Source code for mridc.collections.reconstruction.models.zf

# coding=utf-8
__author__ = "Dimitrios Karkalousos"

from abc import ABC
from typing import Any, Dict, Tuple, Union

import numpy as np
import torch
from omegaconf import DictConfig, OmegaConf
from pytorch_lightning import Trainer

from mridc.collections.common.parts.fft import ifft2c
from mridc.collections.common.parts.utils import check_stacked_complex, coil_combination
from mridc.collections.reconstruction.models.base import BaseMRIReconstructionModel, BaseSensitivityModel
from mridc.collections.reconstruction.parts.utils import center_crop_to_smallest
from mridc.core.classes.common import typecheck

__all__ = ["ZF"]


[docs]class ZF(BaseMRIReconstructionModel, ABC): """ Zero-Filled reconstruction using either root-sum-of-squares (RSS) or SENSE (SENSitivity Encoding), as presented \ in Pruessmann KP, Weiger M, Scheidegger MB, Boesiger P. References ---------- .. Pruessmann KP, Weiger M, Scheidegger MB, Boesiger P. SENSE: Sensitivity encoding for fast MRI. Magn Reson \ Med 1999; 42:952-962. """ def __init__(self, cfg: DictConfig, trainer: Trainer = None): # init superclass super().__init__(cfg=cfg, trainer=trainer) zf_cfg_dict = OmegaConf.to_container(cfg, resolve=True) self.zf_method = zf_cfg_dict.get("zf_method") self.fft_type = zf_cfg_dict.get("fft_type") # Initialize the sensitivity network if use_sens_net is True self.use_sens_net = zf_cfg_dict.get("use_sens_net") if self.use_sens_net: self.sens_net = BaseSensitivityModel( zf_cfg_dict.get("sens_chans"), zf_cfg_dict.get("sens_pools"), fft_type=self.fft_type, mask_type=zf_cfg_dict.get("sens_mask_type"), normalize=zf_cfg_dict.get("sens_normalize"), )
[docs] @staticmethod def process_inputs(y, mask): """ Process the inputs to the method. Parameters ---------- y: Subsampled k-space data. list of torch.Tensor, shape [batch_size, n_coils, n_x, n_y, 2] mask: Sampling mask. list of torch.Tensor, shape [1, 1, n_x, n_y, 1] Returns ------- y: Subsampled k-space data. randomly selected y mask: Sampling mask. randomly selected mask r: Random index. """ if isinstance(y, list): r = np.random.randint(len(y)) y = y[r] mask = mask[r] else: r = 0 return y, mask, r
[docs] @typecheck() def forward( self, y: torch.Tensor, sensitivity_maps: torch.Tensor, mask: torch.Tensor, target: torch.Tensor = None, ) -> Union[list, Any]: """ Forward pass of the zero-filled method. Parameters ---------- y: Subsampled k-space data. torch.Tensor, shape [batch_size, n_coils, n_x, n_y, 2] sensitivity_maps: Coil sensitivity maps. torch.Tensor, shape [batch_size, n_coils, n_x, n_y, 2] mask: Sampling mask. torch.Tensor, shape [1, 1, n_x, n_y, 1] init_pred: Initial prediction. torch.Tensor, shape [batch_size, n_x, n_y, 2] target: Target data to compute the loss. torch.Tensor, shape [batch_size, n_x, n_y, 2] Returns ------- pred: torch.Tensor, shape [batch_size, n_x, n_y, 2] Predicted data. """ sensitivity_maps = self.sens_net(y, mask) if self.use_sens_net else sensitivity_maps pred = coil_combination( ifft2c(y, fft_type=self.fft_type), sensitivity_maps, method=self.zf_method.upper(), dim=1 ) pred = check_stacked_complex(pred) _, pred = center_crop_to_smallest(target, pred) return pred
[docs] def test_step(self, batch: Dict[float, torch.Tensor], batch_idx: int) -> Tuple[str, int, torch.Tensor]: """ Test step. Parameters ---------- batch: Batch of data. Dict of torch.Tensor, shape [batch_size, n_coils, n_x, n_y, 2] batch_idx: Batch index. int Returns ------- name: Name of the volume. str slice_num: Slice number. int pred: Predicted data. torch.Tensor, shape [batch_size, n_x, n_y, 2] """ y, sensitivity_maps, mask, init_pred, target, fname, slice_num, _ = batch y, mask, _ = self.process_inputs(y, mask) prediction = self.forward(y, sensitivity_maps, mask, target) slice_num = int(slice_num) name = str(fname[0]) # type: ignore key = f"{name}_images_idx_{slice_num}" # type: ignore output = torch.abs(prediction).detach().cpu() target = torch.abs(target).detach().cpu() output = output / output.max() # type: ignore target = target / target.max() # type: ignore error = torch.abs(target - output) self.log_image(f"{key}/target", target) self.log_image(f"{key}/reconstruction", output) self.log_image(f"{key}/error", error) return name, slice_num, prediction.detach().cpu().numpy()