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Enhanced bone assessment of the shoulder using zero-echo time MRI with deep-learning image reconstruction.
Ensle, Falko; Kaniewska, Malwina; Lohezic, Maelene; Guggenberger, Roman.
Afiliação
  • Ensle F; Diagnostic and Interventional Radiology, University Hospital Zurich, University Zurich, Zurich, Switzerland. falko.ensle@usz.ch.
  • Kaniewska M; University of Zurich (UZH), Raemistrasse 100, CH-8091, Zurich, Switzerland. falko.ensle@usz.ch.
  • Lohezic M; Diagnostic and Interventional Radiology, University Hospital Zurich, University Zurich, Zurich, Switzerland.
  • Guggenberger R; University of Zurich (UZH), Raemistrasse 100, CH-8091, Zurich, Switzerland.
Skeletal Radiol ; 53(12): 2597-2606, 2024 Dec.
Article em En | MEDLINE | ID: mdl-38658419
ABSTRACT

OBJECTIVES:

To assess a deep learning-based reconstruction algorithm (DLRecon) in zero echo-time (ZTE) MRI of the shoulder at 1.5 Tesla for improved delineation of osseous findings.

METHODS:

In this retrospective study, 63 consecutive exams of 52 patients (28 female) undergoing shoulder MRI at 1.5 Tesla in clinical routine were included. Coronal 3D isotropic radial ZTE pulse sequences were acquired in the standard MR shoulder protocol. In addition to standard-of-care (SOC) image reconstruction, the same raw data was reconstructed with a vendor-supplied prototype DLRecon algorithm. Exams were classified into three subgroups no pathological findings, degenerative changes, and posttraumatic changes, respectively. Two blinded readers performed bone assessment on a 4-point scale (0-poor, 3-perfect) by qualitatively grading image quality features and delineation of osseous pathologies including diagnostic confidence in the respective subgroups. Quantitatively, signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of bone were measured. Qualitative variables were compared using the Wilcoxon signed-rank test for ordinal data and the McNemar test for dichotomous variables; quantitative measures were compared with Student's t-testing.

RESULTS:

DLRecon scored significantly higher than SOC in all visual metrics of image quality (all, p < 0.03), except in the artifact category (p = 0.37). DLRecon also received superior qualitative scores for delineation of osseous pathologies and diagnostic confidence (p ≤ 0.03). Quantitatively, DLRecon achieved superior CNR (95 CI [1.4-3.1]) and SNR (95 CI [15.3-21.5]) of bone than SOC (p < 0.001).

CONCLUSION:

DLRecon enhanced image quality in ZTE MRI and improved delineation of osseous pathologies, allowing for increased diagnostic confidence in bone assessment.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Imageamento por Ressonância Magnética / Aprendizado Profundo Limite: Adult / Aged / Aged80 / Female / Humans / Male / Middle aged Idioma: En Revista: Skeletal Radiol Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Suíça

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Imageamento por Ressonância Magnética / Aprendizado Profundo Limite: Adult / Aged / Aged80 / Female / Humans / Male / Middle aged Idioma: En Revista: Skeletal Radiol Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Suíça