Image quality comparison of lower extremity CTA between CT routine reconstruction algorithms and deep learning reconstruction

Mu, Chunlin; Zhang, Xinyue; Yan, Jing; Xu, Min; Wang, Yun; Xue, Huadan; Chen, Yuexin; Jin, Zhengyu

doi:10.1186/s12880-023-00988-6

Cited by 6 publications

(4 citation statements)

References 30 publications

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“…DLR also showed better agreement between different observers in making the measurements [88,89]: this means that different doctors or researchers using DLR were more likely to obtain similar results compared to using MBIR or HIR [89,90].…”

Section: Image Reconstructionmentioning

confidence: 88%

Artificial Intelligence in Lung Cancer Screening: The Future Is Now

Cellina,

Cacioppa,

Cè

et al. 2023

Cancers

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Lung cancer has one of the worst morbidity and fatality rates of any malignant tumour. Most lung cancers are discovered in the middle and late stages of the disease, when treatment choices are limited, and patients’ survival rate is low. The aim of lung cancer screening is the identification of lung malignancies in the early stage of the disease, when more options for effective treatments are available, to improve the patients’ outcomes. The desire to improve the efficacy and efficiency of clinical care continues to drive multiple innovations into practice for better patient management, and in this context, artificial intelligence (AI) plays a key role. AI may have a role in each process of the lung cancer screening workflow. First, in the acquisition of low-dose computed tomography for screening programs, AI-based reconstruction allows a further dose reduction, while still maintaining an optimal image quality. AI can help the personalization of screening programs through risk stratification based on the collection and analysis of a huge amount of imaging and clinical data. A computer-aided detection (CAD) system provides automatic detection of potential lung nodules with high sensitivity, working as a concurrent or second reader and reducing the time needed for image interpretation. Once a nodule has been detected, it should be characterized as benign or malignant. Two AI-based approaches are available to perform this task: the first one is represented by automatic segmentation with a consequent assessment of the lesion size, volume, and densitometric features; the second consists of segmentation first, followed by radiomic features extraction to characterize the whole abnormalities providing the so-called “virtual biopsy”. This narrative review aims to provide an overview of all possible AI applications in lung cancer screening.

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Section: Image Reconstructionmentioning

confidence: 88%

Artificial Intelligence in Lung Cancer Screening: The Future Is Now

Cellina,

Cacioppa,

Cè

et al. 2023

Cancers

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“…The reconstructed images, including non-enhanced and enhanced images, were categorized into four groups. Group A was reconstructed using AIDR 3D (FC08), whereas Group B was reconstructed using AiCE (body sharp kernel) [ 15 ]. All reconstructions were performed with a slice thickness of 1.0 mm and a slice interval of 0.8 mm, with a pixel matrix of 512 × 512.…”

Section: Methodsmentioning

confidence: 99%

A comparative analysis of deep learning and hybrid iterative reconstruction algorithms with contrast-enhancement-boost post-processing on the image quality of indirect computed tomography venography of the lower extremities

Du,

Sui,

Zhao

et al. 2024

BMC Med Imaging

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Purpose To examine whether there is a significant difference in image quality between the deep learning reconstruction (DLR [AiCE, Advanced Intelligent Clear-IQ Engine]) and hybrid iterative reconstruction (HIR [AIDR 3D, adaptive iterative dose reduction three dimensional]) algorithms on the conventional enhanced and CE-boost (contrast-enhancement-boost) images of indirect computed tomography venography (CTV) of lower extremities. Materials and methods In this retrospective study, seventy patients who underwent CTV from June 2021 to October 2022 to assess deep vein thrombosis and varicose veins were included. Unenhanced and enhanced images were reconstructed for AIDR 3D and AiCE, AIDR 3D-boost and AiCE-boost images were obtained using subtraction software. Objective and subjective image qualities were assessed, and radiation doses were recorded. Results The CT values of the inferior vena cava (IVC), femoral vein ( FV), and popliteal vein (PV) in the CE-boost images were approximately 1.3 (1.31–1.36) times higher than in those of the enhanced images. There were no significant differences in mean CT values of IVC, FV, and PV between AIDR 3D and AiCE, AIDR 3D-boost and AiCE-boost images. Noise in AiCE, AiCE-boost images was significantly lower than in AIDR 3D and AIDR 3D-boost images ( P < 0.05). The SNR (signal-to-noise ratio), CNR (contrast-to-noise ratio), and subjective scores of AiCE-boost images were the highest among 4 groups, surpassing AiCE, AIDR 3D, and AIDR 3D-boost images (all P < 0.05). Conclusion In indirect CTV of the lower extremities images, DLR with the CE-boost technique could decrease the image noise and improve the CT values, SNR, CNR, and subjective image scores. AiCE-boost images received the highest subjective image quality score and were more readily accepted by radiologists.

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“…Detectability of simulated lung lesions was best with the smoothest level in DLR; a dose reduction potential of 81% to 94% was assumed. An overview of recently published articles on deep learning–based image reconstruction 5,9,47–87 is given in Table 5.…”

Section: Image Reconstructionmentioning

confidence: 99%

Computed Tomography 2.0

Lell

Kachelrieß

2023

Invest Radiol

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Computed tomography (CT) dramatically improved the capabilities of diagnostic and interventional radiology. Starting in the early 1970s, this imaging modality is still evolving, although tremendous improvements in scan speed, volume coverage, spatial and soft tissue resolution, as well as dose reduction have been achieved. Tube current modulation, automated exposure control, anatomy-based tube voltage (kV) selection, advanced x-ray beam filtration, and iterative image reconstruction techniques improved image quality and decreased radiation exposure. Cardiac imaging triggered the demand for high temporal resolution, volume acquisition, and high pitch modes with electrocardiogram synchronization. Plaque imaging in cardiac CT as well as lung and bone imaging demand for high spatial resolution. Today, we see a transition of photon-counting detectors from experimental and research prototype setups into commercially available systems integrated in patient care. Moreover, with respect to CT technology and CT image formation, artificial intelligence is increasingly used in patient positioning, protocol adjustment, and image reconstruction, but also in image preprocessing or postprocessing. The aim of this article is to give an overview of the technical specifications of up-to-date available whole-body and dedicated CT systems, as well as hardware and software innovations for CT systems in the near future.

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Image quality comparison of lower extremity CTA between CT routine reconstruction algorithms and deep learning reconstruction

Cited by 6 publications

References 30 publications

Artificial Intelligence in Lung Cancer Screening: The Future Is Now

Artificial Intelligence in Lung Cancer Screening: The Future Is Now

A comparative analysis of deep learning and hybrid iterative reconstruction algorithms with contrast-enhancement-boost post-processing on the image quality of indirect computed tomography venography of the lower extremities

Computed Tomography 2.0

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