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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: Aug 24, 2025

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

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Toward Robust Partial-Image Based Template Matching Techniques for MRI-Guided Interventions.

Eung-Joo Lee¹, Setareh Farzinfard², Pavel Yarmolenko²

¹CAMCA, Dept of Radiology, MGH and Harvard Medical School, Boston, MA, USA. eungjoo.y.lee@gmail.com.

Journal of Digital Imaging

|October 22, 2022

Summary

This summary is machine-generated.

This study introduces an MRI-safe needle guidance toolkit for precise needle placement in medical procedures. The system accurately matches patient skin grids with MRI images, improving interventional radiology accuracy.

Keywords:

Arthrography Image registration MRI-guided interventions Needle-based procedures Template matching

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Area of Science:

Medical Imaging
Interventional Radiology
Biomedical Engineering

Background:

Accurate needle placement is critical for MRI-guided interventions.
Existing methods may lack precision and ease of use.
Needle-based procedures require reliable guidance systems.

Purpose of the Study:

To develop and evaluate an MRI-safe needle guidance toolkit.
To enable accurate needle angulation and entry point positioning.
To enhance MRI-guided interventions like arthrography and biopsies.

Main Methods:

Development of a flexible, patterned silicone 2D grid.
Automatic matching of the physical grid to MRI planning images using phase-only correlation.
Utilizing a 2-degree-of-freedom hand-held needle guide.

Main Results:

The toolkit provides intuitive and accurate needle angulation and entry point positioning.
The image matching algorithm demonstrated robustness against rotation, displacement, and Rician noise.
Average entry point location estimation accuracy was 0.12 ±0.2 mm.

Conclusions:

The developed MRI-safe needle guidance toolkit shows significant promise for improving accuracy in MRI-guided interventions.
The automatic template matching algorithm is effective and accurate for needle entry point localization.
Further studies are warranted to assess end-to-end accuracy in a clinical setting.