Deep Learning CT Image Reconstruction in Clinical Practice

Arndt, Clemens; Güttler, Felix; Heinrich, Andreas; Bürckenmeyer, Florian; Diamantis, Ioannis; Teichgräber, Ulf

doi:10.1055/a-1248-2556

Cited by 48 publications

(31 citation statements)

References 36 publications

(39 reference statements)

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“…Our results are also in line with a review by Arndt et al who evaluated DLIR phantom and body studies and concluded that DLIR algorithms improve image quality with the potential for radiation dose reduction [ 30 ]. Another review by Zhang et al confirms that DLIR preserves image quality better at low doses compared to other image reconstruction techniques [ 20 ].…”

Section: Discussionsupporting

confidence: 92%

Deep learning versus iterative image reconstruction algorithm for head CT in trauma

et al. 2022

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Purpose To compare the image quality between a deep learning–based image reconstruction algorithm (DLIR) and an adaptive statistical iterative reconstruction algorithm (ASiR-V) in noncontrast trauma head CT. Methods Head CT scans from 94 consecutive trauma patients were included. Images were reconstructed with ASiR-V 50% and the DLIR strengths: low (DLIR-L), medium (DLIR-M), and high (DLIR-H). The image quality was assessed quantitatively and qualitatively and compared between the different reconstruction algorithms. Inter-reader agreement was assessed by weighted kappa. Results DLIR-M and DLIR-H demonstrated lower image noise (p < 0.001 for all pairwise comparisons), higher SNR of up to 82.9% (p < 0.001), and higher CNR of up to 53.3% (p < 0.001) compared to ASiR-V. DLIR-H outperformed other DLIR strengths (p ranging from < 0.001 to 0.016). DLIR-M outperformed DLIR-L (p < 0.001) and ASiR-V (p < 0.001). The distribution of reader scores for DLIR-M and DLIR-H shifted towards higher scores compared to DLIR-L and ASiR-V. There was a tendency towards higher scores with increasing DLIR strengths. There were fewer non-diagnostic CT series for DLIR-M and DLIR-H compared to ASiR-V and DLIR-L. No images were graded as non-diagnostic for DLIR-H regarding intracranial hemorrhage. The inter-reader agreement was fair-good between the second most and the less experienced reader, poor-moderate between the most and the less experienced reader, and poor-fair between the most and the second most experienced reader. Conclusion The image quality of trauma head CT series reconstructed with DLIR outperformed those reconstructed with ASiR-V. In particular, DLIR-M and DLIR-H demonstrated significantly improved image quality and fewer non-diagnostic images. The improvement in qualitative image quality was greater for the second most and the less experienced readers compared to the most experienced reader.

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Section: Discussionsupporting

confidence: 92%

Deep learning versus iterative image reconstruction algorithm for head CT in trauma

et al. 2022

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“…However, DL technology has not only been used for the analysis and post-processing of already acquired or reconstructed images, but also for image acquisition and image reconstruction itself, as was shown for both computed tomography (CT) and magnetic resonance imaging (MRI) [ 8 , 9 , 10 , 11 , 12 , 13 ].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Applications in Magnetic Resonance Imaging: Has the Future Become Present?

et al. 2021

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Deep learning technologies and applications demonstrate one of the most important upcoming developments in radiology. The impact and influence of these technologies on image acquisition and reporting might change daily clinical practice. The aim of this review was to present current deep learning technologies, with a focus on magnetic resonance image reconstruction. The first part of this manuscript concentrates on the basic technical principles that are necessary for deep learning image reconstruction. The second part highlights the translation of these techniques into clinical practice. The third part outlines the different aspects of image reconstruction techniques, and presents a review of the current literature regarding image reconstruction and image post-processing in MRI. The promising results of the most recent studies indicate that deep learning will be a major player in radiology in the upcoming years. Apart from decision and diagnosis support, the major advantages of deep learning magnetic resonance imaging reconstruction techniques are related to acquisition time reduction and the improvement of image quality. The implementation of these techniques may be the solution for the alleviation of limited scanner availability via workflow acceleration. It can be assumed that this disruptive technology will change daily routines and workflows permanently.

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“…Diese können z. B. die Strahlenexposition von Patient*innen vermindern, wie die SAFIRE-Software und die ADMIRE-Software von Siemens Healthineers, die in verschiedenen Stufen eingesetzt werden kann; oder mittels Postprocessing – also Datenbearbeitung nach der Bildakquisition – zur Artefakt- und Rauschunterdrückung dienen 12 , 13 . So ist bekannt, dass (abdominelle) MRT-Untersuchungen generell anfällig für eine Vielzahl an Artefakten und Rauschursachen sind.…”

Section: Praktische Anwendungenunclassified