Joint multi-contrast variational network reconstruction (jVN) with application to rapid 2D and 3D imaging

• Verfasst von: Polak, Daniel [VerfasserIn]
• Verfasst von: Cauley, Stephen [VerfasserIn]
• Verfasst von: Bilgic, Berkin [VerfasserIn]
• Verfasst von: Gong, Enhao [VerfasserIn]
• Verfasst von: Bachert, Peter [VerfasserIn]
• Verfasst von: Adalsteinsson, Elfar [VerfasserIn]
• Verfasst von: Setsompop, Kawin [VerfasserIn]
• Titel: Joint multi-contrast variational network reconstruction (jVN) with application to rapid 2D and 3D imaging
• Verf.angabe: Daniel Polak, Stephen Cauley, Berkin Bilgic, Enhao Gong, Peter Bachert, Elfar Adalsteinsson, Kawin Setsompop
• Jahr: 2020
• Umfang: 14 S.
• Fussnoten: First published: 04 March 2020 ; Gesehen am 26.11.2021
• Titel Quelle: Enthalten in: Magnetic resonance in medicine
• Ort Quelle: New York, NY [u.a.] : Wiley-Liss, 1984
• Jahr Quelle: 2020
• Band/Heft Quelle: 84(2020), 3, Seite 1456-1469
• ISSN Quelle: 1522-2594
• DOI: doi:10.1002/mrm.28219
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: deep learning
• Sach-SW: Joint multi-contrast reconstruction
• Sach-SW: parallel imaging
• Sach-SW: Wave-CAIPI
• K10plus-PPN: 1779610459
• K10plus-PPN:
  Universitätsbibliothek
• Lokale URL UB: Zum Volltext

Abstract

Purpose To improve the image quality of highly accelerated multi-channel MRI data by learning a joint variational network that reconstructs multiple clinical contrasts jointly. Methods Data from our multi-contrast acquisition were embedded into the variational network architecture where shared anatomical information is exchanged by mixing the input contrasts. Complementary k-space sampling across imaging contrasts and Bunch-Phase/Wave-Encoding were used for data acquisition to improve the reconstruction at high accelerations. At 3T, our joint variational network approach across T1w, T2w and T2-FLAIR-weighted brain scans was tested for retrospective under-sampling at R = 6 (2D) and R = 4 × 4 (3D) acceleration. Prospective acceleration was also performed for 3D data where the combined acquisition time for whole brain coverage at 1 mm isotropic resolution across three contrasts was less than 3 min. Results Across all test datasets, our joint multi-contrast network better preserved fine anatomical details with reduced image-blurring when compared to the corresponding single-contrast reconstructions. Improvement in image quality was also obtained through complementary k-space sampling and Bunch-Phase/Wave-Encoding where the synergistic combination yielded the overall best performance as evidenced by exemplary slices and quantitative error metrics. Conclusion By leveraging shared anatomical structures across the jointly reconstructed scans, our joint multi-contrast approach learnt more efficient regularizers, which helped to retain natural image appearance and avoid over-smoothing. When synergistically combined with advanced encoding techniques, the performance was further improved, enabling up to R = 16-fold acceleration with good image quality. This should help pave the way to very rapid high-resolution brain exams.