title: Profile-encoding reconstruction for multiple-acquisition balanced steady-state free precession imaging.
abstract: The scan-efficiency in multiple-acquisition balanced steady-state free precession imaging can be maintained by accelerating and reconstructing each phase-cycled acquisition individually, but this strategy ignores correlated structural information among acquisitions. Here, an improved acceleration framework is proposed that jointly processes undersampled data across N phase cycles.
  
  
  
  Phase-cycled imaging is cast as a profile-encoding problem, modeling each image as an artifact-free image multiplied with a distinct balanced steady-state free precession profile. A profile-encoding reconstruction (PE-SSFP) is employed to recover missing data by enforcing joint sparsity and total-variation penalties across phase cycles. PE-SSFP is compared with individual compressed-sensing and parallel-imaging (ESPIRiT) reconstructions.
  
  
  
  In the brain and the knee, PE-SSFP yields improved image quality compared to individual compressed-sensing and other tested methods particularly for higher N values. On average, PE-SSFP improves peak SNR by 3.8 ± 3.0 dB (mean ± s.e. across N = 2-8) and structural similarity by 1.4 ± 1.2% over individual compressed-sensing, and peak SNR by 5.6 ± 0.7 dB and structural similarity by 7.1 ± 0.5% over ESPIRiT.
  
  
  
  PE-SSFP attains improved image quality and preservation of high-spatial-frequency information at high acceleration factors, compared to conventional reconstructions. PE-SSFP is a promising technique for scan-efficient balanced steady-state free precession imaging with improved reliability against field inhomogeneity. Magn Reson Med 78:1316-1329, 2017. © 2016 International Society for Magnetic Resonance in Medicine.
authors: Department of Electrical and Electronics Engineering, Bilkent University, Ankara, Turkey.|National Magnetic Resonance Research Center (UMRAM), Bilkent University, Ankara, Turkey.
authors: Efe
authors: E
authors: Ilicak
authors: Department of Electrical and Electronics Engineering, Bilkent University, Ankara, Turkey.
authors: Lutfi Kerem
authors: LK
authors: Senel
authors: Department of Electrical and Electronics Engineering, Bilkent University, Ankara, Turkey.
authors: Erdem
authors: E
authors: Biyik
authors: Department of Electrical and Electronics Engineering, Bilkent University, Ankara, Turkey.|National Magnetic Resonance Research Center (UMRAM), Bilkent University, Ankara, Turkey.|Neuroscience Program, Graduate School of Engineering and Science, Bilkent University, Ankara, Turkey.
authors: Tolga
authors: T
authors: Çukur
mesh_terms: D000465:Algorithms; D016477:Artifacts; D001921:Brain / Q000000981:diagnostic imaging; D003198:Computer Simulation; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D007717:Knee / Q000000981:diagnostic imaging; D008279:Magnetic Resonance Imaging / Q000379:methods*; D019047:Phantoms, Imaging; D059629:Signal-To-Noise Ratio
keywords: SSFP; banding artifact; compressed sensing; encoding; magnetization profile; reconstruction