3D grid distortion correction in MRI

Unlock Precision: How 3D Imaging Corrects MRI Distortion

Elliot Brynn in Tech & Innovation December 2025 • 4 min read.

"A breakthrough in MRI technology ensures geometrical accuracy for diagnosis and research, even in open-bore systems."

Magnetic Resonance Imaging (MRI) is a cornerstone of modern medicine, providing detailed views of the human body. However, MRI images can suffer from geometrical distortions due to magnetic field inhomogeneity and gradient non-linearity. These distortions can compromise the accuracy of diagnoses and research findings, particularly in open-bore MRI systems where the magnetic field may be less uniform.

Traditionally, these distortions are corrected using Spherical Harmonics Coefficients (SHC) provided by the MRI system vendor. These SHC sets describe the magnetic fields within a limited 'homogeneity sphere.' Outside this sphere, distortions increase, leading to signal loss and inaccurate imaging. This limitation has spurred the development of new methods to improve geometrical accuracy across the entire imaging volume.

This article explores a cutting-edge solution: an automated method for accurately measuring and correcting geometrical distortion in MRI scans using a 3D-lattice phantom. This innovative approach promises to enhance MRI image precision, providing more reliable data for clinical and research applications. We'll break down the method, its validation, and its potential impact on the future of MRI technology.

The 3D-Lattice Phantom: A New Approach to MRI Accuracy

The core of this new method lies in the use of a 3D-lattice phantom. This phantom is a structure with fiducial points (reference points) arranged in a 3D grid. The researchers chose this design for its ease of manufacture and its isotropic structure, meaning it has uniform properties in all directions. This is important because it allows for accurate assessment of distortions regardless of the orientation of the scan.

The phantom's connected structure allows for a unique assessment of the relationship between fiducial points, even when distortions are severe. This is a significant advantage over previous methods that used isolated spheres, which made it difficult to determine the adjacency relationship between points when distortions were present.

Automated Fiducial Point Extraction: The method involves automatically extracting the apparent positions of these fiducial points from 2D or 3D images of the phantom.
Adaptive Cross Prototypes: To overcome geometrical distortions, the researchers employ adaptive 3D cross prototypes. These prototypes are determined based on local image features, allowing for accurate identification of fiducial points even in distorted images.
Adjacency Relationship Determination: The algorithm determines the adjacency relationship between fiducial points, creating a map of how points are connected in the distorted image. This is crucial for accurate distortion correction.

To validate the method and compare different phantom parameters, the researchers developed in-house software to simulate MRI images. This allowed them to evaluate the accuracy of the method under various conditions, including different levels of noise and distortion. Simulated images also enable testing of different phantom geometries without the need for physical manufacturing.

The Future of Accurate MRI

This new automated method represents a significant step forward in MRI technology. By accurately measuring and correcting geometrical distortions, it promises to deliver more precise and reliable images for clinical diagnoses and research applications.

The use of a 3D-lattice phantom and adaptive cross prototypes allows for robust performance even in challenging imaging environments, such as open-bore MRI systems with significant magnetic field inhomogeneity. The method's ability to determine the adjacency relationship between fiducial points is a key advantage over previous approaches.

While further research is needed to address manufacturing issues and generalize the geometrical distortion model, this innovation holds great potential for improving the accuracy and reliability of MRI imaging in the future.

About this Article -

This article was crafted using a human-AI hybrid and collaborative approach. AI assisted our team with initial drafting, research insights, identifying key questions, and image generation. Our human editors guided topic selection, defined the angle, structured the content, ensured factual accuracy and relevance, refined the tone, and conducted thorough editing to deliver helpful, high-quality information.See our About page for more information.

This article is based on research published under:

DOI-LINK: 10.1016/j.mri.2018.10.011, Alternate LINK

Title: A Fully Automated Method For Accurate Measurement Of Geometrical Distortion In Magnetic Resonance Imaging Of A 3D-Lattice Phantom

Subject: Radiology, Nuclear Medicine and imaging

Journal: Magnetic Resonance Imaging

Publisher: Elsevier BV

Authors: S. Mangione, R. Acquaviva, G. Garbo

Published: 2019-04-01

Everything You Need To Know

What are the primary causes of geometrical distortions in MRI scans, and why are open-bore systems more susceptible?

Geometrical distortions in MRI scans are primarily caused by magnetic field inhomogeneity and gradient non-linearity. These imperfections in the magnetic field can warp the images, leading to inaccurate measurements and diagnoses. Open-bore MRI systems are particularly susceptible to these distortions due to their less uniform magnetic fields compared to closed-bore systems. This can compromise the reliability of both clinical and research applications relying on precise anatomical representation.

How does the new method of correcting MRI distortion differ from traditional methods using Spherical Harmonics Coefficients (SHC)?

Traditional methods use Spherical Harmonics Coefficients (SHC) provided by MRI system vendors to correct geometrical distortions. However, SHC sets are limited to a 'homogeneity sphere,' and distortions increase outside this area, causing signal loss and imaging inaccuracies. The new method addresses this limitation by providing a way to measure and correct distortions across the entire imaging volume, leading to more accurate and reliable MRI results, particularly beneficial for open-bore systems where field uniformity is a greater challenge.

How does the automated method using a 3D-lattice phantom work to measure and correct geometrical distortions in MRI scans?

The automated method utilizes a 3D-lattice phantom, a structure with fiducial points arranged in a 3D grid, to measure and correct geometrical distortions. The process involves automated fiducial point extraction using adaptive 3D cross prototypes to identify the reference points even in distorted images. Then, the algorithm determines the adjacency relationship between these fiducial points, mapping how they connect in the distorted image. This allows for precise correction of the geometrical inaccuracies, enhancing the overall precision of MRI imaging. The design of the phantom enables accurate assessment of distortions regardless of the scan's orientation.

What advantages does the 3D-lattice phantom offer compared to previous methods that used isolated spheres for MRI distortion correction?

The 3D-lattice phantom's design, with its interconnected fiducial points, offers a significant advantage over previous methods that used isolated spheres. The connected structure allows for a unique assessment of the relationship between fiducial points, even when distortions are severe. This makes it easier to determine the adjacency relationship between points, which is crucial for accurate distortion correction. The isotropic structure of the phantom, having uniform properties in all directions, ensures that distortions are accurately assessed regardless of the scan's orientation, leading to more reliable corrections.

What are the potential implications of the new automated method for improving the accuracy and reliability of MRI in clinical diagnoses and research applications, especially in open-bore systems?

The new automated method offers significant potential for improving the accuracy and reliability of MRI in both clinical diagnoses and research applications. By precisely correcting geometrical distortions, it ensures that anatomical representations are more accurate, leading to better diagnostic outcomes. Moreover, this advancement is particularly beneficial for open-bore MRI systems, where magnetic field uniformity is a challenge. The simulation software developed for validation also allows for testing different phantom geometries and conditions, optimizing the method for various MRI settings and contributing to ongoing advancements in MRI technology and its applications. Missing from the text is specific information about clinical trials, the specific types of diagnoses that would be most improved, or the expected cost of implementing this new technology.