Deep Learning-based Acceleration and Denoising of 0.55T MRI for Enhanced Conspicuity of Vestibular Schwannoma Post Contrast Administration DOI
Maximilian Hinsen, Armin M. Nagel,

Rafael Heiß

et al.

Published: May 13, 2025

Abstract Background and Purpose Deep-learning (DL) based MRI denoising techniques promise improved image quality shorter examination times. This advancement is particularly beneficial for 0.55T MRI, where the inherently lower signal-to-noise (SNR) ratio can compromise quality. Sufficient SNR crucial reliable detection of vestibular schwannoma (VS). The objective this study to evaluate VS conspicuity acquisition time (TA) examinations with contrast agents using a DL-denoising algorithm. Materials Methods From January 2024 October 2024, we retrospectively included 30 patients (9 women). We acquired clinical reference protocol cerebellopontine angle containing T1w fat-saturated (fs) axial (number signal averages [NSA] 4) Spectral Attenuated Inversion Recovery (SPAIR) coronal (NSA 2) sequences after agent (CA) application without advanced DL-based (w/o DL). reconstructed fs CA sequence SPAIR full mode change NSA secondly 1 (DL & 1NSA). Each was rated on 5-point Likert scale (1: insufficient, 3: moderate, clinically sufficient; 5: perfect) for: overall quality; conspicuity, artifacts. Secondly, analyzed reliability size measurements. Two radiologists focus head neck imaging performed reading Wilcoxon Signed-Rank Test used non-parametric statistical comparison. Results DL 4NSA axial/coronal achieved highest IQ (median 4.9). (IQ) 1NSA higher (M: 4.0) than ; median 4.0 versus 3.5, each p < 0.01). Similarly, best 4.9), decreased &1NSA 4.1) but still sufficient w/o 3.7, TA post-contrast were 8:59 minutes 3:24 DL& 1NSA. Conclusions underlines that reduce by more half while simultaneously improving

Language: Английский

Deep Learning-based Acceleration and Denoising of 0.55T MRI for Enhanced Conspicuity of Vestibular Schwannoma Post Contrast Administration DOI
Maximilian Hinsen, Armin M. Nagel,

Rafael Heiß

et al.

Published: May 13, 2025

Abstract Background and Purpose Deep-learning (DL) based MRI denoising techniques promise improved image quality shorter examination times. This advancement is particularly beneficial for 0.55T MRI, where the inherently lower signal-to-noise (SNR) ratio can compromise quality. Sufficient SNR crucial reliable detection of vestibular schwannoma (VS). The objective this study to evaluate VS conspicuity acquisition time (TA) examinations with contrast agents using a DL-denoising algorithm. Materials Methods From January 2024 October 2024, we retrospectively included 30 patients (9 women). We acquired clinical reference protocol cerebellopontine angle containing T1w fat-saturated (fs) axial (number signal averages [NSA] 4) Spectral Attenuated Inversion Recovery (SPAIR) coronal (NSA 2) sequences after agent (CA) application without advanced DL-based (w/o DL). reconstructed fs CA sequence SPAIR full mode change NSA secondly 1 (DL & 1NSA). Each was rated on 5-point Likert scale (1: insufficient, 3: moderate, clinically sufficient; 5: perfect) for: overall quality; conspicuity, artifacts. Secondly, analyzed reliability size measurements. Two radiologists focus head neck imaging performed reading Wilcoxon Signed-Rank Test used non-parametric statistical comparison. Results DL 4NSA axial/coronal achieved highest IQ (median 4.9). (IQ) 1NSA higher (M: 4.0) than ; median 4.0 versus 3.5, each p < 0.01). Similarly, best 4.9), decreased &1NSA 4.1) but still sufficient w/o 3.7, TA post-contrast were 8:59 minutes 3:24 DL& 1NSA. Conclusions underlines that reduce by more half while simultaneously improving

Language: Английский

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