Add non-static and kt sampling (#280)

* kt sampling mask functions: `KtGaussian1DMaskFunc`, `KtRadialMaskFunc`, `KtUniformMaskFunc`,
* Non-static sampling (dynamic/multislice) dicitated by the `MaskFuncMode`, which can be STATIC, MULTISLICE, DYNAMIC
* Corresponding tests

Author: georgeyiasemis

direct/common/subsample.py

@@ -1,20 +1,23 @@
-# coding=utf-8
 # Copyright (c) DIRECT Contributors
+
+from __future__ import annotations
+
 from dataclasses import dataclass
-from typing import Optional, Tuple
+from typing import Optional
 
 from omegaconf import MISSING
 
 from direct.config.defaults import BaseConfig
+from direct.types import MaskFuncMode
 
 
 @dataclass
 class MaskingConfig(BaseConfig):
     name: str = MISSING
-    accelerations: Tuple[int, ...] = (5,)  # Ideally Union[float, int].
-    center_fractions: Optional[Tuple[float, ...]] = (0.1,)  # Ideally Optional[Tuple[float, ...]]
+    accelerations: tuple[float, ...] = (5.0,)
+    center_fractions: Optional[tuple[float, ...]] = (0.1,)
     uniform_range: bool = False
-    image_center_crop: bool = False
+    mode: MaskFuncMode = MaskFuncMode.STATIC
 
-    val_accelerations: Tuple[int, ...] = (5, 10)
-    val_center_fractions: Optional[Tuple[float, ...]] = (0.1, 0.05)
+    val_accelerations: tuple[float, ...] = (5.0, 10.0)
+    val_center_fractions: Optional[tuple[float, ...]] = (0.1, 0.05)

direct/common/subsample_config.py

@@ -317,7 +317,7 @@ def __call__(self, sample: dict[str, Any]) -> dict[str, Any]:
             Sample with `sampling_mask` key.
         """
         if not self.shape:
-            shape = sample["kspace"].shape[-3:]
+            shape = sample["kspace"].shape[1:]
         elif any(_ is None for _ in self.shape):  # Allow None as values.
             kspace_shape = list(sample["kspace"].shape[1:-1])
             shape = tuple(_ if _ else kspace_shape[idx] for idx, _ in enumerate(self.shape)) + (2,)
@@ -328,9 +328,6 @@ def __call__(self, sample: dict[str, Any]) -> dict[str, Any]:
 
         sampling_mask = self.mask_func(shape=shape, seed=seed, return_acs=False)
 
-        if sample["kspace"].ndim == 5:
-            sampling_mask = sampling_mask.unsqueeze(0)
-
         if "padding" in sample:
             sampling_mask = T.apply_padding(sampling_mask, sample["padding"])
 

direct/data/mri_transforms.py

@@ -8,6 +8,7 @@
 from enum import Enum
 from typing import NewType, Union
 
+import numpy as np
 import torch
 from omegaconf.omegaconf import DictConfig
 from torch import nn as nn
@@ -19,6 +20,7 @@
 FileOrUrl = NewType("FileOrUrl", PathOrString)
 HasStateDict = Union[nn.Module, torch.optim.Optimizer, torch.optim.lr_scheduler._LRScheduler, GradScaler]
 TensorOrNone = Union[None, torch.Tensor]
+TensorOrNdarray = Union[torch.Tensor, np.ndarray]
 
 
 class DirectEnum(str, Enum):
@@ -59,6 +61,12 @@ class TransformKey(DirectEnum):
     SCALING_FACTOR = "scaling_factor"
 
 
+class MaskFuncMode(DirectEnum):
+    STATIC = "static"
+    DYNAMIC = "dynamic"
+    MULTISLICE = "multislice"
+
+
 class IntegerListOrTupleStringMeta(type):
     """Metaclass for the :class:`IntegerListOrTupleString` class.
 

direct/types.py

@@ -25,12 +25,13 @@
 )
 from direct.launch import launch
 from direct.train import setup_train
+from direct.types import MaskFuncMode
 
 
 def create_test_transform_cfg(transforms_type):
     transforms_config = TransformsConfig(
         normalization=NormalizationTransformConfig(scaling_key="masked_kspace"),
-        masking=MaskingConfig(name="FastMRIRandom"),
+        masking=MaskingConfig(name="FastMRIRandom", mode=MaskFuncMode.STATIC),
         cropping=CropTransformConfig(crop="(32, 32)"),
         sensitivity_map_estimation=SensitivityMapEstimationTransformConfig(estimate_sensitivity_maps=True),
         transforms_type=transforms_type,