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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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Updated: May 16, 2025

Magnetic Resonance Imaging Assessment of Carcinogen-induced Murine Bladder Tumors

Magnetic Resonance Imaging Assessment of Carcinogen-induced Murine Bladder Tumors

Published on: March 29, 2019

Optimizing bladder magnetic resonance imaging: accelerating scan time and improving image quality through deep

Erjia Guo¹, Li Chen¹, Lili Xu^2,3

¹Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China.

Abdominal Radiology (New York)

|April 1, 2025

Summary

This summary is machine-generated.

Deep learning (DL) reconstruction for bladder MRI significantly reduces acquisition time and improves image quality compared to standard T2-weighted turbo-spin-echo (TSE) imaging. This advanced technique offers similar diagnostic confidence for bladder cancer evaluation.

Keywords:

Bladder cancer Deep learning Magnetic resonance imaging

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Last Updated: May 16, 2025

Magnetic Resonance Imaging Assessment of Carcinogen-induced Murine Bladder Tumors

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Published on: November 30, 2022

Area of Science:

Radiology
Medical Imaging
Artificial Intelligence in Medicine

Background:

Standard T2-weighted turbo-spin-echo (TSE) imaging (T2_S) is crucial for bladder cancer evaluation.
Longer acquisition times and image artifacts can limit the diagnostic utility of T2_S.

Purpose of the Study:

To evaluate the value of deep learning (DL) reconstructed T2-weighted imaging (T2_DL) for bladder MRI.
To compare T2_DL with standard T2_S regarding acquisition time, image quality, and diagnostic confidence.

Main Methods:

Prospective enrollment of 28 patients with suspected bladder cancer.
Acquisition of T2_S and T2_DL sequences in three planes.
Independent radiologist evaluation of image quality, artifacts, noise, and diagnostic confidence using a Likert scale.
T2 scoring based on the Vesical Imaging-Reporting and Data System (VI-RADS).

Main Results:

T2_DL acquisition time was reduced by 49.4% (axial) and 43.8% (coronal/sagittal) compared to T2_S.
T2_DL demonstrated superior image quality with reduced artifacts and noise (p < 0.05).
Diagnostic confidence and VI-RADS scoring were similar between T2_DL and T2_S (p > 0.05).

Conclusions:

Deep learning reconstruction is feasible for clinical bladder MRI.
T2_DL offers reduced acquisition time and enhanced image quality.
T2_DL provides comparable diagnostic confidence to T2_S for bladder cancer assessment.