Improvement of Neurovascular Imaging Using Ultra-High-Resolution Computed Tomography Angiography.

Cerebral arteries Computed tomography Computed tomography angiography Image enhancement Resolution

Journal

Clinical neuroradiology
ISSN: 1869-1447
Titre abrégé: Clin Neuroradiol
Pays: Germany
ID NLM: 101526693

Informations de publication

Date de publication:
13 Oct 2023
Historique:
received: 24 07 2023
accepted: 23 08 2023
medline: 13 10 2023
pubmed: 13 10 2023
entrez: 13 10 2023
Statut: aheadofprint

Résumé

To evaluate diagnostic image quality of ultra-high-resolution computed tomography angiography (UHR-CTA) in neurovascular imaging as compared to normal resolution CT-angiography (NR-CTA). In this retrospective single-center study brain and neck CT-angiography was performed using an ultra-high-resolution computed tomography scanner (n = 82) or a normal resolution CT scanner (NR-CTA; n = 73). Ultra-high-resolution images were reconstructed with a 1024 × 1024 matrix and a slice thickness of 0.25 mm, whereas NR-CT images were reconstructed with a 512 × 512 matrix and a slice thickness of 0.5 mm. Three blinded neuroradiologists assessed overall image quality, artifacts, image noise, overall contrast and diagnostic confidence using a 4-point Likert scale. Furthermore, the visualization and delineation of supra-aortic arteries with an emphasis on the visualization of small intracerebral vessels was assessed using a cerebral vascular score, also utilizing a 4-point Likert scale. Quantitative analyses included signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), noise and the steepness of gray value transition. Radiation exposure was determined by comparison of computed tomography dose index (CTDIvol), dose length product (DLP) and mean effective dose. Interrater agreement was evaluated via determining Fleiss-Kappa. Ultra-high-resolution CT-angiography (UHR-CTA) yielded excellent image quality with superior quantitative (SNR: p < 0.001, CNR: p < 0.001, steepness of gray value transition: p < 0.001) and qualitative results (overall image quality: 4 (Inter quartile range (IQR) = 4-4); p < 0.001, diagnostic confidence: 4 (IQR = 4-4); p < 0.001) compared to NR-CT (overall image quality: 3 (IQR = 3-3), diagnostic confidence: 3 (IQR = 3-4)). Furthermore, UHR-CT enabled significantly superior delineation and visualization of all vascular segments, from proximal extracranial vessels to the smallest peripheral cerebral branches (e.g. 4 (3-4) vs. NR-CTA PICA: 3 (2-3); UHR-CTA P4: 4 (IQR = 3-4) vs. NR-CTA P4: 2 (IQR = 2-3); UHR-CTA M4: 4 (IQR = 4-4) vs. NR-CTA M4: 3 (IQR = 2-3); UHR-CTA A4: 4 (IQR = 3-4) vs. NR-CTA A4: 2 (IQR = 2-3); all p < 0.001). Noteworthy, a reduced mean effective dose was observed when applying UHR-CT (NR-CTA: 1.8 ± 0.3 mSv; UHR-CTA: 1.5 ± 0.5 mSv; p < 0.001). Ultra-high-resolution CT-angiography improves image quality in neurovascular imaging allowing the depiction and evaluation of small peripheral cerebral arteries. It may thus improve the detection of pathologies in small cerebrovascular lesions and the resulting diagnosis.

Identifiants

pubmed: 37831106
doi: 10.1007/s00062-023-01348-1
pii: 10.1007/s00062-023-01348-1
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Informations de copyright

© 2023. The Author(s).

Références

Oostveen LJ, et al. Physical evaluation of an ultra-high-resolution CT scanner. Eur Radiol. 2020; https://doi.org/10.1007/s00330-019-06635-5 .
doi: 10.1007/s00330-019-06635-5 pubmed: 32215693 pmcid: 7338809
Mergen V, et al. Ultra-high-resolution coronary CT angiography with photon-counting detector CT: feasibility and image characterization. Invest Radiol. 2022;57(12):780–8.
doi: 10.1097/RLI.0000000000000897 pubmed: 35640019 pmcid: 10184822
Symons R, et al. Photon-counting CT for vascular imaging of the head and neck: first in vivo human results. Invest Radiol. 2018;53(3):135.
doi: 10.1097/RLI.0000000000000418 pubmed: 28926370 pmcid: 5792306
Ohara A, et al. Improved image quality of temporal bone CT with an ultrahigh-resolution CT scanner: clinical pilot studies. Jpn J Radiol. 2020;38(9):878.
doi: 10.1007/s11604-020-00987-5 pubmed: 32394364 pmcid: 7452920
Latina J, et al. Ultra-high-resolution coronary CT angiography for assessment of patients with severe coronary artery calcification: initial experience. Radiol Cardiothorac Imaging. 2021;3(4):e210053.
doi: 10.1148/ryct.2021210053 pubmed: 34498007 pmcid: 8415143
Motoyama S, et al. Ultra-high-resolution computed tomography angiography for assessment of coronary artery stenosis. Circ J. 2018;82(7):1844–51.
doi: 10.1253/circj.CJ-17-1281 pubmed: 29743388
Shanbhag SM, et al. Prototype ultrahigh-resolution computed tomography for chest imaging: initial human experience. J Comput Assist Tomogr. 2019;43(5):805–10.
doi: 10.1097/RCT.0000000000000917 pubmed: 31490890
Ohno Y, et al. Comparison of lung CT number and airway dimension evaluation capabilities of ultra-high-resolution CT, using different scan modes and reconstruction methods including deep learning reconstruction, with those of multi-detector CT in a QIBA phantom study. Eur Radiol. 2022; https://doi.org/10.1007/s00330-022-08983-1 .
doi: 10.1007/s00330-022-08983-1 pubmed: 35841417
Anam C, et al. An improved method of automated noise measurement system in CT images. J Biomed Phys Eng. 2021;11(2):163.
pubmed: 33937124 pmcid: 8064134
Chun M, Choi YH, Kim JH. Automated measurement of CT noise in patient images with a novel structure coherence feature. Phys Med Biol. 2015;60(23):9107.
doi: 10.1088/0031-9155/60/23/9107 pubmed: 26561914
Dobbins JT III, et al. Intercomparison of methods for image quality characterization. II. Noise power spectruma. Med Phys. 2006;33(5):1454.
doi: 10.1118/1.2188819 pubmed: 16752580
Ucar FA, et al. Feasibility of ultra-high resolution supra-aortic CT angiography: an assessment of diagnostic image quality and radiation dose. Tomography. 2021;7(4):711–20.
doi: 10.3390/tomography7040059 pubmed: 34842838 pmcid: 8628996
Ogawa K, et al. Visualization of small visceral arteries on abdominal CT angiography using ultra-high-resolution CT scanner. Jpn J Radiol. 2021; https://doi.org/10.1007/s11604-021-01124-6 .
doi: 10.1007/s11604-021-01124-6 pubmed: 34117985 pmcid: 8587239
Shanbhag SM, Chen MY. Ultra-high-resolution coronary CT angiography: the “final frontier”—are we there yet? Radiol Cardiothorac Imaging. 2021;3(4):e210196.
doi: 10.1148/ryct.2021210196 pubmed: 34498013 pmcid: 8415137
Hino T, et al. Detectability of the artery of Adamkiewicz on computed tomography angiography of the aorta by using ultra-high-resolution computed tomography. Jpn J Radiol. 2020;38(7):658–65.
doi: 10.1007/s11604-020-00943-3 pubmed: 32170567
Yoshioka K, et al. Ultra-high-resolution CT angiography of the artery of Adamkiewicz: a feasibility study. Neuroradiology. 2018;60(1):109–15.
doi: 10.1007/s00234-017-1927-7 pubmed: 29080918
Fukushima Y, 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.
doi: 10.1016/j.ejrad.2022.110294 pubmed: 35427840
Dangelmaier J, et al. Experimental feasibility of spectral photon-counting computed tomography with two contrast agents for the detection of endoleaks following endovascular aortic repair. Eur Radiol. 2018;28(8):3318–25.
doi: 10.1007/s00330-017-5252-7 pubmed: 29460069 pmcid: 6028848
Boedeker K. Aquilion Precision ultra-high resolution CT:quantifying diagnostic image quality. 2018.
Grochowski C, Staśkiewicz G. Ultra high field TOF-MRA: a method to visualize small cerebral vessels. 7T TOF-MRA sequence parameters on different MRI scanners—literature review. Neurol Neurochir Pol. 2017;51(5):411–8.
doi: 10.1016/j.pjnns.2017.06.011 pubmed: 28774679
Harteveld AA, et al. 7‑T MRI in cerebrovascular diseases: challenges to overcome and initial results. Top Magn Reson Imaging. 2016;25(2):89–100.
doi: 10.1097/RMR.0000000000000080 pubmed: 27049246
Kraff O, et al. MRI at 7 Tesla and above: demonstrated and potential capabilities. J Magn Reson Imaging. 2015;41(1):13–33.
doi: 10.1002/jmri.24573 pubmed: 24478137
Simoni A, et al. Innovative tool for automatic detection of arterial stenosis on cone beam computed tomography. Appl Sci. 2023;13(2):805.
doi: 10.3390/app13020805
Stampfl S, et al. Initial experience with a new distal intermediate and aspiration catheter in the treatment of acute ischemic stroke: clinical safety and efficacy. J Neurointervent Surg. 2016;8(7):714–8.
doi: 10.1136/neurintsurg-2015-011801
Kurre W, et al. Stent retriever thrombectomy of small caliber intracranial vessels using pREset LITE: safety and efficacy. Clin Neuroradiol. 2017;27(3):351–60.
doi: 10.1007/s00062-016-0497-0 pubmed: 26795038

Auteurs

Felix A Ucar (FA)

Department of Neuroradiology, University Medical Center Mainz, Langenbeckstr. 1, 55131, Mainz, Germany.

Marius Frenzel (M)

Department of Neuroradiology, University Medical Center Mainz, Langenbeckstr. 1, 55131, Mainz, Germany.

Andrea Kronfeld (A)

Department of Neuroradiology, University Medical Center Mainz, Langenbeckstr. 1, 55131, Mainz, Germany.

Sebastian Altmann (S)

Department of Neuroradiology, University Medical Center Mainz, Langenbeckstr. 1, 55131, Mainz, Germany.

Antoine P Sanner (AP)

Department of Neuroradiology, University Medical Center Mainz, Langenbeckstr. 1, 55131, Mainz, Germany.
Department of Computer Science, Fraunhofer IGD, Technical University Darmstadt, Fraunhoferstraße 5, 64283, Darmstadt, Germany.

Mario Alberto Abello Mercado (MAA)

Department of Neuroradiology, University Medical Center Mainz, Langenbeckstr. 1, 55131, Mainz, Germany.

Timo Uphaus (T)

Department of Neurology, University Medical Center Mainz, Langenbeckstr. 1, 55131, Mainz, Germany.

Marc A Brockmann (MA)

Department of Neuroradiology, University Medical Center Mainz, Langenbeckstr. 1, 55131, Mainz, Germany.

Ahmed E Othman (AE)

Department of Neuroradiology, University Medical Center Mainz, Langenbeckstr. 1, 55131, Mainz, Germany. ahmed.e.othman@googlemail.com.

Classifications MeSH