The application of deep learning image reconstruction in dual-energy CT virtual non-contrast CT urography

Abstract

Abstract:

ObjectiveTo investigate the effect of dual-energy CT (DECT) virtual non-contrast (VNC) images reconstructed by deep learning image reconstruction (DLIR) on the image quality and measurements of renal calculus in CT urography (CTU).MethodsThe clinical and imaging data of 90 patients who underwent abdominal and pelvic non-contrast CT examination followed by a nephrographic-phase DE CTU during September 2022 to April 2023 were retrospectively analyzed. The non-contrast CT images were reconstructed with ASIR-V with 70% weight (TNC-AR70). Four groups of VNC images were reconstructed based on medium level and high level DLIR for venous phase and delay phase, namely venous phase-VNC-DLIR medium (VP-VNC-DM), venous phase-VNC-DLIR high (VP-VNC-DH), delay phase-VNC-DLIR medium (DP-VNC-DM), and delay phase-VNC-DLIR high (DP-VNC-DH). The radiation doses of TNC and VNC in venous phase and delay phase were recorded. The mean CT value, image noise (SD), signal-to-noise ratio (SNR) and contrast-to-noise (CNR) were recorded and compared among the five groups. Two radiologists independently assessed the overall image qua-lity and lesion visibility with 5-point Likert scale. Additionally, according to results of TNC, Bland-Altman was used to analyze the measurement differences between VNC and TNC in mean CT value and mean size of renal calculus.ResultsIn the objective assessments, the image quality of the VNC-DH group was better than that of TNC-AR70, and there was no statistically significant difference in CT value among the five groups of images (P>0.05). DP-VNC-DH showed the lowest SD and the highest SNR and CNR values. In the subjective assessments, DP-VNC-DH achieved the best subject scores on image qua-lity, and VP-VNC-DH achieved the best subject scores on lesion visibility. Furthermore, Bland-Altman analysis showed that there was a strong overall agreement between VNC and TNC for renal calculus characterization (allP>0.005).ConclusionsVNC generated by DLIR may provide high-quality image compared with the non-contrast images reconstructed with ASIR-V 70% in CTU.The combination of the VNC images generated by DLIR-H from venous phase and delay phase could replace TNC scanning,reducing the radiation dose of CTU scans.

Key words:CT urography,Dual-energy,CT Deep learning image reconstruction,Virtual non-contrast image,Renal calculus

CLC Number:

QIAN Jiale, FAN Jing, ZHU Hong, WANG Luotong, KONG Deyan. The application of deep learning image reconstruction in dual-energy CT virtual non-contrast CT urography[J]. Journal of Diagnostics Concepts & Practice, 2024, 23(02): 139-145.

Figures/Tables6

Parameters	TNC（n=90）	VP（n=90）	DP（n=90）
Tube voltage (kV)	120	80/140	80/140
Tube current mode	Smart mA	GSI Assist	GSI Assist
Collimation (mm)	80	80	80
Scanning mode	Helical	Helical	Helical
Matrix	512×512	512×512	512×512
Thickness (mm)	1.25	1.25	1.25
Reconstruction	AR70	VNC+DM/DH	VNC+DM/DH
CTDI_vol（mGy）	9.05±2.63	8.99±2.75	8.99±2.75

Table 1

Table 2

Comparison of objective parameters of virtual non-contrast and real non-contrast images of different phases

Indice	TNC-AR70	VP-VNC-DM	VP-VNC-DH	DP-VNC-DM	DP-VNC-DH	F	P
CT（HU）
Liver	57.09±7.79	58.06±6.33	58.14±6.30	59.12±7.02	59.26±6.96	1.481	0.207
Spleen	50.81±3.17	50.31±3.93	50.56±4.64	51.25±3.07	51.32±3.01	1.300	0.269
Kidney	34.19±4.41^d,e	36.15±7.20	36.33±7.09	38.32±4.62^a	37.93±3.84^a	7.699	<0.001
Fat	-108.95±3.67^d,e	-107.47±5.17	-107.65±4.96^d,e	-105.36±5.09^a,c	-105.36±5.02^a,c	9.496	<0.001
SD（HU）
Liver	11.31±1.58^b,c,d,e	8.83±0.78^a,c,e	6.89±0.65^a,b,d,e	8.45±0.95^a,c,e	6.33±0.68^a,b,c,d	349.022	<0.001
Spleen	10.20±1.65^b,c,d,e	8.98±3.04^a,c,e	7.17±3.18^a,b,d,e	8.08±0.81^a,c,e	6.00±0.58^a,b,c,d	50.902	<0.001
Kidney	9.90±2.50^b,c,d,e	10.10±2.25^a,c,d,e	8.61±2.20^a,b,e	8.10±1.43^a,b,e	6.38±1.33^a,b,c,d	51.360	<0.001
Fat	7.77±1.95^b,d	9.31±4.56^a,c,e	7.04±4.66^b,d	8.59±1.53^a,c,e	6.69±4.56^b,d	7.566	<0.001
SNR
Liver	5.16±1.09^b,c,d,e	6.64±1.03^a,c,d,e	8.53±1.35^a,b,d,e	7.10±1.26^a,b,c,e	9.49±1.64^a,b,c,d	153.403	<0.001
Spleen	5.11±0.89^b,c,d,e	5.82±0.90^a,c,d,e	7.42±1.22^a,b,d,e	6.40±0.67^a,b,c,e	8.62±0.88^a,b,c,d	197.779	<0.001
Kidney	3.70±1.14^c,d,e	3.76±1.13^c,d,e	4.50±1.49^a,b,e	4.88±1.08^a,b,e	6.19±1.40^a,b,c,d	58.707	<0.001
CNR
Liver	23.19±6.53^b,d	18.89±5.36^a,c,e	25.88±7.97^b,d	19.17±5.65^a,c,e	26.07±7.78^b,d	24.087	<0.001
Spleen	22.28±6.16^b,d	17.93±4.85^a,c,e	24.60±7.30^b,d	18.20±5.40^a,c,e	24.79±7.36^b,d	25.349	<0.001
Kidney	19.90±5.45^b,d	16.29±4.53^a,c,e	22.32±6.74^b,d	16.63±5.09^a,c,e	22.58±6.78^b,d	24.307	<0.001

Table 2

Figure 1

Figure 2

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Figure 4

References14

[1]	ZHANG X, CHEN J, YU N, et al. Reducing contrast medium dose with low photon energy images in renal dual-energy spectral CT angiography and adaptive statistical iterative reconstruction (ASIR)[J].Br J Radiol,2021,94(1120):20200974.
[2]	ZENG S E, DU M Y, YU Y, et al. Ultrasound, CT, and MR imaging for evaluation of cystic renal masses[J].J Ultrasound Med,2022,41(4):807-819.
[3]	GEYER L L, SCHOEPF U J, MEINEL F G, et al. State of the art: Iterative CT reconstruction techniques[J].Radio-logy,2015,276(2):339-357.
[4]	ALTHOFF C E, GÜNTHER R W, HAMM B, et al. Intra-arterial ultra low iodine CT angiography of renal transplant arteries[J].Cardiovasc Intervent Radiol,2014,37(4):1062-1067. doi:10.1007/s00270-014-0838-9pmid:24464257
[5]	VIRARKAR M K, VULASALA S S R, GUPTA A V, et al. Virtual Non-contrast Imaging in The Abdomen and The Pelvis: An Overview[J].Semin Ultrasound CT MR,2022,43(4):293-310. doi:10.1053/j.sult.2022.03.004pmid:35738815
[6]	CHENG Y, SUN J, LI J, et al. The added value of virtual unenhanced images obtained from dual-energy CT urography in the detection and measurement of urinary stone[J].Urology,2022,166:118-125.
[7]	MA G, HAN D, DANG S, et al. Replacing true unenhanced imaging in renal carcinoma with virtual unenhanced images in dual-energy spectral CT: a feasibility study[J].Clin Radiol,2021,76(1):81,e21-e27.
[8]	XIAO J M, HIPPE D S, ZECEVIC M, et al. Virtual unenhanced dual-energy ct images obtained with a multimaterial decomposition algorithm: diagnostic value for renal mass and urinary stone evaluation[J].Radiology,2021,298(3):611-619. doi:10.1148/radiol.2021192448pmid:33464180
[9]	MEYER M, NELSON R C, VERNUCCIO F, et al. Virtual unenhanced images at dual-energy CT: influence on renal lesion characterization[J].Radiology,2019,291(2):381-390. doi:10.1148/radiol.2019181100pmid:30860450
[10]	DELABIE A, BOUZERAR R, PICHOIS R, et al. Diagnostic performance and image quality of deep learning image reconstruction (DLIR) on unenhanced low-dose abdominal CT for urolithiasis[J].Acta Radiol,2022,63(9):1283-1292.
[11]	YOO Y J, CHOI I Y, YEOM S K, et al. Evaluation of abdominal ct obtained using a deep learning-based image reconstruction engine compared with CT using adaptive statistical iterative reconstruction[J].J Belg Soc Radiol,2022,106(1):15. doi:10.5334/jbsr.2638pmid:35480337
[12]	PARK J, SHIN J, MIN I K, et al. Image quality and lesion detectability of lower-dose abdominopelvic CT obtained using deep learning image reconstruction[J].Korean J Radiol,2022,23(4):402-412. doi:10.3348/kjr.2021.0683pmid:35289146
[13]	SEO J Y, JOO I, YOON J H, et al. Deep learning-based reconstruction of virtual monoenergetic images of kVp-switching dual energy CT for evaluation of hypervascular liver lesions: Comparison with standard reconstruction technique[J].Eur J Radiol,2022,154:110390.
[14]	ZHONG J, XIA Y, CHEN Y, et al. Deep learning image reconstruction algorithm reduces image noise while alters radiomics features in dual-energy CT in comparison with conventional iterative reconstruction algorithms: a phantom study[J].Eur Radiol,2023,33(2):812-824.