ArticleJapanese journal of radiology2026
Deep learning reconstruction-applied PROPELLER diffusion-weighted imaging in uterine malignancies: a comparison study with MUSE.
Article in Japanese journal of radiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
purposeTo compare the image quality of diffusion-weighted imaging (DWI) between deep learning reconstruction (DLR)-applied Periodically Rotated Overlapping ParallEL Lines with Enhanced Reconstruction (PROPELLER) and MUltiplexed Sensitivity Encoding (MUSE) sequences in MRI of uterine malignancies. MATERIALS AND
methodsThis retrospective study included 66 MRI examinations of 48 patients. The cohort consisted of cases with uterine malignancies (cervical cancer (n = 50), endometrial cancer (n = 15) and endometrial stromal sarcoma (n = 1)) at different clinical settings (initial staging, post-treatment, or recurrence). DWI was performed using both DLR-applied PROPELLER and MUSE sequences using b-values of 0 and 800 s/mm
resultsIn the qualitative assessment, DLR-applied PROPELLER-DWI showed significantly fewer artifacts and better lesion conspicuity than MUSE-DWI. No significant difference was found in overall image quality or sharpness. Inter-reader agreement was slight to moderate, except for strong agreement on DLR-applied PROPELLER-DWI lesion conspicuity (κ = 0.696, good). Quantitatively, the mean ADC values were significantly higher with DLR-applied PROPELLER-DWI compared to MUSE-DWI for both readers (p < 0.001). Furthermore, both aSNR and aCNR were significantly higher with DLR-applied PROPELLER-DWI compared to MUSE-DWI.
conclusionsDLR-applied PROPELLER-DWI demonstrated fewer artifacts and better lesion conspicuity than MUSE-DWI, with higher measured aSNR and aCNR. However, DLR-applied PROPELLER-DWI yielded significantly higher mean ADC values.
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