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Excitation-Scanning Hyperspectral Imaging Microscopy to Efficiently Discriminate Fluorescence Signals

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Published on: August 22, 2019

Improved field-mapping and artifact correction in multispectral imaging.

Brady Quist^1,2, Xinwei Shi^1,2, Hans Weber¹

¹Department of Radiology, Stanford University, Stanford, California, USA.

Magnetic Resonance in Medicine

|March 7, 2017

Summary

This summary is machine-generated.

This study introduces improved methods for magnetic resonance imaging (MRI) near metal implants. The new techniques enhance B0 field-map estimation and deblurring, leading to clearer images with reduced artifacts and no extra scan time.

Keywords:

MRI of arthroplasty field-mapping image artifacts metal implants

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Identification of Metal Oxide Nanoparticles in Histological Samples by Enhanced Darkfield Microscopy and Hyperspectral Mapping

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Published on: December 8, 2015

Area of Science:

Medical Imaging
Biomedical Engineering
Magnetic Resonance Imaging

Background:

Metallic implants in patients undergoing MRI can cause significant image artifacts.
Accurate B0 field-map estimation is crucial for correcting these artifacts.
Existing methods for field-map estimation and deblurring near metal are limited by noise and distortion.

Purpose of the Study:

To develop an improved method for B0 field-map estimation, deblurring, and image combination for multispectral imaging (MSI) near metallic implants.
To reduce the impact of noise and artifacts in MRI scans acquired in the presence of metal.

Main Methods:

A goodness-of-fit field-map estimation technique using only MSI data was developed.
A novel deblurring technique and image combination scheme were proposed, utilizing the improved field map.
The proposed methods were compared to conventional techniques in phantom studies and in vivo scans of subjects with metallic implants (knee, hip, spine).

Main Results:

The goodness-of-fit field-map estimation demonstrated reduced sensitivity to noise and bias compared to the center-of-mass technique.
The new deblurring approach showed significantly less sensitivity to noise and distortion than current methods.
A favorable balance between deblurring and distortion was achieved.

Conclusions:

The developed methods enable field-mapping with enhanced noise robustness.
Deblurred images exhibit reduced distortion and improved signal-to-noise ratio.
These advancements facilitate better visualization of anatomy near metallic implants without increasing scan time.