The tutorial accompanying the pull request #7308 for MONAI core, which adds SURE-loss and Conjugate Gradient

README.md

@@ -309,3 +309,7 @@ This tutorial shows the use cases of training and validating a 3D Latent Diffusi
 
 ##### [2D latent diffusion model](./generative/2d_ldm)
 This tutorial shows the use cases of training and validating a 2D Latent Diffusion Model.
+
+##### [SURE-Based Robust MRI Reconstruction with Diffusion Models (SMRD)](./generative/smrd)
+Example shows the use case of inference with a pre-trained score function while taking into account available measurements,
+using the SURE-Based Robust MRI Reconstruction with Diffusion Models (SMRD) method.

generative/README.md

@@ -14,3 +14,7 @@ Example shows the use cases of training and validating a 3D Latent Diffusion Mod
 
 ## [Brats 2D latent diffusion model](./2d_ldm/README.md)
 Example shows the use cases of training and validating a 2D Latent Diffusion Model on axial slices from Brats 2016&2017 data.
+
+## [SURE-Based Robust MRI Reconstruction with Diffusion Models (SMRD)](./smrd/README.md)
+Example shows the use case of inference with a pre-trained score function while taking into account available measurements,
+using the SURE-Based Robust MRI Reconstruction with Diffusion Models (SMRD) method.

generative/smrd/README.md

@@ -0,0 +1,5 @@
+SURE-Based Robust MRI Reconstruction with Diffusion Models (SMRD). MICCAI 2023 (https://link.springer.com/chapter/10.1007/978-3-031-43898-1_20)
+
+![SMRD](figures/SMRD.png)
+It show cases how the conjugate gradient method can be used to enforece meausrement consistency in diffusion model based MRI reconstruction; it also shows
+how the SURE-based method can be used to perform early stopping, so less iteratios and artifacts are introduced during the generation of the reconstructured image.

generative/smrd/configs/demo/SMRD-brain_T2-noise005-R8.yaml

@@ -0,0 +1,133 @@
+user: csgm-mri-langevin
+model_type: ncsnv2
+seed: 42
+device: cuda
+batch_size: 1
+repeat: 1
+
+# The pre-trained NCSNV2 checkpoint
+gen_ckpt: checkpoints/mri-unet-smrd.pth
+
+## weights of different losses
+mse: 5.
+
+## start from different noise level of langevin
+start_iter: 1155
+
+# can be decreased for super-resolution
+image_size:
+  - 384
+  - 384
+
+## files
+input_dir: ./datasets/brain_T2/
+maps_dir: ./datasets/brain_T2_maps/
+anatomy: brain
+
+early_stop: stop
+
+## Acceleration
+R: 8
+pattern: equispaced
+exp_names: 0
+orientation: vertical
+
+## SMRD hyperparameters
+num_cg_iter: 5
+window_size: 160
+lambda_lr: 0.2
+init_lambda_update: 1154
+last_lambda_update: 1655
+
+## Lambda
+lambda_init: 2.0
+lambda_end: 2.0
+lambda_func: learnable
+
+exp_name: admm-learn-sure_brain_noise_005_R8
+learning_loss: SURE
+
+## Input noise
+noise_std: 0.005
+
+
+# logging
+save_latent: false
+save_images: true
+save_dataloader_every: 1000000
+save_iter: 100
+
+debug: false
+world_size: 1
+multiprocessing: false
+port: 12345
+
+langevin_config:
+    training:
+      batch_size: 4
+      n_epochs: 500000
+      n_iters: 320001
+      snapshot_freq: 10000
+      snapshot_sampling: true
+      anneal_power: 2
+      log_all_sigmas: false
+
+    sampling:
+      batch_size: 4
+      data_init: false
+      step_lr: 5e-5
+      n_steps_each: 4
+      ckpt_id: 5000
+      final_only: true
+      fid: false
+      denoise: true
+      num_samples4fid: 10000
+      inpainting: false
+      interpolation: false
+      n_interpolations: 8
+
+    fast_fid:
+      batch_size: 1000
+      num_samples: 1000
+      step_lr: 0.0000009
+      n_steps_each: 3
+      begin_ckpt: 100000
+      end_ckpt: 80000
+      verbose: false
+      ensemble: false
+
+    test:
+      begin_ckpt: 5000
+      end_ckpt: 80000
+      batch_size: 100
+
+    data:
+      dataset: "mri-mvue"
+      image_size: 384
+      channels: 2
+      logit_transform: false
+      uniform_dequantization: false
+      gaussian_dequantization: false
+      random_flip: false
+      rescaled: false
+      num_workers: 8
+
+    model:
+      sigma_begin: 232
+      num_classes: 2311
+      ema: true
+      ema_rate: 0.999
+      spec_norm: false
+      sigma_dist: geometric
+      sigma_end: 0.0066
+      normalization: InstanceNorm++
+      nonlinearity: elu
+      ngf: 128
+
+    optim:
+      weight_decay: 0.000
+      optimizer: "Adam"
+      lr: 0.0001
+      beta1: 0.9
+      amsgrad: false
+      eps: 0.001

generative/smrd/models/__init__.py

@@ -0,0 +1,10 @@
+# Copyright (c) MONAI Consortium
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#     http://www.apache.org/licenses/LICENSE-2.0
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.

generative/smrd/models/ema.py

@@ -0,0 +1,58 @@
+# Copyright (c) MONAI Consortium
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#     http://www.apache.org/licenses/LICENSE-2.0
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import torch.nn as nn
+
+
+class EMAHelper(object):
+    def __init__(self, mu=0.999):
+        self.mu = mu
+        self.shadow = {}
+
+    def register(self, module):
+        if isinstance(module, nn.DataParallel):
+            module = module.module
+        for name, param in module.named_parameters():
+            if param.requires_grad:
+                self.shadow[name] = param.data.clone()
+
+    def update(self, module):
+        if isinstance(module, nn.DataParallel):
+            module = module.module
+        for name, param in module.named_parameters():
+            if param.requires_grad:
+                self.shadow[name].data = (1.0 - self.mu) * param.data + self.mu * self.shadow[name].data
+
+    def ema(self, module):
+        if isinstance(module, nn.DataParallel):
+            module = module.module
+        for name, param in module.named_parameters():
+            if param.requires_grad:
+                param.data.copy_(self.shadow[name].data)
+
+    def ema_copy(self, module):
+        if isinstance(module, nn.DataParallel):
+            inner_module = module.module
+            module_copy = type(inner_module)(inner_module.config).to(inner_module.config.device)
+            module_copy.load_state_dict(inner_module.state_dict())
+            module_copy = nn.DataParallel(module_copy)
+        else:
+            module_copy = type(module)(module.config).to(module.config.device)
+            module_copy.load_state_dict(module.state_dict())
+        # module_copy = copy.deepcopy(module)
+        self.ema(module_copy)
+        return module_copy
+
+    def state_dict(self):
+        return self.shadow
+
+    def load_state_dict(self, state_dict):
+        self.shadow = state_dict