16 research outputs found
DMCVR: Morphology-Guided Diffusion Model for 3D Cardiac Volume Reconstruction
Accurate 3D cardiac reconstruction from cine magnetic resonance imaging
(cMRI) is crucial for improved cardiovascular disease diagnosis and
understanding of the heart's motion. However, current cardiac MRI-based
reconstruction technology used in clinical settings is 2D with limited
through-plane resolution, resulting in low-quality reconstructed cardiac
volumes. To better reconstruct 3D cardiac volumes from sparse 2D image stacks,
we propose a morphology-guided diffusion model for 3D cardiac volume
reconstruction, DMCVR, that synthesizes high-resolution 2D images and
corresponding 3D reconstructed volumes. Our method outperforms previous
approaches by conditioning the cardiac morphology on the generative model,
eliminating the time-consuming iterative optimization process of the latent
code, and improving generation quality. The learned latent spaces provide
global semantics, local cardiac morphology and details of each 2D cMRI slice
with highly interpretable value to reconstruct 3D cardiac shape. Our
experiments show that DMCVR is highly effective in several aspects, such as 2D
generation and 3D reconstruction performance. With DMCVR, we can produce
high-resolution 3D cardiac MRI reconstructions, surpassing current techniques.
Our proposed framework has great potential for improving the accuracy of
cardiac disease diagnosis and treatment planning. Code can be accessed at
https://github.com/hexiaoxiao-cs/DMCVR.Comment: Accepted in MICCAI 202
Dealing With Heterogeneous 3D MR Knee Images: A Federated Few-Shot Learning Method With Dual Knowledge Distillation
Federated Learning has gained popularity among medical institutions since it
enables collaborative training between clients (e.g., hospitals) without
aggregating data. However, due to the high cost associated with creating
annotations, especially for large 3D image datasets, clinical institutions do
not have enough supervised data for training locally. Thus, the performance of
the collaborative model is subpar under limited supervision. On the other hand,
large institutions have the resources to compile data repositories with
high-resolution images and labels. Therefore, individual clients can utilize
the knowledge acquired in the public data repositories to mitigate the shortage
of private annotated images. In this paper, we propose a federated few-shot
learning method with dual knowledge distillation. This method allows joint
training with limited annotations across clients without jeopardizing privacy.
The supervised learning of the proposed method extracts features from limited
labeled data in each client, while the unsupervised data is used to distill
both feature and response-based knowledge from a national data repository to
further improve the accuracy of the collaborative model and reduce the
communication cost. Extensive evaluations are conducted on 3D magnetic
resonance knee images from a private clinical dataset. Our proposed method
shows superior performance and less training time than other semi-supervised
federated learning methods. Codes and additional visualization results are
available at https://github.com/hexiaoxiao-cs/fedml-knee
Identification of Early Diagnostic and Prognostic Biomarkers via WGCNA in Stomach Adenocarcinoma
Stomach adenocarcinoma (STAD) is a leading cause of cancer deaths, and the outcome of the patients remains dismal for the lack of effective biomarkers of early detection. Recent studies have elucidated the landscape of genomic alterations of gastric cancer and reveal some biomarkers of advanced-stage gastric cancer, however, information about early-stage biomarkers is limited. Here, we adopt Weighted Gene Co-expression Network Analysis (WGCNA) to screen potential biomarkers for early-stage STAD using RNA-Seq and clinical data from TCGA database. We find six gene clusters (or modules) are significantly correlated with the stage-I STADs. Among these, five hub genes, i.e., MS4A1, THBS2, VCAN, PDGFRB, and KCNA3 are identified and significantly de-regulated in the stage-I STADs compared with the normal stomach gland tissues, which suggests they can serve as potential early diagnostic biomarkers. Moreover, we show that high expression of VCAN and PDGFRB is associated with poor prognosis of STAD. VCAN encodes a large chondroitin sulfate proteoglycan that is the main component of the extracellular matrix, and PDGFRB encodes a cell surface tyrosine kinase receptor for members of the platelet-derived growth factor (PDGF) family. Consistently, Gene Ontology (GO) analysis of differentially expressed genes in the STADs indicates terms associated with extracellular matrix and receptor ligand activity are significantly enriched. Protein-protein network interaction analysis (PPI) and Gene Set Enrichment Analysis (GSEA) further support the core role of VCAN and PDGFRB in the tumorigenesis. Collectively, our study identifies the potential biomarkers for early detection and prognosis of STAD
Constructing a Tough Shield around the Wellbore by Stabilizing the Multi-Scale Structure of Granular Plugging Zone in Deep Fractured Reservoirs
Fracture plugging zone with low strength is one of the key reasons for plugging failure in deep fractured reservoirs. Forming a high-strength plugging zone is a key engineering problem to be solved in wellbore strengthening. In this chapter, wellbore strengthening mechanisms of plugging zone for wellbore strengthening in deep fractured reservoirs are revealed from a relationship between mechanical structure and strength standpoint. Physical granular bridging materials dislocation and crushing under pressure fluctuation induce the strong force chains network failure, which leads to macroscale friction or shear failure of plugging zone. The main methods to improve microscale materials stability are to increase friction resistance, exert embedding effect, and strengthen bonding effect. Factors, which strengthen the meso-structure stability, include increasing shear strength and proportion of strong force chains. Key measures to strengthen the macrostructure stability of plugging zone are by improving its compactness, controlling its length, and ensuring the stability timeliness
Solar Ring Mission: Building a Panorama of the Sun and Inner-heliosphere
Solar Ring (SOR) is a proposed space science mission to monitor and study the
Sun and inner heliosphere from a full 360{\deg} perspective in the ecliptic
plane. It will deploy three 120{\deg}-separated spacecraft on the 1-AU orbit.
The first spacecraft, S1, locates 30{\deg} upstream of the Earth, the second,
S2, 90{\deg} downstream, and the third, S3, completes the configuration. This
design with necessary science instruments, e.g., the Doppler-velocity and
vector magnetic field imager, wide-angle coronagraph, and in-situ instruments,
will allow us to establish many unprecedented capabilities: (1) provide
simultaneous Doppler-velocity observations of the whole solar surface to
understand the deep interior, (2) provide vector magnetograms of the whole
photosphere - the inner boundary of the solar atmosphere and heliosphere, (3)
provide the information of the whole lifetime evolution of solar featured
structures, and (4) provide the whole view of solar transients and space
weather in the inner heliosphere. With these capabilities, Solar Ring mission
aims to address outstanding questions about the origin of solar cycle, the
origin of solar eruptions and the origin of extreme space weather events. The
successful accomplishment of the mission will construct a panorama of the Sun
and inner-heliosphere, and therefore advance our understanding of the star and
the space environment that holds our life.Comment: 41 pages, 6 figures, 1 table, to be published in Advances in Space
Researc
Unconstrained 3D shape programming with light-induced stress gradient
Programming 2D sheets to form 3D shapes is significant for flexible electronics, soft robots, and biomedical devices. Stress regulation is one of the most used methods, during which external force is usually needed to keep the stress, leading to complex processing setups. Here, by introducing dynamic diselenide bonds into shape-memory materials, unconstrained shape programming with light is achieved. The material could hold and release internal stress by themselves through the shape-memory effect, simplifying programming setups. The fixed stress could be relaxed by light to form stress gradients, leading to out-of-plane deformations through asymmetric contractions. Benefiting from the variability of light irradiation, complex 3D configurations can be obtained conveniently from 2D polymer sheets. Besides, remotely controlled "4D assembly" and actuation, including object transportation and self-lifting, can be achieved by sequential deformation. Taking advantage of the high spatial resolution of light, this material can also produce 3D microscopic patterns. The light-induced stress gradients significantly simplify 3D shape programming procedures with improved resolution and complexity and have great potential in soft robots, smart actuators, and anti-counterfeiting techniques.This work was financially supported by National Natural Science Foundation of China (21734006) and the Foundation for Innovative Research Group of National Natural Science Foundation of China (21821001)