Application of deep learning-based image reconstruction with three low scheme in renal artery angiography

doi:10.3877/cma.j.issn.1674-3253.2025.01.014

Abstract

Abstract:

Objective

The best scheme of renal artery angiography （CTA） was discussed by comparing the three low scheme of deep learning image reconstruction （DLIR）（low radiation dose， low injection rate， low contrast agent dose） with the low energy spectrum （DECT） scheme and the conventional scheme of adaptive statistical iterative Reconstruction -V（ASIR-V）.

Methods

Ninety patients receiving renal artery CTA examination were divided into conventional regimen group （S group 30 cases）， DECT regimen group （DE group 30 cases） and three low regimen group （L group 30 cases） according to the scanning regimen. The dose of contrast agent， injection speed， volume CT dose index and dose length product of the three groups were recorded， and the effective dose was calculated. 60%ASIR-V reconstruction was performed in groups S and DE. DLR -H reconstruction was carried out for group L. Objective image evaluation indexes included abdominal aorta and double renal artery CT values， standard deviation （SD） values， signal-tonoise ratio （SNR） and contrast-to-noise ratio （CNR）. Subjective evaluation was performed by two clinically experienced diagnostic physicians who scored reconstructed images by double-blind method. Objective evaluation parameters such as CT， SD， SNR and CNR values of the three groups were analyzed by one-way analysis of variance， subjective scores were analyzed by Kruskal-Wallis test， and the consistency of subjective scores of two radiologists was analyzed by Kappa test.

Results

Compared with group S， the effective dose in group L was reduced by 35%， the contrast dose by 34%， and the injection speed of contrast agent by 30%.Compared with DE group， the contrast dose was decreased by 42%， the contrast dose was decreased by 36%，and the contrast injection speed was decreased by 30%. SD of L group was smaller than that of S group， SNR and CNR values were larger than that of S group （P＜0.05）， and the CT value and noise of DE group were the highest （P＜0.05）， CNR value was the largest （P＜0.05）. The average score of five factors was the highest in L group （P＜0.05）. The subjective image quality scores of renal arteries from two radiologists were also consistent（Kappa=0.895， P＜0.001； Kappa=0.643， P＜0.001； Kappa=0.764， P＜0.001）.

Conclusions

Three low regimen compared with the low energy spectrum scheme and conventional regimen significantly reduced the radiation dose，the contrast dose， and the injection speed， and it has the best comprehensive image quality， which is the best scheme of renal artery CTA， and can have the highest image evaluation in the absence of advantages of objective data.

Key words: Deep learning, Image reconstruction, Adaptive statistical iterative reconstruction, Renal artery angiography, Three low scheme

Yue Zhang, Ke Zhang, Sisi Deng, Qing Xiang, Yahao Guo, Jian Cao, Tao Luo, Zhan'ao Meng. Application of deep learning-based image reconstruction with three low scheme in renal artery angiography[J]. Chinese Journal of Endourology(Electronic Edition), 2025, 19(01): 76-82.

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Figures/Tables 6

组别	例数	年龄［岁，（ $\overset{ˉ}{x}$ ±s）］	性别（例）		身高［cm，（ $\overset{ˉ}{x}$ ±s）］	体质量［kg，（ $\overset{ˉ}{x}$ ±s）］	BMI［kg/m²，（ $\overset{ˉ}{x}$ ±s）］	对比剂剂量［ml，（ $\overset{ˉ}{x}$ ±s）］	CT容积剂量指数［mGy，（ $\overset{ˉ}{x}$ ±s）］	剂量长度乘积［mGy×cm，（ $\overset{ˉ}{x}$ ±s）］	有效剂量［mSv，（ $\overset{ˉ}{x}$ ±s）］
组别	例数	年龄［岁，（ $\overset{ˉ}{x}$ ±s）］	男	女	身高［cm，（ $\overset{ˉ}{x}$ ±s）］	体质量［kg，（ $\overset{ˉ}{x}$ ±s）］	BMI［kg/m²，（ $\overset{ˉ}{x}$ ±s）］	对比剂剂量［ml，（ $\overset{ˉ}{x}$ ±s）］	CT容积剂量指数［mGy，（ $\overset{ˉ}{x}$ ±s）］	剂量长度乘积［mGy×cm，（ $\overset{ˉ}{x}$ ±s）］	有效剂量［mSv，（ $\overset{ˉ}{x}$ ±s）］
DE组	30	54±11	20	10	166±8	67±13	24±4	101±19	22	657±103	9.9±1.5
S组	30	55±15	19	11	165±9	65±12	24±4	97±19	22±4	587±154	8.8±2.3
L组	30	54±15	23	7	165±9	64±11	24±4	64±11	13±3	380±87	5.7±1.3
统计值		F=0.036	χ²=3.271		F=0.171	F=0.498	F=0.433	F=43.350	F=175.116	F=67.330	F=67.330
P值		0.964	0.195		0.844	0.609	0.650	＜0.001	＜0.001	＜0.001	＜0.001

组别	例数	年龄［岁，（ $\overset{ˉ}{x}$ ±s）］	性别（例）		身高［cm，（ $\overset{ˉ}{x}$ ±s）］	体质量［kg，（ $\overset{ˉ}{x}$ ±s）］	BMI［kg/m²，（ $\overset{ˉ}{x}$ ±s）］	对比剂剂量［ml，（ $\overset{ˉ}{x}$ ±s）］	CT容积剂量指数［mGy，（ $\overset{ˉ}{x}$ ±s）］	剂量长度乘积［mGy×cm，（ $\overset{ˉ}{x}$ ±s）］	有效剂量［mSv，（ $\overset{ˉ}{x}$ ±s）］
组别	例数	年龄［岁，（ $\overset{ˉ}{x}$ ±s）］	男	女	身高［cm，（ $\overset{ˉ}{x}$ ±s）］	体质量［kg，（ $\overset{ˉ}{x}$ ±s）］	BMI［kg/m²，（ $\overset{ˉ}{x}$ ±s）］	对比剂剂量［ml，（ $\overset{ˉ}{x}$ ±s）］	CT容积剂量指数［mGy，（ $\overset{ˉ}{x}$ ±s）］	剂量长度乘积［mGy×cm，（ $\overset{ˉ}{x}$ ±s）］	有效剂量［mSv，（ $\overset{ˉ}{x}$ ±s）］
DE组	30	54±11	20	10	166±8	67±13	24±4	101±19	22	657±103	9.9±1.5
S组	30	55±15	19	11	165±9	65±12	24±4	97±19	22±4	587±154	8.8±2.3
L组	30	54±15	23	7	165±9	64±11	24±4	64±11	13±3	380±87	5.7±1.3
统计值		F=0.036	χ²=3.271		F=0.171	F=0.498	F=0.433	F=43.350	F=175.116	F=67.330	F=67.330
P值		0.964	0.195		0.844	0.609	0.650	＜0.001	＜0.001	＜0.001	＜0.001

级别	例数	CT值			SD值			SNR值			CNR值
级别	例数	AO	RRA	LRA	AO	RRA	LRA	AO	RRA	LRA	AO	RRA	LRA
DE组	30	1524±273	1287±229	1193±206	36.2±8.2	38±8	41±9	44±13	36±9	30±6	72±17	65±21	60±23
S组	30	439±71	390±64	394±62	17.7±6.6	23±8	24±8	28±12	20±9	19±10	30±8	28±15	25±6
L组	30	400±63	356±59	371±49	11.1±2.6	12±4	13±4	38±12	31±7	30±6	41±12	40±16	41±23
F值		238.065	230.278	224.238	130.962	128.006	140.375	12.260	28.009	22.919	78.014	35.177	36.458
P值		＜0.001	＜0.001	＜0.001	＜0.001	＜0.001	＜0.001	＜0.001	＜0.001	＜0.001	＜0.001	＜0.001	＜0.001

组别	例数	血管清晰度	血管噪声	小分支可见度	末端纹理显示	诊断可信度	医师评分1	医师评分2	kappa值	P值
DE组	30	4.88±0.28	4.87±0.32	2.78±0.50	2.70±0.47	3.45±0.55	3.73±0.29	3.75±0.30	0.895	＜0.001
S组	30	2.90±0.28	2.73±0.47	3.57±0.39	3.02±0.33	2.98±0.38	3.04±0.13	3.04±0.18	0.643	＜0.001
L组	30	4.27±0.41	4.27±0.43	4.65±0.46	4.60±0.46	4.71±0.45	4.55±0.22	4.45±0.27	0.764	＜0.001
H值		74.767	72.956	69.679	70.526	63.053	77.536	74.008
P值		＜0.001	＜0.001	＜0.001	＜0.001	＜0.001	＜0.001	＜0.001

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