Ramin Jafari1, Richard K G Do2, Yousef Mazaheri Tehrani1,2, Ty Cashen3, Sagar Mandava3, Maggie Fung3, Ersin Bayram3, and Ricardo Otazo1,2
1Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, United States, 2Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, United States, 3GE Healthcare, Waukesha, WI, United States
1Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, United States, 2Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, United States, 3GE Healthcare, Waukesha, WI, United States
To use deep learning to reconstruct motion-resolved dynamic images from multicoil undersampled radial data without image quality degradation and 800-fold reduction in reconstruction time compared to the iterative XD-GRASP algorithm.
Figure 2.
Comparison of XD-GRASP (left) and proposed XD-NET (right) on a healthy
volunteer. a) Images for 3 motion states (end-expiration, center,
end-inspiration). B) Corresponding correlation curve and coefficient (r), SSIM
and PSNR.
Figure 3.
Comparison of XD-GRASP (left) and proposed XD-NET (right) on a patient with
liver metastasis. a) Images for 3 motion states (end-expiration, center,
end-inspiration). B) Corresponding correlation curve and coefficient (r), SSIM
and PSNR.