US 12,008,690 B2
Iterative hierarchal network for regulating medical image reconstruction
Mahmoud Mostapha, Princeton, NJ (US); Boris Mailhe, Plainsboro, NJ (US); Mariappan S. Nadar, Plainsboro, NJ (US); Simon Arberet, Princeton, NJ (US); and Marcel Dominik Nickel, Herzogenaurach (DE)
Assigned to Siemens Healthineers AG, Forchheim (DE)
Filed by Siemens Healthcare GmbH, Erlangen (DE)
Filed on Jan. 22, 2021, as Appl. No. 17/155,630.
Claims priority of provisional application 63/118,115, filed on Nov. 25, 2020.
Prior Publication US 2022/0165002 A1, May 26, 2022
Int. Cl. G06K 9/00 (2022.01); G06N 3/04 (2023.01); G06N 3/08 (2023.01); G06T 11/00 (2006.01); G16H 30/20 (2018.01); G16H 30/40 (2018.01)
CPC G06T 11/008 (2013.01) [G06N 3/04 (2013.01); G06N 3/08 (2013.01); G06T 11/006 (2013.01); G16H 30/20 (2018.01); G16H 30/40 (2018.01); G06T 2210/41 (2013.01); G06T 2211/424 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A method for reconstruction of a medical image in a medical imaging system, the method comprising:
scanning, by the medical imaging system, a patient, the scanning resulting in measurements;
reconstructing, by an image processor, the medical image from the measurements, the reconstructing including a regularizer implemented with a machine-learned network comprising iterative hierarchal convolutional networks, each of the iterative hierarchal convolutional networks including both down-sampling and up-sampling nested within other down-sampling and up-sampling; and
displaying the medical image.