Zhennong Chen

Noise2Noise diffusion for thin-slice brain CT denoising without clean training data

Zhennong Chen, Siyeop Yoon, Matthew Tivnan, Junyoung Park et autres

Thin-slice and ultra-high-resolution (UHR) computed tomography (CT) images usually suffer from excessive noise due to limited radiation dose being distributed into small detector units. In the context of deep learning-based image denoising, it is challenging to obtain clean training data from real …

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Diffusion Model-Based Motion Correction in Portable Computed Tomography for Brain: A Human Observer Study

Zhennong Chen, Quirin Strotzer, Min Lang, Maryam Vejdani-Jahromi et autres

RATIONALE AND OBJECTIVES: To evaluate the clinical performance of a diffusion model-based motion correction algorithm for portable brain CT. MATERIALS AND METHODS: We retrospectively collected 67 portable brain CT scans with corresponding fixed CT scans acquired within ±2 days as reference. A …

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SAMed-2: Selective Memory Enhanced Medical Segment Anything Model

Zhiling Yan, Sifan Song, Dingjie Song, Yiwei Li et autres

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An end-to-end neural network for 4D cardiac CT reconstruction using single-beat scans

Zhenyao Yan, Zhennong Chen, Liang Li, Li Zhang et autres

Abstract Objective. Motion artifacts remain a significant challenge in cardiac CT imaging, often impairing the accurate detection and diagnosis of cardiac diseases. These artifacts result from involuntary cardiac motion, and traditional mitigation methods typically rely on retrospective rescans, which increase radiation exposure …

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Implicit Image-to-Image Schrödinger Bridge for image restoration

Yuang Wang, Siyeop Yoon, Matthew Tivnan, Sifan Song et autres

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Head CT Scan Motion Artifact Correction via Diffusion-Based Generative Models

Zhennong Chen, Siyeop Yoon, Quirin Strotzer, Rehab Naeem Khalid et autres

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Cine cardiac magnetic resonance segmentation using temporal-spatial adaptation of prompt-enabled segment-anything-model: a feasibility study

Zhennong Chen, Sekeun Kim, Hui Ren, Sung‐Hwan Kim et autres

BACKGROUND: We propose an approach to adapt a segmentation foundation model, segment-anything-model (SAM), for cine cardiovascular magnetic resonance (CMR) segmentation and evaluate its generalization performance on unseen datasets. METHODS: We present our model, cineCMR-SAM, which introduces a temporal-spatial attention mechanism to produce …

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Portable head CT motion artifact correction via diffusion-based generative model

Zhennong Chen, Siyeop Yoon, Quirin Strotzer, Rehab Naeem Khalid et autres

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Abstract 4143921: Adaptation of Prompt-enabled Segment-Anything-Model Enhance the Accuracy and Generalizability of Cine Cardiac Magnetic Resonance Segmentation

Zhennong Chen, S. H. Kim, Hui Ren, Sung‐Hwan Kim et autres

Introduction: Accurate segmentation of cine cardiac magnetic resonance (CMR) throughout the cardiac cycle is essential for comprehensive cardiac functional analysis. However, current deep-learning (DL) approaches often suffer from reduced accuracy on unseen datasets due to generalizability issues. The Segment-Anything Model (SAM) is …

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Temporal-Spatial Adaptation of Promptable SAM Enhance Accuracy and Generalizability of Cine CMR Segmentation

Zhennong Chen, Sekeun Kim, Hui Ren, Quanzheng Li et autres

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Optimizing Conditional DDPM for Head CT Motion Artifact Reduction: Brain vs. Skull and 3D vs. 2D.

Zhennong Chen, Matthew Tivnan, Siyeop Yoon, Rui Hu et autres

In this study, we introduce a conditional Denoising Diffusion Probabilistic Model (DDPM) approach that employs motion-corrupted images generated by FBP as the condition to reduce motion artifacts in 3D head CT scans. We address two critical questions in this application. First, how …

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Hallucination Index: An Image Quality Metric for Generative Reconstruction Models

Matthew Tivnan, Siyeop Yoon, Zhennong Chen, Xiang Li et autres

Generative image reconstruction algorithms such as measurement conditioned diffusion models are increasingly popular in the field of medical imaging. These powerful models can transform low signal-to-noise ratio (SNR) inputs into outputs with the appearance of high SNR. However, the outputs can have …