Improving spatial resolution and diagnostic confidence with thinner slice and deep learning image reconstruction in contrast-enhanced abdominal CT

Cao, Le; Liu, Xiang; Qu, Tingting; Cheng, Yannan; Li, Jianying; Li, Yanan; Niu, Xinyi; Tian, Qian; Guo, Jianxin

doi:10.1007/s00330-022-09146-y

Cited by 17 publications

(7 citation statements)

References 24 publications

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“…Compared to DLIR-L, DLIR-H is more capable of reducing noise. In this study, the SNR and CNR of the images reconstructed with DLIR-H were higher than those reconstructed with DLIR-L, which is consistent with previous work ( 13 , 17 , 18 ); moreover, the ERS of DLIR-H was slightly lower than that of DLIR-L, but not significantly so. CNR and SNR are fairly critical to quality CTA, but some loss of spatial resolution is tolerable.…”

Section: Discussionsupporting

confidence: 92%

The application value of a vendor-specific deep learning image reconstruction algorithm in “triple low” head and neck computed tomography angiography

Zhang,

Lin,

Zhang

et al. 2024

Quant Imaging Med Surg

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Background Head and neck computed tomography angiography (CTA) technology has become the noninvasive imaging method of choice for the diagnosis and long-term follow-up of vascular lesions of the head and neck. However, issues of radiation safety and contrast nephropathy associated with CTA examinations remain concerns. In recent years, deep learning image reconstruction (DLIR) algorithms have been increasingly used in clinical studies, demonstrating their potential for dose optimization. This study aimed to investigate the value of using a DLIR algorithm to reduce radiation and contrast doses in head and neck CTA. Methods A total of 100 patients were prospectively enrolled and randomly divided into two groups. Group A (50 patients) consisted of those who underwent 70-kVp CTA with a low contrast volume and injection rate and who were classified according to the reconstruction algorithm into subgroups A1 [DLIR at high weighting (DLIR-H)], A2 [DLIR at low weighting (DLIR-L)], and A3 [volume-based adaptive statistical iterative reconstruction with 50% weighting (ASIR-V50%)]. Meanwhile, group B (50 patients) consisted of those who underwent standard radiation and contrast doses at 100 kVp with ASIR-V50% reconstruction. The computed tomography (CT) attenuation, background noise, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and subjective image quality score (SIQS) were statistically compared for several vessels among the four groups. Results Group A showed significant reductions in contrast dosage, injection rate, and radiation dose of 36.09%, 20.88%, and 47.80%, respectively, compared to group B (all P<0.001). The four groups differed significantly in terms of background noise (all P<0.05) with group A1 having the lowest value. Group A1 also had significantly higher SNR and CNR values compared to group B in all vessels (all P<0.05) except the M1 of the middle cerebral artery for the SNR. Group A1 also had the highest SIQS, followed by the A2, B, and A3 groups. The SIQS showed good agreement between the two reviewers in all groups, with κ values between 0.88 and 1. Conclusions Compared to the standard-dose protocol using 100 kVp and ASIR-V50%, a protocol of 70 kVp combined with DLIR-H significantly reduces the radiation dose, contrast dose, and injection rate in head and neck CTA while still significantly improving image quality for patients with a standard body size.

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Section: Discussionsupporting

confidence: 92%

The application value of a vendor-specific deep learning image reconstruction algorithm in “triple low” head and neck computed tomography angiography

Zhang,

Lin,

Zhang

et al. 2024

Quant Imaging Med Surg

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“…A radiologist with 4-year-experience in radiology conducted the quantitative image evaluation using the open-source imQuest software version 7.1 (Duke University; https://deckard.duhs.duke.edu/~samei/tg233.html ) (Supplementary Note S2 ) [ 5 , 8 , 9 , 13 ]. Regions of interest (ROI) were selected on the 5 mm AV-50 images.…”

Section: Methodsmentioning

confidence: 99%

“…Five radiologists with 1- to 6-year-experience in radiology performed the qualitative image quality assessment (Supplementary Note S3 ) [ 6 , 7 , 9 , 11 – 16 , 18 ]. The readers independently rated image quality in terms of image noise, contrast, sharpness, texture, small structure visibility, and evaluated overall diagnostic acceptability of images and lesion conspicuity.…”

Section: Methodsmentioning

confidence: 99%

“…However, the application and investigation of iodine maps for diagnosis is hindered by low spatial resolution and severe image noise. The slice thickness of 5-mm and 1.25-mm is used for diagnosis in current clinical routine [ 9 ], with the 5-mm being more common for the abdomen due to the patient radiation concern. The 5-mm images display with lower image noise; however, may suffer from the low spatial resolution due to partial volume effect.…”

Section: Introductionmentioning

confidence: 99%

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Deep learning image reconstruction generates thinner slice iodine maps with improved image quality to increase diagnostic acceptance and lesion conspicuity: a prospective study on abdominal dual-energy CT

Zhong,

Wang,

Yan

et al. 2024

BMC Med Imaging

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Background To assess the improvement of image quality and diagnostic acceptance of thinner slice iodine maps enabled by deep learning image reconstruction (DLIR) in abdominal dual-energy CT (DECT). Methods This study prospectively included 104 participants with 136 lesions. Four series of iodine maps were generated based on portal-venous scans of contrast-enhanced abdominal DECT: 5-mm and 1.25-mm using adaptive statistical iterative reconstruction-V (Asir-V) with 50% blending (AV-50), and 1.25-mm using DLIR with medium (DLIR-M), and high strength (DLIR-H). The iodine concentrations (IC) and their standard deviations of nine anatomical sites were measured, and the corresponding coefficient of variations (CV) were calculated. Noise-power-spectrum (NPS) and edge-rise-slope (ERS) were measured. Five radiologists rated image quality in terms of image noise, contrast, sharpness, texture, and small structure visibility, and evaluated overall diagnostic acceptability of images and lesion conspicuity. Results The four reconstructions maintained the IC values unchanged in nine anatomical sites (all p > 0.999). Compared to 1.25-mm AV-50, 1.25-mm DLIR-M and DLIR-H significantly reduced CV values (all p < 0.001) and presented lower noise and noise peak (both p < 0.001). Compared to 5-mm AV-50, 1.25-mm images had higher ERS (all p < 0.001). The difference of the peak and average spatial frequency among the four reconstructions was relatively small but statistically significant (both p < 0.001). The 1.25-mm DLIR-M images were rated higher than the 5-mm and 1.25-mm AV-50 images for diagnostic acceptability and lesion conspicuity (all P < 0.001). Conclusions DLIR may facilitate the thinner slice thickness iodine maps in abdominal DECT for improvement of image quality, diagnostic acceptability, and lesion conspicuity.

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“…Deep-learning reconstruction (DLR) algorithms produce CT images with significantly less noise than when using iterative reconstructions, leading, thus, to increased contrast-to-noise ratio and improved lesion detectability [ 8 ]. In contrast-enhanced CT, DLR methods have been shown to significantly improve image spatial resolution and diagnostic confidence for the detection of hepatic lesions [ 9 ], and this even with the use of thin slices of 1.25 mm [ 10 ] while maintaining low radiation exposure [ 7 ]. Finally, streak artifacts produced from low-dose acquisition are also subtracted by means of DLR [ 7 ].…”

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confidence: 99%

AI-based approaches in the daily practice of abdominal imaging

Schmidt

2023

Eur Radiol

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Improving spatial resolution and diagnostic confidence with thinner slice and deep learning image reconstruction in contrast-enhanced abdominal CT

Cited by 17 publications

References 24 publications

The application value of a vendor-specific deep learning image reconstruction algorithm in “triple low” head and neck computed tomography angiography

The application value of a vendor-specific deep learning image reconstruction algorithm in “triple low” head and neck computed tomography angiography

Deep learning image reconstruction generates thinner slice iodine maps with improved image quality to increase diagnostic acceptance and lesion conspicuity: a prospective study on abdominal dual-energy CT

AI-based approaches in the daily practice of abdominal imaging

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