Advertisement

A deep-leaning reconstruction algorithm that improves the image quality of low-tube-voltage coronary CT angiography

Published:November 24, 2021DOI:https://doi.org/10.1016/j.ejrad.2021.110070

      Highlights

      • Deep-leaning reconstruction algorithm can improve the image quality of low-tube-voltage coronary CT angiography.
      • Deep-leaning reconstruction with high level did not decrease the sharpness of the coronary artery.

      Abstract

      Purpose

      To assess the image quality (IQ) of low tube voltage coronary CT angiography (CCTA) images reconstructed with deep learning image reconstruction (DLIR).

      Methods

      According to body mass index (BMI), eighty patients who underwent 70kVp CCTA (Group A, N = 40, BMI ≤ 26 kg/m2) or 80kVp CCTA (Group B, N = 40, BMI > 26 kg/m2) were prospectively included. All images were reconstructed with four algorithms, including filtered back-projection (FBP), adaptive statistical iterative reconstruction-Veo at a level of 50% (ASiR-V50%), and DLIR at medium (DLIR-M) and high (DLIR-H) levels. Image noise, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR) and edge rise distance (ERD) within aorta root and coronary arteries were calculated. The IQ was subjectively evaluated by using a 5-point scale.

      Results

      Compared with FBP, ASiR-V50% and DLIR-M, DLIR-H led to the lowest noise (Group A: 24.7 ± 5.0HU; Group B, 21.6 ± 2.8 HU), highest SNR (Group A, 24.9 ± 5.0; Group B, 28.0 ± 5.8), CNR (Group A, 42.2 ± 15.2; Group B, 43.6 ± 10.5) and lowest ERD (Group A, 1.49 ± 0.30 mm; Group B, 1.50 ± 0.22 mm) with statistical significance (all P < 0.05). For the objective assessment, the percentages of 4 and 5 IQ scores were significantly higher for DLIR-H (Group A, 93.8%; Group B,90.0%) and DLIR-M (Group A, 85.6%; Group B,86.9 %) compared to ASiR-V50% (Group A, 58.8%; Group B, 58.8%) and FBP (Group A, 34.4%; Group B, 33.1%) algorithms (all P < 0.05).

      Conclusion

      The application of DLIR significantly improves both objective and subjective IQ in low tube voltage CCTA compared with ASiR-V and FBP, which may promote a further radiation dose reduction in CCTA.

      Keywords

      Abbreviations:

      ASiR-V (adaptive statistical iterative reconstruction-Veo), AO (Aortic root), FBP (Filtered back-projection), BMI (Body mass index), bpm (Beats per minute), CAD (Coronary artery disease), CCTA (Coronary CT angiography), CNR (Contrast-noise ratio), DNN (Deep neural networks), CPR (Curved planar reformation), CTDIvol (Volumetric CT dose index), DLIR (Deep learning–based image reconstruction), DLP (Dose-length product), ED (Effective dose), ERD (Edge rise distance), HR (Heart rates), ICA (Invasive coronary angiography), IQ (Image quality), ROI (Region of interest), SD (Standard deviation), SNR (Signal-to-noise ratio), VR (Volume rendering)
      To read this article in full you will need to make a payment

      References

        • Williams M.C.
        • Hunter A.
        • Shah A.S.V.
        • Assi V.
        • Lewis S.
        • Smith J.
        • Berry C.
        • Boon N.A.
        • Clark E.
        • Flather M.
        • Forbes J.
        • McLean S.
        • Roditi G.
        • van Beek E.J.R.
        • Timmis A.D.
        • Newby D.E.
        Use of Coronary Computed Tomographic Angiography to Guide Management of Patients With Coronary Disease.
        J Am Coll Cardiol. 2016; 67: 1759-1768https://doi.org/10.1016/j.jacc.2016.02.026
      1. SCOT-HEART investigators. CT coronary angiography in patients with suspected angina due to coronary heart disease (SCOT-HEART): an open-label, parallel-group, multicentre trial. Lancet. 2015 Jun 13;385(9985):2383-91. doi: 10.1016/S0140-6736(15)60291-4. Epub 2015 Mar 15. Erratum in: Lancet. 2015 Jun 13;385(9985):2354.

        • Einstein A.J.
        • Henzlova M.J.
        • Rajagopalan S.
        Estimating risk of cancer associated with radiation exposure from 64-slice computed tomography coronary angiography.
        JAMA. 2007 Jul 18; 298: 317-323https://doi.org/10.1001/jama.298.3.317
        • Brenner D.J.
        • Hall E.J.
        Computed tomography–an increasing source of radiation exposure.
        N Engl J Med. 2007 Nov 29; 357: 2277-2284https://doi.org/10.1056/NEJMra072149
      2. Stocker TJ, Deseive S, Leipsic J, et al., Reduction in radiation exposure in cardiovascular computed tomography imaging: results from the PROspective multicenter registry on radiaTion dose Estimates of cardiac CT angIOgraphy iN daily practice in 2017 (PROTECTION VI). Eur Heart J. 2018 Nov 1;39(41):3715-3723. doi: 10.1093/eurheartj/ehy546.

        • Alkadhi H.
        • Leschka S.
        Radiation dose of cardiac computed tomography - what has been achieved and what needs to be done.
        Eur Radiol. 2011 Mar; 21 (Epub 2010 Oct 19): 505-509https://doi.org/10.1007/s00330-010-1984-3
        • LaBounty T.M.
        • Leipsic J.
        • Poulter R.
        • Wood D.
        • Johnson M.
        • Srichai M.B.
        • Cury R.C.
        • Heilbron B.
        • Hague C.
        • Lin F.Y.
        • Taylor C.
        • Mayo J.R.
        • Thakur Y.
        • Earls J.P.
        • Mancini G.B.J.
        • Dunning A.
        • Gomez M.J.
        • Min J.K.
        Coronary CT angiography of patients with a normal body mass index using 80 kVp versus 100 kVp: a prospective, multicenter, multivendor randomized trial.
        AJR Am J Roentgenol. 2011; 197: W860-W867https://doi.org/10.2214/AJR.11.6787
        • Chen Y.
        • Liu Z.
        • Li M.
        Reducing both radiation and contrast doses in coronary CT angiography in lean patients on a 16-cm wide-detector CT using 70 kVp and ASiR-V algorithm, in comparison with the conventional 100-kVp protocol.
        Eur Radiol. 2019 Jun; 29 (Epub 2018 Nov 30): 3036-3043https://doi.org/10.1007/s00330-018-5837-9
        • Willemink M.J.
        • Noël P.B.
        The evolution of image reconstruction for CT-from filtered back projection to artificial intelligence.
        Eur Radiol. 2019 May; 29 (Epub 2018 Oct 30): 2185-2195https://doi.org/10.1007/s00330-018-5810-7
      3. Greffier J, Frandon J, Larbi A, et al., Pereira F. CT iterative reconstruction algorithms: a task-based image quality assessment. Eur Radiol. 2020 Jan;30(1):487-500. doi: 10.1007/s00330-019-06359-6. Epub 2019 Jul 29.

      4. Jiahua Fan, Meghan Yue, and Roman Melnyk. Healthcare GE. Benefits of ASiR-V* Reconstruction for Reducing Patient Radiation Dose and Preserving Diagnostic Quality in CT Exams. Available from: https://www.gehealthcare.co.uk/-/media/6862ed3b10424182924e03a49f4a46d7.

      5. Park CJ, Kim KW, Lee HJ, et al., Contrast-Enhanced CT with Knowledge-Based Iterative Model Reconstruction for the Evaluation of Parotid Gland Tumors: A Feasibility Study. Korean J Radiol. 2018 Sep-Oct;19(5):957-964. doi: 10.3348/kjr.2018.19.5.957. Epub 2018 Aug 6.

        • Tatsugami F.
        • Higaki T.
        • Nakamura Y.
        • Yu Z.
        • Zhou J.
        • Lu Y.
        • Fujioka C.
        • Kitagawa T.
        • Kihara Y.
        • Iida M.
        • Awai K.
        Deep learning-based image restoration algorithm for coronary CT angiography.
        Eur Radiol. 2019; 29: 5322-5329https://doi.org/10.1007/s00330-019-06183-y
        • Bernard A.
        • Comby P.-O.
        • Lemogne B.
        • Haioun K.
        • Ricolfi F.
        • Chevallier O.
        • Loffroy R.
        Deep learning reconstruction versus iterative reconstruction for cardiac CT angiography in a stroke imaging protocol: reduced radiation dose and improved image quality.
        Quant Imaging Med Surg. 2021; 11: 392-401https://doi.org/10.21037/qims10.21037/qims-20-626
        • Liu P.
        • Wang M.
        • Wang Y.
        • Yu M.
        • Wang Y.
        • Liu Z.
        • Li Y.
        • Jin Z.
        Impact of Deep Learning-based Optimization Algorithm on Image Quality of Low-dose Coronary CT Angiography with Noise Reduction: A Prospective Study.
        Acad Radiol. 2020; 27: 1241-1248https://doi.org/10.1016/j.acra.2019.11.010
        • Hong J.H.
        • Park E.-A.
        • Lee W.
        • Ahn C.
        • Kim J.-H.
        Incremental Image Noise Reduction in Coronary CT Angiography Using a Deep Learning-Based Technique with Iterative Reconstruction.
        Korean J Radiol. 2020; 21: 1165https://doi.org/10.3348/kjr.2020.0020
      6. Benz DC, Benetos G, Rampidis G, et al., Validation of deep-learning image reconstruction for coronary computed tomography angiography: Impact on noise, image quality and diagnostic accuracy. J Cardiovasc Comput Tomogr. 2020 Sep-Oct;14(5):444-451. doi: 10.1016/j.jcct.2020.01.002. Epub 2020 Jan 13.

        • McCollough C.H.
        CT dose: how to measure, how to reduce.
        Health Phys. 2008 Nov; 95: 508-517https://doi.org/10.1097/01.HP.0000326343.35884.03
        • Mileto A.
        • Guimaraes L.S.
        • McCollough C.H.
        • Fletcher J.G.
        • Yu L.
        State of the Art in Abdominal CT: The Limits of Iterative Reconstruction Algorithms.
        Radiology. 2019; 293: 491-503https://doi.org/10.1148/radiol.2019191422
      7. Hsieh J, Liu E, Nett B, Tang J, Thibault J, Sahney S. Healthcare GE. A new era of image reconstruction: TrueFidelity™ Technical white paper on deep learning image reconstruction; 2019 [1/1/2010]; Available from: https://www.gehealthcare.com/-/jssmedia/040dd213fa89463287155151fdb01922.pdf.

        • Greffier J.
        • Hamard A.
        • Pereira F.
        • Barrau C.
        • Pasquier H.
        • Beregi J.P.
        • Frandon J.
        Image quality and dose reduction opportunity of deep learning image reconstruction algorithm for CT: a phantom study.
        Eur Radiol. 2020; 30: 3951-3959https://doi.org/10.1007/s00330-020-06724-w
        • Kim J.H.
        • Yoon H.J.
        • Lee E.
        • Kim I.
        • Cha Y.K.
        • Bak S.H.
        Validation of Deep-Learning Image Reconstruction for Low-Dose Chest Computed Tomography Scan: Emphasis on Image Quality and Noise.
        Korean J Radiol. 2021; 22: 131https://doi.org/10.3348/kjr.2020.0116
        • Kim I.
        • Kang H.
        • Yoon H.J.
        • Chung B.M.
        • Shin N.-Y.
        Deep learning-based image reconstruction for brain CT: improved image quality compared with adaptive statistical iterative reconstruction-Veo (ASiR-V).
        Neuroradiology. 2021; 63: 905-912https://doi.org/10.1007/s00234-020-02574-x
      8. Cao L, Liu X, Li J, et al., A study of using a deep learning image reconstruction to improve the image quality of extremely low-dose contrast-enhanced abdominal CT for patients with hepatic lesions. Br J Radiol. 2021 Feb 1;94(1118):20201086. doi: 10.1259/bjr.20201086. Epub 2020 Dec 11.