Optimizing image quality with high-resolution, deep-learning-based diffusion-weighted imaging in breast cancer patients at 1.5 T
The objective of this study was to evaluate a high-resolution deep-learning (DL)-based diffusion-weighted imaging (DWI) sequence for breast magnetic resonance imaging (MRI) in comparison to a standard DWI sequence (DWIStd) at 1.5 T. It is a prospective study of 38 breast cancer patients, who were sc...
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| Hauptverfasser: | , , , , , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
10 August 2024
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| In: |
Diagnostics
Year: 2024, Jahrgang: 14, Heft: 16, Pages: 1-10 |
| ISSN: | 2075-4418 |
| DOI: | 10.3390/diagnostics14161742 |
| Online-Zugang: | Verlag, kostenfrei, Volltext: https://doi.org/10.3390/diagnostics14161742 Verlag, kostenfrei, Volltext: https://www.mdpi.com/2075-4418/14/16/1742 
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| Verfasserangaben: | Susann-Cathrin Olthof, Elisabeth Weiland, Thomas Benkert, Daniel Wessling, Daniel Leyhr, Saif Afat, Konstantin Nikolaou and Heike Preibsch |
MARC
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| 245 | 1 | 0 | |a Optimizing image quality with high-resolution, deep-learning-based diffusion-weighted imaging in breast cancer patients at 1.5 T |c Susann-Cathrin Olthof, Elisabeth Weiland, Thomas Benkert, Daniel Wessling, Daniel Leyhr, Saif Afat, Konstantin Nikolaou and Heike Preibsch |
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| 520 | |a The objective of this study was to evaluate a high-resolution deep-learning (DL)-based diffusion-weighted imaging (DWI) sequence for breast magnetic resonance imaging (MRI) in comparison to a standard DWI sequence (DWIStd) at 1.5 T. It is a prospective study of 38 breast cancer patients, who were scanned with DWIStd and DWIDL. Both DWI sequences were scored for image quality, sharpness, artifacts, contrast, noise, and diagnostic confidence with a Likert-scale from 1 (non-diagnostic) to 5 (excellent). The lesion diameter was evaluated on b 800 DWI, apparent diffusion coefficient (ADC), and the second subtraction (SUB) of the contrast-enhanced T1 VIBE. SNR was also calculated. Statistics included correlation analyses and paired t-tests. High-resolution DWIDL offered significantly superior image quality, sharpness, noise, contrast, and diagnostic confidence (each p < 0.02)). Artifacts were significantly higher in DWIDL by one reader (M = 4.62 vs. 4.36 Likert scale, p < 0.01) without affecting the diagnostic confidence. SNR was higher in DWIDL for b 50 and ADC maps (each p = 0.07). Acquisition time was reduced by 22% in DWIDL. The lesion diameters in DWI b 800DL and Std and ADCDL and Std were respectively 6% lower compared to the 2nd SUB. A DL-based diffusion sequence at 1.5 T in breast MRI offers a higher resolution and a faster acquisition, including only minimally more artefacts without affecting the diagnostic confidence. | ||
| 650 | 4 | |a breast MRI at 1.5 T | |
| 650 | 4 | |a high-resolution deep-learning DWI | |
| 650 | 4 | |a histological proven breast cancer patients | |
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| 700 | 1 | |a Benkert, Thomas |e VerfasserIn |4 aut | |
| 700 | 1 | |a Wessling, Daniel |e VerfasserIn |0 (DE-588)1307202861 |0 (DE-627)186760521X |4 aut | |
| 700 | 1 | |a Leyhr, Daniel |e VerfasserIn |4 aut | |
| 700 | 1 | |a Afat, Saif |e VerfasserIn |4 aut | |
| 700 | 1 | |a Nikolaou, Konstantin |e VerfasserIn |4 aut | |
| 700 | 1 | |a Preibsch, Heike |e VerfasserIn |4 aut | |
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