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Magnetic Resonance Imaging

Magnetic Resonance Imaging

Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...

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Related Experiment Video

Updated: Jun 19, 2026

Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function

Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized ¹²⁹Xe MRI to Evaluate Lung Structure-Function

Published on: April 12, 2024

Uncertainty-Aware, End-to-End Deep Learning for Functional Lung MRI Quantification Using ¹²⁹Xe and ¹H MRI.

Joshua R Astley^1,2, Helen Marshall^1,2, Laurie J Smith¹

¹POLARIS, School of Medicine and Population Health, The University of Sheffield, 18 Claremont Crescent, S10 2TA, Sheffield, United Kingdom.

Radiology. Cardiothoracic Imaging

|June 18, 2026

Summary

This summary is machine-generated.

This study introduces an automated deep learning method for predicting lung ventilation defects using specialized MRI scans. The approach accurately estimates ventilation defect percentage (VDP) without manual input, offering reliable clinical insights.

Keywords:

Functional Imaging Lung MRI Multi-Modal Uncertainty-Aware

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Last Updated: Jun 19, 2026

Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function

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Published on: April 12, 2024

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Published on: November 21, 2023

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

Published on: June 21, 2024

Area of Science:

Radiology and Medical Imaging
Artificial Intelligence in Medicine
Pulmonary Medicine

Background:

Accurate assessment of lung ventilation is crucial for diagnosing and managing pulmonary diseases.
Current methods for quantifying ventilation defects, such as segmentation-derived VDP, often require manual intervention, which can be time-consuming and prone to variability.
Advanced imaging techniques like hyperpolarized ¹²⁹Xe MRI offer detailed functional lung information but require sophisticated analysis pipelines.

Purpose of the Study:

To develop and validate an end-to-end deep learning pipeline for the automatic prediction of ventilation defect percentage (VDP).
To integrate coregistered functional ¹²⁹Xe MRI and structural ¹H MRI data without manual segmentation.
To incorporate uncertainty quantification into the deep learning framework to assess prediction confidence.

Main Methods:

A retrospective study utilizing 574 paired ¹²⁹Xe MRI and ¹H MRI scans from healthy individuals and patients with lung diseases.
An uncertainty-aware convolutional neural network framework employing Monte Carlo dropout for uncertainty estimation.
Test-time augmentation was used to evaluate model robustness and simulate test-retest repeatability.

Main Results:

The deep learning pipeline achieved a median absolute error of 1.01% for VDP prediction, comparable to manual segmentation methods (P = .70).
The model demonstrated strong clinical classification accuracy of 91% (95% CI: 68, 94).
The uncertainty quantification provided confidence groupings for VDP predictions.

Conclusions:

An automated, uncertainty-aware deep learning approach can accurately predict VDP from multi-modal MRI without manual segmentation.
The developed framework offers performance comparable to traditional segmentation-based methods.
This approach enhances the efficiency and reliability of functional lung imaging analysis.