ISSN 1004-4140
CN 11-3017/P
| Citation: |
LI Z, WANG Z W, YU G F, et al. Deep Learning Reconstruction Algorithm Combined with “Double Low” Dose for Liver CT Enhancement[J]. CT Theory and Applications, xxxx, x(x): 1-7. DOI: 10.15953/j.ctta.2024.306. (in Chinese).
|
Objective: Exploration of the application of a deep-learning reconstruction algorithm (DLIR) and adaptive statistical iterative reconstruction (ASIR-V) based on the combination of low radiation dose and low iodine contrast agent in liver CT enhancement. Methods: A total of 82 patients who underwent abdominal enhanced CT were prospectively selected and randomly separated into groups A and B. Group A (control group) received a conventional dose (tube voltage 120 kVp; iodine contrast 85mL) and inferior portal image reconstruction was applied using 30%, 50%, and 70% ASIR-V (AV 30, AV 50, AV 70). In Group B (experimental group), image reconstruction was based on medium- and high-intensity deep learning (DLIR-M, DLIR-H). Image noise (SD), signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), lesion contrast-to-noise ratio (LLR), quality factor (FOM), effective radiation dose (ED), and iodine intake were calculated. Subjective image quality results were obtained for different reconstruction methods at different doses. No significant differences in gender, age, and BMI between groups A and B were found. For 38.40% effective dose and 23.53% reduction in the contrast agent dosage, no significant SD differences were found between DLIR-M and AV 50, DLIR-H and liver parenchyma and AV 70. Only DLIR-M and AV 50 in the portal SNR were not statistically significant. No significant differences were found between DLIR-M and AV 70 in liver parenchyma and portal CNR. Concerning LLR and FOM, no significant differences were found between DLIR-M and AV 70. For various subjective image quality assessments, DLIR at double low doses outperformed AVIR-V, especially DLIR-H. Conclusions: DLIR can improve image quality and the ability to detect liver low contrast lesions at “double low” (low radiation dose low contrast) compared to ASIR-V.
| [1] |
MAYO-SMITH W W, HARA A K, MAHESH M, et al. How I do it: Managing radiation dose in CT[J]. Radiology, 2014, 273(3): 657-672. DOI: 10.1148/radiol.14132328.
|
| [2] |
HYOJIN K, MIN J L, CHULKYUN A, et al. Low dose of contrast agent and low radiation liver computed tomography with deep-learning-based contrast boosting model in participants at high-risk for hepatocellular carcinoma: prospective, randomized, double-blind study[J]. European Radiology, 2023, 33(5): 3660-3670. DOI: 10.1007/s00330-023-09520-4.
|
| [3] |
HAUBOLD J, HOSCH R, UMUTLU L, et al. Contrast agent dose reduction in computed tomography with deep learning using a conditional generative adversarial network[J]. European Radiology, 2021, 31(8): 1-9.
|
| [4] |
SAE-JIN P, DONG-YOON K, KYOUNG-HEE S, et al. Immediate Mild Reactions to CT with Iodinated Contrast Media: Strategy of Contrast Media Readministration without Corticosteroids[J]. Radiology, 2018, 288(3): 710-716. DOI: 10.1148/radiol.2018172524.
|
| [5] |
KUN K H, SEOK K C, KYOUNG N J, et al. Cardiac CTA image quality of adaptivestatistical iterative reconstruction-V versus deep learning reconstruction “TrueFidelity” in children with congenital heart disease[J]. Medicine, 2022, 101(42): e31169-e31169. DOI: 10.1097/MD.0000000000031169.
|
| [6] |
LE C, XIANG L, JIANYING L, et al. A study of using a deep learning image reconstructionto improve the image quality of extremely low dose contrast-enhanced abdominal CT for patients with hepatic lesions[J]. The British Journal of Radiology, 2020, 94(1118): 20201086-20201086.
|
| [7] |
汪琴, 严伟杰, 袁元, 等. 深度学习重建算法在上腹部能谱CT小血管显示中的研究[J]. CT理论与应用研究, 2025, 34(1): 37-43. DOI: 10.15953/j.ctta.2024.168.
WANG Q, YAN W J, YUAN Y, et al. Study of deep learning reconstruction algorithmin the display of CT[J]. Research on CT Theory and Applied Technology, 2025, 34(1): 37-43. DOI: 10.15953/j.ctta.2024.168.
|
| [8] |
TAEHEE L, HEE J Y, YOUNG J P, et al. Deep learning-based iodine contrast-augmenting algorithm for low-contrast-dose liver CT to assess hypovascular hepatic metastasis[J]. Abdominal radiology (New York), 2023, 48(11): 3430-3440. DOI: 10.1007/s00261-023-04039-0.
|
| [9] |
PEIJIE L, ZHEN L, YAN C, et al. Deep learning reconstruction CT for liver metastases: Low-dose dual-energy vs standard-dose single-energy[J]. European Radiology, 2023, 34(1): 28-38. DOI: 10.1007/s00330-023-10033-3.
|
| [10] |
温德英, 杨杰尹, 汪琴, 等. 深度学习重建算法在上腹部CT成像中的应用[J]. CT理论与应用研究, 2022, 31(3): 329-336. DOI: 10.15953/j.ctta.2021.005.
WEN D Y, YANG J Y, WANG Q, et al. Application of a deep learning reconstructionalgorithm in CT imaging of the upper abdomen[J]. CT Theory and Applied Research, 2022, 31(3): 329-336. DOI: 10.15953/j.ctta.2021.005.
|
| [11] |
褚冰倩, 沈艺, 宋建, 等. 深度学习重建提高腹部能谱CT图像质量和病灶诊断的可行性研究[J]. 中国CT和MRI杂志, 2024, 22(6): 154-157. DOI: 10.3969/j.issn.1672-5131.2024.06.049.
CHU B Q, SHEN Y, SONG J, et al. Feasibility study of deep learning reconstruction to improve CT image quality of abdominal energy spectrum and lesion diagnosis[J]. Chinese Journal of CT and MRI, 2024, 22(6): 154-157. DOI: 10.3969/j.issn.1672-5131.2024.06.049.
|
| [12] |
魏巍, 杨旭, 童小雨, 等. 深度学习重建算法联合低剂量增强CT对肝脏低对比度病灶显示的影响[J]. 中国医学计算机成像杂志, 2024, 30(3): 333-337. DOI: 10.3969/j.issn.1006-5741.2024.03.013.
WEI W, YANG X, TONG X Y, et al. Effect of deep learning reconstruction algorithm combined with low dose enhanced CT on liver low contrast focus display[J]. Chinese Journal of Medical Computer Imaging, 2024, 30(3): 333-337. DOI: 10.3969/j.issn.1006-5741.2024.03.013.
|
| [13] |
MARIN D, NELSON R C, SCHINDERA S T, et al. Low-tube-voltage, high-tube-current multidetector abdominal CT: improved image quality and decreased radiation dose with adaptive statistical iterative reconstruction algorithm--initial clinical experience[J]. Radiology, 2010, 254(1): 145-53.
|
| [14] |
DAMIANO C, DOMENICO S D, ANTONELLA G D, et al. Low-dose liver CT: Image quality and diagnostic accuracy of deep learning image reconstruction algorithm[J]. European Radiology, 2023, 34(4): 2384-2393. DOI: 10.1007/s00330-023-10171-8.
|
| [15] |
JIUMING J, LEI M, XIN L, et al. The value of deep learning image reconstruction in improving the quality of low-dose chest CT images[J]. Diagnostics, 2022, 12(10): 2560-2560. DOI: 10.3390/diagnostics12102560.
|
| [16] |
YIFAN B, SHUO Y, XINGCHAO L, et al. Impact of deep learning-based image reconstruction on image quality and lesion visibility in renal computed tomographyat different doses[J]. Quantitative Imaging in Medicine and Surgery, 2023, 13(4): 2197-2207. DOI: 10.21037/qims-22-852.
|
| [17] |
姜宁宁. 多层螺旋CT双低剂量和常规剂量在结肠癌肿瘤侵犯、肠系膜血管分布中准确率的效果对比[J]. 中外医学研究, 2024, 22(24): 83-86.
JIANG N N. Comparison of the accuracy of multi-layer spiral CT double low dose andconventional dose in tumor invasion and mesenteric vascularity of colon cancer[J]. Chinese and Foreign Medical Research, 2024, 22(24): 83-86.
|
| [18] |
胡淑敏, 祁冬, 乔晓春, 等. “双低”(低对比剂、低管电压)技术结合多模型迭代重建算法在肾脏双期CT增强扫描中的可行性[J]. 吉林医学, 2021, 42(9): 2123-2125.
HU S M, QI D, QIAO X C, et al. The feasibility of "double low" (low contrastagent, low tube voltage) technology combined with multi-model iterative reconstruction algorithm in kidney dual-stage CT enhanced scan[J]. Jilin Medical, 2021, 42(9): 2123-2125.
|
| [19] |
MENG B, CONG W, XI Y, et al. Model and reconstruction of a K-edge contrast agent distribution with an X-ray photon-counting detector[J]. Optics Express, 2017, 25(8): 9378-9392. DOI: 10.1364/OE.25.009378.
|
| [20] |
CHUNG E Y, YOU S J, LEE H, et al. Possible contrast media reduction with low kev monoenergetic images in the detection of focal liver lesions: A dual-energy CT animal study[J]. PLoS ONE, 2017, 10(7): e0133170.
|
| [21] |
PEIJIE L, NANA L, BRIAN H, et al. Is it possible to use low-dose deep learning reconstruction for the detection of liver metastases on CT routinely?[J]. European Radiology, 2022, 33(3): 1629-1640. DOI: 10.1007/s00330-022-09206-3.
|
| [22] |
JUNE P, JAESEUNG S, KYUNG I M, et al. Image quality and lesion detectability of lower-dose abdominopelvic CT obtained using deep learning image reconstruction[J]. Korean journal of radiology, 2022, 23(4):
|
| [23] |
XIE, Y R, WANG Y C, SHAN H, et al. Application of deep-learning reconstruction algorithm for enhanced CT scan of upper abdomen under different radiation doses: focus on noise, contrast-to-noise ratio and image quality[J]. Chinese Journal of Academic Radiology, 2022, 5(2): 107-121. DOI: 10.1007/s42058-021-00086-y.
|
| [24] |
ZHOU Z, GONG H, HSIEH S, et al. Image quality evaluation in deep-learning-based CT noise reduction using virtual imaging trial methods: Contrast-dependent spatial resolution[J]. Medical Physics, 2024, 51(8): 5399-5413. DOI: 10.1002/mp.17029.
|
| [25] |
SOLOMON J, LYU P, MARIN D, et al. Noise and spatial resolution properties of a commercially available deep learning based CT reconstruction algorithm[J]. Medical Physics, 2020, 47(9): 3961-3971. DOI: 10.1002/mp.14319.
|
| 1. |
徐舒宁，桂志国，尚禹. 漫射相关光谱在脑血流自动调节中的研究. 中国医疗设备. 2025(04): 44-49 .
|
Supported by: Beijing Renhe Information Technology Co. Ltd 
DownLoad: