Advanced Guided Diffuse Optical Tomography for Next-Generation Cancer Diagnosis and Treatment

Publication ID: 24-11857289_0005_PTD

Published: November 07, 2025

Category:Future Evolutions & Paradigm Shifts

Legal Citation

pr1or.art Inc., “Advanced Guided Diffuse Optical Tomography for Next-Generation Cancer Diagnosis and Treatment,” Published Technical Disclosure No. 24-11857289_0005_PTD, Published November 07, 2025, available at https://archive.pr1or.art/24-11857289_0005_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. 11,857,289.

Summary of the Inventive Concept

An innovative, high-resolution, real-time guided diffuse optical tomography (DOT) system that integrates machine learning and multimodal imaging for personalized cancer diagnosis and treatment planning, revolutionizing the field of cancer care.

Background and Problem Solved

The original patent, 'Systems and methods of optimizing functional images of a lesion region using guided diffuse optical tomography,' provided a crucial step in cancer diagnosis using DOT. However, the limitations of the original patent, such as motion artifacts and lack of personalization, hinder its widespread adoption. The new inventive concept addresses these limitations by introducing machine learning-based motion correction, personalized treatment planning, and multimodal imaging integration, enabling more accurate and effective cancer diagnosis and treatment.

Detailed Description of the Inventive Concept

The advanced guided DOT system comprises a DOT device, a machine learning module, and a registration module. The DOT device acquires high-resolution, real-time functional data from the lesion region, while the machine learning module predicts and corrects for motion artifacts, ensuring accurate data analysis. The registration module integrates the functional data with other imaging modalities, providing a comprehensive understanding of the lesion region. The system enables personalized treatment planning by analyzing the functional images to determine the optimal treatment strategy for each patient. Additionally, the system can monitor treatment efficacy over time, providing valuable insights for healthcare providers.

Novelty and Inventive Step

The new inventive concept introduces several novel aspects, including the integration of machine learning for motion correction, personalized treatment planning, and multimodal imaging integration. These advancements overcome the limitations of the original patent, providing a more accurate, effective, and personalized approach to cancer diagnosis and treatment.

Alternative Embodiments and Variations

Alternative embodiments of the inventive concept could include the use of different machine learning algorithms, various imaging modalities, or different registration techniques. Additionally, the system could be adapted for use in other medical applications, such as cardiovascular disease diagnosis or neurological disorder treatment.

Potential Commercial Applications and Market

The advanced guided DOT system has significant commercial potential in the cancer diagnosis and treatment market, estimated to be worth billions of dollars. The system's ability to provide personalized treatment planning, accurate diagnosis, and effective treatment monitoring makes it an attractive solution for healthcare providers, medical research institutions, and pharmaceutical companies.

Section 103 Obviousness Analysis (PHOSITA)

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Field of Art

Medical imaging, specifically diffuse optical tomography (DOT) for cancer diagnostics, requiring expertise in optical imaging, signal processing, machine learning, and medical image analysis

Person of Ordinary Skill (PHOSITA) Profile

A biomedical engineer or medical imaging specialist with advanced degrees in bioengineering, electrical engineering, or medical physics, possessing knowledge of medical imaging techniques, signal processing algorithms, and computational image reconstruction methods

Obviousness Rationale

A person of ordinary skill would recognize that integrating machine learning for motion correction and multimodal image registration represents a predictable extension of existing DOT imaging techniques. The source patent's foundational DOT methodology provides a clear technical framework that naturally suggests advanced computational enhancements. The PTD's proposed variations represent incremental improvements using well-established techniques in medical imaging and machine learning.

Obvious Combinations & Variations

Source Patent Element

DOT device for acquiring lesion functional data and reference functional data from different tissue regions

PTD Variation

Adding machine learning module to predict and correct motion artifacts in acquired functional data

Obviousness Reasoning

Motion artifact correction is a known challenge in medical imaging, and applying machine learning techniques to address this issue represents a predictable solution using standard signal processing approaches

Source Patent Element

Functional image generation using voxel-based reconstruction techniques

PTD Variation

Integrating multimodal imaging registration to provide comprehensive lesion region analysis

Obviousness Reasoning

Cross-modality image registration is a standard technique in medical imaging, and combining different imaging data sources to enhance diagnostic capabilities is an obvious optimization strategy

Source Patent Element

Pseudoinverse matrix techniques for image reconstruction

PTD Variation

Implementing personalized treatment planning based on functional image analysis

Obviousness Reasoning

Extracting diagnostic insights from medical imaging data is a standard clinical practice, and using computational methods to derive treatment recommendations represents a logical extension of existing image analysis techniques

Source Patent Element

Functional imaging of lesion regions using DOT

PTD Variation

Real-time monitoring of treatment efficacy through sequential functional image generation

Obviousness Reasoning

Longitudinal tracking of medical conditions is a well-established clinical approach, and applying this methodology to DOT imaging represents a predictable and obvious enhancement

35 U.S.C. § 103 Summary: Based on the teachings of US Patent 11857289 and the published technical disclosure, a person of ordinary skill in medical imaging would find the proposed variations involving machine learning-enhanced DOT imaging, multimodal registration, and personalized treatment planning to be obvious extensions of existing medical imaging techniques. The incremental computational and methodological improvements represent predictable advancements that would be readily conceived by a skilled practitioner in the field of medical imaging and diagnostic technologies.

Original Patent Information

Patent Number	US 11,857,289
Title	Systems and methods of optimizing functional images of a lesion region using guided diffuse optical tomography
Assignee(s)	Washington University