Upload brain_image_synthesis_latent_diffusion_model version 1.0.1

.gitattributes

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 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+docs/figure_1.png filter=lfs diff=lfs merge=lfs -text

LICENSE

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configs/inference.json

@@ -0,0 +1,112 @@
+{
+    "imports": [
+        "$import torch",
+        "$from datetime import datetime",
+        "$from pathlib import Path"
+    ],
+    "bundle_root": ".",
+    "dataset_dir": "",
+    "dataset": "",
+    "evaluator": "",
+    "inferer": "",
+    "load_old": 1,
+    "model_dir": "$@bundle_root + '/models'",
+    "output_dir": "$@bundle_root + '/output'",
+    "create_output_dir": "$Path(@output_dir).mkdir(exist_ok=True)",
+    "gender": 0.0,
+    "age": 0.1,
+    "ventricular_vol": 0.2,
+    "brain_vol": 0.4,
+    "device": "$torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')",
+    "conditioning": "$torch.tensor([[@gender, @age, @ventricular_vol, @brain_vol]]).to(@device).unsqueeze(1)",
+    "out_file": "$datetime.now().strftime('sample_%H%M%S_%d%m%Y') + '_' + str(@gender) + '_' + str(@age) + '_' + str(@ventricular_vol) + '_' + str(@brain_vol)",
+    "autoencoder_def": {
+        "_target_": "monai.networks.nets.AutoencoderKL",
+        "spatial_dims": 3,
+        "in_channels": 1,
+        "out_channels": 1,
+        "latent_channels": 3,
+        "channels": [
+            64,
+            128,
+            128,
+            128
+        ],
+        "num_res_blocks": 2,
+        "norm_num_groups": 32,
+        "norm_eps": 1e-06,
+        "attention_levels": [
+            false,
+            false,
+            false,
+            false
+        ],
+        "with_encoder_nonlocal_attn": false,
+        "with_decoder_nonlocal_attn": false
+    },
+    "network_def": "@autoencoder_def",
+    "load_autoencoder_path": "$@model_dir + '/autoencoder.pt'",
+    "load_autoencoder_func": "$@autoencoder_def.load_old_state_dict if bool(@load_old) else @autoencoder_def.load_state_dict",
+    "load_autoencoder": "$@load_autoencoder_func(torch.load(@load_autoencoder_path))",
+    "autoencoder": "$@autoencoder_def.to(@device)",
+    "diffusion_def": {
+        "_target_": "monai.networks.nets.DiffusionModelUNet",
+        "spatial_dims": 3,
+        "in_channels": 7,
+        "out_channels": 3,
+        "channels": [
+            256,
+            512,
+            768
+        ],
+        "num_res_blocks": 2,
+        "attention_levels": [
+            false,
+            true,
+            true
+        ],
+        "norm_num_groups": 32,
+        "norm_eps": 1e-06,
+        "resblock_updown": true,
+        "num_head_channels": [
+            0,
+            512,
+            768
+        ],
+        "with_conditioning": true,
+        "transformer_num_layers": 1,
+        "cross_attention_dim": 4,
+        "upcast_attention": true,
+        "use_flash_attention": false
+    },
+    "load_diffusion_path": "$@model_dir + '/model.pt'",
+    "load_diffusion_func": "$@diffusion_def.load_old_state_dict if bool(@load_old) else @diffusion_def.load_state_dict",
+    "load_diffusion": "$@load_diffusion_func(torch.load(@load_diffusion_path))",
+    "diffusion": "$@diffusion_def.to(@device)",
+    "scheduler": {
+        "_target_": "monai.networks.schedulers.DDIMScheduler",
+        "_requires_": [
+            "@load_diffusion",
+            "@load_autoencoder"
+        ],
+        "beta_start": 0.0015,
+        "beta_end": 0.0205,
+        "num_train_timesteps": 1000,
+        "schedule": "scaled_linear_beta",
+        "clip_sample": false
+    },
+    "noise": "$torch.randn((1, 3, 20, 28, 20)).to(@device)",
+    "set_timesteps": "[email protected]_timesteps(num_inference_steps=50)",
+    "sampler": {
+        "_target_": "scripts.sampler.Sampler",
+        "_requires_": "@set_timesteps"
+    },
+    "sample": "[email protected]_fn(@noise, @autoencoder, @diffusion, @scheduler, @conditioning)",
+    "saver": {
+        "_target_": "SaveImage",
+        "_requires_": "@create_output_dir",
+        "output_dir": "@output_dir",
+        "output_postfix": "@out_file"
+    },
+    "run": "$@saver(@sample[0][0])"
+}

configs/logging.conf

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+[loggers]
+keys=root
+
+[handlers]
+keys=consoleHandler
+
+[formatters]
+keys=fullFormatter
+
+[logger_root]
+level=INFO
+handlers=consoleHandler
+
+[handler_consoleHandler]
+class=StreamHandler
+level=INFO
+formatter=fullFormatter
+args=(sys.stdout,)
+
+[formatter_fullFormatter]
+format=%(asctime)s - %(name)s - %(levelname)s - %(message)s

configs/metadata.json

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+{
+    "schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20240725.json",
+    "version": "1.0.1",
+    "changelog": {
+        "1.0.1": "update to huggingface hosting",
+        "1.0.0": "Initial release"
+    },
+    "monai_version": "1.4.0",
+    "pytorch_version": "2.5.1",
+    "numpy_version": "1.26.4",
+    "required_packages_version": {
+        "nibabel": "5.3.2"
+    },
+    "task": "Brain image synthesis",
+    "description": "A generative model for creating high-resolution 3D brain MRI based on UK Biobank",
+    "authors": "Walter H. L. Pinaya, Petru-Daniel Tudosiu, Jessica Dafflon, Pedro F Da Costa, Virginia Fernandez, Parashkev Nachev, Sebastien Ourselin, and M. Jorge Cardoso",
+    "copyright": "Copyright (c) MONAI Consortium",
+    "data_source": "https://www.ukbiobank.ac.uk/",
+    "data_type": "nibabel",
+    "image_classes": "T1w head MRI with 1x1x1 mm voxel size",
+    "eval_metrics": {
+        "fid": 0.0076,
+        "msssim": 0.6555,
+        "4gmsssim": 0.3883
+    },
+    "intended_use": "This is a research tool/prototype and not to be used clinically",
+    "references": [
+        "Pinaya, Walter HL, et al. \"Brain imaging generation with latent diffusion models.\" MICCAI Workshop on Deep Generative Models. Springer, Cham, 2022."
+    ],
+    "network_data_format": {
+        "inputs": {
+            "image": {
+                "type": "tabular",
+                "num_channels": 1,
+                "dtype": "float32",
+                "value_range": [
+                    0,
+                    1
+                ],
+                "format": "nii",
+                "spatial_shape": [
+                    160,
+                    224,
+                    160
+                ],
+                "is_patch_data": false,
+                "channel_def": {
+                    "0": "Gender",
+                    "1": "Age",
+                    "2": "Ventricular volume",
+                    "3": "Brain volume"
+                }
+            }
+        },
+        "outputs": {
+            "pred": {
+                "type": "image",
+                "format": "image",
+                "num_channels": 1,
+                "spatial_shape": [
+                    160,
+                    224,
+                    160
+                ],
+                "dtype": "float32",
+                "value_range": [
+                    0,
+                    1
+                ],
+                "modality": "MR",
+                "is_patch_data": false,
+                "channel_def": {
+                    "0": "T1w"
+                }
+            }
+        }
+    }
+}

docs/README.md

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+# Brain Imaging Generation with Latent Diffusion Models
+
+### **Authors**
+
+Walter H. L. Pinaya, Petru-Daniel Tudosiu, Jessica Dafflon, Pedro F Da Costa, Virginia Fernandez, Parashkev Nachev,
+Sebastien Ourselin, and M. Jorge Cardoso
+
+### **Tags**
+Synthetic data, Latent Diffusion Model, Generative model, Brain Imaging
+
+## **Model Description**
+This model is trained using the Latent Diffusion Model architecture [1] and is used for the synthesis of conditioned 3D
+brain MRI data. The model is divided into two parts: an autoencoder with a KL-regularisation model that compresses data
+into a latent space and a diffusion model that learns to generate conditioned synthetic latent representations. This
+model is conditioned on age, sex, the volume of ventricular cerebrospinal fluid, and brain volume normalised for head size.
+
+![](./figure_1.png) <br>
+<p align="center">
+Figure 1 - Synthetic image from the model. </p>
+
+
+## **Data**
+The model was trained on brain data from 31,740 participants from the UK Biobank [2]. We used high-resolution 3D T1w MRI with voxel size of 1mm3, resulting in volumes with 160 x 224 x 160 voxels
+
+#### **Preprocessing**
+We used UniRes [3] to perform a rigid body registration to a common MNI space for image pre-processing. The voxel intensity was normalised to be between [0, 1].
+
+## **Performance**
+This model achieves the following results on UK Biobank: an FID of 0.0076, an MS-SSIM of 0.6555, and a 4-G-R-SSIM of 0.3883.
+
+Please, check Table 1 of the original paper for more details regarding evaluation results.
+
+
+## **commands example**
+
+Execute sampling:
+
+```shell
+python -m monai.bundle run --config_file configs/inference.json --gender 1.0 --age 0.7 --ventricular_vol 0.7 --brain_vol 0.5
+```
+
+All conditioning are expected to have values between 0 and 1
+
+## Using a new version of the model
+
+If you want to use the checkpoints from a newly fine-tuned model, you need to set parameter load_old to 0 when you run inference,
+to avoid the function load_old_state_dict being called instead of load_state_dict to be called, currently default, as it is
+required to load the checkpoint from the original GenerativeModels repository.
+
+```shell
+python -m monai.bundle run --config_file configs/inference.json --gender 1.0 --age 0.7 --ventricular_vol 0.7 --brain_vol 0.5 --load_old 0
+```
+
+## **Citation Info**
+
+```bibtex
+@inproceedings{pinaya2022brain,
+  title={Brain imaging generation with latent diffusion models},
+  author={Pinaya, Walter HL and Tudosiu, Petru-Daniel and Dafflon, Jessica and Da Costa, Pedro F and Fernandez, Virginia and Nachev, Parashkev and Ourselin, Sebastien and Cardoso, M Jorge},
+  booktitle={MICCAI Workshop on Deep Generative Models},
+  pages={117--126},
+  year={2022},
+  organization={Springer}
+}
+```
+
+## **References**
+
+Example:
+
+[1] Pinaya, Walter HL, et al. "Brain imaging generation with latent diffusion models." MICCAI Workshop on Deep Generative Models. Springer, Cham, 2022.
+
+[2] Sudlow, Cathie, et al. "UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age." PLoS medicine 12.3 (2015): e1001779.
+
+[3] Brudfors, Mikael, et al. "MRI super-resolution using multi-channel total variation." Annual Conference on Medical Image Understanding and Analysis. Springer, Cham, 2018.

models/autoencoder.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4f0f1c27f699698e3f4360650189231b558ba2bb47867a6d2353fe1b108a5429
+size 55123965

models/model.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:bdd08aa8b747a848830621476f26a579c7f5a6879b05d86733d1563589983af5
+size 2212893985

scripts/sampler.py

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+from __future__ import annotations
+
+import torch
+import torch.nn as nn
+from monai.utils import optional_import
+from torch.cuda.amp import autocast
+
+tqdm, has_tqdm = optional_import("tqdm", name="tqdm")
+
+
+class Sampler:
+    def __init__(self) -> None:
+        super().__init__()
+
+    @torch.no_grad()
+    def sampling_fn(
+        self,
+        input_noise: torch.Tensor,
+        autoencoder_model: nn.Module,
+        diffusion_model: nn.Module,
+        scheduler: nn.Module,
+        conditioning: torch.Tensor,
+    ) -> torch.Tensor:
+        if has_tqdm:
+            progress_bar = tqdm(scheduler.timesteps)
+        else:
+            progress_bar = iter(scheduler.timesteps)
+
+        image = input_noise
+        cond_concat = conditioning.squeeze(1).unsqueeze(-1).unsqueeze(-1).unsqueeze(-1)
+        cond_concat = cond_concat.expand(list(cond_concat.shape[0:2]) + list(input_noise.shape[2:]))
+        for t in progress_bar:
+            with torch.no_grad():
+                model_output = diffusion_model(
+                    torch.cat((image, cond_concat), dim=1),
+                    timesteps=torch.Tensor((t,)).to(input_noise.device).long(),
+                    context=conditioning,
+                )
+                image, _ = scheduler.step(model_output, t, image)
+
+        with torch.no_grad():
+            with autocast():
+                sample = autoencoder_model.decode_stage_2_outputs(image)
+
+        return sample