{
  "_ilex": {
    "architecture": "ilex.models.asl_denoising.model.AslDenoisingDAE",
    "constructor_kwargs": {},
    "format": "ilex",
    "framework_version": {
      "equinox": "0.13.8",
      "ilex": "0.0.0.dev0",
      "jax": "0.10.0",
      "jaxlib": "0.10.0",
      "numpy": "2.4.4",
      "safetensors": "0.7.0"
    },
    "has_state": false,
    "origin": "ilex-native",
    "weights_status": "bundled"
  },
  "authors": "Hales P. W. (Great Ormond Street Hospital / UCL Institute of Child Health)",
  "copyright": "patrickhales/asl-denoising is copyright (c) Patrick Hales,\nGPL-3.0-licensed on the code + the released Keras .h5\nweights. The ilex JAX / Equinox port code is licensed under\nGPL-3.0 to preserve the upstream's license; this port is NOT\navailable under the Apache-2.0 track that covers ilex's\npermissive-licensed ports.\n",
  "data_type": "image",
  "description": "patrickhales/asl-denoising (Hales et al., *JMRI* 2020) is a\n5-Conv2D U-Net-shaped denoising autoencoder for arterial spin\nlabelling (ASL) MRI difference images (dM). The model takes\nsingle-repetition raw dM images (high-noise, single label-\ncontrol subtraction) and emits a denoised dM image\napproximating the multi-repetition mean (low-noise, averaged\nover typically 10 repetitions). The model also suppresses\ntransient artefacts from head motion, arterial-transit\nvariation, and spurious perfusion signals that survive a\nsingle-rep subtraction.\n\nv0 ships **one variant** (the upstream's released\n``DaeTrainedModel.h5``; trained on 28,820 dM images over 67\nearly-stopped epochs of a planned 100). The architecture is\nparameter-fixed at 149,441 trainable scalars.\n",
  "equinox_version": "0.13.8",
  "ilex_version": "0.0.0.dev0",
  "image_classes": "Inputs are 2D ASL difference images (per-slice ``(1, H, W)``\nchannels-first per-sample). The upstream trains and infers at\n``(128, 128)``; the JAX model is shape-invariant for any\n``(H, W)`` divisible by 4 (two stride-2 max-pools and matching\nupsamples). The input is z-normalised by the consumer's\npreprocessing using the published mean/std constants.\n",
  "intended_use": "Research / preprocessing use for ASL MRI sequences, downstream\nof the standard label-control subtraction. Improves\nsingle-repetition or short-acquisition dM image quality by\napproximating the multi-rep mean output, opening up workflows\nwhere acquisition time is constrained (paediatric, clinical\nsame-day-scan windows) and the conventional 10-repetition\naveraging is not feasible.\n\nv0 of the ilex port wraps the JAX forward only; consumers are\nresponsible for: (a) the label-control subtraction (the upstream's\n``dMraw`` input is already a difference image); (b) the\n``(128, 128)`` per-slice resize -- the upstream uses\n``skimage.transform.resize`` with anti-aliasing; (c) the\nintensity clip to the 0.1-99.9 percentile envelope; (d) the\nz-normalisation ``(x - 10.411559) / 27.749531`` (constants from\nthe bundle's ``_ilex.preprocessing`` block). The model emits a\nnormalised output that consumers un-normalise the same way.\n\nNot a clinical diagnostic tool. The training data is paediatric\n(the upstream cohort is from Great Ormond Street Hospital); adult\ngeneralisation is plausible but **not validated** by the\nupstream.\n",
  "jax_version": "0.10.0",
  "network_data_format": {
    "inputs": {},
    "outputs": {}
  },
  "numpy_version": "2.4.4",
  "pred_classes": "Output is a denoised dM image of the same shape ``(1, H, W)``,\nalso in z-normalised space (consumer un-normalises by\n``output * std + mean`` to recover the dM intensity scale).\n\nNo segmentation, classification, or auxiliary head outputs.\n",
  "references": [
    "Hales P. W., Pfeuffer J., Clark C. A. (2020). Combined denoising and suppression of transient artefacts in arterial spin labelling MRI using deep learning. *Journal of Magnetic Resonance Imaging*. doi 10.1002/jmri.27255.",
    "Upstream code + weights -- github.com/patrickhales/asl-denoising (GPL-3.0)."
  ],
  "schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20240725.json",
  "task": "simultaneous denoising and transient-artefact suppression in arterial spin labelling (ASL) MRI difference images",
  "version": "0.0.0"
}