Engineered Bacteria with Genetic Circuits Accumulating Nanomagnets as MRI Contrast Agents.

The demand for highly efficient cancer diagnostic tools increases alongside the high cancer incidence nowadays. Moreover, there is an imperative need for novel cancer treatment therapies that lack the side effects of conventional treatment options. Developments in this aspect employ magnetic nanoparticles (MNPs) for biomedical applications due to their stability, biocompatibility, and magnetic properties. Certain organisms, including many bacteria, can synthesize magnetic nanocrystals, which help their spatial orientation and survival by sensing the earth's geomagnetic field. This work aims to convert Escherichia coli to accumulate magnetite, which can further be coupled with drug delivery modules. The authors design magnetite accumulating bacterial machines using genetic circuitries hiring Mms6 with iron-binding activity and essential in magnetite crystal formation. The work demonstrates that the combinatorial effect of Mms6 with ferroxidase, iron transporter protein, and material binding peptide enhances the paramagnetic behavior of the cells in magnetic resonance imaging (MRI) measurements. Cellular machines are also engineered to display Mms6 peptide on the cell surface via an autotransporter protein that shows augmented MRI performance. The findings are promising for endowing a probiotic bacterium, able to accumulate magnetite intracellularly or extracellularly, serving as a theranostics agent for cancer diagnostics via MRI scanning and hyperthermia treatment.

Sheared two-dimensional radiofrequency excitation for off-resonance robustness and fat suppression in reduced field-of-view imaging.

PURPOSE: Two-dimensional (2D) echo-planar radiofrequency (RF) pulses are widely used for reduced field-of-view (FOV) imaging in applications such as diffusion-weighted imaging. However, long pulse durations render the 2D RF pulses sensitive to off-resonance effects, causing local signal losses in reduced-FOV images. This work aims to achieve off-resonance robustness for 2D RF pulses via a sheared trajectory design. THEORY AND METHODS: A sheared 2D RF pulse design is proposed to reduce pulse durations while covering identical excitation k-space extent as a standard 2D RF pulse. For a given shear angle, the number of sheared trajectory lines is minimized to obtain the shortest pulse duration, such that the excitation replicas are repositioned outside the slice stack to guarantee unlimited slice coverage. A target fat/water signal ratio of 5% is chosen to achieve robust fat suppression. RESULTS: Simulations, imaging experiments on a custom head and neck phantom, and in vivo imaging experiments in the spinal cord at 3 T demonstrate that the sheared 2D RF design provides significant improvement in image quality while preserving profile sharpnesses. In regions with high off-resonance effects, the sheared 2D RF pulse improves the signal by more than 50% when compared to the standard 2D RF pulse. CONCLUSION: The proposed sheared 2D RF design successfully reduces pulse durations, exhibiting significantly improved through-plane off-resonance robustness, while providing unlimited slice coverage and high fidelity fat suppression. This method will be especially beneficial in regions suffering from a variety of off-resonance effects, such as spinal cord and breast.

Simultaneous use of individual and joint regularization terms in compressive sensing: Joint reconstruction of multi-channel multi-contrast MRI acquisitions.

Multi-contrast images are commonly acquired together to maximize complementary diagnostic information, albeit at the expense of longer scan times. A time-efficient strategy to acquire high-quality multi-contrast images is to accelerate individual sequences and then reconstruct undersampled data with joint regularization terms that leverage common information across contrasts. However, these terms can cause features that are unique to a subset of contrasts to leak into the other contrasts. Such leakage-of-features may appear as artificial tissues, thereby misleading diagnosis. The goal of this study is to develop a compressive sensing method for multi-channel multi-contrast magnetic resonance imaging (MRI) that optimally utilizes shared information while preventing feature leakage. Joint regularization terms group sparsity and colour total variation are used to exploit common features across images while individual sparsity and total variation are also used to prevent leakage of distinct features across contrasts. The multi-channel multi-contrast reconstruction problem is solved via a fast algorithm based on Alternating Direction Method of Multipliers. The proposed method is compared against using only individual and only joint regularization terms in reconstruction. Comparisons were performed on single-channel simulated and multi-channel in-vivo datasets in terms of reconstruction quality and neuroradiologist reader scores. The proposed method demonstrates rapid convergence and improved image quality for both simulated and in-vivo datasets. Furthermore, while reconstructions that solely use joint regularization terms are prone to leakage-of-features, the proposed method reliably avoids leakage via simultaneous use of joint and individual terms, thereby holding great promise for clinical use.

Anisotropic dynamics of a self-assembled colloidal chain in an active bath.

Anisotropic macromolecules exposed to non-equilibrium (active) noise are very common in biological systems, and an accurate understanding of their anisotropic dynamics is therefore crucial. Here, we experimentally investigate the dynamics of isolated chains assembled from magnetic microparticles at a liquid-air interface and moving in an active bath consisting of motile E. coli bacteria. We investigate both the internal chain dynamics and the anisotropic center-of-mass dynamics through particle tracking. We find that both the internal and center-of-mass dynamics are greatly enhanced compared to the passive case, i.e., a system without bacteria, and that the center-of-mass diffusion coefficient D features a non-monotonic dependence as a function of the chain length. Furthermore, our results show that the relationship between the components of D parallel and perpendicular with respect to the direction of the applied magnetic field is preserved in the active bath compared to the passive case, with a higher diffusion in the parallel direction, in contrast to previous findings in the literature. We argue that this qualitative difference is due to subtle differences in the experimental geometry and conditions and the relative roles played by long-range hydrodynamic interactions and short-range collisions.

Reduced field-of-view DWI with robust fat suppression and unrestricted slice coverage using tilted 2D RF excitation.

PURPOSE: Reduced field-of-view (rFOV) diffusion-weighted imaging (DWI) using 2D echo-planar radiofrequency (2DRF) excitation has been widely and successfully applied in clinical settings. The purpose of this work is to further improve its clinical utility by overcoming slice coverage limitations without any scan time penalty while providing robust fat suppression. THEORY AND METHODS: During multislice imaging with 2DRF pulses, periodic sidelobes in the slice direction cause partial saturation, limiting the slice coverage. In this work, a tilting of the excitation plane is proposed to push the sidelobes out of the imaging section while preserving robust fat suppression. The 2DRF pulse is designed using Shinnar-Le Roux algorithm on a rotated excitation k-space. The performance of the method is validated via simulations, phantom experiments, and high in-plane resolution in vivo DWI of the spinal cord. RESULTS: Results show that rFOV DWI using the tilted 2DRF pulse provides increased signal-to-noise ratio, extended coverage, and robust fat suppression, without any scan time penalty. CONCLUSION: Using a tilted 2DRF excitation, a high-resolution rFOV DWI method with robust fat suppression and unrestricted slice coverage is presented. This method will be beneficial in clinical applications needing large slice coverage, for example, axial imaging of the spine, prostate, or breast. Magn Reson Med 76:1668-1676, 2016. © 2016 International Society for Magnetic Resonance in Medicine.

Targeted vessel reconstruction in non-contrast-enhanced steady-state free precession angiography.

Image quality in non-contrast-enhanced (NCE) angiograms is often limited by scan time constraints. An effective solution is to undersample angiographic acquisitions and to recover vessel images with penalized reconstructions. However, conventional methods leverage penalty terms with uniform spatial weighting, which typically yield insufficient suppression of aliasing interference and suboptimal blood/background contrast. Here we propose a two-stage strategy where a tractographic segmentation is employed to auto-extract vasculature maps from undersampled data. These maps are then used to incur spatially adaptive sparsity penalties on vascular and background regions. In vivo steady-state free precession angiograms were acquired in the hand, lower leg and foot. Compared with regular non-adaptive compressed sensing (CS) reconstructions (CSlow ), the proposed strategy improves blood/background contrast by 71.3 ± 28.9% in the hand (mean ± s.d. across acceleration factors 1-8), 30.6 ± 11.3% in the lower leg and 28.1 ± 7.0% in the foot (signed-rank test, P < 0.05 at each acceleration). The proposed targeted reconstruction can relax trade-offs between image contrast, resolution and scan efficiency without compromising vessel depiction.

Enhanced phase-sensitive SSFP reconstruction for fat-water separation in phased-array acquisitions.

PURPOSE: To propose and assess a method to improve the reliability of phase-sensitive fat-water separation for phased-array balanced steady-state free precession (bSSFP) acquisitions. Phase-sensitive steady-state free precession (PS-SSFP) is an efficient fat-water separation technique that detects the phase difference between neighboring bands in the bSSFP magnetization profile. However, large spatial variations in the sensitivity profiles of phased-array coils can lead to noisy phase estimates away from the coil centers, compromising tissue classification. MATERIALS AND METHODS: We first perform region-growing phase correction in individual coil images via unsupervised selection of a fat-voxel seed near the peak of each coil's sensitivity profile. We then use an optimal linear combination of phase-corrected images to segregate fat and water signals. The proposed method was demonstrated on noncontrast-enhanced SSFP angiograms of the thigh, lower leg, and foot acquired at 1.5T using an 8-channel coil. Individual coil PS-SSFP with a common seed selection for all coils, individual coil PS-SSFP with coil-wise seed selection, PS-SSFP after coil combination, and IDEAL reconstructions were also performed. Water images reconstructed via PS-SSFP methods were compared in terms of the level of fat suppression and the similarity to reference IDEAL images (signed-rank test). RESULTS: While tissue misclassification was broadly evident across regular PS-SSFP images, the proposed method achieved significantly higher levels of fat suppression (P < 0.005) and increased similarity to reference IDEAL images (P < 0.005). CONCLUSION: The proposed method enhances fat-water separation in phased-array acquisitions by producing improved phase estimates across the imaging volume. J. Magn. Reson. Imaging 2016;44:148-157.

Hadamard slice encoding for reduced-FOV diffusion-weighted imaging.

PURPOSE: To improve the clinical utility of diffusion-weighted imaging (DWI) by extending the slice coverage of a high-resolution reduced field-of-view technique. THEORY: Challenges in achieving high spatial resolution restrict the use of DWI in assessment of small structures such as the spinal cord. A reduced field-of-view method with 2D echo-planar radiofrequency (RF) excitation was recently proposed for high-resolution DWI. Here, a Hadamard slice-encoding scheme is proposed to double the slice coverage by exploiting the periodicity of the 2D echo-planar RF excitation profile. METHODS: A 2D echo-planar RF pulse and matching multiband refocusing RF pulses were designed using the Shinnar-Le Roux algorithm to reduce band interference, and variable-rate selective excitation to shorten the pulse durations. Hadamard-encoded images were resolved through a phase-preserving image reconstruction. The performance of the method was evaluated via simulations, phantom experiments, and in vivo high-resolution axial DWI of spinal cord. RESULTS: The proposed scheme successfully extends the slice coverage, while preserving the sharp excitation profile and the reliable fat suppression of the original method. For in vivo axial DWI of the spinal cord, an in-plane resolution of 0.7 × 0.7 mm(2) was achieved with 16 slices. CONCLUSION: The proposed Hadamard slice-encoding scheme doubles the slice coverage of the 2D echo-planar RF reduced field-of-view method without any scan-time penalty.

DEQ-MPI: A Deep Equilibrium Reconstruction With Learned Consistency for Magnetic Particle Imaging.

Magnetic particle imaging (MPI) offers unparalleled contrast and resolution for tracing magnetic nanoparticles. A common imaging procedure calibrates a system matrix (SM) that is used to reconstruct data from subsequent scans. The ill-posed reconstruction problem can be solved by simultaneously enforcing data consistency based on the SM and regularizing the solution based on an image prior. Traditional hand-crafted priors cannot capture the complex attributes of MPI images, whereas recent MPI methods based on learned priors can suffer from extensive inference times or limited generalization performance. Here, we introduce a novel physics-driven method for MPI reconstruction based on a deep equilibrium model with learned data consistency (DEQ-MPI). DEQ-MPI reconstructs images by augmenting neural networks into an iterative optimization, as inspired by unrolling methods in deep learning. Yet, conventional unrolling methods are computationally restricted to few iterations resulting in non-convergent solutions, and they use hand-crafted consistency measures that can yield suboptimal capture of the data distribution. DEQ-MPI instead trains an implicit mapping to maximize the quality of a convergent solution, and it incorporates a learned consistency measure to better account for the data distribution. Demonstrations on simulated and experimental data indicate that DEQ-MPI achieves superior image quality and competitive inference time to state-of-the-art MPI reconstruction methods.

COVID-19 Detection From Respiratory Sounds With Hierarchical Spectrogram Transformers.

Monitoring of prevalent airborne diseases such as COVID-19 characteristically involves respiratory assessments. While auscultation is a mainstream method for preliminary screening of disease symptoms, its utility is hampered by the need for dedicated hospital visits. Remote monitoring based on recordings of respiratory sounds on portable devices is a promising alternative, which can assist in early assessment of COVID-19 that primarily affects the lower respiratory tract. In this study, we introduce a novel deep learning approach to distinguish patients with COVID-19 from healthy controls given audio recordings of cough or breathing sounds. The proposed approach leverages a novel hierarchical spectrogram transformer (HST) on spectrogram representations of respiratory sounds. HST embodies self-attention mechanisms over local windows in spectrograms, and window size is progressively grown over model stages to capture local to global context. HST is compared against state-of-the-art conventional and deep-learning baselines. Demonstrations on crowd-sourced multi-national datasets indicate that HST outperforms competing methods, achieving over 90% area under the receiver operating characteristic curve (AUC) in detecting COVID-19 cases.

Automated Parameter Selection for Accelerated MRI Reconstruction via Low-Rank Modeling of Local k-Space Neighborhoods.

PURPOSE: Image quality in accelerated MRI rests on careful selection of various reconstruction parameters. A common yet tedious and error-prone practice is to hand-tune each parameter to attain visually appealing reconstructions. Here, we propose a parameter tuning strategy to automate hybrid parallel imaging (PI) - compressed sensing (CS) reconstructions via low-rank modeling of local k-space neighborhoods (LORAKS) supplemented with sparsity regularization in wavelet and total variation (TV) domains. METHODS: For low-rank regularization, we leverage a soft-thresholding operation based on singular values for matrix rank selection in LORAKS. For sparsity regularization, we employ Stein's unbiased risk estimate criterion to select the wavelet regularization parameter and local standard deviation of reconstructions to select the TV regularization parameter. Comprehensive demonstrations are presented on a numerical brain phantom and in vivo brain and knee acquisitions. Quantitative assessments are performed via PSNR, SSIM and NMSE metrics. RESULTS: The proposed hybrid PI-CS method improves reconstruction quality compared to PI-only techniques, and it achieves on par image quality to reconstructions with brute-force optimization of reconstruction parameters. These results are prominent across several different datasets and the range of examined acceleration rates. CONCLUSION: A data-driven parameter tuning strategy to automate hybrid PI-CS reconstructions is presented. The proposed method achieves reliable reconstructions of accelerated multi-coil MRI datasets without the need for exhaustive hand-tuning of reconstruction parameters.

Assessment of glymphatic function in narcolepsy using DTI-ALPS index.

INTRODUCTION: Sleep is a modulator of glymphatic activity which is altered in various sleep disorders. Narcolepsy is a sleep disorder characterized by excessive daytime sleepiness (EDS), rapid onset of rapid eye movement (REM) sleep, cataplexy, disturbed night sleep with fragmentation. It is categorized into two types, type 1 (NT1) and type 2 (NT2) depending on the presence of cataplexy and/or absence of orexin. We sought for alterations in glymphatic activity in narcoleptic patients using diffusion tensor imaging (DTI) along perivascular space (ALPS) index on magnetic resonance imaging (MRI). MATERIAL AND METHODS: Adult patients diagnosed with NT1 or NT2 who had polysomnography (PSG) and MRI with DTI were included in the study. Sleep recording included Epworth Sleepiness Scale (ESS) score, sleep latency during multiple sleep latency test (MSLT), sleep efficiency during night PSG, wake after sleep onset (WASO), REM sleep latency during PSG, percentage of non-REM (NREM), REM sleep and wakefulness during night PSG. DTI-ALPS index was calculated for each patient and age-sex matched healthy control(HC)s. RESULTS: The study group was composed of 25 patients [F/M = 15/10, median age = 34 (29.5-44.5)], 14 with NT1 and 11 with NT2 disease. ESS, WASO and percentage of wakefulness were significantly higher in NT1 patients (p < 0.05). Mean DTI-ALPS was not significantly different neither between narcoleptic patients and HCs, nor between NT1 and NT2 patients (all, p > 0.05). However, DTI-ALPS was negatively correlated with WASO (r = -0.745, p = 0.013) and percentage of wakefulness (r = -0.837, p = 0.005) in NT1 patients. DTI-ALPS correlated negatively with percentage of N1 sleep (r = -0.781, p = 0.005) but positively with REM percentage (r = 0.618, p = 0.043) in NT2 patients. CONCLUSION: In this study, DTI-ALPS was not significantly different in narcoleptic patients than the HCs. However, the glymphatic index as assessed by DTI-ALPS correlated with PSG parameters; negatively with WASO, percentage of wakefulness in NT1, percentage of N1 sleep in NT2, and positively with REM sleep in NT2. A tendency for a reduction in DTI-ALPS in NT1 patients compared to both NT2 patients and HCs was also found. These findings might show the first evidence of an alteration of glymphatic activity, especially in NT1 patients, thus warrant further prospective studies in larger size of narcoleptic patient cohorts.

Antibacterial properties and osteoblast interactions of microfluidically synthesized chitosan - SPION composite nanoparticles.

In this research, a multi-step microfluidic reactor was used to fabricate chitosan - superparamagnetic iron oxide composite nanoparticles (Ch - SPIONs), where composite formation using chitosan was aimed to provide antibacterial property and nanoparticle stability for magnetic resonance imaging (MRI). Monodispersed Ch - SPIONs had an average particle size of 8.8 ± 1.2 nm with a magnetization value of 32.0 emu/g. Ch - SPIONs could be used as an MRI contrast agent by shortening T2 relaxation parameter of the surrounding environment, as measured on a 3 T MRI scanner. In addition, Ch - SPIONs with concentrations less than 1 g/L promoted bone cell (osteoblast) viability up to 7 days of culture in vitro in the presence of 0.4 T external static magnetic field. These nanoparticles were also tested against Staphylococcus aureus (S. aureus) and Pseudomonas aeruginosa (P. aeruginosa), which are dangerous pathogens that cause infection in tissues and biomedical devices. Upon interaction of Ch - SPIONs with S. aureus and P. aeruginosa at 0.01 g/L concentration, nearly a 2-fold reduction in the number of colonies was observed for both bacteria strains at 48 h of culture. Results cumulatively showed that Ch - SPIONs were potential candidates as a cytocompatible and antibacterial agent that can be targeted to biofilm and imaged using an MRI.

High-throughput large scale microfluidic assembly of iron oxide nanoflowers@PS-b-PAA polymeric micelles as multimodal nanoplatforms for photothermia and magnetic imaging.

Magnetic nanoparticles have been extensively explored as theranostic agents both in academic and clinical settings. Their self-assembly into nanohybrids using block copolymers can lead to new nanostructures with high functionalities and performances. Herein, we demonstrate a high-throughput and scalable method to elaborate magnetic micelles by the assembly of iron oxide magnetite nanoflowers, an efficient nanoheater, and the block copolymer Poly(styrene)-block-poly(acrylic acid) via a microfluidic-assisted nanoprecipitation method. We show that the size and shape of the magnetomicelles can be easily tuned by modulating the residence time in the microfluidic channel. In addition to their biocompatibility, we demonstrate the potential of these magnetic nanohybrids as multimodal theranostic platforms capable of generating heat by photothermia and functioning as negative contrast agents in magnetic resonance imaging and as imaging tracers in magnetic particle imaging. Notably, they outperform currently commercially available particles in terms of imaging functionalities.

Simultaneous temperature and viscosity estimation capability via magnetic nanoparticle relaxation.

PURPOSE: Magnetic particle imaging (MPI) is emerging as a highly promising imaging modality. Magnetic nanoparticles (MNPs) are used as imaging tracers in MPI, and their relaxation behavior provides the foundation for its functional imaging capability. Since MNPs are also utilized in magnetic fluid hyperthermia (MFH) and MPI enables localized MFH, temperature mapping arises as an important application area of MPI. To achieve accurate temperature estimations, however, one must also take into account the confounding effects of viscosity on the MPI signal. In this work, we analyze the effects of temperature and viscosity on MNP relaxation and determine temperature and viscosity sensitivities of relaxation time constant estimations via TAURUS (TAU estimation via Recovery of Underlying mirror Symmetry) at a wide range of operating points to empower simultaneous mapping of these two parameters. METHODS: A total of 15 samples were prepared to reach four target viscosity levels (0.9-3.6 mPa · $\cdot$ s) at five different temperatures (25-45 ∘ $^\circ$ C). Experiments were performed on a magnetic particle spectrometer (MPS) setup at 60 different operating points at drive field amplitudes ranging between 5 and 25 mT and frequencies ranging between 1 and 7 kHz. To enable these extensive experiments, an in-house arbitrary-waveform MPS setup with temperature-controlled heating capability was developed. The operating points were divided into four groups with comparable signal levels to maximize signal gain during rapid signal acquisition. The relaxation time constants were estimated via TAURUS, by restoring the underlying mirror symmetry property of the positive and negative half cycles of the time-domain MNP response. The relative time constants with respect to the drive field period, τ ̂ $\hat{\tau }$ , were computed to enable quantitative comparison across different operating points. At each operating point, a linear fit was performed to τ ̂ $\hat{\tau }$ as a function of each functional parameter (i.e., temperature or viscosity). The slopes of these linear fits were utilized to compute the temperature and viscosity sensitivities of TAURUS. RESULTS: Except for outlier behaviors at 1 kHz, the following global trends were observed: τ ̂ $\hat{\tau }$ decreases with drive field amplitude, increases with drive field frequency, decreases with temperature, and increases with viscosity. The temperature sensitivity varies slowly across the operating points and reaches a maximum value of 1.18%/ ∘ $^\circ$ C. In contrast, viscosity sensitivity is high at low frequencies around 1 kHz with a maximum value of 13.4%/(mPa · $\cdot$ s) but rapidly falls after 3 kHz. These results suggest that the simultaneous estimation of temperature and viscosity can be achieved by performing measurements at two different drive field settings that provide complementary temperature/viscosity sensitivities. Alternatively, temperature estimation alone can be achieved with a single measurement at drive field frequencies above 3 kHz, where viscosity sensitivity is minimized. CONCLUSIONS: This work demonstrates highly promising temperature and viscosity sensitivities for TAURUS, highlighting its potential for simultaneous estimation of these two environmental parameters via MNP relaxation. The findings of this work reveal the potential of a hybrid MPI-MFH system for real-time monitored and localized thermal ablation treatment of cancer.

2D RF pulse design for optimized reduced field-of-view imaging at 1.5T and 3T.

Two-dimensional spatially selective radiofrequency (2DRF) excitation pulses are widely used for reduced field-of-view (FOV) targeted high-resolution diffusion weighted imaging (DWI), especially for anatomically small regions such as the spinal cord and prostate. The reduction in FOV achieved by 2DRF pulses significantly improve the in-plane off-resonance artifacts in single-shot echo planar imaging (ss-EPI). However, long durations of 2DRF pulses create a sensitivity to through-plane off-resonance effects, especially at 3T where the off-resonance field doubles with respect to 1.5T. This work proposes a parameter-based optimization approach to design 2DRF pulses with blips along the slice-select axis, with the goal of maximizing slab sharpness while minimizing off-resonance effects on 1.5T and 3T MRI scanners, separately. Extensive Bloch simulations are performed to evaluate the off-resonance robustness of 2DRF pulses. Three different metrics are proposed to quantify the similarity between the actual and ideal 2D excitation profiles, based on the signals within and outside the targeted reduced-FOV region. In addition, simulations on a digital brain phantom are performed for visual comparison purposes. The results show that maintaining a sharp profile is the primary design requirement at 1.5T, necessitating the usage of relatively high slab sharpness with a time-bandwidth product (TBW) around 8-10. In contrast, off-resonance robustness is the primary design requirement at 3T, requiring the usage of a moderate slap sharpness with TBW around 5-7.

Rapid TAURUS for Relaxation-Based Color Magnetic Particle Imaging.

Magnetic particle imaging (MPI) is a rapidly developing medical imaging modality that exploits the non-linear response of magnetic nanoparticles (MNPs). Color MPI widens the functionality of MPI, empowering it with the capability to distinguish different MNPs and/or MNP environments. The system function approach for color MPI relies on extensive calibrations that capture the differences in the harmonic responses of the MNPs. An alternative calibration-free x-space-based method called TAURUS estimates a map of the relaxation time constant, τ , by recovering the underlying mirror symmetry in the MPI signal. However, TAURUS requires a back and forth scanning of a given region, restricting its usage to slow trajectories with constant or piecewise constant focus fields (FFs). In this work, we propose a novel technique to increase the performance of TAURUS and enable τ map estimation for rapid and multi-dimensional trajectories. The proposed technique is based on correcting the distortions on mirror symmetry induced by time-varying FFs. We demonstrate via simulations and experiments in our in-house MPI scanner that the proposed method successfully estimates high-fidelity τ maps for rapid trajectories that provide orders of magnitude reduction in scanning time (over 300 fold for simulations and over 8 fold for experiments) while preserving the calibration-free property of TAURUS.

TranSMS: Transformers for Super-Resolution Calibration in Magnetic Particle Imaging.

Magnetic particle imaging (MPI) offers exceptional contrast for magnetic nanoparticles (MNP) at high spatio-temporal resolution. A common procedure in MPI starts with a calibration scan to measure the system matrix (SM), which is then used to set up an inverse problem to reconstruct images of the MNP distribution during subsequent scans. This calibration enables the reconstruction to sensitively account for various system imperfections. Yet time-consuming SM measurements have to be repeated under notable changes in system properties. Here, we introduce a novel deep learning approach for accelerated MPI calibration based on Transformers for SM super-resolution (TranSMS). Low-resolution SM measurements are performed using large MNP samples for improved signal-to-noise ratio efficiency, and the high-resolution SM is super-resolved via model-based deep learning. TranSMS leverages a vision transformer module to capture contextual relationships in low-resolution input images, a dense convolutional module for localizing high-resolution image features, and a data-consistency module to ensure measurement fidelity. Demonstrations on simulated and experimental data indicate that TranSMS significantly improves SM recovery and MPI reconstruction for up to 64-fold acceleration in two-dimensional imaging.

Partial FOV Center Imaging (PCI): A Robust X-Space Image Reconstruction for Magnetic Particle Imaging.

Magnetic Particle Imaging (MPI) is an emerging medical imaging modality that images the spatial distribution of superparamagnetic iron oxide (SPIO) nanoparticles using their nonlinear response to applied magnetic fields. In standard x-space approach to MPI, the image is reconstructed by gridding the speed-compensated nanoparticle signal to the instantaneous position of the field free point (FFP). However, due to safety limits on the drive field, the field-of-view (FOV) needs to be covered by multiple relatively small partial field-of-views (pFOVs). The image of the entire FOV is then pieced together from individually processed pFOVs. These processing steps can be sensitive to non-ideal signal conditions such as harmonic interference, noise, and relaxation effects. In this work, we propose a robust x-space reconstruction technique, Partial FOV Center Imaging (PCI), with substantially simplified pFOV processing. PCI first forms a raw image of the entire FOV by mapping MPI signal directly to pFOV center locations. The corresponding MPI image is then obtained by deconvolving this raw image by a compact kernel, whose fully-known shape solely depends on the pFOV size. We analyze the performance of the proposed reconstruction via extensive simulations, as well as imaging experiments on our in-house FFP MPI scanner. The results show that PCI offers a trade-off between noise robustness and interference robustness, outperforming standard x-space reconstruction in terms of both robustness against non-ideal signal conditions and image quality.