ArticleSkeletal radiology2022
Clinical evaluation of a phantom-based deep convolutional neural network for whole-body-low-dose and ultra-low-dose CT skeletal surveys.
Article in Skeletal radiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep learning applications in visual data for benign and malignant hematologic conditions: a systematic review and visual glossary.Haematologica · 2023Pooled it
- Multicontrast Multiphase Intestinal Imaging Using Photon-Counting CT: A Feasibility Study in Rats.Investigative radiology · 2025Article
- Preserving noise texture through training data curation for deep learning denoising of high-resolution cardiac EID-CT.Medical physics · 2025Article
- Improved noise reduction in photon-counting detector CT using prior knowledge-aware iterative denoising neural network.Journal of medical imaging (Bellingham, Wash.) · 2024Article
- Image quality evaluation in deep-learning-based CT noise reduction using virtual imaging trial methods: Contrast-dependent spatial resolution.Medical physics · 2024Article
- Enhancing diagnostic accuracy of multiple myeloma through ML-driven analysis of hematological slides: new dataset and identification model to support hematologists.Scientific reports · 2024Article
- Recent advances in imaging and artificial intelligence (AI) for quantitative assessment of multiple myeloma.American journal of nuclear medicine and molecular imaging · 2024Review
- Pie-Net: Prior-information-enabled deep learning noise reduction for coronary CT angiography acquired with a photon counting detector CT.Medical physics · 2023Article
- Clinical applications of photon counting detector CT.European radiology · 2023Review
- Contrast- and noise-dependent spatial resolution measurement for deep convolutional neural network-based noise reduction in CT using patient data.Proceedings of SPIE--the International Society for Optical Engineering · 2023Article
- PKAID-Net: Prior Knowledge Aware Iterative Denoising Neural Network for Photon Counting Detector CT.Proceedings of SPIE--the International Society for Optical Engineering · 2023Article
- A dense search challenge phantom fabricated with pixel-based 3D printing for precise detectability assessment.Proceedings of SPIE--the International Society for Optical Engineering · 2023Article
- Photon-counting Detector CT with Deep Learning Noise Reduction to Detect Multiple Myeloma.Radiology · 2023Article
- Dedicated convolutional neural network for noise reduction in ultra-high-resolution photon-counting detector computed tomography.Physics in medicine and biology · 2022Article
- A minimum SNR criterion for computed tomography object detection in the projection domain.Medical physics · 2022Article
- Dual-Contrast Biphasic Liver Imaging With Iodine and Gadolinium Using Photon-Counting Detector Computed Tomography: An Exploratory Animal Study.Investigative radiology · 2022Article
- Deep Learning-Based Image Noise Quantification Framework for Computed Tomography.Journal of computer assisted tomographyArticle
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9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThis study evaluated the clinical utility of a phantom-based convolutional neural network noise reduction framework for whole-body-low-dose CT skeletal surveys. MATERIALS AND
methodsThe CT exams of ten patients with multiple myeloma were retrospectively analyzed. Exams were acquired with routine whole-body-low-dose CT protocol and projection noise insertion was used to simulate 25% dose exams. Images were reconstructed with either iterative reconstruction or filtered back projection with convolutional neural network post-processing. Diagnostic quality and structure visualization were blindly rated (subjective scale ranging from 0 [poor] to 100 [excellent]) by three musculoskeletal radiologists for iterative reconstruction and convolutional neural network images at routine whole-body-low-dose and 25% dose CT.
resultsFor the diagnostic quality rating, the convolutional neural network outscored iterative reconstruction at routine whole-body-low-dose CT (convolutional neural network: 95 ± 5, iterative reconstruction: 85 ± 8) and at the 25% dose level (convolutional neural network: 79 ± 10, iterative reconstruction: 22 ± 13). Convolutional neural network applied to 25% dose was rated inferior to iterative reconstruction applied to routine dose. Similar trends were observed in rating experiments focusing on structure visualization.
conclusionResults indicate that the phantom-based convolutional neural network noise reduction framework can improve visualization of critical structures within CT skeletal surveys. At matched dose level, the convolutional neural network outscored iterative reconstruction for all conditions studied. The image quality improvement of convolutional neural network applied to 25% dose indicates a potential for dose reduction; however, the 75% dose reduction condition studied is not currently recommended for clinical implementation.
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