US 12,462,450 B2
Methods and system for low dose x ray computed tomography perfusion imaging
Jayavardhana Rama Gubbi Lakshminarasimha, Bangalore (IN); Viswanath Pamulakanty Sudarshan, Bangalore (IN); Vartika Sengar, Bangalore (IN); Arpan Pal, Kolkata (IN); and Pavan Kumar Reddy Kancham, Bangalore (IN)
Assigned to TATA CONSULTANCY SERVICES LIMITED, Mumbai (IN)
Filed by Tata Consultancy Services Limited, Mumbai (IN)
Filed on Feb. 27, 2023, as Appl. No. 18/175,484.
Claims priority of application No. 202221018042 (IN), filed on Mar. 28, 2022.
Prior Publication US 2023/0326101 A1, Oct. 12, 2023
Int. Cl. G06T 11/00 (2006.01)
CPC G06T 11/006 (2013.01) [G06T 11/005 (2013.01); G06T 2207/10081 (2013.01)] 12 Claims
OG exemplary drawing
 
1. A processor implemented method comprising:
acquiring, via one or more hardware processors, a plurality of frames from a low-dose X-ray computerized tomography (CT) perfusion scan of a subject;
identifying, via the one or more hardware processors, one or more contrast enhanced frames, from among the plurality of frames;
obtaining, via the one or more hardware processors, a plurality of tissue enhancement measurements (C) from the one or more contrast enhanced frames, wherein the plurality of tissue enhancement measurements (C) is obtained from the one or more contrast enhanced frames by using an equation
C=CaR⊙DV, and
wherein Ca is a block-circulant construction of an arterial input function (AIF), D(·) outputs a diagonal matrix with elements of V along corresponding diagonal, V is a perfusion map constructed from the one or more contrast enhanced frames, R represents an imaging system, and ⊙ represents element-wise product;
modelling, via the one or more hardware processors, an optimization problem for joint estimation of a set of structural CT images and a perfusion map, based on the plurality of tissue enhancement measurements (C); and
solving, via the one or more hardware processors, the optimization problem by determining an average structural prior image (Ü) and a functional image (V) with a 3D image-gradient-based prior and another patch-based prior with a fixed patch length, comprising, iteratively performing, till a calculated relative change in V is below a threshold:
reconstructing a pre-defined number of frames (M) of a structural image obtained using the 3D image-gradient-based prior and averaging the M frames to obtain the average structural prior image (Ü); and
applying deconvolution term and the patch-based prior information with information from the average structural prior image (U), to obtain the functional image V.