Ultrafast T2-weighted MR imaging of the urinary bladder using deep learning-accelerated HASTE at 3 Tesla
Abstract Objective This prospective study aimed to assess the feasibility of a half-Fourier single-shot turbo spin echo sequence (HASTE) with deep learning (DL) reconstruction for ultrafast imaging of the bladder with reduced susceptibility to motion artifacts. Methods 50 patients underwent pelvic T...
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BMC
2025-07-01
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| Series: | BMC Medical Imaging |
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| Online Access: | https://doi.org/10.1186/s12880-025-01810-1 |
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| author | Li Yan Qinxuan Tan David Kohnert Marcel Dominik Nickel Elisabeth Weiland Felix Kubicka Paul Jahnke Dominik Geisel Moritz Wagner Thula Walter-Rittel |
| author_facet | Li Yan Qinxuan Tan David Kohnert Marcel Dominik Nickel Elisabeth Weiland Felix Kubicka Paul Jahnke Dominik Geisel Moritz Wagner Thula Walter-Rittel |
| author_sort | Li Yan |
| collection | DOAJ |
| description | Abstract Objective This prospective study aimed to assess the feasibility of a half-Fourier single-shot turbo spin echo sequence (HASTE) with deep learning (DL) reconstruction for ultrafast imaging of the bladder with reduced susceptibility to motion artifacts. Methods 50 patients underwent pelvic T2w imaging at 3 Tesla using the following MR sequences in sagittal orientation without antiperistaltic premedication: T2-TSE (time of acquisition [TA]: 2.03–4.00 min), standard HASTE (TA: 0.65–1.10 min), and DL-HASTE (TA: 0.25–0.47 min), with a slice thickness of 3 mm and a varying number of slices (25–45). Three radiologists evaluated the image quality of the three sequences quantitatively and qualitatively. Results Overall image quality of DL-HASTE (average score: 5) was superior to HASTE and T2-TSE (p < .001). DL-HASTE provided the clearest bladder wall delineation, especially in the apical part of the bladder (p < .001). SNR (36.3 ± 6.3) and CNR (50.3 ± 19.7) were the highest on DL-HASTE, followed by T2-TSE (33.1 ± 6.3 and 44.3 ± 21.0, respectively; p < .05) and HASTE (21.7 ± 5.4 and 35.8 ± 17.5, respectively; p < .01). A limitation of DL-HASTE and HASTE was the susceptibility to urine flow artifact within the bladder, which was absent or only minimal on T2-TSE. Diagnostic confidence in assessment of the bladder was highest with the combination of DL-HASTE and T2-TSE (p < .05). Conclusion DL-HASTE allows for ultrafast imaging of the bladder with high image quality and is a promising addition to T2-TSE. |
| format | Article |
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| institution | DOAJ |
| issn | 1471-2342 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | BMC |
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| series | BMC Medical Imaging |
| spelling | doaj-art-204fd74a236c48faadae1d0fc699b81b2025-08-20T03:06:31ZengBMCBMC Medical Imaging1471-23422025-07-0125111010.1186/s12880-025-01810-1Ultrafast T2-weighted MR imaging of the urinary bladder using deep learning-accelerated HASTE at 3 TeslaLi Yan0Qinxuan Tan1David Kohnert2Marcel Dominik Nickel3Elisabeth Weiland4Felix Kubicka5Paul Jahnke6Dominik Geisel7Moritz Wagner8Thula Walter-Rittel9Department of Radiology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin Institute of HealthDepartment of Radiology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin Institute of HealthDepartment of Radiology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin Institute of HealthMR Application Predevelopment, Siemens Healthcare GmbHMR Application Predevelopment, Siemens Healthcare GmbHDepartment of Radiology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin Institute of HealthDepartment of Radiology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin Institute of HealthDepartment of Radiology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin Institute of HealthDepartment of Radiology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin Institute of HealthDepartment of Radiology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin Institute of HealthAbstract Objective This prospective study aimed to assess the feasibility of a half-Fourier single-shot turbo spin echo sequence (HASTE) with deep learning (DL) reconstruction for ultrafast imaging of the bladder with reduced susceptibility to motion artifacts. Methods 50 patients underwent pelvic T2w imaging at 3 Tesla using the following MR sequences in sagittal orientation without antiperistaltic premedication: T2-TSE (time of acquisition [TA]: 2.03–4.00 min), standard HASTE (TA: 0.65–1.10 min), and DL-HASTE (TA: 0.25–0.47 min), with a slice thickness of 3 mm and a varying number of slices (25–45). Three radiologists evaluated the image quality of the three sequences quantitatively and qualitatively. Results Overall image quality of DL-HASTE (average score: 5) was superior to HASTE and T2-TSE (p < .001). DL-HASTE provided the clearest bladder wall delineation, especially in the apical part of the bladder (p < .001). SNR (36.3 ± 6.3) and CNR (50.3 ± 19.7) were the highest on DL-HASTE, followed by T2-TSE (33.1 ± 6.3 and 44.3 ± 21.0, respectively; p < .05) and HASTE (21.7 ± 5.4 and 35.8 ± 17.5, respectively; p < .01). A limitation of DL-HASTE and HASTE was the susceptibility to urine flow artifact within the bladder, which was absent or only minimal on T2-TSE. Diagnostic confidence in assessment of the bladder was highest with the combination of DL-HASTE and T2-TSE (p < .05). Conclusion DL-HASTE allows for ultrafast imaging of the bladder with high image quality and is a promising addition to T2-TSE.https://doi.org/10.1186/s12880-025-01810-1Magnetic resonance imagingDeep learningHASTETurbo spin echo sequenceBladder imaging |
| spellingShingle | Li Yan Qinxuan Tan David Kohnert Marcel Dominik Nickel Elisabeth Weiland Felix Kubicka Paul Jahnke Dominik Geisel Moritz Wagner Thula Walter-Rittel Ultrafast T2-weighted MR imaging of the urinary bladder using deep learning-accelerated HASTE at 3 Tesla BMC Medical Imaging Magnetic resonance imaging Deep learning HASTE Turbo spin echo sequence Bladder imaging |
| title | Ultrafast T2-weighted MR imaging of the urinary bladder using deep learning-accelerated HASTE at 3 Tesla |
| title_full | Ultrafast T2-weighted MR imaging of the urinary bladder using deep learning-accelerated HASTE at 3 Tesla |
| title_fullStr | Ultrafast T2-weighted MR imaging of the urinary bladder using deep learning-accelerated HASTE at 3 Tesla |
| title_full_unstemmed | Ultrafast T2-weighted MR imaging of the urinary bladder using deep learning-accelerated HASTE at 3 Tesla |
| title_short | Ultrafast T2-weighted MR imaging of the urinary bladder using deep learning-accelerated HASTE at 3 Tesla |
| title_sort | ultrafast t2 weighted mr imaging of the urinary bladder using deep learning accelerated haste at 3 tesla |
| topic | Magnetic resonance imaging Deep learning HASTE Turbo spin echo sequence Bladder imaging |
| url | https://doi.org/10.1186/s12880-025-01810-1 |
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