Value of vendor-agnostic deep learning image denoising in brain computed tomography: A multi-scanner study

Abstract

Purpose

To evaluate the effect of a vendor-agnostic deep learning denoising (DLD) algorithm on diagnostic image quality of non-contrast cranial computed tomography (ncCT) across five CT scanners.

Materials and Methods

This retrospective single-center study included ncCT data of 150 consecutive patients (30 for each of the five scanners) who had undergone routine imaging after minor head trauma. The images were reconstructed using filtered back projection (FBP) and a vendor-agnostic DLD method. Using a 4-point Likert scale, three readers performed a subjective evaluation assessing the following quality criteria: overall diagnostic image quality, image noise, gray matter-white matter differentiation (GM-WM), artifacts, sharpness, and diagnostic confidence. Objective analysis included evaluation of noise, contrast-to-noise ratio (CNR), signal-to-noise ratio (SNR), and an artifact index for the posterior fossa.

Results

In subjective image quality assessment, DLD showed constantly superior results compared to FBP in all categories and for all scanners (p<0.05) across all readers. The objective image quality analysis showed significant improvement in noise, SNR, and CNR as well as for the artifact index using DLD for all scanners (p<0.001).

Conclusion

The vendor-agnostic deep learning denoising algorithm provided significantly superior results in the subjective as well as in the objective analysis of ncCT images of patients with minor head trauma concerning all parameters compared to the FBP reconstruction. This effect has been observed in all five included scanners.

Key Points

• Significant improvement of image quality for 5 scanners due to the vendor-agnostic DLD

• Subjects were patients with routine imaging after minor head trauma

• Reduction of artifacts in the posterior fossa due to the DLD

• Access to improved image quality even for older scanners from different vendors

Citation Format

• Kapper C, Müller L, Kronfeld A et al. Value of vendor-agnostic deep learning image denoising in brain computed tomography: A multi-scanner study. Fortschr Röntgenstr 2024; DOI 10.1055/a-2290-4781

Zusammenfassung

Ziel

Auswertung der Wirkung eines herstellerunabhängigen Deep Learning Denoising-Algorithmus (DLD) auf die diagnostische Bildqualität kontrastloser kranialer Computertomografie (ncCT) für fünf CT-Scanner im Vergleich.

Material und Methoden

Diese retrospektive monozentrische Studie schloss ncCT-Daten von 150 konsekutiven Patienten (30 für jeden der fünf Scanner) ein, bei denen nach einem leichten Kopftrauma eine Routinebildgebung erfolgt war. Die Bilder wurden mittels gefilterter Rückprojektion (FBP) und einer herstellerunabhängigen DLD-Methode rekonstruiert. Anhand einer 4-Punkte-Likert-Skala führten drei Reader eine subjektive Bewertung durch, bei der die Qualitätskriterien allgemeine diagnostische Bildqualität, Bildrauschen, Differenzierung zwischen grauer und weißer Substanz (GM-WM), Artefakte, Bildschärfe und diagnostische Sicherheit bewertet wurden. Die objektive Analyse umfasste die Bewertung des Rauschens, des Kontrast-Rausch-Verhältnisses (CNR), des Signal-Rausch-Verhältnisses (SNR) und einen Artefaktindex für die Fossa cranii posterior.

Ergebnisse

Bei der subjektiven Auswertung der Bildqualität zeigte DLD im Vergleich zu FBP in allen Bewertungskategorien und für alle Scanner konstant bessere Ergebnisse (p<0,05) bei allen Readern. Die objektive Bildqualitätsanalyse zeigte bei allen Scannern eine signifikante Verbesserung des Rauschens, der SNR und der CNR sowie des Artefaktindexes durch das DLD (p<0,001).

Schlussfolgerung

Der herstellerunabhängige Deep Learning Denoising-Algorithmus lieferte im Vergleich zur FBP-Rekonstruktion bei allen Parametern sowohl in der subjektiven als auch in der objektiven Analyse deutlich bessere Ergebnisse für ncCT-Bilder von Patienten nach einem leichten Schädeltrauma. Dieser Effekt wurde bei allen fünf einbezogenen Scannern beobachtet.

Kernaussagen

• Hochsignifikante Verbesserung der Bildqualität für alle 5 Scanner durch das herstellerunabhängige DLD

• Eingeschlossen wurden Patienten mit Routinebildgebung nach leichtem Schädeltrauma

• Verringerung von Artefakten in der hinteren Schädelgrube durch das DLD

• Zugang zu verbesserter Bildqualität auch für ältere Geräte unterschiedlicher Hersteller möglich

Keywords

CT - deep learning denoising - vendor-agnostic - artificial intelligence - brain

Publication History

Received: 14 December 2023

Accepted after revision: 15 March 2024

Article published online:
15 May 2024

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