MRI reconstruction with joint global regularization and transform learning
Computerized Medical Imaging and Graphics, cilt.53, ss.1-8, 2016 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 53
- Basım Tarihi: 2016
- Doi Numarası: 10.1016/j.compmedimag.2016.06.004
- Dergi Adı: Computerized Medical Imaging and Graphics
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
- Sayfa Sayıları: ss.1-8
- Anahtar Kelimeler: Global regularization, Image reconstruction, Magnetic resonance, Sparsity, Transform learning
- Kırklareli Üniversitesi Adresli: Evet
Özet
Sparsity based regularization has been a popular approach to remedy the measurement scarcity in image reconstruction. Recently, sparsifying transforms learned from image patches have been utilized as an effective regularizer for the Magnetic Resonance Imaging (MRI) reconstruction. Here, we infuse additional global regularization terms to the patch-based transform learning. We develop an algorithm to solve the resulting novel cost function, which includes both patchwise and global regularization terms. Extensive simulation results indicate that the introduced mixed approach has improved MRI reconstruction performance, when compared to the algorithms which use either of the patchwise transform learning or global regularization terms alone.