Deep-learning-based image reconstruction in dynamic contrast-enhanced abdominal CT: image quality and lesion detection among reconstruction strength levels

Kaga, Tetsuro; Noda, Yoshifumi; Fujimoto, Keita; Suto, Taketo; Kawai, Nobuyuki; Miyoshi, Toshiharu; Hyodo, Fuminori

doi:10.1016/j.crad.2021.03.010

Cited by 25 publications

(25 citation statements)

References 19 publications

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“…Kaga et al and Park et al showed a statistically significantly higher mean CT value in liver tissue in DLR than in hybrid IR [16,22]. However, these differences were clinically not relevant with maximum differences of 0.5-0.6 HU.…”

Section: Descriptive Resultsmentioning

confidence: 93%

“…Five of five studies that described diagnostic confidence in the liver showed significantly higher confidence of GE Healthcare's DLR than hybrid IR, rated by experienced radiologists. Results on the five-point scale of the comparison of hybrid IR vs DLR were as follows: (3.75 vs 4.0, p < 0.05) Cao et al [13], (4.20 vs 4.72, p < 0.05) Jensen et al [15], (3.92 vs 4.29, p < 0.0001) Kaga et al [16], (2.98 vs 3.59, p < 0.002) Li et al [18], and (3.1 vs 4.1, p < 0.001) Noda et al [21].…”

Section: Quantitative Resultsmentioning

confidence: 97%

“…Six articles showed CT values of GE Healthcare's ASiR-V 30-50% (hybrid IR) and TrueFidelity (DLR) reconstruction techniques, so these papers were included in the meta-analysis [13,[15][16][17][18]21]. CT values were analyzed for the liver, spleen, and pancreas.…”

Section: Quantitative Resultsmentioning

confidence: 99%

“…Finally, 16 articles were included in the systematic review. Of these 16 [11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26], 6 articles [13,[15][16][17][18]21] were included in the meta-analysis (Fig. 1).…”

Section: Study Selectionmentioning

confidence: 99%

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The effect of deep learning reconstruction on abdominal CT densitometry and image quality: a systematic review and meta-analysis

et al. 2021

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Objective To determine the difference in CT values and image quality of abdominal CT images reconstructed by filtered back-projection (FBP), hybrid iterative reconstruction (IR), and deep learning reconstruction (DLR). Methods PubMed and Embase were systematically searched for articles regarding CT densitometry in the abdomen and the image reconstruction techniques FBP, hybrid IR, and DLR. Mean differences in CT values between reconstruction techniques were analyzed. A comparison between signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of FBP, hybrid IR, and DLR was made. A comparison of diagnostic confidence between hybrid IR and DLR was made. Results Sixteen articles were included, six being suitable for meta-analysis. In the liver, the mean difference between hybrid IR and DLR was − 0.633 HU (p = 0.483, SD ± 0.902 HU). In the spleen, the mean difference between hybrid IR and DLR was − 0.099 HU (p = 0.925, SD ± 1.061 HU). In the pancreas, the mean difference between hybrid IR and DLR was − 1.372 HU (p = 0.353, SD ± 1.476 HU). In 14 articles, CNR was described. In all cases, DLR showed a significantly higher CNR. In 9 articles, SNR was described. In all cases but one, DLR showed a significantly higher SNR. In all cases, DLR showed a significantly higher diagnostic confidence. Conclusions There were no significant differences in CT values reconstructed by FBP, hybrid IR, and DLR in abdominal organs. This shows that these reconstruction techniques are consistent in reconstructing CT values. DLR images showed a significantly higher SNR and CNR, compared to FBP and hybrid IR. Key Points CT values of abdominal CT images are similar between deep learning reconstruction (DLR), filtered back-projection (FBP), and hybrid iterative reconstruction (IR). DLR results in improved image quality in terms of SNR and CNR compared to FBP and hybrid IR images. DLR can thus be safely implemented in the clinical setting resulting in improved image quality without affecting CT values.

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Section: Descriptive Resultsmentioning

confidence: 93%

Section: Quantitative Resultsmentioning

confidence: 97%

Section: Quantitative Resultsmentioning

confidence: 99%

Section: Study Selectionmentioning

confidence: 99%

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The effect of deep learning reconstruction on abdominal CT densitometry and image quality: a systematic review and meta-analysis

et al. 2021

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“…In contrast, raw data for the LDCT group were reconstructed with DLIR (TrueFidelity™; GE Healthcare) at high strength level with 5-mm section thickness and 0% overlap. In this study, we used a high strength level of DLIR because this level has been proved to strongly reduce background noise compared with low and middle strength levels of DLIR and ASiR-V in several studies [10][11][12][13]18]. The CTDI vol , size-specific dose estimates (SSDE), and doselength product (DLP) were recorded from the dose report.…”

Section: Imaging Techniquementioning

confidence: 99%

Unenhanced abdominal low-dose CT reconstructed with deep learning-based image reconstruction: image quality and anatomical structure depiction

et al. 2022

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Purpose To evaluate the utility of deep learning-based image reconstruction (DLIR) algorithm in unenhanced abdominal low-dose CT (LDCT). Materials and methods Two patient groups were included in this prospective study: 58 consecutive patients who underwent unenhanced abdominal standard-dose CT reconstructed with hybrid iterative reconstruction (SDCT group) and 48 consecutive patients who underwent unenhanced abdominal LDCT reconstructed with high strength level of DLIR (LDCT group). The background noise and signal-to-noise ratio (SNR) of the liver, pancreas, spleen, kidney, abdominal aorta, inferior vena cava, and portal vein were calculated. Two radiologists qualitatively assessed the overall image noise, overall image quality, and abdominal anatomical structures depiction. Quantitative and qualitative parameters and size-specific dose estimates (SSDE) were compared between SDCT and LDCT groups. Results The background noise was lower in LDCT group than in SDCT group (P = 0.02). SNRs were higher in LDCT group than in SDCT group (P < 0.001–0.004) except for the liver. Overall image noise was superior in LDCT group than in SDCT group (P < 0.001). Overall image quality was not different between SDCT and LDCT groups (P = 0.25–0.26). Depiction of almost all abdominal anatomical structures was equal to or better in LDCT group than in SDCT group (P < 0.001–0.88). The SSDE was lower in LDCT group (4.0 mGy) than in SDCT group (20.6 mGy) (P < 0.001). Conclusions DLIR facilitates substantial radiation dose reduction of > 75% and significantly reduces background noise. DLIR can maintain image quality and anatomical structure depiction in unenhanced abdominal LDCT.

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Deep-learning CT reconstruction in clinical scans of the abdomen: a systematic review and meta-analysis

et al. 2023

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Deep-learning-based image reconstruction in dynamic contrast-enhanced abdominal CT: image quality and lesion detection among reconstruction strength levels

Cited by 25 publications

References 19 publications

The effect of deep learning reconstruction on abdominal CT densitometry and image quality: a systematic review and meta-analysis

The effect of deep learning reconstruction on abdominal CT densitometry and image quality: a systematic review and meta-analysis

Unenhanced abdominal low-dose CT reconstructed with deep learning-based image reconstruction: image quality and anatomical structure depiction

Deep-learning CT reconstruction in clinical scans of the abdomen: a systematic review and meta-analysis

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