Andrew Palmera Leynes1,2 and Peder E.Z. Larson1,2
1Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, United States, 2UC Berkeley - UC San Francisco Joint Graduate Program in Bioengineering, Berkeley and San Francisco, CA, United States
1Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, United States, 2UC Berkeley - UC San Francisco Joint Graduate Program in Bioengineering, Berkeley and San Francisco, CA, United States
We introduce Deep Non-Linear Inversion (DNLINV), a deep image reconstruction approach that may be used with any hardware and acquisition configuration. We
demonstrate DNLINV on different anatomies and sampling patterns and show high
quality reconstructions at higher acceleration factors.
Figure 4. Calibrationless parallel imaging and compressed
sensing on a T1-weighted brain image. All methods were able to successfully
reconstruct the image at R=4.0. However, at R=8.5, only DNLINV was able to
reconstruct the image without any loss of structure. Furthermore, DNLINV reconstructions
have higher apparent SNR.
Figure 5. Autocalibrating parallel imaging with CAIPI
sampling on a T1-weighted brain image. All methods were able to successfully
reconstruct the image at R=16.0 with DNLINV having the highest apparent SNR. At R=25.0, residual
aliasing artifacts remain on ESPIRiT and ENLIVE while these are largely suppressed
in DNLINV.
