Gregory Lemberskiy1, Jelle Veraart1, Benjamin Ades-aron1, Els Fieremans1, and Dmitry S Novikov1
1Radiology, NYU School of Medicine, New York, NY, United States
1Radiology, NYU School of Medicine, New York, NY, United States
We propose a method of virtual coil compression using random matrix theory, MP-VCC, in which the Marchenko-Pastur distribution defines how many virtual coils may be discarded without loss beyond the PCA precision. MP-VCC is evaluated for PF, regular undersampling, and MB acceleration.
Marchenko-Pastur Virtual Coil Compression (MP-VCC) For the aliased region of MB=2 experiment, we display a (A) local spatial patch, XC, (B) its VC basis XVC, and (C) its eigenvalue spectrum with the MP distribution in black. (D) Spatially varying virtual coil maps, PC, are shown for every experiment.
Statistics of Discarded VCs. Properties of the normalized residuals r=(noisy−denoised)/σ, characterizing the discarded VCs, are evaluated via (A,B) histograms showing Gaussian distribution of r; and (C,D) power spectrum analysis, showing no memory along the measurement dimension (temporal power-spectrum Γ(ω) of residuals is flat) and marginal low-frequency bias along the spatial dimension (spatial power-spectrum Γ(k) of residuals flat for almost all $k$)
