arXiv subjects

Weili Lin

Publications and source records attributed to Weili Lin.

At least 37 records · Page 2Linked to original sources

arXiv2022

Longitudinal Prediction of Postnatal Brain Magnetic Resonance Images via a Metamorphic Generative Adversarial Network

Missing scans are inevitable in longitudinal studies due to either subject dropouts or failed scans. In this paper, we propose a deep learning framework to predict missing scans from acquired scans, catering to longitudinal infant studies. Prediction of infant brain MRI is challenging owing to the rapid contrast and structural changes particularly during the first year of life. We introduce a trustworthy metamorphic generative adversarial network (MGAN) for translating infant brain MRI from one time-point to another. MGAN has three key features: (i) Image translation leveraging spatial and frequency information for detail-preserving mapping; (ii) Quality-guided learning strategy that focuses attention on challenging regions. (iii) Multi-scale hybrid loss function that improves translation of tissue contrast and structural details. Experimental results indicate that MGAN outperforms existing GANs by accurately predicting both contrast and anatomical details.

eess.IV↗

arXiv2022

Observations of the Very Young Type Ia Supernova 2019np with Early-excess Emission

Early-time radiative signals from type Ia supernovae (SNe Ia) can provide important constraints on the explosion mechanism and the progenitor system. We present observations and analysis of SN 2019np, a nearby SN Ia discovered within 1-2 days after the explosion. Follow-up observations were conducted in optical, ultraviolet, and near-infrared bands, covering the phases from $\sim-$16.7 days to $\sim$+367.8 days relative to its $B-$band peak luminosity. The photometric and spectral evolutions of SN 2019np resembles the average behavior of normal SNe Ia. The absolute B-band peak magnitude and the post-peak decline rate are $M_{\rm max}(B)=-19.52 \pm 0.47$mag and $Δm_{\rm15}(B) =1.04 \pm 0.04$mag, respectively. No Hydrogen line has been detected in the near-infrared and nebular-phase spectra of SN 2019np. Assuming that the $^{56}$Ni powering the light curve is centrally located, we find that the bolometric light curve of SN 2019np shows a flux excess up to 5.0% in the early phase compared to the radiative diffusion model. Such an extra radiation perhaps suggests the presence of an additional energy source beyond the radioactive decay of central nickel. Comparing the observed color evolution with that predicted by different models such as interactions of SN ejecta with circumstellar matter (CSM)/companion star, a double-detonation explosion from a sub-Chandrasekhar mass white dwarf (WD), and surface $^{56}$Ni mixing, the latter one is favored.

astro-ph.HE↗

arXiv2021

Learning MRI Artifact Removal With Unpaired Data

Retrospective artifact correction (RAC) improves image quality post acquisition and enhances image usability. Recent machine learning driven techniques for RAC are predominantly based on supervised learning and therefore practical utility can be limited as data with paired artifact-free and artifact-corrupted images are typically insufficient or even non-existent. Here we show that unwanted image artifacts can be disentangled and removed from an image via an RAC neural network learned with unpaired data. This implies that our method does not require matching artifact-corrupted data to be either collected via acquisition or generated via simulation. Experimental results demonstrate that our method is remarkably effective in removing artifacts and retaining anatomical details in images with different contrasts.

eess.IV↗

arXiv2021

A Few-shot Learning Graph Multi-Trajectory Evolution Network for Forecasting Multimodal Baby Connectivity Development from a Baseline Timepoint

Charting the baby connectome evolution trajectory during the first year after birth plays a vital role in understanding dynamic connectivity development of baby brains. Such analysis requires acquisition of longitudinal connectomic datasets. However, both neonatal and postnatal scans are rarely acquired due to various difficulties. A small body of works has focused on predicting baby brain evolution trajectory from a neonatal brain connectome derived from a single modality. Although promising, large training datasets are essential to boost model learning and to generalize to a multi-trajectory prediction from different modalities (i.e., functional and morphological connectomes). Here, we unprecedentedly explore the question: Can we design a few-shot learning-based framework for predicting brain graph trajectories across different modalities? To this aim, we propose a Graph Multi-Trajectory Evolution Network (GmTE-Net), which adopts a teacher-student paradigm where the teacher network learns on pure neonatal brain graphs and the student network learns on simulated brain graphs given a set of different timepoints. To the best of our knowledge, this is the first teacher-student architecture tailored for brain graph multi-trajectory growth prediction that is based on few-shot learning and generalized to graph neural networks (GNNs). To boost the performance of the student network, we introduce a local topology-aware distillation loss that forces the predicted graph topology of the student network to be consistent with the teacher network. Experimental results demonstrate substantial performance gains over benchmark methods. Hence, our GmTE-Net can be leveraged to predict atypical brain connectivity trajectory evolution across various modalities. Our code is available at https: //github.com/basiralab/GmTE-Net.

q-bio.NC↗

arXiv2021

Minute-cadence Observations of the LAMOST Fields with the TMTS: I. Methodology of Detecting Short-period Variables and Results from the first-year Survey

Tsinghua University-Ma Huateng Telescopes for Survey (TMTS), located at Xinglong Station of NAOC, has a field of view upto 18 deg^2. The TMTS has started to monitor the LAMOST sky areas since 2020, with the uninterrupted observations lasting for about 6 hours on average for each sky area and a cadence of about 1 minute. Here we introduce the data analysis and preliminary scientific results for the first-year observations, which covered 188 LAMOST plates ( about 1970 deg^2). These observations have generated over 4.9 million uninterrupted light curves, with at least 100 epochs for each of them. These light curves correspond to 4.26 million Gaia-DR2 sources, among which 285 thousand sources are found to have multi-epoch spectra from the LAMOST. By analysing these light curves with the Lomb-Scargle periodograms, we identify more than 3700 periodic variable star candidates with periods below 7.5 hours, primarily consisting of eclipsing binaries and Delta Scuti stars. Those short-period binaries will provide important constraints on theories of binary evolution and possible sources for space gravitational wave experiments in the future. Moreover, we also identified 42 flare stars by searching rapidly-evolving signals in the light curves. The densely-sampled light curves from the TMTS allow us to better quantify the shapes and durations for these flares.

astro-ph.IM↗

arXiv2021

ASASSN-14ms:the Most Energetic Known Explosion of a Type Ibn Supernova and its Physical Origin

ASASSN-14ms may represent the most luminous Type Ibn supernova (SN~Ibn) ever detected, with an absolute U-band magnitude brighter than -22.0 mag and a total bolometric luminosity >1.0x10^{44} erg/s near maximum light. The early-time spectra of this SN are characterized by a blue continuum on which are superimposed narrow P~Cygni profile lines of He I, suggesting the presence of slowly moving (~1000 km/s), He-rich circumstellar material (CSM). At 1--2 months after maximum brightness, the He I line profiles become only slightly broader, with blueshifted velocities of 2000--3000 km/s, consistent with the CSM shell being continuously accelerated by the SN light and ejecta. Like most SNe~Ibn, the light curves of ASASSN-14ms show rapid post-peak evolution, dropping by ~7 mag in the V band over three months. Such a rapid post-peak decline and high luminosity can be explained with interaction between SN ejecta and helium-rich CSM of 0.9~M_{\odot} at a distance of~10^{15} cm. The CSM around ASASSN-14ms is estimated to originate from a pre-explosion event with a mass-loss rate of 6.7~M_\odot /yr (assuming a velocity of ~1000 km/s), which is consistent with abundant He-rich material violently ejected during the late Wolf-Rayet (WN9-11 or Opfe) stage. After examining the light curves for a sample of SNe~Ibn, we find that the more luminous ones tend to have slower post-peak decline rates, reflecting that the observed differences may arise primarily from discrepancies in the CSM distribution around the massive progenitors.

astro-ph.HE↗

arXiv2021

SN 2015bf: a fast declining type II supernova with flash-ionised signatures

We present optical and ultraviolet photometry, as well as optical spectra, for the type II supernova (SN) 2015bf. Our observations cover the phases from $\sim 2$ to $\sim 200$ d after explosion. The first spectrum is characterised by a blue continuum with a blackbody temperature of $\sim 24,000$K and flash-ionised emission lines. After about one week, the spectra of SN 2015bf evolve like those of a regular SN II. From the luminosity of the narrow emission component of H$α$, we deduce that the mass-loss rate is larger than $\sim 3.7\times10^{-3}\,{\rm M_\odot\,yr^{-1}}$. The disappearance of the flash features in the first week after explosion indicates that the circumstellar material is confined within $\sim 6 \times 10^{14}$ cm. Thus, we suggest that the progenitor of SN 2015bf experienced violent mass loss shortly before the supernova explosion. The multiband light curves show that SN 2015bf has a high peak luminosity with an absolute visual magnitude $M_V = -18.11 \pm 0.08$ mag and a fast post-peak decline with a $V$-band decay of $1.22 \pm 0.09$ mag within $\sim 50$ d after maximum light. Moreover, the $R$-band tail luminosity of SN 2015bf is fainter than that of SNe~II with similar peak by 1--2 mag, suggesting a small amount of ${\rm ^{56}Ni}$ ($\sim 0.009\,{\rm M_\odot}$) synthesised during the explosion. Such a low nickel mass indicates that the progenitor of SN 2015bf could be a super-asymptotic-giant-branch star that collapsed owing to electron capture.

astro-ph.HE↗

arXiv2021

Supernova luminosity powered by magnetar-disk system

Magnetars are one of the potential power sources for some energetic supernova explosions such as type I superluminous supernovae (SLSNe I) and broad-lined type Ic supernovae (SNe Ic-BL). In order to explore the possible link between these two subclasses of supernovae (SNe), we study the effect of fallback accretion disk on magnetar evolution and magnetar-powered SNe. In this scenario, the interaction between a magnetar and a fallback accretion disk would accelerate the spin of the magnetar in the accretion regime but could result in substantial spin-down of the magnetars in the propeller regime. Thus, the initial rotation of the magnetar plays a less significant role in the spin evolution. Such a magnetar-disk interaction scenario can explain well the light curves of both SNe Ic-BL and SLSNe I, for which the observed differences are sensitive to the initial magnetic field of the magnetar and the fallback mass and timescale for the disk. Compared to the magnetars powering the SNe Ic-BL, those accounting for more luminous SNe usually maintain faster rotation and have relatively lower effective magnetic fields around peak time. In addition, the association between SLSNe I and long gamma-ray bursts, if observed in the future, could be explained in the context of magnetar-disk system.

astro-ph.HE↗

arXiv2021

Optical and Ultraviolet Monitoring of the Black Hole X-ray Binary MAXI J1820+070/ASASSN-18ey for 18 Months

MAXI J1820+070 is a low-mass black hole X-ray binary system with high luminosity in both optical and X-ray bands during the outburst periods. We present extensive photometry in X-ray, ultraviolet, and optical bands, as well as densely-cadenced optical spectra, covering the phase from the beginning of optical outburst to $\sim$550 days. During the rebrightening process, the optical emission preceded the X-ray by 20.80 $\pm$ 2.85 days. The spectra are characterized by blue continua and emission features of Balmer series, He I, He II lines and broad Bowen blend. The pseudo equivalent width (pEW) of emission lines are found to show anticorrelations with the X-ray flux measured at comparable phases, which is due to the increased suppression by the optical continuum. At around the X-ray peak, the full width at half maximums (FWHMs) of H$_β$ and He II $λ$4686 tend to stabilize at 19.4 Angstrom and 21.8 Angstrom, which corresponds to the line forming region at a radius of 1.7 and 1.3 R_sun within the disk. We further analyzed the absolute fluxes of the lines and found that the fluxes of H$_β$ and He II $λ$4686 show positive correlations with the X-ray flux, favoring that the irradiation model is responsible for the optical emission. However, the fact that X-ray emission experiences a dramatic flux drop at t$\sim$200 days after the outburst, while the optical flux only shows little variations suggests that additional energy such as viscous energy may contribute to the optical radiation in addition to the X-ray irradiation.

astro-ph.HE↗

arXiv2021

The Peculiar Transient AT2018cow: A Possible Origin of A Type Ibn/IIn Supernova

We present our photometric and spectroscopic observations on the peculiar transient AT2018cow. The multi-band photometry covers from peak to $\sim$70 days and the spectroscopy ranges from 5 to $\sim$50 days. The rapid rise ($t_{\mathrm{r}}$$\lesssim$2.9 days), high luminosity ($M_{V,\mathrm{peak}}\sim-$20.8 mag) and fast decline after peak make AT2018cow stand out of any other optical transients. While we find that its light curves show high resemblance to those of type Ibn supernovae. Moreover, the spectral energy distribution remains high temperature of $\sim$14,000 K after $\sim$15 days since discovery. The spectra are featureless in the first 10 days, while some broad emission lines due to H, He, C and O emerge later, with velocity declining from $\sim$14,000 km s$^{-1}$ to $\sim$3000 km s$^{-1}$ at the end of our observations. Narrow and weak He I emission lines emerge in the spectra at $t>$20 days since discovery. These emission lines are reminiscent of the features seen in interacting supernovae like type Ibn and IIn subclasses. We fit the bolometric light curves with a model of circumstellar interaction (CSI) and radioactive decay (RD) of \Ni and find a good fit with ejecta mass $M_{\mathrm{ej}}\sim$3.16 M$_{\odot}$, circumstellar material mass $M_{\mathrm{CSM}}\sim$0.04 M$_{\odot}$, and ejected \Ni mass $M_{^{56}\mathrm{Ni}}\sim$0.23 M$_{\odot}$. The CSM shell might be formed in an eruptive mass ejection of the progenitor star. Furthermore, host environment of AT2018cow implies connection of AT2018cow with massive stars. Combining observational properties and the light curve fitting results, we conclude that AT2018cow might be a peculiar interacting supernova originated from a massive star.

astro-ph.HE↗

arXiv2020

Navigator-Free Submillimeter Diffusion Imaging using Multishot-encoded Simultaneous Multi-slice (MUSIUM)

The ability to achieve submillimter isotropic resolution diffusion MR imaging (dMRI) is critically important to study fine-scale brain structures, particularly in the cortex. One of the major challenges in performing submillimeter dMRI is the inherently low signal-to-noise ratio (SNR). While approaches capable of mitigating the low SNR in high resolution dMRI have been proposed, namely simultaneous multi-slab (SMSlab) and generalized slice dithered enhanced resolution with simultaneous multislice (gSlider-SMS), limitations are associated with these approaches. The SMSlab sequences suffer from the slab boundary artifacts and require additional navigators for phase estimation. On the other hand, gSlider sequences require relatively high RF power and peak amplitude, which increase the SAR and complicate the RF excitation. In this work, we developed a navigator-free multishot-encoded simultaneous multi-slice (MUSIUM) imaging approach on a 3T MR scanner, achieving enhanced SNR, low RF power and peak amplitude, and being free from slab boundary artifacts. The dMRI with ultrahigh resolution (0.86 mm isotropic resolution), whole brain coverage and ~12.5 minute acquisition time were achieved, revealing detailed structures at cortical and white matter areas. The simulated and in vivo results also demonstrated that the MUSIUM imaging was minimally affected by the motion. Taken together, the MUSIUM imaging is a promising approach to achieve submillimeter diffusion imaging on 3T MR scanners within clinically feasible scan time.

physics.med-ph↗

arXiv2020

Co-evolution of Functional Brain Network at Multiple Scales during Early Infancy

The human brains are organized into hierarchically modular networks facilitating efficient and stable information processing and supporting diverse cognitive processes during the course of development. While the remarkable reconfiguration of functional brain network has been firmly established in early life, all these studies investigated the network development from a "single-scale" perspective, which ignore the richness engendered by its hierarchical nature. To fill this gap, this paper leveraged a longitudinal infant resting-state functional magnetic resonance imaging dataset from birth to 2 years of age, and proposed an advanced methodological framework to delineate the multi-scale reconfiguration of functional brain network during early development. Our proposed framework is consist of two parts. The first part developed a novel two-step multi-scale module detection method that could uncover efficient and consistent modular structure for longitudinal dataset from multiple scales in a completely data-driven manner. The second part designed a systematic approach that employed the linear mixed-effect model to four global and nodal module-related metrics to delineate scale-specific age-related changes of network organization. By applying our proposed methodological framework on the collected longitudinal infant dataset, we provided the first evidence that, in the first 2 years of life, the brain functional network is co-evolved at different scales, where each scale displays the unique reconfiguration pattern in terms of modular organization.

q-bio.NC↗

arXiv2020

Multi-Site Infant Brain Segmentation Algorithms: The iSeg-2019 Challenge

To better understand early brain growth patterns in health and disorder, it is critical to accurately segment infant brain magnetic resonance (MR) images into white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF). Deep learning-based methods have achieved state-of-the-art performance; however, one of major limitations is that the learning-based methods may suffer from the multi-site issue, that is, the models trained on a dataset from one site may not be applicable to the datasets acquired from other sites with different imaging protocols/scanners. To promote methodological development in the community, iSeg-2019 challenge (http://iseg2019.web.unc.edu) provides a set of 6-month infant subjects from multiple sites with different protocols/scanners for the participating methods. Training/validation subjects are from UNC (MAP) and testing subjects are from UNC/UMN (BCP), Stanford University, and Emory University. By the time of writing, there are 30 automatic segmentation methods participating in iSeg-2019. We review the 8 top-ranked teams by detailing their pipelines/implementations, presenting experimental results and evaluating performance in terms of the whole brain, regions of interest, and gyral landmark curves. We also discuss their limitations and possible future directions for the multi-site issue. We hope that the multi-site dataset in iSeg-2019 and this review article will attract more researchers on the multi-site issue.

eess.IV↗

arXiv2020

Probing Tissue Microarchitecture of the Baby Brain via Spherical Mean Spectrum Imaging

During the first years of life, the human brain undergoes dynamic spatially-heterogeneous changes, involving differentiation of neuronal types, dendritic arborization, axonal ingrowth, outgrowth and retraction, synaptogenesis, and myelination. To better quantify these changes, this article presents a method for probing tissue microarchitecture by characterizing water diffusion in a spectrum of length scales, factoring out the effects of intra-voxel orientation heterogeneity. Our method is based on the spherical means of the diffusion signal, computed over gradient directions for a fixed set of diffusion weightings (i.e., b-values). We decompose the spherical mean series at each voxel into a spherical mean spectrum (SMS), which essentially encodes the fractions of spin packets undergoing fine- to coarse-scale diffusion processes, characterizing hindered and restricted diffusion stemming respectively from extra- and intra-neurite water compartments. From the SMS, multiple orientation distribution invariant indices can be computed, allowing for example the quantification of neurite density, microscopic fractional anisotropy ($μ$FA), per-axon axial/radial diffusivity, and free/restricted isotropic diffusivity. We show maps of these indices for baby brains, demonstrating that microscopic tissue features can be extracted from the developing brain for greater sensitivity and specificity to development related changes. Also, we demonstrate that our method, called spherical mean spectrum imaging (SMSI), is fast, accurate, and can overcome the biases associated with other state-of-the-art microstructure models.

physics.med-ph↗

arXiv2020

Real-Time Quality Assessment of Pediatric MRI via Semi-Supervised Deep Nonlocal Residual Neural Networks

In this paper, we introduce an image quality assessment (IQA) method for pediatric T1- and T2-weighted MR images. IQA is first performed slice-wise using a nonlocal residual neural network (NR-Net) and then volume-wise by agglomerating the slice QA results using random forest. Our method requires only a small amount of quality-annotated images for training and is designed to be robust to annotation noise that might occur due to rater errors and the inevitable mix of good and bad slices in an image volume. Using a small set of quality-assessed images, we pre-train NR-Net to annotate each image slice with an initial quality rating (i.e., pass, questionable, fail), which we then refine by semi-supervised learning and iterative self-training. Experimental results demonstrate that our method, trained using only samples of modest size, exhibit great generalizability, capable of real-time (milliseconds per volume) large-scale IQA with near-perfect accuracy.

cs.CV↗

arXiv2020

Multifold Acceleration of Diffusion MRI via Slice-Interleaved Diffusion Encoding (SIDE)

Diffusion MRI (dMRI) is a unique imaging technique for in vivo characterization of tissue microstructure and white matter pathways. However, its relatively long acquisition time implies greater motion artifacts when imaging, for example, infants and Parkinson's disease patients. To accelerate dMRI acquisition, we propose in this paper (i) a diffusion encoding scheme, called Slice-Interleaved Diffusion Encoding (SIDE), that interleaves each diffusion-weighted (DW) image volume with slices that are encoded with different diffusion gradients, essentially allowing the slice-undersampling of image volume associated with each diffusion gradient to significantly reduce acquisition time, and (ii) a method based on deep learning for effective reconstruction of DW images from the highly slice-undersampled data. Evaluation based on the Human Connectome Project (HCP) dataset indicates that our method can achieve a high acceleration factor of up to 6 with minimal information loss. Evaluation using dMRI data acquired with SIDE acquisition demonstrates that it is possible to accelerate the acquisition by as much as 50 folds when combined with multi-band imaging.

physics.med-ph↗

arXiv2019

FRNET: Flattened Residual Network for Infant MRI Skull Stripping

Skull stripping for brain MR images is a basic segmentation task. Although many methods have been proposed, most of them focused mainly on the adult MR images. Skull stripping for infant MR images is more challenging due to the small size and dynamic intensity changes of brain tissues during the early ages. In this paper, we propose a novel CNN based framework to robustly extract brain region from infant MR image without any human assistance. Specifically, we propose a simplified but more robust flattened residual network architecture (FRnet). We also introduce a new boundary loss function to highlight ambiguous and low contrast regions between brain and non-brain regions. To make the whole framework more robust to MR images with different imaging quality, we further introduce an artifact simulator for data augmentation. We have trained and tested our proposed framework on a large dataset (N=343), covering newborns to 48-month-olds, and obtained performance better than the state-of-the-art methods in all age groups.

cs.CV↗

arXiv2019

Spherical U-Net on Cortical Surfaces: Methods and Applications

Convolutional Neural Networks (CNNs) have been providing the state-of-the-art performance for learning-related problems involving 2D/3D images in Euclidean space. However, unlike in the Euclidean space, the shapes of many structures in medical imaging have a spherical topology in a manifold space, e.g., brain cortical or subcortical surfaces represented by triangular meshes, with large inter-subject and intrasubject variations in vertex number and local connectivity. Hence, there is no consistent neighborhood definition and thus no straightforward convolution/transposed convolution operations for cortical/subcortical surface data. In this paper, by leveraging the regular and consistent geometric structure of the resampled cortical surface mapped onto the spherical space, we propose a novel convolution filter analogous to the standard convolution on the image grid. Accordingly, we develop corresponding operations for convolution, pooling, and transposed convolution for spherical surface data and thus construct spherical CNNs. Specifically, we propose the Spherical U-Net architecture by replacing all operations in the standard U-Net with their spherical operation counterparts. We then apply the Spherical U-Net to two challenging and neuroscientifically important tasks in infant brains: cortical surface parcellation and cortical attribute map development prediction. Both applications demonstrate the competitive performance in the accuracy, computational efficiency, and effectiveness of our proposed Spherical U-Net, in comparison with the state-of-the-art methods.

cs.CV↗