "metadata": {},
   "outputs": [],
   "source": [
    "import base64,io,zipfile\n",
    "payload='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'\n",
    "Path('data').mkdir(exist_ok=True)\n",
    "with zipfile.ZipFile(io.BytesIO(base64.b64decode(payload))) as z:z.extractall('data')\n",
    "[str(p) for p in Path('data').rglob('*') if p.is_file()]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa8d807e",
   "metadata": {},
   "source": [
    "## Baseline\n",
    "Replace this transparent heuristic with your training and inference pipeline, while keeping the output contract unchanged."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0358a520",
   "metadata": {},
   "outputs": [],
   "source": [
    "import zipfile\n",
    "import nibabel as nib\n",
    "import numpy as np\n",
    "out=Path('predictions/images');out.mkdir(parents=True,exist_ok=True)\n",
    "for path in Path('data/mr').glob('*.nii.gz'):\n",
    "    image=nib.load(path); prediction=np.asarray(image.dataobj,dtype=np.float32)*1000-500\n",
    "    nib.save(nib.Nifti1Image(prediction,image.affine,image.header),out/path.name)\n",
    "with zipfile.ZipFile('submission.zip','w',zipfile.ZIP_DEFLATED) as z:\n",
    "    for path in out.glob('*.nii.gz'):z.write(path,path.relative_to('predictions'))\n",
    "list(out.glob('*'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b864cf5",
   "metadata": {},
   "source": [
    "## Checks\n",
    "Inspect the archive before upload. Hidden reference values are never included."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0de2c8c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "with zipfile.ZipFile('submission.zip') as z: print(z.namelist())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df82a07f",
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   "source": [
    "## Next steps\n",
    "Import the full SynthRAD2023 cohort only after reviewing its current license and citation requirements in `datasets/CATALOG.md`."
   ]
  }
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