arXiv subjects

Hongyu An

Publications and source records attributed to Hongyu An.

At least 37 records · Page 2Linked to original sources

arXiv2024

Ultrahigh-temperature ferromagnetism in ultrathin insulating films with ripple-infinite-layer structure

Ferromagnetism and electrical insulation are often at odds, signifying an inherent trade off. The simultaneous optimization of both in one material, essential for advancing spintronics and topological electronics, necessitates the individual manipulation over various degrees of freedom of strongly correlated electrons. Here, by selective control of the spin exchange and Coulomb interactions, we report the achievement of SrFeO2 thin films with resistivity above 106 Ohm.cm and strong magnetization with Curie temperature extrapolated to be 1200 K. Robust ferromagnetism is obtained down to 1.0 nm thickness on substrate and 2.0 nm for freestanding films. Featuring an out of plane oriented ripple infinite layer structure, this ferromagnetic insulating phase is obtained through extensive reduction of as grown brownmillerite SrFeO2.5 films at high compressive strains. Pronounced spin Hall magnetoresistance signals up to 0.0026 is further demonstrated with a Pt Hall bar device. Our findings promise emerging spintronic and topological electronic functionalities harnessing spin dynamics with minimized power dissipations.

cond-mat.mtrl-sci↗

arXiv2024

Small metal artifact detection and inpainting in cardiac CT images

Background: Quantification of cardiac motion on pre-treatment CT imaging for stereotactic arrhythmia radiotherapy patients is difficult due to the presence of image artifacts caused by metal leads of implantable cardioverter-defibrillators (ICDs). New methods are needed to accurately reduce the metal artifacts in already reconstructed CTs to recover the otherwise lost anatomical information. Purpose: To develop a methodology to automatically detect metal artifacts in cardiac CT scans and inpaint the affected volume with anatomically consistent structures and values. Methods: ECG-gated 4DCT scans of 12 patients who underwent cardiac radiation therapy for treating ventricular tachycardia were collected. The metal artifacts in the images were manually contoured. A 2D U-Net deep learning (DL) model was developed to segment the metal artifacts. A dataset of synthetic CTs was prepared by adding metal artifacts from the patient images to artifact-free CTs. A 3D image inpainting DL model was trained to refill the metal artifact portion in the synthetic images with realistic values. The inpainting model was evaluated by analyzing the automated segmentation results of the four heart chambers on the synthetic dataset. Additionally, the raw cardiac patient cases were qualitatively inspected. Results: The artifact detection model produced a Dice score of 0.958 +- 0.008. The inpainting model was able to recreate images with a structural similarity index of 0.988 +- 0.012. With the chamber segmentations improved surface Dice scores from 0.684 +- 0.247 to 0.964 +- 0.067 and the Hausdorff distance reduced from 3.4 +- 3.9 mm to 0.7 +- 0.7 mm. The inpainting model's use on cardiac patient CTs was visually inspected and the artifact-inpainted images were visually plausible. Conclusion: We successfully developed two deep models to detect and inpaint metal artifacts in cardiac CT images.

physics.med-ph↗

arXiv2024

Preliminary Results of Neuromorphic Controller Design and a Parkinson's Disease Dataset Building for Closed-Loop Deep Brain Stimulation

Parkinson's Disease afflicts millions of individuals globally. Emerging as a promising brain rehabilitation therapy for Parkinson's Disease, Closed-loop Deep Brain Stimulation (CL-DBS) aims to alleviate motor symptoms. The CL-DBS system comprises an implanted battery-powered medical device in the chest that sends stimulation signals to the brains of patients. These electrical stimulation signals are delivered to targeted brain regions via electrodes, with the magnitude of stimuli adjustable. However, current CL-DBS systems utilize energy-inefficient approaches, including reinforcement learning, fuzzy interface, and field-programmable gate array (FPGA), among others. These approaches make the traditional CL-DBS system impractical for implanted and wearable medical devices. This research proposes a novel neuromorphic approach that builds upon Leaky Integrate and Fire neuron (LIF) controllers to adjust the magnitude of DBS electric signals according to the various severities of PD patients. Our neuromorphic controllers, on-off LIF controller, and dual LIF controller, successfully reduced the power consumption of CL-DBS systems by 19% and 56%, respectively. Meanwhile, the suppression efficiency increased by 4.7% and 6.77%. Additionally, to address the data scarcity of Parkinson's Disease symptoms, we built Parkinson's Disease datasets that include the raw neural activities from the subthalamic nucleus at beta oscillations, which are typical physiological biomarkers for Parkinson's Disease.

cs.NE↗

arXiv2024

SPICER: Self-Supervised Learning for MRI with Automatic Coil Sensitivity Estimation and Reconstruction

Deep model-based architectures (DMBAs) integrating physical measurement models and learned image regularizers are widely used in parallel magnetic resonance imaging (PMRI). Traditional DMBAs for PMRI rely on pre-estimated coil sensitivity maps (CSMs) as a component of the measurement model. However, estimation of accurate CSMs is a challenging problem when measurements are highly undersampled. Additionally, traditional training of DMBAs requires high-quality groundtruth images, limiting their use in applications where groundtruth is difficult to obtain. This paper addresses these issues by presenting SPICE as a new method that integrates self-supervised learning and automatic coil sensitivity estimation. Instead of using pre-estimated CSMs, SPICE simultaneously reconstructs accurate MR images and estimates high-quality CSMs. SPICE also enables learning from undersampled noisy measurements without any groundtruth. We validate SPICE on experimentally collected data, showing that it can achieve state-of-the-art performance in highly accelerated data acquisition settings (up to 10x).

eess.IV↗

arXiv2024

Magnetic Bimeron Traveling on the Domain Wall

Domain wall bimerons (DWBMs) are nanoscale spin textures residing within the magnetic domain walls of in-plane magnets. In this study, we employ both numerical and analytical methods to explore the stabilization of Néel-type domain wall bimerons and their dynamics when excited by spin-orbit torque. Our findings reveal two unique and intriguing dynamic mechanisms, which depend on the polarization direction of the spin current: In the first scenario, the magnetic domain wall serves as a track that confines the motion of the bimeron and effectively suppresses the skyrmion Hall effect. In the second scenario, pushing the magnetic domain wall triggers a rapid sliding of the bimeron along the wall. This process significantly enhances the dynamics of the bimeron, resulting in a velocity increase of approximately 40 times compared to skyrmions and bimeron solitons. Our results highlight the potential advantages of the skyrmion Hall effect in developing energy-efficient spintronic devices based on domain wall bimerons.

cond-mat.mes-hall↗

arXiv2024

Pseudo-MRI-Guided PET Image Reconstruction Method Based on a Diffusion Probabilistic Model

Anatomically guided PET reconstruction using MRI information has been shown to have the potential to improve PET image quality. However, these improvements are limited to PET scans with paired MRI information. In this work we employed a diffusion probabilistic model (DPM) to infer T1-weighted-MRI (deep-MRI) images from FDG-PET brain images. We then use the DPM-generated T1w-MRI to guide the PET reconstruction. The model was trained with brain FDG scans, and tested in datasets containing multiple levels of counts. Deep-MRI images appeared somewhat degraded than the acquired MRI images. Regarding PET image quality, volume of interest analysis in different brain regions showed that both PET reconstructed images using the acquired and the deep-MRI images improved image quality compared to OSEM. Same conclusions were found analysing the decimated datasets. A subjective evaluation performed by two physicians confirmed that OSEM scored consistently worse than the MRI-guided PET images and no significant differences were observed between the MRI-guided PET images. This proof of concept shows that it is possible to infer DPM-based MRI imagery to guide the PET reconstruction, enabling the possibility of changing reconstruction parameters such as the strength of the prior on anatomically guided PET reconstruction in the absence of MRI.

eess.IV↗

arXiv2024

A Plug-and-Play Image Registration Network

Deformable image registration (DIR) is an active research topic in biomedical imaging. There is a growing interest in developing DIR methods based on deep learning (DL). A traditional DL approach to DIR is based on training a convolutional neural network (CNN) to estimate the registration field between two input images. While conceptually simple, this approach comes with a limitation that it exclusively relies on a pre-trained CNN without explicitly enforcing fidelity between the registered image and the reference. We present plug-and-play image registration network (PIRATE) as a new DIR method that addresses this issue by integrating an explicit data-fidelity penalty and a CNN prior. PIRATE pre-trains a CNN denoiser on the registration field and "plugs" it into an iterative method as a regularizer. We additionally present PIRATE+ that fine-tunes the CNN prior in PIRATE using deep equilibrium models (DEQ). PIRATE+ interprets the fixed-point iteration of PIRATE as a network with effectively infinite layers and then trains the resulting network end-to-end, enabling it to learn more task-specific information and boosting its performance. Our numerical results on OASIS and CANDI datasets show that our methods achieve state-of-the-art performance on DIR.

eess.IV↗

arXiv2023

DDPET-3D: Dose-aware Diffusion Model for 3D Ultra Low-dose PET Imaging

As PET imaging is accompanied by substantial radiation exposure and cancer risk, reducing radiation dose in PET scans is an important topic. Recently, diffusion models have emerged as the new state-of-the-art generative model to generate high-quality samples and have demonstrated strong potential for various tasks in medical imaging. However, it is difficult to extend diffusion models for 3D image reconstructions due to the memory burden. Directly stacking 2D slices together to create 3D image volumes would results in severe inconsistencies between slices. Previous works tried to either apply a penalty term along the z-axis to remove inconsistencies or reconstruct the 3D image volumes with 2 pre-trained perpendicular 2D diffusion models. Nonetheless, these previous methods failed to produce satisfactory results in challenging cases for PET image denoising. In addition to administered dose, the noise levels in PET images are affected by several other factors in clinical settings, e.g. scan time, medical history, patient size, and weight, etc. Therefore, a method to simultaneously denoise PET images with different noise-levels is needed. Here, we proposed a Dose-aware Diffusion model for 3D low-dose PET imaging (DDPET-3D) to address these challenges. We extensively evaluated DDPET-3D on 100 patients with 6 different low-dose levels (a total of 600 testing studies), and demonstrated superior performance over previous diffusion models for 3D imaging problems as well as previous noise-aware medical image denoising models. The code is available at: https://github.com/xxx/xxx.

eess.IV↗

arXiv2023

Block Coordinate Plug-and-Play Methods for Blind Inverse Problems

Plug-and-play (PnP) prior is a well-known class of methods for solving imaging inverse problems by computing fixed-points of operators combining physical measurement models and learned image denoisers. While PnP methods have been extensively used for image recovery with known measurement operators, there is little work on PnP for solving blind inverse problems. We address this gap by presenting a new block-coordinate PnP (BC-PnP) method that efficiently solves this joint estimation problem by introducing learned denoisers as priors on both the unknown image and the unknown measurement operator. We present a new convergence theory for BC-PnP compatible with blind inverse problems by considering nonconvex data-fidelity terms and expansive denoisers. Our theory analyzes the convergence of BC-PnP to a stationary point of an implicit function associated with an approximate minimum mean-squared error (MMSE) denoiser. We numerically validate our method on two blind inverse problems: automatic coil sensitivity estimation in magnetic resonance imaging (MRI) and blind image deblurring. Our results show that BC-PnP provides an efficient and principled framework for using denoisers as PnP priors for jointly estimating measurement operators and images.

eess.IV↗

arXiv2023

Impact of inherent energy barrier on spin-orbit torques in magnetic-metal/semimetal heterojunctions

Spintronic devices are based on heterojunctions of two materials with different magnetic and electronic properties. Although an energy barrier is naturally formed even at the interface of metallic heterojunctions, its impact on spin transport has been overlooked. Here, using diffusive spin Hall currents, we provide evidence that the inherent energy barrier governs the spin transport even in metallic systems. We find a sizable field-like torque, much larger than the damping-like counterpart, in Ni$_{81}$Fe$_{19}$/Bi$_{0.1}$Sb$_{0.9}$ bilayers. This is a distinct signature of barrier-mediated spin-orbit torques, which is consistent with our theory that predicts a strong modification of the spin mixing conductance induced by the energy barrier. Our results suggest that the spin mixing conductance and the corresponding spin-orbit torques are strongly altered by minimizing the work function difference in the heterostructure. These findings provide a new mechanism to control spin transport and spin torque phenomena by interfacial engineering of metallic heterostructures.

cond-mat.mtrl-sci↗

arXiv2022

Self-Supervised Deep Equilibrium Models for Inverse Problems with Theoretical Guarantees

Deep equilibrium models (DEQ) have emerged as a powerful alternative to deep unfolding (DU) for image reconstruction. DEQ models-implicit neural networks with effectively infinite number of layers-were shown to achieve state-of-the-art image reconstruction without the memory complexity associated with DU. While the performance of DEQ has been widely investigated, the existing work has primarily focused on the settings where groundtruth data is available for training. We present self-supervised deep equilibrium model (SelfDEQ) as the first self-supervised reconstruction framework for training model-based implicit networks from undersampled and noisy MRI measurements. Our theoretical results show that SelfDEQ can compensate for unbalanced sampling across multiple acquisitions and match the performance of fully supervised DEQ. Our numerical results on in-vivo MRI data show that SelfDEQ leads to state-of-the-art performance using only undersampled and noisy training data.

eess.IV↗

arXiv2022

Synthetic PET via Domain Translation of 3D MRI

Historically, patient datasets have been used to develop and validate various reconstruction algorithms for PET/MRI and PET/CT. To enable such algorithm development, without the need for acquiring hundreds of patient exams, in this paper we demonstrate a deep learning technique to generate synthetic but realistic whole-body PET sinograms from abundantly-available whole-body MRI. Specifically, we use a dataset of 56 $^{18}$F-FDG-PET/MRI exams to train a 3D residual UNet to predict physiologic PET uptake from whole-body T1-weighted MRI. In training we implemented a balanced loss function to generate realistic uptake across a large dynamic range and computed losses along tomographic lines of response to mimic the PET acquisition. The predicted PET images are forward projected to produce synthetic PET time-of-flight (ToF) sinograms that can be used with vendor-provided PET reconstruction algorithms, including using CT-based attenuation correction (CTAC) and MR-based attenuation correction (MRAC). The resulting synthetic data recapitulates physiologic $^{18}$F-FDG uptake, e.g. high uptake localized to the brain and bladder, as well as uptake in liver, kidneys, heart and muscle. To simulate abnormalities with high uptake, we also insert synthetic lesions. We demonstrate that this synthetic PET data can be used interchangeably with real PET data for the PET quantification task of comparing CT and MR-based attenuation correction methods, achieving $\leq 7.6\%$ error in mean-SUV compared to using real data. These results together show that the proposed synthetic PET data pipeline can be reasonably used for development, evaluation, and validation of PET/MRI reconstruction methods.

physics.med-ph↗

arXiv2022

Image Reconstruction for MRI using Deep CNN Priors Trained without Groundtruth

We propose a new plug-and-play priors (PnP) based MR image reconstruction method that systematically enforces data consistency while also exploiting deep-learning priors. Our prior is specified through a convolutional neural network (CNN) trained without any artifact-free ground truth to remove undersampling artifacts from MR images. The results on reconstructing free-breathing MRI data into ten respiratory phases show that the method can form high-quality 4D images from severely undersampled measurements corresponding to acquisitions of about 1 and 2 minutes in length. The results also highlight the competitive performance of the method compared to several popular alternatives, including the TGV regularization and traditional UNet3D.

eess.IV↗

arXiv2021

Deformation-Compensated Learning for Image Reconstruction without Ground Truth

Deep neural networks for medical image reconstruction are traditionally trained using high-quality ground-truth images as training targets. Recent work on Noise2Noise (N2N) has shown the potential of using multiple noisy measurements of the same object as an alternative to having a ground-truth. However, existing N2N-based methods are not suitable for learning from the measurements of an object undergoing nonrigid deformation. This paper addresses this issue by proposing the deformation-compensated learning (DeCoLearn) method for training deep reconstruction networks by compensating for object deformations. A key component of DeCoLearn is a deep registration module, which is jointly trained with the deep reconstruction network without any ground-truth supervision. We validate DeCoLearn on both simulated and experimentally collected magnetic resonance imaging (MRI) data and show that it significantly improves imaging quality.

eess.IV↗

arXiv2021

Current-induced magnetization switching in a chemically disordered A1 CoPt single layer

We report the first demonstration of the current-induced magnetization switching in a perpendicularly magnetized A1 CoPt single layer. We show that good perpendicular magnetic anisotropy can be obtained in a wide composition range of the A1 Co1-xPtx single layers, which allows to fabricate perpendicularly magnetized CoPt single layer with composition gradient to break the inversion symmetry of the structure. By fabricating the gradient CoPt single layer, we have evaluated the SOT efficiency and successfully realized the SOT-induced magnetization switching. Our study provides an approach to realize the current-induced magnetization in the ferromagnetic single layers without attaching SOT source materials.

cond-mat.mtrl-sci↗

arXiv2020

Deep Image Reconstruction using Unregistered Measurements without Groundtruth

One of the key limitations in conventional deep learning based image reconstruction is the need for registered pairs of training images containing a set of high-quality groundtruth images. This paper addresses this limitation by proposing a novel unsupervised deep registration-augmented reconstruction method (U-Dream) for training deep neural nets to reconstruct high-quality images by directly mapping pairs of unregistered and artifact-corrupted images. The ability of U-Dream to circumvent the need for accurately registered data makes it widely applicable to many biomedical image reconstruction tasks. We validate it in accelerated magnetic resonance imaging (MRI) by training an image reconstruction model directly on pairs of undersampled measurements from images that have undergone nonrigid deformations.

eess.IV↗

arXiv2020

RARE: Image Reconstruction using Deep Priors Learned without Ground Truth

Regularization by denoising (RED) is an image reconstruction framework that uses an image denoiser as a prior. Recent work has shown the state-of-the-art performance of RED with learned denoisers corresponding to pre-trained convolutional neural nets (CNNs). In this work, we propose to broaden the current denoiser-centric view of RED by considering priors corresponding to networks trained for more general artifact-removal. The key benefit of the proposed family of algorithms, called regularization by artifact-removal (RARE), is that it can leverage priors learned on datasets containing only undersampled measurements. This makes RARE applicable to problems where it is practically impossible to have fully-sampled groundtruth data for training. We validate RARE on both simulated and experimentally collected data by reconstructing a free-breathing whole-body 3D MRIs into ten respiratory phases from heavily undersampled k-space measurements. Our results corroborate the potential of learning regularizers for iterative inversion directly on undersampled and noisy measurements.

eess.IV↗

arXiv2020

Bone Material Analogues for PET/MRI Phantoms

Purpose: To develop bone material analogues that can be used in construction of phantoms for simultaneous PET/MRI systems. Methods: Plaster was used as the basis for the bone material analogues tested in this study. It was mixed with varying concentrations of an iodinated CT contrast, a gadolinium-based MR contrast agent, and copper sulfate to modulate the attenuation properties and MRI properties (T1 and T2*). Attenuation was measured with CT and 68Ge transmission scans, and MRI properties were measured with quantitative ultrashort echo time pulse sequences. A proof-of-concept skull was created by plaster casting. Results: Undoped plaster has a 511 keV attenuation coefficient (~0.14 cm-1) similar to cortical bone (0.10-0.15 cm-1), but slightly longer T1 (~500 ms) and T2* (~1.2 ms) MR parameters compared to bone (T1 ~ 300 ms, T2* ~ 0.4 ms). Doping with the iodinated agent resulted in increased attenuation with minimal perturbation to the MR parameters. Doping with a gadolinium chelate greatly reduced T1 and T2*, resulting in extremely short T1 values when the target T2* values were reached, while the attenuation coefficient was unchanged. Doping with copper sulfate was more selective for T2* shortening and achieved comparable T1 and T2* values to bone (after 1 week of drying), while the attenuation coefficient was unchanged. Conclusions: Plaster doped with copper sulfate is a promising bone material analogue for a PET/MRI phantom, mimicking the MR properties (T1 and T2*) and 511 keV attenuation coefficient of human cortical bone.

physics.med-ph↗