arXiv subjects

Ivan Etoku Oiye

Publications and source records attributed to Ivan Etoku Oiye.

3 recordsLinked to original sources

arXiv2026

Design and Optimization of a Pole-less 0.2 T C-Type MRI Magnet

Low-field MRI is increasingly considered accessible for imaging owing to its lower cost, simpler infrastructure requirements, and potential for mobile and point-of-care deployment. A central challenge is achieving clinically useful field strength and homogeneity while keeping the magnet lightweight and maintaining patient accessibility. This work presents the design and magnetostatic simulation of a pole-less, 0.2 T, C-type bipolar magnet comprising two cylindrical N52 permanent magnets and four concentric rings that replace traditional pole pieces to enhance field homogeneity and reduce weight in bipolar magnet designs. Geometric parameters, including each magnet ring thickness, height, angular anchorage, spacing between magnets, and the magnets' vertical offset relative to the horizontal yokes, were manually investigated to improve magnetic field homogeneity in a 20 cm DSV. Simulations were performed in CST Studio Suite, yielding a peak field of 0.2 T, with a peak inhomogeneity of 1.43 mT across the 20 cm DSV and a total weight of 590 kg. A pole piece design with comparable dimensions, used as a benchmark for inhomogeneity and weight, was designed and simulated. It yielded a peak field of 0.15 T and a weight of 890 kg, with a 0.7 mT inhomogeneity over a 20 cm DSV. This study demonstrates the feasibility of replacing the traditional pole pieces with magnet rings to reduce weight while enhancing patient access with the C-magnet structure in yoked MRI systems.

physics.med-ph↗

arXiv2025

A Hands-On Workshop for Constructing a Low-Field MRI System in Three Days

Access to Magnetic Resonance Imaging system assembly knowledge can be expanded by leveraging open-source hardware and software, simplified installation requirements, and collaborative training initiatives. To this end, we conducted a three-day workshop to construct an operational 0.27T MRI scanner. The workshop hosted 16 participants, including faculty, postdoctoral fellows, trainers, and students, who collaborated to build the scanner using open-source hardware and software components. Teams were designated to focus on various subsystems, including the magnet, passive shimming, radiofrequency (RF) coils, gradient coils, data acquisition, and reconstruction. Pre-workshop preparation involved simulation-based design processes and fabrication techniques, which incorporated configuring MaRCoS and PyPulseq libraries, CNC machining, and 3D printing. During the workshop, participants assembled an H-shaped magnet, which achieved a peak magnetic field strength of 0.269T. Passive shimming effectively reduced the field inhomogeneity from 3mT to 2mT. A 3 cm diameter RF solenoid was built and tuned to 11.4 MHz. The gradients exhibited less than 5% non-linearity in simulations and were fabricated by CNC machining copper plates. The assembled system was used to acquire a 2D spin echo of a water phantom. Following the workshop, the system was further optimized to scan relaxometry phantoms. A post-workshop survey was carried out, revealing over 87% satisfaction. The constructed scanner represents a valuable platform for educational initiatives, pulse sequence development, and preclinical research imaging efforts.

physics.ins-det↗

arXiv2025

Multi-stage PDE-based image processing techniques for noisy MRI scans

Image denoising and image segmentation play essential roles in image processing. Partial differential equations (PDE)-based methods have proven to show reliable results when incorporated in both denoising and segmentation of images. In our work, we discuss a multi-stage PDE-based image processing approach. It relies upon the nonlinear diffusion for noise removal and clustering and region growing for segmentation. In the first stage of the approach, the raw image is computed from noisy measurement data. The second stage aims to filter out the noise using anisotropic diffusion. We couple these stages into one optimisation problem which allows us to incorporate a diffusion coefficient based on a presegmented image. The third stage performs the final segmentation of the image. We demonstrate our approach on both images for which the ground truth is known and on MR measurements made by an experimental, inexpensive scanner.

math.NA↗