Image quality assessment of artificial intelligence iterative reconstruction for low dose aortic CTA: A feasibility study of 70 kVp and reduced contrast medium volume

Li, Wanjiang; You, Yongchun; Zhong, Sihua; Tao, Shuai; Liao, Kai; Yu, Jianqun; Zhao, Jichun; Li, Zhenlin; Lu, Chin‐Song

doi:10.1016/j.ejrad.2022.110221

Cited by 23 publications

(15 citation statements)

References 40 publications

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“…The HIR suppresses the image noise, but may lead to "plastic" images and degrade diagnostic accuracy [13]. The AIIR, a deep-learning-based reconstruction algorithm, was originally trained with standard and stimulated multi-dose level CT image aiming to generate images with lower noise and to improve low contrast detectability [14]. This newly developed reconstruction algorithm is capable of denoising low-dose images while maintaining the image quality compared to the SDCT images [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

“…The AIIR, a deep-learning-based reconstruction algorithm, was originally trained with standard and stimulated multi-dose level CT image aiming to generate images with lower noise and to improve low contrast detectability [14]. This newly developed reconstruction algorithm is capable of denoising low-dose images while maintaining the image quality compared to the SDCT images [14][15][16]. Thus, the radiation exposure could be theoretically further reduced with the AIIR, but few studies on ULDCT scans have been conducted.…”

Section: Introductionmentioning

confidence: 99%

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Ultra-low-dose CT reconstructed with the artificial intelligence iterative reconstruction algorithm (AIIR) in 18F-FDG total-body PET/CT examination: a preliminary study

Zheng

et al. 2023

EJNMMI Phys

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Purpose To investigate the feasibility of ultra-low-dose CT (ULDCT) reconstructed with the artificial intelligence iterative reconstruction (AIIR) algorithm in total-body PET/CT imaging. Methods The study included both the phantom and clinical parts. An anthropomorphic phantom underwent CT imaging with ULDCT (10mAs) and standard-dose CT (SDCT) (120mAs), respectively. ULDCT was reconstructed with AIIR and hybrid iterative reconstruction (HIR) (expressed as ULDCT-AIIRphantom and ULDCT-HIRphantom), respectively, and SDCT was reconstructed with HIR (SDCT-HIRphantom) as control. In the clinical part, 52 patients with malignant tumors underwent the total-body PET/CT scan. ULDCT with AIIR (ULDCT-AIIR) and HIR (ULDCT-HIR), respectively, was reconstructed for PET attenuation correction, followed by the SDCT reconstructed with HIR (SDCT-HIR) for anatomical location. PET/CT images’ quality was qualitatively assessed by two readers. The CTmean, as well as the CT standard deviation (CTsd), SUVmax, SUVmean, and the SUV standard deviation (SUVsd), was recorded. The signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated and compared. Results The image quality of ULDCT-HIRphantom was inferior to the SDCT-HIRphantom, but no significant difference was found between the ULDCT-AIIRphantom and SDCT-HIRphantom. The subjective score of ULDCT-AIIR in the neck, chest and lower limb was equivalent to that of SDCT-HIR. Besides the brain and lower limb, the change rates of CTmean in thyroid, neck muscle, lung, mediastinum, back muscle, liver, lumbar muscle, first lumbar spine and sigmoid colon were −2.15, −1.52, 0.66, 2.97, 0.23, 8.91, 0.06, −4.29 and 8.78%, respectively, while all CTsd of ULDCT-AIIR was lower than that of SDCT-HIR. Except for the brain, the CNR of ULDCT-AIIR was the same as the SDCT-HIR, but the SNR was higher. The change rates of SUVmax, SUVmean and SUVsd were within $$\pm$$ ± 3% in all ROIs. For the lesions, the SUVmax, SUVsd and TBR showed no significant difference between PET-AIIR and PET-HIR. Conclusion The SDCT-HIR could not be replaced by the ULDCT-AIIR at date, but the AIIR algorithm decreased the image noise and increased the SNR, which can be implemented under special circumstances in PET/CT examination.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Ultra-low-dose CT reconstructed with the artificial intelligence iterative reconstruction algorithm (AIIR) in 18F-FDG total-body PET/CT examination: a preliminary study

Zheng

et al. 2023

EJNMMI Phys

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“…Over time, a variety of such algorithms have been developed and put in commercial use, despite the fact that the performance in case of metal objects with complex geometry is still not satisfying 5 . Lately, deep learning technique is being introduced to the field of CT imaging, showing promise as a new concept for image reconstruction that can be particularly useful for low dose acquisitions 12,13 . This inspires a similar thinking whether deep learning can also be utilized to benefit MAC algorithms.…”

Section: Introductionmentioning

confidence: 99%

Preclinical validation of a novel deep learning‐based metal artifact correction algorithm for orthopedic CT imaging

Guo,

Zou,

Zhang

et al. 2023

J Applied Clin Med Phys

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PurposeTo validate a novel deep learning‐based metal artifact correction (MAC) algorithm for CT, namely, AI‐MAC, in preclinical setting with comparison to conventional MAC and virtual monochromatic imaging (VMI) technique.Materials and methodsAn experimental phantom was designed by consecutively inserting two sets of pedicle screws (size Φ 6.5 × 30‐mm and Φ 7.5 × 40‐mm) into a vertebral specimen to simulate the clinical scenario of metal implantation. The resulting MAC, VMI, and AI‐MAC images were compared with respect to the metal‐free reference image by subjective scoring, as well as by CT attenuation, image noise, signal‐to‐noise ratio (SNR), contrast‐to‐noise ratio (CNR), and correction accuracy via adaptive segmentation of the paraspinal muscle and vertebral body.ResultsThe AI‐MAC and VMI images showed significantly higher subjective scores than the MAC image (all p < 0.05). The SNRs and CNRs on the AI‐MAC image were comparable to the reference (all p > 0.05), whereas those on the VMI were significantly lower (all p < 0.05). The paraspinal muscle segmented on the AI‐MAC image was 4.6% and 5.1% more complete to the VMI and MAC images for the Φ 6.5 × 30‐mm screws, and 5.0% and 5.1% for the Φ 7.5 × 40‐mm screws, respectively. The vertebral body segmented on the VMI was closest to the reference, with only 3.2% and 7.4% overestimation for Φ 6.5 × 30‐mm and Φ 7.5 × 40‐mm screws, respectively.ConclusionsUsing metal‐free reference as the ground truth for comparison, the AI‐MAC outperforms VMI in characterizing soft tissue, while VMI is useful in skeletal depiction.

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“…The HIR suppresses the image noise, but may lead to "plastic" images and degrade diagnostic accuracy [13]. The AIIR is a deep-learning based reconstruction algorithm that trains the deep learning network with a big dataset of CT images [14]. This newly developed reconstruction algorithm is capable of denoising low-dose images while maintaining the image quality compared to the SDCT images [14][15][16].…”

mentioning

confidence: 99%

“…The AIIR is a deep-learning based reconstruction algorithm that trains the deep learning network with a big dataset of CT images [14]. This newly developed reconstruction algorithm is capable of denoising low-dose images while maintaining the image quality compared to the SDCT images [14][15][16]. Thus, the radiation exposure could be theoretically further reduced with the AIIR, but few studies on the ULDCT scans has been conducted.…”

mentioning

confidence: 99%

Ultra-low dose CT reconstructed with the artificial intelligence iterative reconstruction algorithm ( AIIR) in 18F-FDG Total-body PET/CT examination: a preliminary study

Hu¹,

Zheng²,

Yu³

et al. 2022

Preprint

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Purpose: To investigate the feasibility of ultra-low dose CT (ULDCT) reconstructed with the artificial intelligence iterative reconstruction (AIIR) algorithm in total-body PET/CT imaging.Methods: The study included both the phantom and clinical part. An anthropomorphic phantom underwent CT imaging with ULDCT (10mAs) and standard-dose CT (SDCT) (120mAs) respectively. ULDCT was reconstructed with AIIR and hybrid iterative reconstruction (HIR) (expressed as ULDCT-AIIRphantom and ULDCT-HIRphantom) respectively, and SDCT was reconstructed with HIR (SDCT-HIRphantom) as control. In the clinical part, 52 patients with malignant tumours underwent the total-body PET/CT scann. ULDCT with AIIR (ULDCT-AIIR) and HIR (ULDCT-HIR) respectively was reconstructed for PET attenuation correction, followed by the SDCT reconstructed with HIR (SDCT-HIR) for anatomical location. PET/CT images quality were qualitatively assessed by two readers. The CTmean as well as the CT standard deviation (CTsd), SUVmax, SUVmean, and the SUV standard deviation (SUVsd) were recorded. The signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated and compared.Results: The image quality of ULDCT-HIRphantom was inferior to the SDCT-HIRphantom, but no significant difference was found between the ULDCT-AIIRphantom and SDCT-HIRphantom. The subjective score of ULDCT-AIIR in the neck, throax and lower limb was equivalent to that of SDCT-HIR. Besides the brain and lower limb, the CTmean of the lesion and other regiongs in ULDCT-AIIR didn’t fluctuate too much, while the CTsd of ULDCT-AIIR was lower than that of SDCT-HIR. Except for the brain, the CNR of ULDCT-AIIR was the same as the SDCT-HIR, but the SNR was higher. The SUVmax, SUVmean and SUVsd of PET-AIIR had a slightly change in the whole body. For the lesions, the SUVmax, SUVsd and TBR showed no significant difference between PET-AIIR and PET-HIR. Conclusion: The SDCT-HIR was not replaced by the ULDCT-AIIR, but the AIIR algorithm decreased the image noise and increased the SNR, which can be implemented under special circumstance in PET/CT examination.

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Image quality assessment of artificial intelligence iterative reconstruction for low dose aortic CTA: A feasibility study of 70 kVp and reduced contrast medium volume

Cited by 23 publications

References 40 publications

Ultra-low-dose CT reconstructed with the artificial intelligence iterative reconstruction algorithm (AIIR) in 18F-FDG total-body PET/CT examination: a preliminary study

Ultra-low-dose CT reconstructed with the artificial intelligence iterative reconstruction algorithm (AIIR) in 18F-FDG total-body PET/CT examination: a preliminary study

Preclinical validation of a novel deep learning‐based metal artifact correction algorithm for orthopedic CT imaging

Ultra-low dose CT reconstructed with the artificial intelligence iterative reconstruction algorithm ( AIIR) in 18F-FDG Total-body PET/CT examination: a preliminary study

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