Magnetic Imaging Research Articles

Deep learning methods for Hamiltonian parameter estimation and magnetic domain image generation in twisted van der Waals magnets

The application of twist engineering in van der Waals magnets has opened new frontiers in the field of two-dimensional magnetism, yielding distinctive magnetic domain structures. Despite the introduction of numerous theoretical methods, limitations persist in terms of accuracy or efficiency due to the complex nature of the magnetic Hamiltonians pertinent to these systems. In this study, we introduce a deep-learning approach to tackle these challenges. Utilizing customized, fully connected networks, we develop two deep-neural-network kernels that facilitate efficient and reliable analysis of twisted van der Waals magnets. Our regression model is adept at estimating the magnetic Hamiltonian parameters of twisted bilayer CrI3 from its magnetic domain images generated through atomistic spin simulations. The ‘generative model’ excels in producing precise magnetic domain images from the provided magnetic parameters. The trained networks for these models undergo thorough validation, including statistical error analysis and assessment of robustness against noisy injections. These advancements not only extend the applicability of deep-learning methods to twisted van der Waals magnets but also streamline future investigations into these captivating yet poorly understood systems.

Brain magnetic resonance imaging image classification for Alzheimer's disease and its hardware acceleration

<span lang="EN-US">Alzheimer's is a progressive neurodegenerative disorder and is considered the sixth leading cause of death after cancer and heart attack. Early detection and diagnosis provide individuals to go through a wider variety of clinical trials and get multiple medical benefits. Research on the application of deep learning and machine learning to the early detection of Alzheimer's disease has recently gained considerable attention. In this paper, we propose a deep learning classification framework to classify the individual with different progression stages of Alzheimer's disease such as mild cognitive impairment (MCI) and cognitive normal (CN). The dataset from Alzheimer’s disease neuroimaging initiative (ADNI) is considered in this paper which is a multisite having collection of Neuroimaging data for researchers. Structural magnetic resonance imaging (MRI) images are considered from the ADNI data set and feature extraction is done using a 2D discrete wavelet transform. 97% of data reduction is achieved during data pre-processing. The algorithm is trained and validated. The algorithm is accelerated in Nvidia Tx2 graphics processing unit (GPU) to get the better throughput. The result shows our algorithm outperforms the other deep learning algorithms with 91.56% accuracy. </span>

On Constructing of Magnetic and Gravity Images of Mercury from Satellite Data

On Constructing of Magnetic and Gravity Images of Mercury from Satellite Data

A non-invasive AI-based system for precise grading of anosmia in COVID-19 using neuroimaging

COVID-19 (Coronavirus), an acute respiratory disorder, is caused by SARS-CoV-2 (coronavirus severe acute respiratory syndrome). The high prevalence of COVID-19 infection has drawn attention to a frequent illness symptom: olfactory and gustatory dysfunction. The primary purpose of this manuscript is to create a Computer-Assisted Diagnostic (CAD) system to determine whether a COVID-19 patient has normal, mild, or severe anosmia. To achieve this goal, we used fluid-attenuated inversion recovery (FLAIR) Magnetic Resonance Imaging (FLAIR-MRI) and Diffusion Tensor Imaging (DTI) to extract the appearance, morphological, and diffusivity markers from the olfactory nerve. The proposed system begins with the identification of the olfactory nerve, which is performed by a skilled expert or radiologist. It then proceeds to carry out the subsequent primary steps: (i) extract appearance markers (i.e., 1st and 2nd order markers), morphology/shape markers (i.e., spherical harmonics), and diffusivity markers (i.e., Fractional Anisotropy (FA) & Mean Diffusivity (MD)), (ii) apply markers fusion based on the integrated markers, and (iii) determine the decision and corresponding performance metrics based on the most-promising classifier. The current study is unusual in that it ensemble bags the learned and fine-tuned ML classifiers and diagnoses olfactory bulb (OB) anosmia using majority voting. In the 5-fold approach, it achieved an accuracy of 94.1%, a balanced accuracy (BAC) of 92.18%, precision of 91.6%, recall of 90.61%, specificity of 93.75%, F1 score of 89.82%, and Intersection over Union (IoU) of 82.62%. In the 10-fold approach, stacking continued to demonstrate impressive results with an accuracy of 94.43%, BAC of 93.0%, precision of 92.03%, recall of 91.39%, specificity of 94.61%, F1 score of 91.23%, and IoU of 84.56%. In the leave-one-subject-out (LOSO) approach, the model continues to exhibit notable outcomes, achieving an accuracy of 91.6%, BAC of 90.27%, precision of 88.55%, recall of 87.96%, specificity of 92.59%, F1 score of 87.94%, and IoU of 78.69%. These results indicate that stacking and majority voting are crucial components of the CAD system, contributing significantly to the overall performance improvements. The proposed technology can help doctors assess which patients need more intensive clinical care.

Open-source device for high sensitivity magnetic particle spectroscopy, relaxometry, and hysteresis loop tracing.

Magnetic nanoparticles (MNPs) are used extensively across numerous disciples, with applications including Magnetic Particle Imaging (MPI), targeted hyperthermia, deep brain stimulation, immunoassays, and thermometry. The assessment of MNPs, especially those being designed for MPI, is performed with magnetic particle spectrometers, relaxometers, loop tracers, or similar devices. Despite the many applications and the need for particle assessment, there are few consolidated resources for designing or building such a MNP assessment system. Here, we describe the design and performance of an open-source device capable of spectroscopy, relaxometry, and loop tracing. We show example measurements from the device and quantify the detection sensitivity by measuring a dilution series of Synomag-D 70nm (from 0.5 mg Fe/ml to 7 ng Fe/ml) with a 10 mT drive field at 23.8kHz. The device measures 260pg Fe with SNR = 1 and 1.3 ng at SNR = 5 in spectroscopy mode in under one second of measurement time. The system has a dynamic range of 60 μg to 260pg Fe without changing the hardware configuration. As an example application, we characterize Synomag-D's relaxation time constant for drive fields 2-18 mT and compare the magnetization responses of two commonly used MNPs.

Magnetic field analysis and modeling of gradient coils based on ferromagnetic coupling inside magnetically shielded cylinder

To accurately analyze the magnetic field of gradient coils inside a magnetically shielded cylinder (MSC) and enhance the precision of remanence compensation, analytical models for calculating the magnetic field of the gradient coils are proposed. First, the coupling between the high-permeability MSC and the coils is accurately analyzed based on Green’ s function method, magnetic field boundary conditions and image method. Secondly, the current density of four commonly used gradient coils is analyzed, and analytical models for the magnetic fields of these four types of gradient coils are established. Finally, the validity of the analytical models is proved through finite element simulations and experimental tests. The experimental results indicate that within the range of x,z∈−80,80mm (± 0.5R), the analytical values have good consistency with experimental values, and the maximum relative error between the two is only 0.91%. The analytical models will be beneficial to the research on the coil optimization design inside the MSC.

Restoration of Vector Magnetization Image from Vector Scanning-SQUID Microscope Measurement

Abstract A generalized mathematical framework to treat image data measured by the scanning superconducting quantum interference device (SQUID) microscope using a three-dimensional vector pickup coil is presented. The blurring of the images originating from the effects of diamagnetism due to the superconductivity of the sensor, the non-zero sensor size, and the finite sensor-to-sample separation are numerically reduced. We use a lattice model of the measurement system, and singular value decomposition and the Moore-Penrose pseudo-inverse matrix are employed to handle ill-conditioned matrices we encounter in the numerical processes. Based on a numerical model, measurement of the vector magnetization distributed on a sample surface, and the image restoration using the present procedure are demonstrated.

Evaluating the glymphatic system via magnetic resonance diffusion tensor imaging along the perivascular spaces in brain tumor patients

PurposeTo investigate glymphatic system function in patients with brain tumors, including both primary and secondary tumors, using diffusion tensor imaging along perivascular spaces (DTI-ALPS).MethodsWe retrospectively analyzed the MR DTI of 24 patients with unilateral brain tumors and compared them with age and sex-matched controls. We compared the DTI-ALPS index of the ipsi- and contralateral brain hemispheres. The region of interest was placed in the periventricular vessels adjacent to the lateral ventricles. Differences between sex, age, and kind of tumor (primary or brain metastasis) were evaluated. Correlations between DTI-ALPS index and age and the tumor's apparent diffusion coefficient (ADC) were also investigated.ResultsThe DTI-ALPS index was significantly lower (p < 0.05) in the tumor-affected hemisphere (mean = 1.26 ± 0.24) than contralateral (mean = 1.43 ± 0.28). A comparison with healthy controls revealed no significant difference on the matched ipsilateral side. However, the DTI-ALPS index of the contralateral side of the patients was larger than the HC. Additionally, no statistically significant differences were found when analyzing the DTI-ALPS index vs. age, sex, and tumor entity. Additionally, we did not find a correlation between the DTI-ALPS index and patient age or tumor ADC.ConclusionThe decreased DTI-ALPS index in the tumor-affected hemisphere may be related to impaired glymphatic system function. However, cancer is often a systemic disease; thus, the DTI-ALPS index from the contralateral brain hemisphere may not generally be considered as a normal control. Nonetheless, the DTI-ALPS index does not only reflect diffusion in the perivascular spaces but it can also be influenced by factors such as axonal degeneration. Therefore, it does not directly reflect brain waste clearance and changes in the index should be interpreted carefully.

Risk Factors and Clinical Significance of Ultra-Long-Term Microischemia After Intracranial Aneurysm Embolization.

This study aimed to explore influencing factors and clinical significance of ultra-long-term microischemia following intracranial aneurysm (IA) embolization and establish a theoretical foundation for reducing both the incidence of ultra-long-term microischemia and cognitive dysfunction in patients post embolization. A retrospective analysis was conducted on data from 147 patients who received endovascular treatment for IAs. Patients were categorized into microischemic and control (non-microischemic) groups on the based on the findings of high-resolution magnetic resonance vessel wall imaging (HR-VWI) examinations performed 3days postoperatively and 6months postoperatively. Risk factors for the occurrence of ultra-long-term microischemia were determined by univariate analysis and multivariate logistic regression analysis. Out of 147 patients included in the study, 51 (34.69%) developed microischemia while the remaining 96 (65.31%) did not experience this condition. Analysis revealed that factors such as sex, age, history of underlying diseases (hypertension, diabetes mellitus), aneurysmal site characteristics, the presence or absence of stenosis in the aneurysm-bearing artery, modified Fisher score at admission, Barthel's index at discharge, immunoinflammatory index at 3days postoperatively and at the 6-month follow-up, the presence or absence of aneurysmal wall enhancement, and the presence or absence of aneurysmal lumen showed no statistically significant differences between the two groups (all P > 0.05). By contrast, variables like in operative time, rupture status of the aneurysm before surgery according to World Federation of Neurologic Surgeons (WFNS) grade, aneurysm size, number of stents used, number of guidewires and catheters used, and Evans index between the two groups were found to have statistically significant disparities between those who developed microischemia and those who did not (P < 0.05). A subsequent multivariate analysis revealed that aneurysm size, Evans index, and the number of stents used were independent risk factors for the occurrence of ultra-long-term microischemia after surgical intervention of aneurysms (P < 0.05). The receiver operating characteristic (ROC) curves of the patients were constructed on the basis of risk factors determined through multivariate logistic regression analysis. Results indicated that aneurysm size (area under ROC curve (AUC) 0.619, sensitivity 94.7%, specificity 17.1%, P = 0.049), Evans index (AUC 0.670, sensitivity 96.4%, specificity 26.8%, P = 0.004), and number of stents (AUC 0.639, sensitivity 44.6%, specificity 90.2%, P < 0.001) effectively predicted the occurrence of microischemia. The incidence of cognitive dysfunction was higher in the microischemic group than in the control group (P < 0.05), and a greater number of microischemic foci was associated with a higher incidence of cognitive dysfunction. The proportion of microschemia foci in the thalamus and basal ganglia in patients with cognitive dysfunction (60.87%) was significantly higher than that in patients without cognitive dysfunction (34.55%) (P < 0.05). Aneurysm size, Evans index > 0.3, and the quantity of stents were independent risk factors for the occurrence of ultra-long-term microischemia after aneurysm embolization and provided good predictive performance. Cognitive dysfunction was closely associated with microischemia, with its severity increasing with an increase in the number of ischemic foci.

Impact of string connection and contact defects on electrical current distribution in solar cells and modules: A model validated by magnetic field imaging

AbstractModeling of solar modules and their components is essential to quantify geometrical, optical, and electrical losses and to improve the designs and technologies in terms of performance. In most loss analysis models, the current share among the busbars of the solar cell is assumed to be equal since a symmetrical distribution of the metallization is given. The impact of string terminal connection on the current distribution among the ribbons and the resulting changes in ohmic losses has not been studied yet. In this study, a MATLAB model is developed to consider the impact of the string connector terminal position on the current distribution and the ohmic losses in the ribbons and in string connector. The model allows for the analysis of the impact of contact defects scenarios in ribbons and string connectors on the current distribution. Results show that the highest current flows at the closest busbar to the string connector terminal while the current decreases at the busbars farther away from the terminal due to higher ohmic resistance of the current path. The higher the ohmic resistance of the string connector, the more inhomogeneous the current share at busbars. Simulating a 9 busbar M6 half‐cell with 1 × 0.08 mm2 string connector, positioning the string connector terminal at the leftmost or rightmost ribbon results in 0.4 W less power compared to center connection configuration, where the string connector terminal is positioned at the center ribbon. Furthermore, simulation results show that inhomogeneity of current causes about 2.1% reduction in module power compared to the case of evenly distributed cell current, considering a 120‐haf‐cell module with the same string connector. Regarding contact defect analysis, exemplary simulations show the impact of the position of detached ribbons on the power or efficiency loss. Considering left or right connection configuration, detaching the leftmost or rightmost ribbon results in higher power loss compared to other ribbons. Detaching one cell ribbon completely from the string connector results in about 0.2%abs decrease in cell efficiency, while detaching the outer ribbon along all strings of a 120‐half‐cell module results in power loss of about 0.8%. The developed model is validated by performing magnetic field imaging (MFI) measurements, in which the magnetic flux density induced by the current carried by the ribbons is measured.

Expanding the Treatable Imaging Profile in Patients With Large Ischemic Stroke: Subanalysis From a Randomized Clinical Trial.

We aimed to examine the boundary of the ischemic core volume in patients undergoing endovascular thrombectomy (EVT) versus those receiving medical management to determine the minimum optimal size for favorable treatment outcomes. This is a prespecified substudy of the RESCUE-Japan LIMIT (Recovery by Endovascular Salvage for Cerebral Ultra-Acute Embolism-Japan Large Ischemic Core Trial). Patients with large vessel occlusion were enrolled between November 2018 and September 2021 with a National Institutes of Health Stroke Scale score of at least 6 on admission and an Alberta Stroke Program Early Computed Tomography Score value of 3 to 5. We investigated the correlation between optimal quantified ischemic core volume, assessed solely using magnetic resonance diffusion-weighted imaging, and functional outcomes (modified Rankin Scale score, 0-3) at 90 days by predictive marginal plots. Final infarct volume and safety outcomes (symptomatic intracerebral hemorrhage and mortality) were also assessed. Of the 203 cases, 168 patients (85 in the EVT group versus 83 in the medical management group) were included. The median (interquartile range) core volume was 94 (65-160) mL in patients with EVT and 115 (71-141) mL in the medical management group (P=0.72). The predictive marginal probabilities of the 2 groups intersected at 128 mL for estimating functional outcomes. Symptomatic intracerebral hemorrhage and mortality within 90 days had overlay margins through all core volumes in both groups. The median final infarct volume (interquartile range) was smaller in the EVT group (142 [80-223] mL versus 211 [123-289] mL in the medical management group; P<0.001). In this prespecified analysis of a randomized clinical trial involving patients with large ischemic strokes, patients with an estimated core volume of up to 128 mL on diffusion-weighted imaging benefit from EVT. URL: https://www.clinicaltrials.gov; Unique identifier: NCT03702413.

Evaluation of split renal dysfunction using radiomics based on magnetic resonance diffusion-weighted imaging.

Accurate and noninvasive assessment of split renal dysfunction is crucial, while there is lack of corresponding method clinically. To investigate the feasibility of using diffusion-weighted imaging (DWI)-based radiomics models to evaluate split renal dysfunction. We enrolled patients with impaired and normal renal function undergoing renal DWI examination. Glomerular filtration rate (GFR, mL/min) was measured using 99mTc-DTPA scintigraphy, which is reference standard of GFR measurement. The kidneys were classified into normal (GFR ≥40), mildly impaired (20≤ GFR<40), moderately impaired (10≤ GFR<20), and severely impaired (GFR<10) renal function groups. Optimized subsets of radiomics features were selected from renal DWI images and radiomics scores (Rad-score) calculated to discriminate groups with different renal function. The radiomics model (Rad-score based) was developed in a training cohort and validated in a test cohort. Evaluations were conducted on the discrimination, calibration, and clinical application of the method. The final analysis included 330 kidneys. Logistic regression was used to develop three radiomics models, model A, B, and C, which were used to distinguish normal from impaired, mild from moderate, and moderate from severe renal function, respectively. The area under the curve of the three models were 0.822, 0.704, and 0.887 in the training cohort and 0.843, 0.717, and 0.897 in the test cohort, respectively, indicating efficient discrimination performance. DWI-based radiomics models have potential for evaluating split renal dysfunction and discriminating between normal and impaired renal function groups and their subgroups.

AI-enabled Lorentz microscopy for quantitative imaging of nanoscale magnetic spin textures

AbstractThe manipulation and control of nanoscale magnetic spin textures are of rising interest as they are potential foundational units in next-generation computing paradigms. Achieving this requires a quantitative understanding of the spin texture behavior under external stimuli using in situ experiments. Lorentz transmission electron microscopy (LTEM) enables real-space imaging of spin textures at the nanoscale, but quantitative characterization of in situ data is extremely challenging. Here, we present an AI-enabled phase-retrieval method based on integrating a generative deep image prior with an image formation forward model for LTEM. Our approach uses a single out-of-focus image for phase retrieval and achieves significantly higher accuracy and robustness to noise compared to existing methods. Furthermore, our method is capable of isolating sample heterogeneities from magnetic contrast, as shown by application to simulated and experimental data. This approach allows quantitative phase reconstruction of in situ data and can also enable near real-time quantitative magnetic imaging.

Enhanced reliability and time efficiency of deep learning-based posterior tibial slope measurement over manual techniques.

Multifaceted factors contribute to inferior outcomes following anterior cruciate ligament (ACL) reconstruction surgery. A particular focus is placed on the posterior tibial slope (PTS). This study introduces the integration of machine learning and artificial intelligence (AI) for efficient measurements of tibial slopes on magnetic resonance imaging images as a promising solution. This advancement aims to enhance risk stratification, diagnostic insights, intervention prognosisand surgical planning for ACL injuries. Images and demographic information from 120 patients who underwent ACL reconstruction surgery were used for this study. An AI-driven model was developed to measure the posterior lateral tibial slope using the YOLOv8 algorithm. The accuracy of the lateral tibial slope, medial tibial slopeand tibial longitudinal axis measurements was assessed, and the results reached high levels of reliability. This study employed machine learning and AI techniques to provide objective, consistentand efficient measurements of tibial slopes on MR images. Three distinct models were developed to derive AI-based measurements. The study results revealed a substantial correlation between the measurements obtained from the AI models and those obtained by the orthopaedic surgeon across three parameters: lateral tibial slope, medial tibial slopeand tibial longitudinal axis. Specifically, the Pearson correlation coefficients were 0.673, 0.850and 0.839, respectively. The Spearman rank correlation coefficients were 0.736, 0.861and 0.738, respectively. Additionally, the interclass correlation coefficients were 0.63, 0.84and 0.84, respectively. This study establishes that the deep learning-based method for measuring posterior tibial slopes strongly correlates with the evaluations of expert orthopaedic surgeons. The time efficiency and consistency of this technique suggest its utility in clinical practice, promising to enhance workflow, risk assessmentand the customization of patient treatment plans. Level III, cross-sectional diagnostic study.

Spin‐Orbit Torque Switching of Magnetization in Ultra‐Thick Ferromagnetic Layers

AbstractCurrent‐induced magnetization switching via spin‐orbit torque (SOT) holds great potential for applications in high‐speed and energy‐efficient magnetic memory and logic devices. In the extensively studied heavy metal/ferromagnet (HM/FM) SOT heterostructures, the thickness of the FM layer is typically restricted to a few nanometers or less due to the rapid spin dephasing, making it challenging to implement thermally stable memory cells with high density. In this study, it is demonstrated that this thickness constraint can be significantly alleviated by utilizing an oxide ferromagnet La0.67Sr0.33MnO3 (LSMO). Through electrical transport and magnetic optical measurements, it is found that the SOT can switch the magnetization in Pt/LSMO heterostructures even at an LSMO thickness of 35 nm, which is one order of magnitude larger than that for metallic FMs, such as CoFeB. Furthermore, based on the FM thickness dependence of the switching current and the domain switching type revealed by magnetic optical Kerr effect imaging (MOKE), a possible picture is proposed to describe the SOT switching in Pt/LSMO, which highlights the critical role of the domain wall propagation in the vertical direction. The work provides valuable insights into the behavior of SOT switching in ultra‐thick FM films, offering new possibilities for their practical applications.

A birdcage transmit, 24-channel conformal receive array coil for sensitive 31P magnetic resonance spectroscopic imaging of the human brain at 7 T.

Phosphorus (31P) magnetic resonance spectroscopic imaging (MRSI) can serve as a critical tool for more direct quantification of brain energy metabolism, tissue pH, and cell membrane turnover. However, the low concentration of 31P metabolites in biological tissue may result in low signal-to-noise ratio (SNR) in 31P MRS images. In this work, we present an innovative design and construction of a 31P radiofrequency coil for whole-brain MRSI at 7 T. Our coil builds on current literature in ultra-high field 31P coil design and offers complete coverage of the brain, including the cerebellum and brainstem. The coil consists of an actively detunable volume transmit (Tx) resonator and a custom 24-channel receive (Rx) array. The volume Tx resonator is a 16-rung high-pass birdcage coil. The Rx coil consists of a 24-element phased array composed of catered loop shapes and sizes built onto a custom, close-fitting, head-shaped housing. The Rx array was designed to provide complete coverage of the head, while minimizing mutual coupling. The Rx configuration had a mean reflection coefficient better than -20 decibels (dB) when the coil was loaded with a human head. The mean mutual coupling ( ) among Rx elements, when loaded with a human head, was -16 dB. In phantom imaging, the phased array produced a central SNR that was 4.4-fold higher than the corresponding central SNR when operating the 31P birdcage as a transceiver. The peripheral SNR was 12-fold higher when applying the optimized phased array. In vivo 3D 31P MRSI experiments produced high-quality spectra in the cerebrum gray and white matter, as well as in the cerebellum. Characteristic phosphorus metabolites related to adenosine triphosphate metabolism and cell membrane turnover were distinguishable across all brain regions. In summary, our results demonstrate the potential of our novel coil for accurate, whole-brain 31P metabolite quantification.

Cardiovascular magnetic resonance 3D LGE imaging for atrial fibrillation ablation

Abstract Objective To assess feasibility and reliability of cardiac magnetic resonance (CMR) imaging in visualizing left atrial (LA) structural changes that patients with paroxysmal atrial fibrillation (PAF) undergo acutely and long-term post ablation. Two different ablation methods i.e pulsed filed ablation (PFA) and cryoablation were studied. Background Pulmonary vein (PV) isolation has become the cornerstone technique for catheter ablation in patients with drug-refractory atrial fibrillation (AF). At present, almost all ablation technologies are mediated by a thermal effect. Recently, pulsed field ablation (PFA) has been introduced as a new energy source. PFA is a non-thermal ablative modality in which high voltage ultra-short pulses are applied to target tissue, while preserving the extracellular matrix architecture, nerves, and microvascular structures. Few data exist showing different structural LA changes between the two methods using Three-Dimensional (3D) Late Gadolinium Enhancement (LGE) Imaging. Methods CMR imaging was performed pre-ablation, acutely (&amp;lt;24 h), and 3 months post-ablation in 10 patients with paroxysmal atrial fibrillation (AF) undergoing pulmonary vein (PV) isolation with PFA (n = 5) or cryoablation (n = 5). 3D LGE and T2-weighted images were analysed. We obtained two data sets of 3D whole heart LGE imaging with high resolution, 20minutes post gadolinium injection. Patients underwent two acquisitions: the conventional respiratory navigated, electrocardiographically-gated 3D fast gradient echo (1.3slice thickness, 256x256 matrix acquisition) and the image-navigated isotropic high resolution 3D LGE with Dixon water-fat separation. 3D LGE data were analysed using a dedicated (ADAS LA) software in order to produce a 3D-LA model visualization of LGE areas. Results In the acute stage, LGE volume was 50% larger after PFA vs. cryoablation (P &amp;lt; 0.001), and oedema on T2 imaging was significantly smaller (visual estimation) on PFA cases. LGE rings surrounding PV ostia were more homogeneous after PFA, with no sign of microvascular damage or intramural hemorrhage as compared to cryoablation cases. In the chronic stage, the majority of acute LGE had disappeared after PFA, whereas most LGE persisted after cryo-ablation. The two 3D LGE sequences used for analysis showed complete agreement in the visualization of atrial scar with superior image quality of the novel sequence. The mean acquisition time for 3D LGE imaging was 11±4minutes for the conventional fast gradient echo sequence, and 5±2minutes for the novel 3D self-image navigated LGE sequence. Conclusions CMR imaging may help in visualization and understanding of LA structural remodeling post different ablation techniques.

B.1 CT Or MRI protocol for acute stroke reperfusion with EVT (COMPARE): an international retrospective cohort study

Background: Patients with an acute ischemic stroke (AIS) are selected to receive reperfusion therapy using either computed tomography (CT-CTA) or magnetic brain imaging (MRI). The aim of this study was to compare CT and MRI as the primary imaging modality for AIS patients undergoing EVT. Methods: Data for AIS patients between January 2018 and January 2021 were extracted from two prospective multicenter EVT cohorts: the ETIS registry in France (MRI) and the OPTIMISE registry in Canada (CT). Demographics, procedural data and outcomes were collected. We assessed the association of qualifying imaging (CT vs. MRI) with time metrics and functional outcome. Results: From January 2018 to January 2021, 4059 patients selected by MRI and 1324 patients selected by CT were included in the study. Demographics were similar between the two groups. The median imaging-to-arterial puncture time was 37 minutes longer in the MRI group. Patients selected by CT had more favorable 90-day functional outcomes (mRS 0-2) as compared to patients selected by MRI (48.5% vs 44.4%; adjusted OR (aOR), 1.54, 95%CI 1.31 to 1.80, p&lt;0.001). Conclusions: Patients with AIS undergoing EVT who were selected with MRI as opposed to CT had longer imaging-to-arterial-puncture delays and worse functional outcomes at 90 days.

A systematic 3-D magnetic particle imaging simulation model for quantitative analysis of reconstruction image quality

Background and objectiveMagnetic particle imaging (MPI) is an emerging imaging technology in medical tomography that utilizes the nonlinear magnetization response of superparamagnetic iron oxide (SPIO) particles to determine the in vivo spatial distribution of nanoparticle contrast agents. The reconstruction image quality of MPI is determined by the characteristics of magnetic particles, the setting of the MPI scanner parameters, and the hardware interference of MPI systems. We explore a feasible method to systematically and quickly analyze the impact of these factors on MPI reconstruction image quality. MethodsWe propose a systematic 3-D MPI simulation model. The MPI simulation model has the capability of quickly producing the simulated reconstruction images of a scanned phantom, and quantitative analysis of MPI reconstruction image quality can be achieved by comparing the differences between the input image and output image. These factors are mainly classified as imaging parameters and interference parameters in our model. In order to reduce the computational time of the simulation model, we introduce GPU parallel programming to accelerate the processing of large complex matrix data. For ease of use, we also construct a reliable, high-performance, and open-source 3-D MPI simulation software tool based on our model. The efficiency of our model is evaluated by using OpenMPIData. To demonstrate the capabilities of our model, we conduct simulation experiments using parameters consistent with a real MPI scanner for improving MPI image quality. ResultsThe experimental results show that our simulation model can systematically and quickly evaluate the impact of imaging parameters and interference parameters on MPI reconstruction image quality. ConclusionsWe developed an easy-to-use and open-source 3-D MPI simulation software tool based on our simulation model incorporating all the stages of MPI formation, from signal acquisition to image reconstruction. In the future, our simulation model has potential guiding significance to practical MPI images.

Multi-sequence generative adversarial network: better generation for enhanced magnetic resonance imaging images.

MRI is one of the commonly used diagnostic methods in clinical practice, especially in brain diseases. There are many sequences in MRI, but T1CE images can only be obtained by using contrast agents. Many patients (such as cancer patients) must undergo alignment of multiple MRI sequences for diagnosis, especially the contrast-enhanced magnetic resonance sequence. However, some patients such as pregnant women, children, etc. find it difficult to use contrast agents to obtain enhanced sequences, and contrast agents have many adverse reactions, which can pose a significant risk. With the continuous development of deep learning, the emergence of generative adversarial networks makes it possible to extract features from one type of image to generate another type of image. We propose a generative adversarial network model with multimodal inputs and end-to-end decoding based on the pix2pix model. For the pix2pix model, we used four evaluation metrics: NMSE, RMSE, SSIM, and PNSR to assess the effectiveness of our generated model. Through statistical analysis, we compared our proposed new model with pix2pix and found significant differences between the two. Our model outperformed pix2pix, with higher SSIM and PNSR, lower NMSE and RMSE. We also found that the input of T1W images and T2W images had better effects than other combinations, providing new ideas for subsequent work on generating magnetic resonance enhancement sequence images. By using our model, it is possible to generate magnetic resonance enhanced sequence images based on magnetic resonance non-enhanced sequence images. This has significant implications as it can greatly reduce the use of contrast agents to protect populations such as pregnant women and children who are contraindicated for contrast agents. Additionally, contrast agents are relatively expensive, and this generation method may bring about substantial economic benefits.