ldctbench.hub.utils

`denoise_dicom(dicom_path, savedir, method, device=None, disable_progress=False)`

Denoise a single DICOM or multiple DICOMs in a folder with a model from the model hub

Parameters:

dicom_path (str) –

Path to a single DICOM file or a folder containig one or more DICOM files
savedir (str) –

Foldername where to store denoised DICOM file(s) in
method (Union[Literal[RESNET, CNN10, DUGAN, QAE, REDCNN, TRANSCT, WGANVGG, BILATERAL], str]) –

Enum item or string, specifying model to load. See ldctbench.hub.methods.Methods for more info.
device (Optional[device], default: None ) –

torch.device, optional
disable_progress (bool, default: False ) –

Disable progress bar (if denoising multiple DICOM files), by default False

Raises:

ValueError –

If provided path is neither a DICOM file nor a folder containing at least one DICOM file

Examples:

A comprehensive example using this function is provided in [Getting Started][denoise-dicom-using-pretrained-models].

Denoise a numpy array with a model from the model hub

Parameters:

x (ndarray) –

Input numpy array. Must be either 2d (single slice) or 3d (with slices-first) order
method (Union[Literal[RESNET, CNN10, DUGAN, QAE, REDCNN, TRANSCT, WGANVGG, BILATERAL], str, Module]) –

Enum item or string, specifying model to load. See ldctbench.hub.methods.Methods for more info. You can also pass a nn.Module directly.
device (Optional[device], default: None ) –

torch.device, optional
num_slices_parallel (int, default: 6 ) –

Number of slices to process in parallel, by default 6
is_normalized (bool, default: False ) –

Whether data in array is already normalized for the model, by default False

Returns:

Raises: