Enhanced Method for Providing Image Data of a Hollow Organ

Publication ID: 24-11857145_0006_PTD

Published: November 07, 2025

Category:Direct Improvements & Enhancements

Legal Citation

pr1or.art Inc., “Enhanced Method for Providing Image Data of a Hollow Organ,” Published Technical Disclosure No. 24-11857145_0006_PTD, Published November 07, 2025, available at https://archive.pr1or.art/24-11857145_0006_PTD

This technical disclosure describes improvements that would be readily apparent to a Person Having Ordinary Skill In The Art (PHOSITA) when considered in combination with the foundational architecture disclosed in U.S. Patent No. 11,857,145.

Summary of the Inventive Concept

The inventive concept provides an improved method for generating image data of a hollow organ by utilizing a third contrast agent, machine learning algorithms, or deep learning algorithms to enhance the accuracy and efficiency of the image data.

Background and Problem Solved

The original patent discloses a method for providing image data of a hollow organ using two contrast agents. However, the method has limitations in terms of accuracy and efficiency. The new inventive concept addresses these limitations by introducing a third contrast agent, machine learning algorithms, or deep learning algorithms to improve the quality of the image data.

Detailed Description of the Inventive Concept

The new inventive concept involves applying a third contrast agent having a third absorption spectrum different from the first and second absorption spectra to a lumen of the hollow organ. This allows for more accurate material separation and improved image data quality. Additionally, the inventive concept incorporates machine learning algorithms or deep learning algorithms to enhance the accuracy and efficiency of the image data generation process. The system for providing image data of a hollow organ includes a computed tomography scanner, a processor, and a display. The processor is configured to generate a representation of the wall of the hollow organ based on the obtained spectrally resolved computed tomography data and the third absorption spectrum.

Novelty and Inventive Step

The new inventive concept introduces a third contrast agent, machine learning algorithms, or deep learning algorithms, which are not present in the original patent. These features provide an improved method for generating image data of a hollow organ, overcoming the limitations of the original patent.

Alternative Embodiments and Variations

Alternative embodiments of the inventive concept may include using different types of contrast agents, varying the absorption spectra, or incorporating additional machine learning or deep learning algorithms. Variations of the system may include using different types of computed tomography scanners or displays.

Potential Commercial Applications and Market

The inventive concept has potential commercial applications in the medical imaging industry, particularly in the field of computed tomography. The improved method for generating image data of a hollow organ can be used in hospitals, clinics, and research institutions, providing more accurate and efficient diagnostic tools.

CPC Classifications

Section	Class	Group
A	A61	A61B6/481
A	A61	A61B6/032
A	A61	A61B6/4241
A	A61	A61B6/469
A	A61	A61B6/50
A	A61	A61B6/5205
A	A61	A61M5/007

Section 103 Obviousness Analysis (PHOSITA)

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Field of Art

Medical Imaging and Diagnostic Radiology, specifically computed tomography (CT) imaging techniques for hollow organ visualization, requiring expertise in medical imaging technology, contrast agent chemistry, and spectral imaging analysis

Person of Ordinary Skill (PHOSITA) Profile

A medical imaging specialist with advanced degree (PhD/MD) in radiology or biomedical engineering, experienced in CT scanning technologies, contrast agent applications, spectral imaging techniques, and familiar with machine learning approaches to medical image processing

Obviousness Rationale

A PHOSITA would recognize that extending the source patent's spectrally resolved CT imaging method by introducing a third contrast agent and incorporating machine learning algorithms represents a predictable technological enhancement within the existing imaging paradigm. The fundamental imaging methodology remains consistent, with the proposed variations representing incremental improvements in contrast agent differentiation and image processing techniques. These modifications align with standard technological progression in medical imaging research.

Obvious Combinations & Variations

Source Patent Element

Spectrally resolved computed tomography data using first and second contrast agents with different absorption spectra

PTD Variation

Adding a third contrast agent with a distinct absorption spectrum to enhance material separation and image resolution

Obviousness Reasoning

Known technique of using multiple contrast agents to improve diagnostic imaging, representing a predictable optimization of existing spectral imaging approaches

Source Patent Element

Material separation algorithm for analyzing CT image data

PTD Variation

Incorporating machine learning and deep learning algorithms to enhance image processing and wall representation generation

Obviousness Reasoning

Applying machine learning to medical image processing is a well-established approach, representing an obvious extension of existing computational techniques in radiology

Source Patent Element

Computed tomography scanning method for hollow organ imaging

PTD Variation

Expanding system configuration to include specialized injectors and enhanced processing capabilities

Obviousness Reasoning

Modular system design improvements represent standard engineering practice for incremental technological enhancement, involving predictable combinations of known elements

Source Patent Element

K-edge imaging algorithm for material differentiation

PTD Variation

Implementing more sophisticated spectral analysis techniques using additional contrast agents and advanced computational methods

Obviousness Reasoning

Refinement of existing spectral imaging algorithms through expanded contrast agent utilization and computational techniques represents an obvious technological progression

Source Patent Element

Region of interest determination in CT imaging

PTD Variation

Enhanced classification using multiple contrast agents and machine learning-based diagnostic approaches

Obviousness Reasoning

Integrating machine learning for diagnostic classification is a natural evolutionary step in medical imaging technology, representing a predictable application of computational intelligence

35 U.S.C. § 103 Summary: Pursuant to 35 U.S.C. § 103, the variations disclosed in this published technical disclosure (PTD) would have been obvious to a person having ordinary skill in the art at the time of invention, as they represent predictable extensions of the technological teachings in US Patent 11857145, involving standard medical imaging techniques, known contrast agent methodologies, and established machine learning approaches to image processing.

Original Patent Information

Patent Number	US 11,857,145
Title	Method for providing image data of a hollow organ
Assignee(s)	Siemens Healthcare Limited