US 2026/0065472 A1Published application

Contrast Agent-Free Virtual Contrast-Enhanced Magnetic Resonance Imaging Apparatus and Data Preprocessing Apparatus and Method thereof

AssigneeThe Hong Kong Polytechnic University

InventorsJing CAI, Wen LI, Yiming SHI

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Abstract

Apparatus and method for contrast agent-free virtual contrast-enhanced magnetic resonance imaging (VCE-MRI) based on a VCE-MRI model of federated learning (FL) are provided. The VCE-MRI model is trained using large-scale, highly heterogeneous multi-center data for data of nasopharyngeal carcinoma (NPC) patients, protecting patient data privacy while guaranteeing high generalization of the model. Apparatus and method for preprocessing VCE-MRI data are also provided. In the preprocessing of the data, a training dataset and/or a test dataset suitable for the FL model from patient data from different medical institutions to be used for model training and/or local verification of the FL model are obtained, respectively. The results of clinical evaluation show that the VCE-MRI model developed in the present application has high generalization and high clinical use value.

Claims16

Dependent claims

A data preprocessing apparatus for contrast agent-free virtual contrast-enhanced magnetic resonance imaging (VCE-MRI), including:

a receiving module for receiving and storing VCE-MRI patient-data files from different medical institutions, wherein the patient-data files contain different data sequences and different views;
a data conversion module for converting the VCE-MRI patient-data file into a three-dimensional MHA file that contains an image array and basic image information data, wherein patient-identifying information in the VCE-MRI patient-data file is removed in the conversion;
a file selection module for selecting a required three-dimensional MHA file from the three-dimensional MHA file, the required three-dimensional MHA file including a required data sequence, wherein the required data sequence includes non-fat-suppressed T1w MRI data, fat-suppressed T2w MRI data, and fat-suppressed contrast enhanced magnetic resonance imaging (CE-MRI) data;
a resampling module for resampling each data in the required data sequence to generate a resampled three-dimensional MHA file;
a registration module for applying image registration to the resampled three-dimensional MHA file to generate a registered three-dimensional MHA file;
a slicing module for conducting MHA to NPY conversion on the registered three-dimensional MHA file such that the registered three-dimensional MHA file is converted into two-dimensional NPY slices;
a slice selection module for excluding broken slices from the two-dimensional NPY slices to generate selected NPY slices that are free from the broken slices so as to ensure end-to-end mapping of MHA to NPY conversion;
a standardization module for applying standardization to process the selected NPY slices to generate a standardized image file;
and
a dataset module for dividing data in the standardized image file into a training dataset and a test dataset.

7 more elements

A data preprocessing method for contrast agent-free virtual contrast-enhanced magnetic resonance imaging (VCE-MRI), including:

receiving and storing VCE-MRI patient-data files from different medical institutions, wherein the patient-data files contain different data sequences and different views;
converting a VCE-MRI patient-data file into a three-dimensional MHA file that contains an image array and basic image information data, wherein patient-identifying information in the VCE-MRI patient-data file is removed in the conversion;
selecting a required three-dimensional MHA file from the three-dimensional MHA file, the required three-dimensional MHA file including a required data sequence, wherein the required data sequence includes a non-fat-suppressed T1w MRI data, a fat-suppressed T2w MRI data, and a fat-suppressed contrast enhanced magnetic resonance imaging (CE-MRI) data;
resampling each data in the required data sequence to generate a resampled three-dimensional MHA file;
applying image registration to the resampled three-dimensional MHA file to generate a registered three-dimensional MHA file;
conducting MHA to NPY conversion on the registered three-dimensional MHA files such that the registered three-dimensional MHA file is converted into two-dimensional NPY slices;
excluding broken slices in the two-dimensional NPY slices to generate selected NPY slices that are free from the broken slices so as to ensure end-to-end mapping of MHA to NPY conversion;
applying standardization to process the selected NPY slice to generate a standardized image file;
and
dividing data in the standardized image file into a training dataset and a test dataset.

7 more elements

Dependencies are inferred from the claim text.

Facts

Application: 19/319,837Filed 5 Sep 2025
Priority: CN 202411238642.55 Sep 2024
Published: 5 Mar 2026
Term ends: Not estimatedThe source records don’t support an estimate.

Classification11 codes

G06T 7/0012
Inspection of images, e.g. flaw detection: biomedical image inspection
G06N 3/045
Architecture, e.g. interconnection topology: combinations of networks
G06N 3/08
Neural networks: learning methods
G16H 30/20
ICT specially adapted for the handling or processing of medical images: for handling medical images, e.g. DICOM, HL7 or PACS

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