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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. 

Abstract PurposeWe examined magnetic field dependent SNR gains and ability to capture them with multichannel receive arrays for human head imaging in going from 7 T, the most commonly used ultrahigh magnetic field (UHF) platform at the present, to 10.5 T, which represents the emerging new frontier of >10 T in UHFs. MethodsElectromagnetic (EM) models of 31‐channel and 63‐channel multichannel arrays built for 10.5 T were developed for 10.5 T and 7 T simulations. A 7 T version of the 63‐channel array with an identical coil layout was also built. Array performance was evaluated in the EM model using a phantom mimicking the size and electrical properties of the human head and a digital human head model. Experimental data was obtained at 7 T and 10.5 T with the 63‐channel array. Ultimate intrinsic SNR (uiSNR) was calculated for the two field strengths using a voxelized cloud of dipoles enclosing the phantom or the digital human head model as a reference to assess the performance of the two arrays and field depended SNR gains. ResultsuiSNR calculations in both the phantom and the digital human head model demonstrated SNR gains at 10.5 T relative to 7 T of 2.6 centrally, ˜2 at the location corresponding to the edge of the brain, ˜1.4 at the periphery. The EM models demonstrated that, centrally, both arrays captured ˜90% of the uiSNR at 7 T, but only ˜65% at 10.5 T, leading only to ˜2‐fold gain in array SNR in going from 7 to 10.5 T. This trend was also observed experimentally with the 63‐channel array capturing a larger fraction of the uiSNR at 7 T compared to 10.5 T, although the percentage of uiSNR captured were slightly lower at both field strengths compared to EM simulation results. ConclusionsMajor uiSNR gains are predicted for human head imaging in going from 7 T to 10.5 T, ranging from ˜2‐fold at locations corresponding to the edge of the brain to 2.6‐fold at the center, corresponding to approximately quadratic increase with the magnetic field. Realistic 31‐ and 63‐channel receive arrays, however, approach the central uiSNR at 7 T, but fail to do so at 10.5 T, suggesting that more coils and/or different type of coils will be needed at 10.5 T and higher magnetic fields. 

Abstract PurposeRecent numerical and empirical results proved that high permittivity materials (HPM) used in pads placed near the subject or directly integrated with coils can increase the SNR and reduce the specific absorption rate (SAR) in MRI. In this paper, we propose an analytical investigation of the effect on the magnetic field distribution of a layer of HPM surrounding an anatomy‐mimicking cylindrical sample. MethodsThe study is based on a reformulation of the Mie scattering for cylindrical geometry, following an approach recently introduced for spherical samples. The total field in each medium is decomposed in terms of inward and outward electromagnetic waves, and the fields are expressed as series of cylindrical harmonics, whose coefficients can be interpreted as classical reflection and transmission coefficients. ResultsOur new formulation allows a quantitative evaluation of the effect of the HPM layer for varying permittivity and thickness, and it provides an intuitive understanding of such effect in terms of propagation and scattering of the RF field. ConclusionWe show how HPM can filter out the modes that only contribute to the noise or RF power deposition, resulting in higher SNR or lower SAR, respectively. Our proposed framework provides physical insight on how to properly design HPM for MRI applications. 

Abstract PurposeTo introduce a method for the estimation of the ideal current patterns (ICP) that yield optimal signal‐to‐noise ratio (SNR) for realistic heterogeneous tissue models in MRI. Theory and MethodsThe ICP were calculated for different surfaces that resembled typical radiofrequency (RF) coil formers. We constructed numerical electromagnetic (EM) bases to accurately represent EM fields generated by RF current sources located on the current‐bearing surfaces. Using these fields as excitations, we solved the volume integral equation and computed the EM fields in the sample. The fields were appropriately weighted to calculate the optimal SNR and the corresponding ICP. We demonstrated how to qualitatively use ICP to guide the design of a coil array to maximize SNR inside a head model. ResultsIn agreement with previous analytic work, ICP formed large distributed loops for voxels in the middle of the sample and alternated between a single loop and a figure‐eight shape for a voxel 3‐cm deep in the sample's cortex. For the latter voxel, a surface quadrature loop array inspired by the shape of the ICP reached of the optimal SNR at 3T, whereas a single loop placed above the voxel reached only of the optimal SNR. At 7T, the performance of the two designs decreased to and , respectively, suggesting that loops could be suboptimal at ultra‐high field MRI. ConclusionICP can be calculated for human tissue models, potentially guiding the design of application‐specific RF coil arrays. 

Abstract Two‐scale topology optimization, combined with the design of microstructure families with a broad range of effective material parameters, is widely used in many fabrication applications to achieve a target deformation behavior for a variety of objects. The main idea of this approach is to optimize the distribution of material properties in the object partitioned into relatively coarse cells, and then replace each cell with microstructure geometry that mimics these material properties. In this paper, we focus on adapting this approach to complex shapes in situations when preserving the shape's surface is essential. Our approach extends any regular (i.e. defined on a regular lattice grid) microstructure family to complex shapes, by enriching it with tiles adapted to the geometry of the cut‐cell. We propose a fully automated and robust pipeline based on this approach, and we show that the performance of the regular microstructure family is only minimally affected by our extension while allowing its use on 2D and 3D shapes of high complexity. 

Many problems in computer graphics can be formulated as finding the global minimum of a function subject to a set of non-linear constraints (Minimize), or finding all solutions of a system of non-linear constraints (Solve). We introduce MiSo, a domain-specific language and compiler for generating efficient C++ code for low-dimensional Minimize and Solve problems, that uses interval methods to guarantee conservative results while using floating point arithmetic. We demonstrate that MiSo-generated code shows competitive performance compared to hand-optimized codes for several computer graphics problems, including high-order collision detection with non-linear trajectories, surface-surface intersection, and geometrical validity checks for finite element simulation. 

Barrier potentials gained popularity as a means for robust contact handling in physical modeling and for modeling self-avoiding shapes. The key to the success of these approaches is adherence to geometric constraints, i.e., avoiding intersections, which are the cause of most robustness problems in complex deformation simulation with contact. However, existing barrier-potential methods may lead to spurious forces and imperfect satisfaction of the geometric constraints. They may have strong resolution dependence, requiring careful adaptation of the potential parameters to the object discretizations. We present a systematic derivation of a continuum potential defined for smooth and piecewise smooth surfaces, starting from identifying a set of natural requirements for contact potentials, including the barrier property, locality, differentiable dependence on shape, and absence of forces in rest configurations. Our potential is formulated independently of surface discretization and addresses the shortcomings of existing potential-based methods while retaining their advantages. We present a discretization of our potential that is a drop-in replacement for the potential used in the incremental potential contact formulation [Li et al. 2020], and compare its behavior to other potential formulations, demonstrating that it has the expected behavior. The presented formulation connects existing barrier approaches, as all recent existing methods can be viewed as a variation of the presented potential, and lays a foundation for developing alternative (e.g., higher-order) versions. 

We introduceTopological Offsets, a novel approach to generate manifold and self-intersection-free offset surfaces that are topologically equivalent to an offset infinitesimally close to the surface. Our approach, by construction, creates a manifold, watertight, and self-intersection-free offset surface strictly enclosing the input, while doing a best effort to move it to a prescribed distance from the input. Differently from existing approaches, we embed the input in a background mesh and insert a topological offset around the input with purely combinatorial operations. The topological offset is then inflated/deflated to match the user-prescribed distance while enforcing that no intersections or non-manifold configurations are introduced. We evaluate the effectiveness and robustness of our approach on the Thingi10k dataset, and show that topological offsets are beneficial in multiple graphics applications, including (1) converting non-manifold surfaces to manifold ones, (2) creating layered offsets, and (3) reliably computing finite offsets. 

We introduce a conceptually simple and efficient algorithm for seamless parametrization, a key element in constructing quad layouts and texture charts on surfaces. More specifically, we consider the construction of parametrizations with prescribedholonomy signaturesi.e., a set of angles at singularities, and rotations along homology loops, preserving which is essential for constructing parametrizations following an input field, as well as for user control of the parametrization structure. Our algorithm performs exceptionally well on a large dataset based on Thingi10k [Zhou and Jacobson 2016], (16156 meshes) as well as on a challenging smaller dataset of [Myles et al. 2014], converging, on average, in 9 iterations. Although the algorithm lacks a formal mathematical guarantee, presented empirical evidence and the connections between convex optimization and closely related algorithms, suggest that a similar formulation can be found for this algorithm in the future.