Model Details#

Model Architecture#

Architecture Type: Convolutional Neural Network (CNN) Network Architecture: 3D UNet + attention blocks

Input#

num_output_samples#

  • Input Type: Integer

  • Input Format: Single integer value

  • Input Parameters: Required input indicates the number of synthetic images the model will generate.

body_region#

  • Input Type: List

  • Input Format: Array of Strings

  • Input Parameters: Required input indicates the region of body the generated CT will focus on

  • Options: [“head”, “chest”, “thorax”, “abdomen”, “pelvis”, “lower”]

anatomy_list#

  • Input Type: List

  • Input Format: Array of Strings

  • Input Parameters: Optional list of 127 anatomical classes (listed in the Additional Information section)

output_size#

  • Input Type: List

  • Input Format: Array of 3 Integers

  • Input Parameters: Optional list of 3 numbers that indicate the x, y, and z size of the CT image.

    • x- and y-axes: 128, 256, 384, 512

    • z-axis: 128, 256, 384, 512, 640, 768

spacing#

  • Input Type: List

  • Input Format: Array of 3 Floats

  • Input Parameters: Optional list of 3 floats that indicate the spacing of the CT image

  • Each element must be in the range: 0.5 to 5.0

controllable_anatomy_size#

  • Input Type: List

  • Input Format: Array of Tuples (String, Float)

  • Input Parameters: Optional list of tuples for up to 10 different anatomies. Each tuple consists of an (organ_name, size_value) pair.

    • organ_name options: [“liver”, “gallbladder”, “stomach”, “pancreas”, “colon”, “lung tumor”, “bone lesion”, “hepatic tumor”, “colon cancer primaries”, “pancreatic tumor”]

    • size_value range: 0.0 to 1.0, or -1 (means not exist/delete this organ)

Output#

  • Output Type(s): Image(s)

  • Output Format: (Neuroimaging Informatics Technology Initiative) NIfTI, (Digital Imaging and Communications in - Medicine) DICOM, and (Nearly Raw Raster Data) Nrrd

  • Output Parameters: Three-Dimensional (3D)

  • Output Description: Synthetic CT image with dimensions up to 512x512x768 and spacing between 0.5mm and 5.0mm, reflecting controllable anatomy sizes as specified. If requested in input parameters, an additional NIfTI file containing the corresponding label map for the anatomy_list is also provided.

Software Integration#

Runtime Engine(s): MONAI Core v.1.4

Supported Hardware Microarchitecture Compatibility:

  • NVIDIA Ampere

  • NVIDIA Hopper

[Preferred/Supported] Operating System(s):

  • Linux

Inference#

Engine: Triton
Test Hardware: A100, H100 (with at least 80GB memory for 512x512x512 images)

Supported Anatomy#

You can find the complete list of supported anatomies for segmentation in the MAISI label_dict.json.

Ethical Considerations#

NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse. Please report security vulnerabilities or NVIDIA AI Concerns here.

License#

The NIM container and model are governed by the NVIDIA software and model evaluation license agreement.

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