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Precise radiation guided by oxygen images has demonstrated superiority over the traditional radiation methods. Electron paramagnetic resonance (EPR) imaging has proven to be the most advanced oxygen imaging modality. However, the main drawback of EPR imaging is the long scan time. For each projection, we usually need to collect the projection many times and then average them to achieve high signal-to-noise ratio (SNR). One approach to fast scan is to reduce the repeating time for each projection. While the projections would be noisy and thus the traditional commonly-use filtered backprojection (FBP) algorithm would not be capable of accurately reconstructing images. Optimization-based iterative algorithms may accurately reconstruct images from noisy projections for they may incorporate prior information into optimization models. Based on the total variation (TV) algorithms for EPR imaging, in this work, we propose a directional TV (DTV) algorithm to further improve the reconstruction accuracy. We construct the DTV constrained, data divergence minimization (DTVcDM) model, derive its Chambolle–Pock (CP) solving algorithm, validate the correctness of the whole algorithm, and perform evaluations via simulated and real data. The experimental results show that the DTV algorithm outperforms the existing TV and FBP algorithms in fast EPR imaging. Compared to the standard FBP algorithm, the proposed algorithm may achieve 10 times of acceleration. [Display omitted] • We propose a DTV algorithm for fast EPR imaging to speed up the scan process 10 times. • This is the first extension and application of DTV algorithm in 3D pulsed EPRI. • We first give a simple but effective model-parameters selection method. • We accelerate the convergence rate by introducing two balanced parameters. [ABSTRACT FROM AUTHOR]

Objective. Electron paramagnetic resonance (EPR) imaging is an advanced in vivo oxygen imaging modality. The main drawback of EPR imaging is the long scanning time. Sparse-view projections collection is an effective fast scanning pattern. However, the commonly-used filtered back projection (FBP) algorithm is not competent to accurately reconstruct images from sparse-view projections because of the severe streak artifacts. The aim of this work is to develop an advanced algorithm for sparse reconstruction of 3D EPR imaging. Methods. The optimization based algorithms including the total variation (TV) algorithm have proven to be effective in sparse reconstruction in EPR imaging. To further improve the reconstruction accuracy, we propose the directional TV (DTV) model and derive its Chambolle-Pock solving algorithm. Results. After the algorithm correctness validation on simulation data, we explore the sparse reconstruction capability of the DTV algorithm via a simulated six-sphere phantom and two real bottle phantoms filled with OX063 trityl solution and scanned by an EPR imager with a magnetic field strength of 250 G. Conclusion. Both the simulated and real data experiments show that the DTV algorithm is superior to the existing FBP and TV-type algorithms and a deep learning based method according to visual inspection and quantitative evaluations in sparse reconstruction of EPR imaging. Significance. These insights gained in this work may be used in the development of fast EPR imaging workflow of practical significance., (© 2024 Institute of Physics and Engineering in Medicine.)

• Propose an edge-preserving total nuclear variation for sparse reconstruction. • Propose a method to dynamically adjust the intensity of gradient diffusion. • Extensive experiments have demonstrated the effectiveness of the proposed method. Currently, slow scanning speed has become a technical bottleneck limiting the development of electron paramagnetic resonance imaging (EPRI), a novel oxygen imaging modality. Image reconstruction from sparse-view projections is an important imaging configuration to achieve fast scanning in EPRI. However, EPR images reconstructed from sparse-view projections using traditional analytic algorithms suffer from sparse artifacts, causing severe image degradation. In this work, we aim to use an optimization-based approach to achieve high-precision sparse reconstruction for EPRI. Methods. Inspired by the success of total nuclear variation (TV N) for denoising multi-channel spectral CT images and adaptive-weighted total variation (awTV) for improving the edge-preserving performance of TV, we proposed an edge-preserving total nuclear variation (EPTV N) minimization algorithm for sparse reconstruction in EPRI. The EPTV N combines the advantage of TV N in modelling structural similarity and low-rank prior between multi-channel images for denoising with the advantage of awTV for edge-preserving, which can better protect the edge structure of images while suppressing sparse artifacts. Results. We designed two numerical phantoms with different properties, a piecewise-constant phantom and a gradient phantom, and a physical phantom, and then performed inverse crime, simulation study and real-data study. Experimental results show that the EPTV N minimization algorithm can reconstruct more accurate EPR images from sparse-view projections for both the numerical and physical phantoms. Significance. The proposed method can effectively suppress sparse artifacts and protect edge structures of reconstructed images, thus achieving high-precision sparse-view reconstruction for EPRI. The insights of this work can also be applied to multi-channel image denoising tasks. [ABSTRACT FROM AUTHOR]

Performing nanoscale scanning electron paramagnetic resonance (EPR) requires three essential ingredients: First, a static magnetic field together with field gradients to Zeeman split the electronic energy levels with spatial resolution; second, a radio frequency (rf) magnetic field capable of inducing spin transitions; finally, a sensitive detection method to quantify the energy absorbed by spins. This is usually achieved by combining externally applied magnetic fields with inductive coils or cavities, fluorescent defects, or scanning probes. Here, we theoretically propose the realization of an EPR scanning sensor merging all three characteristics into a single device: the vortex core stabilized in ferromagnetic thin-film discs. On one hand, the vortex ground state generates a significant static magnetic field and field gradients. On the other hand, the precessional motion of the vortex core around its equilibrium position produces a circularly polarized oscillating magnetic field, which is enough to produce spin transitions. Finally, the spin-magnon coupling broadens the vortex gyrotropic frequency, suggesting a direct measure of the presence of unpaired electrons. Moreover, the vortex core can be displaced by simply using external magnetic fields of a few mT, enabling EPR scanning microscopy with large spatial resolution. Our numerical simulations show that, by using low damping magnets, it is theoretically possible to detect single spins located on the disc's surface. Vortex nanocavities could also attain strong coupling to individual spin molecular qubits with potential applications to mediate qubit-qubit interactions or to implement qubit readout protocols.