Improving the depiction of small intracranial vessels in head computed tomography angiography: a comparative analysis of deep learning reconstruction and hybrid iterative reconstruction

Lian K, Bharatha A, Aviv RI, Symons SP. Interpretation errors in CT angiography of the head and neck and the benefit of double reading. Am J Neuroradiol. 2011;32(11):2132–5. https://doi.org/10.3174/ajnr.A2678.

Article  CAS  PubMed  PubMed Central  Google Scholar 

McKinney AM, Palmer CS, Truwit CL, Karagulle A, Teksam M. Detection of aneurysms by 64-section multidetector CT angiography in patients acutely suspected of having an intracranial aneurysm and comparison with digital subtraction and 3D rotational angiography. Am J Neuroradiol. 2008;29(3):594–602. https://doi.org/10.3174/ajnr.A0848.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Rovira A, Frive E, Rovira A, Sabin JA. Distribution territories and causative mechanisms of ischemic stroke. Eur Radiol. 2005;15:416–26. https://doi.org/10.1007/s00330-004-2633-5.

Article  CAS  PubMed  Google Scholar 

Hamamura T, Hayashida Y, Takeshita Y, Sugimoto K, Ueda I, Futatsuya K, et al. The usefulness of full-iterative reconstruction algorithm for the visualization of cystic artery on CT angiography. Jpn J Radiol. 2019;37(7):526–33. https://doi.org/10.1007/s11604-019-00839-x.

Article  PubMed  Google Scholar 

Willemink MJ, Noel PB. The evaluation of image reconstruction for CT—from filtered back projection to artificial intelligence. Eur Radiol. 2019;29:2185–95. https://doi.org/10.1007/s00330-018-5810-7.

Article  PubMed  Google Scholar 

Katsura M, Sato J, Akahane M, Matusda I, Ishida M, Yasaka K, et al. Comparison of pure and hybrid iterative reconstruction techniques with conventional filtered back projection: Image quality assessment in the cervicothoracic region. Eur J Radiol. 2013;82(2):356–60. https://doi.org/10.1016/j.ejrad.2012.11.004.

Article  PubMed  Google Scholar 

Oostveen LJ, Meijer FJA, Lange F, Smit EJ, Pegge SA, Steens SCA, et al. Deep learning-based reconstruction may improve non-contrast cerebral CT imaging compared to other current reconstruction algorithms. Eur Radiol. 2021;31:5498–506. https://doi.org/10.1007/s00330-020-07668-x.

Article  PubMed  PubMed Central  Google Scholar 

Akagi M, Nakamura Y, Higaki T, Narita K, Honda Y, Zhou, et al. Deep learning reconstruction improves image quality of abdominal ultra-high-resolution CT. Eur Radiol. 2019;29:6163–71. https://doi.org/10.1007/s00330-019-06170-3

Singh R, Digumarthy SR, Muse VV, Kambadakone AR, Blake MA, Tabari A, et al. Image quality and lesion detection on deep learning reconstruction and iterative reconstruction of submillisievert chest and abdominal CT. Am J Roentgenol. 2020;213:566–73. https://doi.org/10.2214/AJR.19.21809.

Article  Google Scholar 

Otgonbaatar C, Ryu JK, Kim S, Seo JW, Shim H, Hwang DH. Improvement of depiction of the intracranial arteries on brain CT angiography using deep learning reconstruction. J Integr Neurosci. 2021;20(4):967–76. https://doi.org/10.31083/j.jin2004097

Fukushima Y, Fushimi Y, Funaki T, Sakata A, Hinoda T, Nakajima S, et al. Evaluation of moyamoya disease in CT angiography using ultra-high-resolution computed tomography: application of deep learning reconstruction. Eur J Radiol. 2022;151: 110294. https://doi.org/10.1016/j.ejrad.2022.110294.

Article  PubMed  Google Scholar 

Terasawa K, Tanaka K, Watanabe N, Takada M, Ikeno Y. Optimization of computed tomography contrast studies with a new, simple dosing regimen incorporating body size: examination of contrast effects in the thoracoabdominal aorta. Radiol Phys Technol. 2021:149–60. https://doi.org/10.1007/s12194-021-00609-3

Terasawa K, Hatcho A. Contrast enhancement technique in brain 3D-CTA studies: optimizing the amount of contrast medium according to scan time based on TDC. Nihon Hoshasen Gijutsu Gakkai Zasshi. 2008;64(6):681–90. https://doi.org/10.6009/jjrt.64.681.

Article  PubMed  Google Scholar 

Boedeker KL, Cooper VN, McNitt-Gray MF. Application of the noise power spectrum in modern diagnostic MDCT: part I. Measurement of the noise power spectra and noise equivalent quanta. Phys Med Biol. 2007;52:4027. https://doi.org/10.1088/0031-9155/52/14/002

Richard S, Husarik DB, Yadava G, Murphy SN, Samei E. Towards task-based assessment of CT performance: system and object MTF across different reconstructions algorithms. Med Phys. 2012;39(7):4115–22. https://doi.org/10.1118/1.4725171.

Article  PubMed  Google Scholar 

Ichikawa K, Hara T, Ohashi K. CT measure. Japanese Society of CT Technology, 2012–2014. http://www.jsct-tech.org/.

Pai BS, Varma RV, Kulkarni RN, Nirmala S, Manjunath LC, Rakshith S. Microsurgical anatomy of the posterior circulation. Neurol India. 2007;55(1):31–41. https://doi.org/10.4103/0028-3886.30424.

Article  PubMed  Google Scholar 

Hiraishi T, Matsushima T, Kawashima M, Nakamura Y, Takahashi Y, Ito H, et al. 3D Computer graphics simulation to optimal surgical exposure during microvascular decompression of the glossopharyngeal nerve. Neurosurg Rev. 2013;36:629–35. https://doi.org/10.1007/s10143-013-0479-5.

Article  PubMed  Google Scholar 

Matsumoto M, Kodama N, Endo Y, Sakuma J, Suzuki K, Sasaki T, et al. Dynamic 3D-CT angiography. Am J Neuroradiol. 2007;28(2):299–304.

CAS  PubMed  PubMed Central  Google Scholar 

Kanda Y. Investigation of the freely available easy-to-use software ‘EZR’ for medical statistics. Bone Marrow Transpl. 2013;48(3):452–8. https://doi.org/10.1038/bmt.2012.244.

Article  CAS  Google Scholar 

Solomon J, Lyu P, Marin D, Samei E. Noise and spatial resolution properties of a commercially available deep learning-based CT reconstruction algorithm. Med Phys. 2020;47(9):3961–71. https://doi.org/10.1002/mp.14319.

Article  PubMed  Google Scholar 

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