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Computed Tomography

Computed Tomography

Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...

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Related Experiment Video

Updated: May 21, 2025

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging

Published on: December 15, 2014

AI-assisted diffuse correlation tomography for identifying breast cancer.

Ruizhi Zhang¹, Jianju Lu², Wenqi Di¹

¹North University of China, State Key Laboratory of Dynamic Measurement Technology, Taiyuan, China.

Journal of Biomedical Optics

|May 19, 2025

Summary

Artificial intelligence (AI)-assisted diffuse correlation tomography (DCT) accurately distinguishes between benign and malignant breast lesions at 97% accuracy. This technique shows promise for early breast cancer diagnosis and treatment monitoring by assessing microvascular blood flow.

Keywords:

artificial intelligence breast cancer clinical imaging diagnosis microvascular blood flow

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Last Updated: May 21, 2025

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging

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Computed Tomography-guided Time-domain Diffuse Fluorescence Tomography in Small Animals for Localization of Cancer Biomarkers

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Published on: August 30, 2013

Area of Science:

Biomedical Engineering
Medical Imaging
Oncology

Background:

Diffuse correlation tomography (DCT) is an emerging noninvasive technique for measuring breast microvascular blood flow.
Validation of DCT for differentiating benign and malignant breast lesions is limited due to instrumentation and algorithmic challenges.

Purpose of the Study:

To develop and validate an AI-assisted DCT system for classifying breast lesions.
To assess the performance of AI models in analyzing DCT-derived blood flow data for lesion characterization.

Main Methods:

An AI-assisted DCT system was developed using a novel source-detector array and image reconstruction algorithm.
DCT images were acquired from 61 female patients.
AI models were trained using blood flow images as feature parameters or global inputs for lesion classification.

Main Results:

DCT measurements demonstrated stability in healthy subjects through longitudinal monitoring.
AI-assisted DCT achieved 97% accuracy in distinguishing between benign and malignant breast lesions.
The system effectively identified functional abnormalities linked to metabolic demands in breast diseases.

Conclusions:

AI-assisted DCT shows significant potential for the early diagnosis of breast cancer.
This technique can aid in timely therapeutic assessment by detecting functional changes before significant tumor or vascular network development.
The study highlights the capability of AI-assisted DCT in characterizing breast lesions noninvasively.