تقرير
Joint multi-contrast Variational Network reconstruction (jVN) with application to rapid 2D and 3D imaging
العنوان: | Joint multi-contrast Variational Network reconstruction (jVN) with application to rapid 2D and 3D imaging |
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المؤلفون: | Polak, Daniel, Cauley, Stephen, Bilgic, Berkin, Gong, Enhao, Bachert, Peter, Adalsteinsson, Elfar, Setsompop, Kawin |
سنة النشر: | 2019 |
المجموعة: | Physics (Other) |
مصطلحات موضوعية: | Electrical Engineering and Systems Science - Image and Video Processing, Physics - Medical Physics |
الوصف: | 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 was 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=4x4 (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 three minutes. 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 exemplarily 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. |
نوع الوثيقة: | Working Paper |
URL الوصول: | http://arxiv.org/abs/1910.03273 |
رقم الأكسشن: | edsarx.1910.03273 |
قاعدة البيانات: | arXiv |
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