ArticleIEEE access : practical innovations, open solutions2018
Structurally-sensitive Multi-scale Deep Neural Network for Low-Dose CT Denoising.
Article in IEEE access : practical innovations, open solutions, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 54 papers, 1 of them a synthesis that pooled it.
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Who cites it
54 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A systematic literature review: deep learning techniques for synthetic medical image generation and their applications in radiotherapy.Frontiers in radiology · 2024Pooled it
- Super-Resolution MR Spectroscopic Imaging via Diffusion Models for Tumor Metabolism Mapping.Journal of imaging informatics in medicine · 2026Article
- Improved Detection of Small (<2 cm) Hepatocellular Carcinoma via Deep Learning-Based Synthetic CT Hepatic Arteriography: A Multi-Center External Validation Study.Diagnostics (Basel, Switzerland) · 2026Article
- A new hybrid model for enhancing low-dose CT images using EfficientNetV2 and WGAN-GP: a multi-loss approach.European journal of medical research · 2025Article
- Deep learning in CT image reconstruction and processing: techniques, performance evaluation, radiation dose, and future perspective.The British journal of radiology · 2025Review
- Wavelet-domain frequency-mixing transformer unfolding network for low-dose computed tomography image denoising.Quantitative imaging in medicine and surgery · 2025Article
- Deep learning-based image domain reconstruction enhances image quality and pulmonary nodule detection in ultralow-dose CT with adaptive statistical iterative reconstruction-V.European radiology · 2025Article
- Better performance of cerebral blood volume images synthesized from arterial spin labeling and standard MRI in separating glioblastoma recurrence from treatment response than arterial spin labeling.Frontiers in oncology · 2025Article
- Self-supervised learning for CT image denoising and reconstruction: a review.Biomedical engineering letters · 2024Review
- Deep-learning-based attenuation map generation in kidney single photon emission computed tomography.EJNMMI physics · 2024Article
- Assessing the Capacity of a Denoising Diffusion Probabilistic Model to Reproduce Spatial Context.IEEE transactions on medical imaging · 2024Article
- Dual stage MRI image restoration based on blind spot denoising and hybrid attention.BMC medical imaging · 2024Article
- Development and validation of a noise insertion algorithm for photon-counting-detector CT.Medical physics · 2024Article
- Weakly supervised low-dose computed tomography denoising based on generative adversarial networks.Quantitative imaging in medicine and surgery · 2024Article
- Subjective and objective image quality of low-dose CT images processed using a self-supervised denoising algorithm.Radiological physics and technology · 2024Article
- Application of learned ideal observers for estimating task-based performance bounds for computed imaging systems.Journal of medical imaging (Bellingham, Wash.) · 2024Article
- CT image denoising methods for image quality improvement and radiation dose reduction.Journal of applied clinical medical physics · 2024Review
- GAN-based medical image small region forgery detection via a two-stage cascade framework.PloS one · 2024Article
- Measurement of proximal contact of single crowns to assess interproximal relief: A pilot study.Heliyon · 2023Article
- Degradation Adaption Local-to-Global Transformer for Low-Dose CT Image Denoising.Journal of digital imaging · 2023Article
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11 authors.
Funding
Abstract
Computed tomography (CT) is a popular medical imaging modality and enjoys wide clinical applications. At the same time, the x-ray radiation dose associated with CT scannings raises a public concern due to its potential risks to the patients. Over the past years, major efforts have been dedicated to the development of Low-Dose CT (LDCT) methods. However, the radiation dose reduction compromises the signal-to-noise ratio (SNR), leading to strong noise and artifacts that downgrade CT image quality. In this paper, we propose a novel 3D noise reduction method, called Structurally-sensitive Multi-scale Generative Adversarial Net (SMGAN), to improve the LDCT image quality. Specifically, we incorporate three-dimensional (3D) volumetric information to improve the image quality. Also, different loss functions for training denoising models are investigated. Experiments show that the proposed method can effectively preserve structural and textural information in reference to normal-dose CT (NDCT) images, and significantly suppress noise and artifacts. Qualitative visual assessments by three experienced radiologists demonstrate that the proposed method retrieves more information, and outperforms competing methods.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.