Zhanli Hu
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View article: Super‐resolution reconstruction of MR vessel wall images using a deep neural network
Super‐resolution reconstruction of MR vessel wall images using a deep neural network Open
Background Magnetic resonance vessel wall imaging (MR‐VWI) is a non‐invasive, high‐resolution technique that enables detailed visualization of vascular structures and plays a crucial role in diagnosing cerebrovascular diseases. However, im…
View article: Automatic dual‐modality breast tumor segmentation in PET/CT images using CT‐guided transformer
Automatic dual‐modality breast tumor segmentation in PET/CT images using CT‐guided transformer Open
Background Breast tumor segmentation is crucial for the diagnosis of breast cancer, as it enables radiologists to rapidly identify areas of interest and facilitate subsequent analysis, diagnosis, and treatment. Present breast tumor segment…
View article: A Novel Autoencoder-Based Design for Channel Estimation in Maritime OFDM Systems
A Novel Autoencoder-Based Design for Channel Estimation in Maritime OFDM Systems Open
This paper introduces a novel autoencoder-based channel estimation framework specifically designed for OFDM systems in the complex and rapidly time-varying maritime channel. We design a novel autoencoder architecture that integrates attent…
View article: Reconstruction of total-body multi parametric images with shortened-duration dynamic [<sup>68</sup>Ga]Ga-PSMA-11 and [<sup>68</sup>Ga]Ga-FAPI-04 PET scans
Reconstruction of total-body multi parametric images with shortened-duration dynamic [<sup>68</sup>Ga]Ga-PSMA-11 and [<sup>68</sup>Ga]Ga-FAPI-04 PET scans Open
Objective. The lengthy 1 h dynamic positron emission tomography (PET) scans discomfort patients, add motion artifacts, and inflate costs, highlighting the need for tech advancements to reduce scan times. Therefore, we attempted to reconstr…
View article: Memory-enhanced and multi-domain learning-based deep unrolling network for medical image reconstruction
Memory-enhanced and multi-domain learning-based deep unrolling network for medical image reconstruction Open
Objective . Reconstructing high-quality images from corrupted measurements remains a fundamental challenge in medical imaging. Recently, deep unrolling (DUN) methods have emerged as a promising solution, combining the interpretability of t…
View article: Prompt Guiding Multi-Scale Adaptive Sparse Representation-driven Network for Low-Dose CT MAR
Prompt Guiding Multi-Scale Adaptive Sparse Representation-driven Network for Low-Dose CT MAR Open
Low-dose CT (LDCT) is capable of reducing X-ray radiation exposure, but it will potentially degrade image quality, even yields metal artifacts at the case of metallic implants. For simultaneous LDCT reconstruction and metal artifact reduct…
View article: Multimodal feature‐guided diffusion model for low‐count PET image denoising
Multimodal feature‐guided diffusion model for low‐count PET image denoising Open
Background To minimize radiation exposure while obtaining high‐quality Positron Emission Tomography (PET) images, various methods have been developed to derive standard‐count PET (SPET) images from low‐count PET (LPET) images. Although dee…
View article: Feasibility exploration of myocardial blood flow synthesis from a simulated static myocardial computed tomography perfusion via a deep neural network
Feasibility exploration of myocardial blood flow synthesis from a simulated static myocardial computed tomography perfusion via a deep neural network Open
Background: Myocardial blood flow (MBF) provides important diagnostic information for myocardial ischemia. However, dynamic computed tomography perfusion (CTP) needed for MBF involves multiple exposures, leading to high radiation doses. Ob…
View article: MacNet: a mobile attention classification network combining convolutional neural network and transformer for the differentiation of cervical cancer
MacNet: a mobile attention classification network combining convolutional neural network and transformer for the differentiation of cervical cancer Open
We have proposed a lightweight neural network method that innovatively combines attention mechanisms with convolutional neural networks (CNNs) to efficiently utilize multiscale information from histopathological images. This integration en…
View article: Bidirectional dynamic frame prediction network for total-body [68Ga]Ga-PSMA-11 and [68Ga]Ga-FAPI-04 PET images
Bidirectional dynamic frame prediction network for total-body [68Ga]Ga-PSMA-11 and [68Ga]Ga-FAPI-04 PET images Open
In this work, we propose a bidirectional dynamic frame prediction network for total-body 68Ga-PSMA and 68Ga-FAPI PET imaging with a reduced scan duration. Visual and quantitative analyses demonstrated that our approach performed well when …
View article: SG-Fusion: A swin-transformer and graph convolution-based multi-modal deep neural network for glioma prognosis
SG-Fusion: A swin-transformer and graph convolution-based multi-modal deep neural network for glioma prognosis Open
The integration of morphological attributes extracted from histopathological images and genomic data holds significant importance in advancing tumor diagnosis, prognosis, and grading. Histopathological images are acquired through microscop…
View article: Deep learning for intracranial aneurysm segmentation using CT angiography
Deep learning for intracranial aneurysm segmentation using CT angiography Open
Objective. This study aimed to employ a two-stage deep learning method to accurately detect small aneurysms (4–10 mm in size) in computed tomography angiography images. Approach. This study included 956 patients from 6 hospitals and a publ…
View article: Fusion of shallow and deep features from 18F-FDG PET/CT for predicting EGFR-sensitizing mutations in non-small cell lung cancer
Fusion of shallow and deep features from 18F-FDG PET/CT for predicting EGFR-sensitizing mutations in non-small cell lung cancer Open
Our findings suggest that deep features significantly enhance the detection of EGFR-sensitizing mutations, especially those extracted with ResNet. Moreover, PET/CT images are more effective than CT-only and PET-only images in producing EGF…
View article: A novel automatic segmentation method directly based on magnetic resonance imaging K-space data for auxiliary diagnosis of glioma
A novel automatic segmentation method directly based on magnetic resonance imaging K-space data for auxiliary diagnosis of glioma Open
These results show the superiority of our method compared to previous methods. The direct performance of lesion segmentation based on K-space data eliminates the time-consuming and tedious image reconstruction process, thus enabling the se…
View article: Few‐shot segmentation framework for lung nodules via an optimized active contour model
Few‐shot segmentation framework for lung nodules via an optimized active contour model Open
Background Accurate segmentation of lung nodules is crucial for the early diagnosis and treatment of lung cancer in clinical practice. However, the similarity between lung nodules and surrounding tissues has made their segmentation a longs…
View article: Fast finite difference/Legendre spectral collocation approximations for a tempered time-fractional diffusion equation
Fast finite difference/Legendre spectral collocation approximations for a tempered time-fractional diffusion equation Open
The present work is concerned with the efficient numerical schemes for a time-fractional diffusion equation with tempered memory kernel. The numerical schemes are established by using a $ L1 $ difference scheme for generalized Caputo fract…
View article: An interactive nuclei segmentation framework with Voronoi diagrams and weighted convex difference for cervical cancer pathology images
An interactive nuclei segmentation framework with Voronoi diagrams and weighted convex difference for cervical cancer pathology images Open
Objective. Nuclei segmentation is crucial for pathologists to accurately classify and grade cancer. However, this process faces significant challenges, such as the complex background structures in pathological images, the high-density dist…
View article: Effect of depth of interaction resolution on the spatial resolution of SIAT aPET
Effect of depth of interaction resolution on the spatial resolution of SIAT aPET Open
Objective. Spatial resolution is a crucial parameter for a positron emission tomography (PET) scanner. The spatial resolution of a high-resolution small animal PET scanner is significantly influenced by the effect of depth of interaction (…
View article: Combining deep learning with a kinetic model to predict dynamic PET images and generate parametric images
Combining deep learning with a kinetic model to predict dynamic PET images and generate parametric images Open
Background Dynamic positron emission tomography (PET) images are useful in clinical practice because they can be used to calculate the metabolic parameters ( K i ) of tissues using graphical methods (such as Patlak plots). K i is more stab…
View article: Synthetic high-energy computed tomography image via a Wasserstein generative adversarial network with the convolutional block attention module
Synthetic high-energy computed tomography image via a Wasserstein generative adversarial network with the convolutional block attention module Open
Experimental results indicate that CBAM is able to suppress the noise and artifacts effectively and suggest that the image synthesized by the proposed method is closest to the high-energy CT image in terms of visual perception and objectiv…
View article: Automatic brain structure segmentation for 18F-fluorodeoxyglucose positron emission tomography/magnetic resonance images via deep learning
Automatic brain structure segmentation for 18F-fluorodeoxyglucose positron emission tomography/magnetic resonance images via deep learning Open
We present a deep learning-based method for the joint segmentation of anatomical and functional PET/MR images. Compared with other single-modality methods, our method greatly improved the accuracy of brain structure delineation, which show…
View article: Synthesizing PET images from High-field and Ultra-high-field MR images Using Joint Diffusion Attention Model
Synthesizing PET images from High-field and Ultra-high-field MR images Using Joint Diffusion Attention Model Open
MRI and PET are crucial diagnostic tools for brain diseases, as they provide complementary information on brain structure and function. However, PET scanning is costly and involves radioactive exposure, resulting in a lack of PET. Moreover…
View article: A classifier-combined method for grading breast cancer based on Dempster-Shafer evidence theory
A classifier-combined method for grading breast cancer based on Dempster-Shafer evidence theory Open
Multiple classifiers can be effectively combined based on D-S evidence theory to improve the prediction of histologic grade in breast cancer.