Is it possible to use low-dose deep learning reconstruction for the detection of liver metastases on CT routinely?

Lyu, Peijie; Liu, Nana; Harrawood, Brian P.; Solomon, Justin; Wang, Huixia; Chen, Yan; Rigiroli, Francesca; Ding, Yuqin; Schwartz, Fides R.; Jiang, Hanyu; Lowry, Carolyn; Wang, Luotong; Samei, Ehsan; Gao, Jianbo; Marin, Daniele

doi:10.1007/s00330-022-09206-3

Cited by 18 publications

(5 citation statements)

References 28 publications

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“…Another study performed by the same team showed that with an increasing dose-reduction rate, the differences were more significant via visual assessment. This finding was in accordance with the results of this study [20] . In summary, the current study highlights significant improvements in the quality of cervical lymph node images on CT using medium- and high-strength DLIR.…”

Section: Discussionsupporting

confidence: 94%

Insights about cervical lymph nodes: Evaluating deep learning–based reconstruction for head and neck computed tomography scan

Lin,

Su,

et al. 2024

European Journal of Radiology Open

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Section: Discussionsupporting

confidence: 94%

Insights about cervical lymph nodes: Evaluating deep learning–based reconstruction for head and neck computed tomography scan

Lin,

Su,

et al. 2024

European Journal of Radiology Open

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“…AiCE) as confirmed by normal dose SBIR, MBIR and FBP. In a liver metastases detection study, Lyu et al 85 demonstrated non-inferiority down to 50% for all size lesions, but down to 70% reduction for metastasis >1 cm. Zhao et al 86 compared RECIST measurement accuracy for DLR between low dose CT (0.07 mSv) and normal dose CT (2.38 mSv) with pulmonary lesion measurements agreeing to better than 2.2% and lymph node measurements agreeing to better than 1.4%.…”

Section: Dose Reduction Potential Using Dlrmentioning

confidence: 96%

Implementation of AI image reconstruction in CT—how is it validated and what dose reductions can be achieved

Brady

2023

The British Journal of Radiology

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CT reconstruction has undergone a substantial change over the last decade with the introduction of iterative reconstruction (IR) and now with deep learning reconstruction (DLR). In this review, DLR will be compared to IR and filtered back-projection (FBP) reconstructions. Comparisons will be made using image quality metrics such as noise power spectrum, contrast-dependent task-based transfer function, and non-prewhitening filter detectability index (dNPW'). Discussion on how DLR has impacted CT image quality, low-contrast detectability, and diagnostic confidence will be provided. DLR has shown the ability to improve in areas that IR is lacking, namely: noise magnitude reduction does not alter noise texture to the degree that IR did, and the noise texture found in DLR is more aligned with noise texture of an FBP reconstruction. Additionally, the dose reduction potential for DLR is shown to be greater than IR. For IR, the consensus was dose reduction should be limited to no more than 15–30% to preserve low-contrast detectability. For DLR, initial phantom and patient observer studies have shown acceptable dose reduction between 44 and 83% for both low- and high-contrast object detectability tasks. Ultimately, DLR is able to be used for CT reconstruction in place of IR, making it an easy “turnkey” upgrade for CT reconstruction. DLR for CT is actively being improved as more vendor options are being developed and current DLR options are being enhanced with second generation algorithms being released. DLR is still in its developmental early stages, but is shown to be a promising future for CT reconstruction.

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“…The six groups of reconstructed images were independently evaluated by two radiologists with more than 5 years of experience in abdominal CT diagnosis and no knowledge of the method of image reconstruction. A ve-point scoring method was used to evaluate each of ve characteristics: image noise, distortion, clarity of the portal vein, visibility of small structures and overall image quality [21,22]. The speci c scoring criteria in the ve areas are shown in Table 1.…”

Section: Subjective Evaluationmentioning

confidence: 99%

Reduced-dose deep learning iterative reconstruction for abdominal computed tomography with low tube-voltage and tube-current

Zhu,

Zhang,

Tian

et al. 2024

Preprint

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Background: Low tube-voltage technique (e.g., 80 kV) could efficiently reduce the radiation dose and increase the contrast enhancement of vascular and parenchymal structures in abdominal CT. However, a high tube current is always required in this setting and limits the dose reduction potential. By using a deep learning iterative reconstruction algorithm (Deep IR), this paper aims to investigate the feasibility of a Deep IR in reducing radiation dose while improving the image quality for abdominal computed tomography (CT) with low tube-voltage and tube-current. Methods: Sixty patients (Male/female, 36/24; Age, 57.72±10.19 years) undergoing the abdominal portal venous phase CT were randomly divided into groups A (100 kV, automatic exposure control [AEC] with reference tube-current of 213 mAs) and B (80 kV, AEC with reference of 130 mAs). Images were reconstructed by hybrid iterative reconstruction (HIR) and Deep IR (levels 1-5). The mean CT and standard deviation (SD) values of four regions of interest (ROI), i.e. liver, spleen, main portal vein and erector spinae at the porta hepatis level in each image serial were measured, and signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated. The image quality was subjectively scored by two radiologists using a 5-point criterion. Results: A significant reduction of radiation dose by 69.94% (5.09±0.9 mSv vs. 1.53±0.37 mSv) were detected in group B when compared to group A. With regard to Deep IR algorithm at various levels, there was no significant change in CT value, but SD gradually increased. Group B had higher CT values than group A, and the portal vein CT values significantly differed between groups (P<0.003). The SNR and CNR in group B with Deep IR at levels 1-5 were higher than those in group A and significantly differed when HIR and Deep IR were applied at levels 1-3 of HIR and Deep IR (P<0.003). The subjective scores (distortion, clarity of the portal vein, visibility of small structures and overall image quality) with Deep IR at levels 4-5 in group B were significantly higher than those in group A with HIR (P<0.003). Conclusion: Deep IR algorithm can reduce radiation dose and improve the image quality of parenchymal organs and portal vein clarity in portal venous phase abdominal CT with low tube-voltage and tube-current.

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Is it possible to use low-dose deep learning reconstruction for the detection of liver metastases on CT routinely?

Cited by 18 publications

References 28 publications

Insights about cervical lymph nodes: Evaluating deep learning–based reconstruction for head and neck computed tomography scan

Insights about cervical lymph nodes: Evaluating deep learning–based reconstruction for head and neck computed tomography scan

Implementation of AI image reconstruction in CT—how is it validated and what dose reductions can be achieved

Reduced-dose deep learning iterative reconstruction for abdominal computed tomography with low tube-voltage and tube-current

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