An official website of the United States government
PurposeAn MRI scanner is equipped with global shim systems for shimming one region of interest (ROI) only. However, it often fails to reach state‐of‐the‐art when shimming two isolated regions of interest simultaneously, even though the two‐area shimming can be essential in scan scenarios, such as bilateral breasts or dyadic brains. To address these challenges, a hybrid active and passive local shimming technique is proposed to simultaneously shim two isolated region‐of‐interest areas within the whole FOV. MethodsA local passive shimming system is constructed by optimized bilateral ferromagnetic chip arrays to compensate for the magnet's significant high‐order B0inhomogeneities at the boundary of the manufacturer's specified homogeneous volume, thus locally improving the available FOV. The local active shimming consists of 40‐channel DC loops powered by 64‐channel current amplifiers. With the optimized current distribution, active shimming can correct the residual low‐order B0inhomogeneities and subject‐specific field inhomogeneities. In addition, active shimming is used to homogenize the center frequencies of the two regions. ResultsWith the implementation of the hybrid active and passive local shimming, the 95% peak‐to‐peak was reduced from 1.92 to 1.12 ppm by 41.7%, and RMS decreased from 0.473 to 0.255 ppm by 46.1% in a two‐phantom experiment. The volume ratio containing MR voxels within a 0.5‐ppm frequency span increased from 64.3% to 81.3% by 26.3%. ConclusionThe proposed hybrid active and passive local shimming technique uses both passive and active local shimming, and it can efficiently shim two areas simultaneously, which is an unmet need for a commercial MRI scanner.
more »
« less
This content will become publicly available on March 24, 2026
MRI4ALL : A Week‐Long Hackathon for the Development of an Open‐Source Ultra‐Low‐Field MRI System
ABSTRACT The goal of the MRI4ALL hackathon, which took place in October 2023, was to develop a functional low‐field MRI scanner in just one week and to release all created source code and resources as open‐source packages. The event was attended by 52 participants from 16 institutions who assembled the scanner on the last day of the hackathon. The scanner's magnetic B0field with a strength of 43 mT and a target field‐of‐view size of 11 cm3was created with a Halbach array made from 990 N40UH permanent magnets, held in place using 3D printed ring formers. Gradient coils were fabricated by gluing enameled copper wire onto 3D printed holders with imprinted wire patterns. A solenoid coil for RF transmission and reception was built by winding 20 turns of Litz wire around a 3D printed cylinder. A Red Pitaya FPGA prototyping board running the MaRCoS framework was used to control the scanner components, and a GPA‐FHDO amplifier board was used to drive the gradients. To simplify the scanner's operation, console software with an intuitive graphical user interface was developed in Python using the PyPulseq package for sequence calculations. Furthermore, the scanner was equipped with a cooling system, as well as options for passive and active shimming. After resolving several technical issues that arose during the assembly, the scanner is now able to acquire MR images with different sequences. While not suitable for real‐world clinical applications, it can be utilized for educational purposes or as a low‐cost prototyping platform. Moreover, it may serve as a reference design for future MRI development projects. All source code and resources are available on the project websitemri4all.org, allowing other groups to replicate the scanner. Evidence Leveln/a Technical EfficacyStage 1.
more »
« less
- Award ID(s):
- 2313156
- PAR ID:
- 10625371
- Publisher / Repository:
- Wiley
- Date Published:
- Journal Name:
- Journal of Magnetic Resonance Imaging
- ISSN:
- 1053-1807
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Abstract PurposeTo determine the feasibility of simultaneous multi‐slice (SMS) real‐time MRI (RT‐MRI) at 0.55T for the evaluation of cardiac function. MethodsCardiac CINE MRI is routinely used to evaluate left‐ventricular (LV) function. The standard is sequential multi‐slice balanced SSFP (bSSFP) over a stack of short‐axis slices using electrocardiogram (ECG) gating and breath‐holds. SMS has been used in CINE imaging to reduce the number of breath‐holds by a factor of 2–4 at 1.5T, 3T, and recently at 0.55T. This work aims to determine if SMS is similarly effective in the RT‐MRI evaluation of cardiac function. We used an SMS bSSFP pulse sequence with golden‐angle spirals at 0.55T with an SMS factor of three. We cover the LV with three acquisitions for SMS, and nine for single‐band (SB). Imaging was performed on 9 healthy volunteers and 1 patient with myocardial fibrosis and sternal wires. A spatio‐temporal constrained reconstruction is used, with regularization parameters selected by a board‐certified cardiologist. Images were quantitatively analyzed with a normalized contrast and an Edge Sharpness (ES) score. ResultsThere was a statistically significant 2‐fold difference in contrast between SMS and SB and no significant difference in ES score. The contrast for SMS and SB were 13.38/29.05 at mid‐diastole and 10.79/22.26 at end‐systole; the ES scores for SMS and SB were 1.77/1.83 at mid‐diastole and 1.50/1.72 at end‐systole. ConclusionsSMS cardiac RT‐MRI at 0.55T is feasible and provides sufficient blood‐myocardium contrast to evaluate LV function in three slices simultaneously without any gating or periodic motion assumptions.more » « less
-
Abstract The Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB), funded by the US National Science Foundation, National Institutes of Health, and Department of Energy, has served structural biologists and Protein Data Bank (PDB) data consumers worldwide since 1999. RCSB PDB, a founding member of the Worldwide Protein Data Bank (wwPDB) partnership, is the US data center for the global PDB archive housing biomolecular structure data. RCSB PDB is also responsible for the security of PDB data, as the wwPDB‐designated Archive Keeper. Annually, RCSB PDB serves tens of thousands of three‐dimensional (3D) macromolecular structure data depositors (using macromolecular crystallography, nuclear magnetic resonance spectroscopy, electron microscopy, and micro‐electron diffraction) from all inhabited continents. RCSB PDB makes PDB data available from its research‐focusedRCSB.orgweb portal at no charge and without usage restrictions to millions of PDB data consumers working in every nation and territory worldwide. In addition, RCSB PDB operates an outreach and educationPDB101.RCSB.orgweb portal that was used by more than 800,000 educators, students, and members of the public during calendar year 2020. This invited Tools Issue contribution describes (i) how the archive is growing and evolving as new experimental methods generate ever larger and more complex biomolecular structures; (ii) the importance of data standards and data remediation in effective management of the archive and facile integration with more than 50 external data resources; and (iii) new tools and features for 3D structure analysis and visualization made available during the past yearviatheRCSB.orgweb portal.more » « less
-
dadi-cli: Automated and distributed population genetic model inference from allele frequency spectraAbstract Summarydadi is a popular software package for inferring models of demographic history and natural selection from population genomic data. But using dadi requires Python scripting and manual parallelization of optimization jobs. We developed dadi-cli to simplify dadi usage and also enable straighforward distributed computing. Availability and Implementationdadi-cli is implemented in Python and released under the Apache License 2.0. The source code is available athttps://github.com/xin-huang/dadi-cli. dadi-cli can be installed via PyPI and conda, and is also available through Cacao on Jetstream2https://cacao.jetstream-cloud.org/.more » « less
-
Abstract BackgroundPlant architecture can influence crop yield and quality. Manual extraction of architectural traits is, however, time-consuming, tedious, and error prone. The trait estimation from 3D data addresses occlusion issues with the availability of depth information while deep learning approaches enable learning features without manual design. The goal of this study was to develop a data processing workflow by leveraging 3D deep learning models and a novel 3D data annotation tool to segment cotton plant parts and derive important architectural traits. ResultsThe Point Voxel Convolutional Neural Network (PVCNN) combining both point- and voxel-based representations of 3D data shows less time consumption and better segmentation performance than point-based networks. Results indicate that the best mIoU (89.12%) and accuracy (96.19%) with average inference time of 0.88 s were achieved through PVCNN, compared to Pointnet and Pointnet++. On the seven derived architectural traits from segmented parts, an R2value of more than 0.8 and mean absolute percentage error of less than 10% were attained. ConclusionThis plant part segmentation method based on 3D deep learning enables effective and efficient architectural trait measurement from point clouds, which could be useful to advance plant breeding programs and characterization of in-season developmental traits. The plant part segmentation code is available athttps://github.com/UGA-BSAIL/plant_3d_deep_learning.more » « less
