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16.05.2024 | Computed Tomography

Low tube voltage and deep-learning reconstruction for reducing radiation and contrast medium doses in thin-slice abdominal CT: a prospective clinical trial

verfasst von: Kenichiro Yoshida, Yasunori Nagayama, Yoshinori Funama, Soichiro Ishiuchi, Toshihiko Motohara, Toshiro Masuda, Takeshi Nakaura, Takatoshi Ishiko, Toshinori Hirai, Toru Beppu

Erschienen in: European Radiology

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Abstract

Objectives

To investigate the feasibility of low-radiation dose and low iodinated contrast medium (ICM) dose protocol combining low-tube voltage and deep-learning reconstruction (DLR) algorithm in thin-slice abdominal CT.

Methods

This prospective study included 148 patients who underwent contrast-enhanced abdominal CT with either 120-kVp (600 mgL/kg, n = 74) or 80-kVp protocol (360 mgL/kg, n = 74). The 120-kVp images were reconstructed using hybrid iterative reconstruction (HIR) (120-kVp-HIR), while 80-kVp images were reconstructed using HIR (80-kVp-HIR) and DLR (80-kVp-DLR) with 0.5 mm thickness. Size-specific dose estimate (SSDE) and iodine dose were compared between protocols. Image noise, CT attenuation, and contrast-to-noise ratio (CNR) were quantified. Noise power spectrum (NPS) and edge rise slope (ERS) were used to evaluate noise texture and edge sharpness, respectively. The subjective image quality was rated on a 4-point scale.

Results

SSDE and iodine doses of 80-kVp were 40.4% (8.1 ± 0.9 vs. 13.6 ± 2.7 mGy) and 36.3% (21.2 ± 3.9 vs. 33.3 ± 4.3 gL) lower, respectively, than those of 120-kVp (both, p < 0.001). CT attenuation of vessels and solid organs was higher in 80-kVp than in 120-kVp images (all, p < 0.001). Image noise of 80-kVp-HIR and 80-kVp-DLR was higher and lower, respectively than that of 120-kVp-HIR (both p < 0.001). The highest CNR and subjective scores were attained in 80-kVp-DLR (all, p < 0.001). There were no significant differences in average NPS frequency and ERS between 120-kVp-HIR and 80-kVp-DLR (p ≥ 0.38).

Conclusion

Compared with the 120-kVp-HIR protocol, the combined use of 80-kVp and DLR techniques yielded superior subjective and objective image quality with reduced radiation and ICM doses at thin-section abdominal CT.

Clinical relevance statement

Scanning at low-tube voltage (80-kVp) combined with the deep-learning reconstruction algorithm may enhance diagnostic efficiency and patient safety by improving image quality and reducing radiation and contrast doses of thin-slice abdominal CT.

Key Points

  • Reducing radiation and iodine doses is desirable; however, contrast and noise degradation can be detrimental.
  • The 80-kVp scan with the deep-learning reconstruction technique provided better images with lower radiation and contrast doses.
  • This technique may be efficient for improving diagnostic confidence and patient safety in thin-slice abdominal CT.
Literatur
1.
Zurück zum Zitat Rehani MM, Yang K, Melick ER et al (2020) Patients undergoing recurrent CT scans: assessing the magnitude. Eur Radiol 30:1828–1836PubMedCrossRef Rehani MM, Yang K, Melick ER et al (2020) Patients undergoing recurrent CT scans: assessing the magnitude. Eur Radiol 30:1828–1836PubMedCrossRef
2.
Zurück zum Zitat Bosch de Basea Gomez M, Thierry-Chef I, Harbron R et al (2023) Risk of hematological malignancies from CT radiation exposure in children, adolescents and young adults. Nat Med 29:3111–3119PubMedPubMedCentralCrossRef Bosch de Basea Gomez M, Thierry-Chef I, Harbron R et al (2023) Risk of hematological malignancies from CT radiation exposure in children, adolescents and young adults. Nat Med 29:3111–3119PubMedPubMedCentralCrossRef
3.
Zurück zum Zitat Gupta S, Motwani SS, Seitter RH et al (2023) Development and validation of a risk model for predicting contrast-associated acute kidney injury in patients with cancer: evaluation in over 46,000 CT examinations. AJR Am J Roentgenol 221:486–501CrossRef Gupta S, Motwani SS, Seitter RH et al (2023) Development and validation of a risk model for predicting contrast-associated acute kidney injury in patients with cancer: evaluation in over 46,000 CT examinations. AJR Am J Roentgenol 221:486–501CrossRef
4.
Zurück zum Zitat Dekker HM, Stroomberg GJ, Prokop M (2022) Tackling the increasing contamination of the water supply by iodinated contrast media. Insights Imaging 13:30PubMedPubMedCentralCrossRef Dekker HM, Stroomberg GJ, Prokop M (2022) Tackling the increasing contamination of the water supply by iodinated contrast media. Insights Imaging 13:30PubMedPubMedCentralCrossRef
5.
Zurück zum Zitat Grist TM, Canon CL, Fishman EK, Kohi MP, Mossa-Basha M (2022) Short-, mid-, and long-term strategies to manage the shortage of iohexol. Radiology 304:289–293PubMedCrossRef Grist TM, Canon CL, Fishman EK, Kohi MP, Mossa-Basha M (2022) Short-, mid-, and long-term strategies to manage the shortage of iohexol. Radiology 304:289–293PubMedCrossRef
6.
Zurück zum Zitat Nakayama Y, Awai K, Funama Y et al (2005) Abdominal CT with low tube voltage: preliminary observations about radiation dose, contrast enhancement, image quality, and noise. Radiology 237:945–951PubMedCrossRef Nakayama Y, Awai K, Funama Y et al (2005) Abdominal CT with low tube voltage: preliminary observations about radiation dose, contrast enhancement, image quality, and noise. Radiology 237:945–951PubMedCrossRef
7.
Zurück zum Zitat Nagayama Y, Oda S, Nakaura T et al (2018) Radiation dose reduction at pediatric CT: use of low tube voltage and iterative reconstruction. Radiographics 38:1421–1440PubMedCrossRef Nagayama Y, Oda S, Nakaura T et al (2018) Radiation dose reduction at pediatric CT: use of low tube voltage and iterative reconstruction. Radiographics 38:1421–1440PubMedCrossRef
8.
Zurück zum Zitat Nakaura T, Nakamura S, Maruyama N et al (2012) Low contrast agent and radiation dose protocol for hepatic dynamic CT of thin adults at 256-detector row CT: effect of low tube voltage and hybrid iterative reconstruction algorithm on image quality. Radiology 264:445–454PubMedCrossRef Nakaura T, Nakamura S, Maruyama N et al (2012) Low contrast agent and radiation dose protocol for hepatic dynamic CT of thin adults at 256-detector row CT: effect of low tube voltage and hybrid iterative reconstruction algorithm on image quality. Radiology 264:445–454PubMedCrossRef
9.
Zurück zum Zitat Schindera ST, Nelson RC, Mukundan Jr S et al (2008) Hypervascular liver tumors: low tube voltage, high tube current multi-detector row CT for enhanced detection-phantom study. Radiology 246:125–132PubMedCrossRef Schindera ST, Nelson RC, Mukundan Jr S et al (2008) Hypervascular liver tumors: low tube voltage, high tube current multi-detector row CT for enhanced detection-phantom study. Radiology 246:125–132PubMedCrossRef
10.
Zurück zum Zitat Marin D, Nelson RC, Samei E et al (2009) Hypervascular liver tumors: low tube voltage, high tube current multidetector CT during late hepatic arterial phase for detection-initial clinical experience. Radiology 251:771–779PubMedCrossRef Marin D, Nelson RC, Samei E et al (2009) Hypervascular liver tumors: low tube voltage, high tube current multidetector CT during late hepatic arterial phase for detection-initial clinical experience. Radiology 251:771–779PubMedCrossRef
11.
12.
Zurück zum Zitat Schindera ST, Torrente JC, Ruder TD et al (2011) Decreased detection of hypovascular liver tumors with MDCT in obese patients: a phantom study. AJR Am J Roentgenol 196:W772–W776CrossRef Schindera ST, Torrente JC, Ruder TD et al (2011) Decreased detection of hypovascular liver tumors with MDCT in obese patients: a phantom study. AJR Am J Roentgenol 196:W772–W776CrossRef
13.
Zurück zum Zitat Nagayama Y, Tanoue S, Tsuji A et al (2018) Application of 80-kVp scan and raw data-based iterative reconstruction for reduced iodine load abdominal-pelvic CT in patients at risk of contrast-induced nephropathy referred for oncological assessment: effects on radiation dose, image quality and renal function. Br J Radiol 91:20170632PubMedPubMedCentralCrossRef Nagayama Y, Tanoue S, Tsuji A et al (2018) Application of 80-kVp scan and raw data-based iterative reconstruction for reduced iodine load abdominal-pelvic CT in patients at risk of contrast-induced nephropathy referred for oncological assessment: effects on radiation dose, image quality and renal function. Br J Radiol 91:20170632PubMedPubMedCentralCrossRef
14.
Zurück zum Zitat Mileto A, Zamora DA, Alessio AM et al (2018) CT detectability of small low-contrast hypoattenuating focal lesions: iterative reconstructions versus filtered back projection. Radiology 289:443–454PubMedCrossRef Mileto A, Zamora DA, Alessio AM et al (2018) CT detectability of small low-contrast hypoattenuating focal lesions: iterative reconstructions versus filtered back projection. Radiology 289:443–454PubMedCrossRef
15.
Zurück zum Zitat Jensen CT, Wagner-Bartak NA, Vu LN et al (2019) Detection of colorectal hepatic metastases is superior at standard radiation dose CT versus reduced dose CT. Radiology 290:400–409PubMedCrossRef Jensen CT, Wagner-Bartak NA, Vu LN et al (2019) Detection of colorectal hepatic metastases is superior at standard radiation dose CT versus reduced dose CT. Radiology 290:400–409PubMedCrossRef
16.
Zurück zum Zitat Mileto A, Guimaraes LS, McCollough CH, Fletcher JG, Yu L (2019) State of the art in abdominal CT: the limits of iterative reconstruction algorithms. Radiology 293:491–503PubMedCrossRef Mileto A, Guimaraes LS, McCollough CH, Fletcher JG, Yu L (2019) State of the art in abdominal CT: the limits of iterative reconstruction algorithms. Radiology 293:491–503PubMedCrossRef
17.
Zurück zum Zitat Samei E, Bakalyar D, Boedeker KL et al (2019) Performance evaluation of computed tomography systems: Summary of AAPM Task Group 233. Med Phys 46:e735–e756PubMedCrossRef Samei E, Bakalyar D, Boedeker KL et al (2019) Performance evaluation of computed tomography systems: Summary of AAPM Task Group 233. Med Phys 46:e735–e756PubMedCrossRef
18.
Zurück zum Zitat Ishihara M, Shiiba M, Maruno H et al (2023) Detection of intracranial aneurysms using deep learning-based CAD system: usefulness of the scores of CNN’s final layer for distinguishing between aneurysm and infundibular dilatation. Jpn J Radiol 41:131–141PubMedCrossRef Ishihara M, Shiiba M, Maruno H et al (2023) Detection of intracranial aneurysms using deep learning-based CAD system: usefulness of the scores of CNN’s final layer for distinguishing between aneurysm and infundibular dilatation. Jpn J Radiol 41:131–141PubMedCrossRef
19.
Zurück zum Zitat Toda N, Hashimoto M, Iwabuchi Y et al (2023) Validation of deep learning-based computer-aided detection software use for interpretation of pulmonary abnormalities on chest radiographs and examination of factors that influence readers’ performance and final diagnosis. Jpn J Radiol 41:38–44PubMedCrossRef Toda N, Hashimoto M, Iwabuchi Y et al (2023) Validation of deep learning-based computer-aided detection software use for interpretation of pulmonary abnormalities on chest radiographs and examination of factors that influence readers’ performance and final diagnosis. Jpn J Radiol 41:38–44PubMedCrossRef
20.
Zurück zum Zitat Ozaki J, Fujioka T, Yamaga E et al (2022) Deep learning method with a convolutional neural network for image classification of normal and metastatic axillary lymph nodes on breast ultrasonography. Jpn J Radiol 40:814–822PubMedCrossRef Ozaki J, Fujioka T, Yamaga E et al (2022) Deep learning method with a convolutional neural network for image classification of normal and metastatic axillary lymph nodes on breast ultrasonography. Jpn J Radiol 40:814–822PubMedCrossRef
21.
Zurück zum Zitat Takamatsu A, Ueno M, Yoshida K, Kobayashi T, Kobayashi S, Gabata T (2023) Performance of artificial intelligence-based software for the automatic detection of lung lesions on chest radiographs of patients with suspected lung cancer. Jpn J Radiol 42:291–299 Takamatsu A, Ueno M, Yoshida K, Kobayashi T, Kobayashi S, Gabata T (2023) Performance of artificial intelligence-based software for the automatic detection of lung lesions on chest radiographs of patients with suspected lung cancer. Jpn J Radiol 42:291–299
22.
Zurück zum Zitat Nakaura T, Yoshida N, Kobayashi N et al (2023) Preliminary assessment of automated radiology report generation with generative pre-trained transformers: comparing results to radiologist-generated reports. Jpn J Radiol 42:190–200 Nakaura T, Yoshida N, Kobayashi N et al (2023) Preliminary assessment of automated radiology report generation with generative pre-trained transformers: comparing results to radiologist-generated reports. Jpn J Radiol 42:190–200
23.
Zurück zum Zitat Nakaura T, Kobayashi N, Yoshida N et al (2023) Update on the use of artificial intelligence in hepatobiliary MR imaging. Magn Reson Med Sci 22:147–156PubMedPubMedCentralCrossRef Nakaura T, Kobayashi N, Yoshida N et al (2023) Update on the use of artificial intelligence in hepatobiliary MR imaging. Magn Reson Med Sci 22:147–156PubMedPubMedCentralCrossRef
24.
Zurück zum Zitat Nagayama Y, Sakabe D, Goto M et al (2021) Deep learning–based reconstruction for lower-dose pediatric CT: technical principles, image characteristics, and clinical implementations. Radiographics 41:1936–1953PubMedCrossRef Nagayama Y, Sakabe D, Goto M et al (2021) Deep learning–based reconstruction for lower-dose pediatric CT: technical principles, image characteristics, and clinical implementations. Radiographics 41:1936–1953PubMedCrossRef
25.
Zurück zum Zitat Ueda D, Kakinuma T, Fujita S et al (2024) Fairness of artificial intelligence in healthcare: review and recommendations. Jpn J Radiol 42:3–15PubMedCrossRef Ueda D, Kakinuma T, Fujita S et al (2024) Fairness of artificial intelligence in healthcare: review and recommendations. Jpn J Radiol 42:3–15PubMedCrossRef
26.
Zurück zum Zitat Nagayama Y, Goto M, Sakabe D et al (2022) Radiation dose optimization potential of deep learning-based reconstruction for multiphase hepatic CT: a clinical and phantom study. Eur J Radiol 151:110280PubMedCrossRef Nagayama Y, Goto M, Sakabe D et al (2022) Radiation dose optimization potential of deep learning-based reconstruction for multiphase hepatic CT: a clinical and phantom study. Eur J Radiol 151:110280PubMedCrossRef
27.
Zurück zum Zitat Goto M, Nagayama Y, Sakabe D et al (2023) Lung-optimized deep-learning-based reconstruction for ultralow-dose CT. Acad Radiol 30:431–440PubMedCrossRef Goto M, Nagayama Y, Sakabe D et al (2023) Lung-optimized deep-learning-based reconstruction for ultralow-dose CT. Acad Radiol 30:431–440PubMedCrossRef
28.
Zurück zum Zitat Zhao R, Sui X, Qin R et al (2022) Can deep learning improve image quality of low-dose CT: a prospective study in interstitial lung disease. Eur Radiol 32:8140–8151PubMedCrossRef Zhao R, Sui X, Qin R et al (2022) Can deep learning improve image quality of low-dose CT: a prospective study in interstitial lung disease. Eur Radiol 32:8140–8151PubMedCrossRef
29.
Zurück zum Zitat Kaga T, Noda Y, Mori T et al (2022) Unenhanced abdominal low-dose CT reconstructed with deep learning-based image reconstruction: image quality and anatomical structure depiction. Jpn J Radiol 40:703–711PubMedPubMedCentralCrossRef Kaga T, Noda Y, Mori T et al (2022) Unenhanced abdominal low-dose CT reconstructed with deep learning-based image reconstruction: image quality and anatomical structure depiction. Jpn J Radiol 40:703–711PubMedPubMedCentralCrossRef
30.
Zurück zum Zitat Funama Y, Nakaura T, Hasegawa A et al (2023) Noise power spectrum properties of deep learning–based reconstruction and iterative reconstruction algorithms: phantom and clinical study. Eur J Radiol 165:110914PubMedCrossRef Funama Y, Nakaura T, Hasegawa A et al (2023) Noise power spectrum properties of deep learning–based reconstruction and iterative reconstruction algorithms: phantom and clinical study. Eur J Radiol 165:110914PubMedCrossRef
31.
Zurück zum Zitat Nagayama Y, Goto M, Sakabe D et al (2022) Radiation dose reduction for 80-kVp pediatric CT using deep learning-based reconstruction: a clinical and phantom study. AJR Am J Roentgenol 219:315–324CrossRef Nagayama Y, Goto M, Sakabe D et al (2022) Radiation dose reduction for 80-kVp pediatric CT using deep learning-based reconstruction: a clinical and phantom study. AJR Am J Roentgenol 219:315–324CrossRef
32.
Zurück zum Zitat Nagayama Y, Iwashita K, Maruyama N et al (2023) Deep learning-based reconstruction can improve the image quality of low radiation dose head CT. Eur Radiol 33:3253–3265PubMedCrossRef Nagayama Y, Iwashita K, Maruyama N et al (2023) Deep learning-based reconstruction can improve the image quality of low radiation dose head CT. Eur Radiol 33:3253–3265PubMedCrossRef
33.
Zurück zum Zitat Higaki T, Nakamura Y, Zhou J et al (2020) Deep learning reconstruction at CT: phantom study of the image characteristics. Acad Radiol 27:82–87PubMedCrossRef Higaki T, Nakamura Y, Zhou J et al (2020) Deep learning reconstruction at CT: phantom study of the image characteristics. Acad Radiol 27:82–87PubMedCrossRef
34.
Zurück zum Zitat Greffier J, Hamard A, Pereira F et al (2020) Image quality and dose reduction opportunity of deep learning image reconstruction algorithm for CT: a phantom study. Eur Radiol 30:3951–3959PubMedCrossRef Greffier J, Hamard A, Pereira F et al (2020) Image quality and dose reduction opportunity of deep learning image reconstruction algorithm for CT: a phantom study. Eur Radiol 30:3951–3959PubMedCrossRef
35.
Zurück zum Zitat Solomon J, Lyu P, Marin D, Samei E (2020) Noise and spatial resolution properties of a commercially available deep learning-based CT reconstruction algorithm. Med Phys 47:3961–3971PubMedCrossRef Solomon J, Lyu P, Marin D, Samei E (2020) Noise and spatial resolution properties of a commercially available deep learning-based CT reconstruction algorithm. Med Phys 47:3961–3971PubMedCrossRef
38.
Zurück zum Zitat Tatsugami F, Higaki T, Sakane H et al (2017) Coronary artery stent evaluation with model-based iterative reconstruction at coronary CT angiography. Acad Radiol 24:975–981PubMedCrossRef Tatsugami F, Higaki T, Sakane H et al (2017) Coronary artery stent evaluation with model-based iterative reconstruction at coronary CT angiography. Acad Radiol 24:975–981PubMedCrossRef
39.
Zurück zum Zitat Tatsugami F, Higaki T, Nakamura Y et al (2019) Deep learning-based image restoration algorithm for coronary CT angiography. Eur Radiol 29:5322–5329PubMedCrossRef Tatsugami F, Higaki T, Nakamura Y et al (2019) Deep learning-based image restoration algorithm for coronary CT angiography. Eur Radiol 29:5322–5329PubMedCrossRef
40.
Zurück zum Zitat Mergen V, Sartoretti T, Baer-Beck M et al (2022) Ultra-high-resolution coronary CT angiography with photon-counting detector CT: feasibility and image characterization. Invest Radiol 57:780–788 Mergen V, Sartoretti T, Baer-Beck M et al (2022) Ultra-high-resolution coronary CT angiography with photon-counting detector CT: feasibility and image characterization. Invest Radiol 57:780–788
41.
Zurück zum Zitat Kanda Y (2013) Investigation of the freely available easy-to-use software ‘EZR’ for medical statistics. Bone Marrow Transplantation 48:452–458PubMedCrossRef Kanda Y (2013) Investigation of the freely available easy-to-use software ‘EZR’ for medical statistics. Bone Marrow Transplantation 48:452–458PubMedCrossRef
42.
Zurück zum Zitat Oostveen LJ, Smit EJ, Dekker HM et al (2023) Abdominopelvic CT image quality: evaluation of thin (0.5-mm) slices using deep learning reconstruction. AJR Am J Roentgenol 220:381–388CrossRef Oostveen LJ, Smit EJ, Dekker HM et al (2023) Abdominopelvic CT image quality: evaluation of thin (0.5-mm) slices using deep learning reconstruction. AJR Am J Roentgenol 220:381–388CrossRef
43.
Zurück zum Zitat Jensen CT, Gupta S, Saleh MM et al (2022) Reduced-dose deep learning reconstruction for abdominal CT of liver metastases. Radiology 303:90–98PubMedCrossRef Jensen CT, Gupta S, Saleh MM et al (2022) Reduced-dose deep learning reconstruction for abdominal CT of liver metastases. Radiology 303:90–98PubMedCrossRef
44.
Zurück zum Zitat Toia GV, Zamora DA, Singleton M et al (2023) Detectability of small low-attenuation lesions with deep learning CT image reconstruction: a 24-reader phantom study. AJR Am J Roentgenol 220:283–295CrossRef Toia GV, Zamora DA, Singleton M et al (2023) Detectability of small low-attenuation lesions with deep learning CT image reconstruction: a 24-reader phantom study. AJR Am J Roentgenol 220:283–295CrossRef
45.
Zurück zum Zitat Lyu P, Liu N, Harrawood B et al (2023) Is it possible to use low-dose deep learning reconstruction for the detection of liver metastases on CT routinely? Eur Radiol 33:1629–1640PubMedCrossRef Lyu P, Liu N, Harrawood B et al (2023) Is it possible to use low-dose deep learning reconstruction for the detection of liver metastases on CT routinely? Eur Radiol 33:1629–1640PubMedCrossRef
46.
Zurück zum Zitat Achenbach S, Chandrashekhar Y, Narula J (2013) The ethics of publishing medical imaging research. JACC Cardiovasc Imaging 6:1351–1353PubMedCrossRef Achenbach S, Chandrashekhar Y, Narula J (2013) The ethics of publishing medical imaging research. JACC Cardiovasc Imaging 6:1351–1353PubMedCrossRef
47.
Zurück zum Zitat Akagi M, Nakamura Y, Higaki T, Narita K, Honda Y, Awai K (2020) Deep learning reconstruction of equilibrium phase CT images in obese patients. Eur J Radiol 133:109349PubMedCrossRef Akagi M, Nakamura Y, Higaki T, Narita K, Honda Y, Awai K (2020) Deep learning reconstruction of equilibrium phase CT images in obese patients. Eur J Radiol 133:109349PubMedCrossRef
48.
Zurück zum Zitat Wang H, Yue S, Liu N et al (2024) Deep learning reconstruction vs standard reconstruction for abdominal CT: the influence of BMI. Eur Radiol 34:1614–1623PubMedCrossRef Wang H, Yue S, Liu N et al (2024) Deep learning reconstruction vs standard reconstruction for abdominal CT: the influence of BMI. Eur Radiol 34:1614–1623PubMedCrossRef
49.
Zurück zum Zitat Greffier J, Frandon J, Si-Mohamed S et al (2021) Comparison of two deep learning image reconstruction algorithms in chest CT images: A task-based image quality assessment on phantom data. Diagn Interv Imaging 103:21–30 Greffier J, Frandon J, Si-Mohamed S et al (2021) Comparison of two deep learning image reconstruction algorithms in chest CT images: A task-based image quality assessment on phantom data. Diagn Interv Imaging 103:21–30
50.
Zurück zum Zitat Kawashima H, Ichikawa K, Takata T, Seto I (2022) Comparative assessment of noise properties for two deep learning CT image reconstruction techniques and filtered back projection. Med Phys 49:6359–6367PubMedCrossRef Kawashima H, Ichikawa K, Takata T, Seto I (2022) Comparative assessment of noise properties for two deep learning CT image reconstruction techniques and filtered back projection. Med Phys 49:6359–6367PubMedCrossRef
Metadaten
Titel
Low tube voltage and deep-learning reconstruction for reducing radiation and contrast medium doses in thin-slice abdominal CT: a prospective clinical trial
verfasst von
Kenichiro Yoshida
Yasunori Nagayama
Yoshinori Funama
Soichiro Ishiuchi
Toshihiko Motohara
Toshiro Masuda
Takeshi Nakaura
Takatoshi Ishiko
Toshinori Hirai
Toru Beppu
Publikationsdatum
16.05.2024
Verlag
Springer Berlin Heidelberg
Erschienen in
European Radiology
Print ISSN: 0938-7994
Elektronische ISSN: 1432-1084
DOI
https://doi.org/10.1007/s00330-024-10793-6

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